# `image` — machine vision

The `image` module is used for machine vision.

## Functions

image.binary_to_grayscale(binary_image_value)

  Returns a converted binary value (0-1) to a grayscale value (0-255).

image.binary_to_rgb(binary_image_value)

  Returns a converted binary value (0-1) to a 3 value RGB888 tuple.

image.binary_to_lab(binary_image_value)

  Returns a converted binary value (0-1) to a 3 value LAB tuple.

  L goes between 0 and 100 and A/B go from -128 to 128.

image.binary_to_yuv(binary_image_value)

  Returns a converted binary value (0-1) to a 3 value YUV tuple.

  Y goes between 0 and 255 and U/V go from -128 to 128.

image.grayscale_to_binary(grayscale_value)

  Returns a converted grayscale value (0-255) to a binary value (0-1).

image.grayscale_to_rgb(grayscale_value)

  Returns a converted grayscale value to a 3 value RGB888 tuple.

  Note

  The OpenMV Cam firmware does the conversion using a RGB565->RGB888 process so this method won’t return the exact values as a pure RGB888 system would. However, it’s true to how the image lib works internally.

image.grayscale_to_lab(grayscale_value)

  Returns a converted grayscale value to a 3 value LAB tuple.

  L goes between 0 and 100 and A/B go from -128 to 128.

  Note

  The OpenMV Cam firmware does the conversion using a RGB565->LAB process so this method won’t return the exact values as a pure LAB system would. However, it’s true to how the image lib works internally.

image.grayscale_to_yuv(grayscale_value)

  Returns a converted grayscale value to a 3 value YUV tuple.

  Y goes between 0 and 255 and U/V go from -128 to 128.

  Note

  The OpenMV Cam firmware does the conversion using a RGB565->YUV process so this method won’t return the exact values as a pure YUV system would. However, it’s true to how the image lib works internally.

image.rgb_to_binary(rgb_tuple)

  Returns a converted 3 value RGB888 tuple to a center range thresholded binary value (0-1).

  Note

  The OpenMV Cam firmware does the conversion using a RGB888->RGB565 process so this method won’t return the exact values as a pure RGB888 system would. However, it’s true to how the image lib works internally.

image.rgb_to_grayscale(rgb_tuple)

  Returns a converted 3 value RGB888 tuple to a grayscale value (0-255).

  Note

  The OpenMV Cam firmware does the conversion using a RGB888->RGB565 process so this method won’t return the exact values as a pure RGB888 system would. However, it’s true to how the image lib works internally.

image.rgb_to_lab(rgb_tuple)

  Returns a converted 3 value RGB888 tuple to a 3 value LAB tuple.

  L goes between 0 and 100 and A/B go from -128 to 128.

  Note

  The OpenMV Cam firmware does the conversion using a RGB888->RGB565 process so this method won’t return the exact values as a pure RGB888 system would. However, it’s true to how the image lib works internally.

image.rgb_to_yuv(rgb_tuple)

  Returns a converted 3 value RGB888 tuple to a 3 value YUV tuple.

  Y goes between 0 and 255 and U/V go from -128 to 128.

  Note

  The OpenMV Cam firmware does the conversion using a RGB888->RGB565 process so this method won’t return the exact values as a pure RGB888 system would. However, it’s true to how the image lib works internally.

image.lab_to_binary(lab_tuple)

  Returns a converted 3 value LAB tuple to a center range thresholded binary value (0-1).

  Note

  The OpenMV Cam firmware does the conversion using a LAB->RGB565 process so this method won’t return the exact values as a pure LAB system would. However, it’s true to how the image lib works internally.

image.lab_to_grayscale(lab_tuple)

  Returns a converted 3 value LAB tuple to a grayscale value (0-255).

  Note

  The OpenMV Cam firmware does the conversion using a LAB->RGB565 process so this method won’t return the exact values as a pure LAB system would. However, it’s true to how the image lib works internally.

image.lab_to_rgb(lab_tuple)

  Returns a converted 3 value LAB tuple to a 3 value RGB888 tuple.

  Note

  The OpenMV Cam firmware does the conversion using a LAB->RGB565 process so this method won’t return the exact values as a pure LAB system would. However, it’s true to how the image lib works internally.

image.lab_to_yuv(lab_tuple)

  Returns a converted 3 value LAB tuple to a 3 value YUV tuple.

  Y goes between 0 and 255 and U/V go from -128 to 128.

  Note

  The OpenMV Cam firmware does the conversion using a LAB->RGB565 process so this method won’t return the exact values as a pure LAB system would. However, it’s true to how the image lib works internally.

image.yuv_to_binary(yuv_tuple)

  Returns a converted 3 value YUV tuple to a center range thresholded binary value (0-1).

  Note

  The OpenMV Cam firmware does the conversion using a YUV->RGB565 process so this method won’t return the exact values as a pure YUV system would. However, it’s true to how the image lib works internally.

image.yuv_to_grayscale(yuv_tuple)

  Returns a converted 3 value YUV tuple to a grayscale value (0-255).

  Note

  The OpenMV Cam firmware does the conversion using a YUV->RGB565 process so this method won’t return the exact values as a pure YUV system would. However, it’s true to how the image lib works internally.

image.yuv_to_rgb(lab_tuple)

  Returns a converted 3 value YUV tuple to a 3 value RGB888 tuple.

  Note

  The OpenMV Cam firmware does the conversion using a YUV->RGB565 process so this method won’t return the exact values as a pure YUV system would. However, it’s true to how the image lib works internally.

image.yuv_to_lab(yuv_tuple)

  Returns a converted 3 value YUV tuple to a 3 value LAB tuple.

  L goes between 0 and 100 and A/B go from -128 to 128.

  Note

  The OpenMV Cam firmware does the conversion using a YUV->RGB565 process so this method won’t return the exact values as a pure YUV system would. However, it’s true to how the image lib works internally.

image.load_decriptor(path)

  Loads a descriptor object from disk.

  `path` is the path to the descriptor file to load.

image.save_descriptor(path, descriptor)

  Saves the descriptor object `descriptor` to disk.

  `path` is the path to the descriptor file to save.

image.match_descriptor(descritor0, descriptor1[, threshold=70[, filter_outliers=False]])

  For LBP descriptors this function returns an integer representing the difference between the two descriptors. You may then threshold/compare this distance metric as necessary. The distance is a measure of similarity. The closer it is to zero the better the LBP keypoint match.

  For ORB descriptors this function returns the `kptmatch` object. See above.

  `threshold` is used for ORB keypoints to filter ambiguous matches. A lower `threshold` value tightens the keypoint matching algorithm. `threshold` may be between 0-100 (int). Defaults to 70.

  `filter_outliers` is used for ORB keypoints to filter out outlier keypoints allow you to raise the `threshold`. Defaults to False.

## class HaarCascade – Feature Descriptor

The Haar Cascade feature descriptor is used for the `Image.find_features()` method. It doesn’t have any methods itself for you to call.

class image.HaarCascade(path[, stages=Auto])

  Loads a Haar Cascade into memory from a Haar Cascade binary file formatted for your OpenMV Cam. If you pass “frontalface” instead of a path then this constructor will load the built-in frontal face Haar Cascade into memory. Additionally, you can also pass “eye” to load a Haar Cascade for eyes into memory. Finally, this method returns the loaded Haar Cascade object for use with `Image.find_features()`.

  `stages` defaults to the number of stages in the Haar Cascade. However, you can specify a lower number of stages to speed up processing the feature detector at the cost of a higher rate of false positives.

  Note

  You can make your own Haar Cascades to use with your OpenMV Cam. First, Google for “<thing> Haar Cascade” to see if someone already made an OpenCV Haar Cascade for an object you want to detect. If not… then you’ll have to generate your own (which is a lot of work). See here for how to make your own Haar Cascade. Then see this script for converting OpenCV Haar Cascades into a format your OpenMV Cam can read.

  Q: What is a Haar Cascade?

  A: A Haar Cascade is a series of contrast checks that are used to determine if an object is present in the image. The contrast checks are split of into stages where a stage is only run if previous stages have already passed. The contrast checks are simple things like checking if the center vertical of the image is lighter than the edges. Large area checks are performed first in the earlier stages followed by more numerous and smaller area checks in later stages.

  Q: How are Haar Cascades made?

  A: Haar Cascades are made by training the generator algorithm against positive and negative labeled images. For example, you’d train the generator algorithm against hundreds of pictures with cats in them that have been labeled as images with cats and against hundreds of images with not cat like things labeled differently. The generator algorithm will then produce a Haar Cascade that detects cats.

## class Histogram – Histogram Object

The histogram object is returned by `Image.get_histogram()`.

Grayscale histograms have one channel with some number of bins. All bins are normalized so that all bins sum to 1.

RGB565 histograms have three channels with some number of bins each. All bins are normalized so that all bins in a channel sum to 1.

class image.histogram

  Please call `Image.get_histogram()` to create this object.

  bins()

    Returns a list of floats for the grayscale histogram.

    You may also get this value doing `[0]` on the object.

  l_bins()

    Returns a list of floats for the RGB565 histogram LAB L channel.

    You may also get this value doing `[0]` on the object.

  a_bins()

    Returns a list of floats for the RGB565 histogram LAB A channel.

    You may also get this value doing `[1]` on the object.

  b_bins()

    Returns a list of floats for the RGB565 histogram LAB B channel.

    You may also get this value doing `[2]` on the object.

  get_percentile(percentile)

    Computes the CDF of the histogram channels and returns a `image.percentile` object with the values of the histogram at the passed in `percentile` (0.0 - 1.0) (float). So, if you pass in 0.1 this method will tell you (going from left-to-right in the histogram) what bin when summed into an accumulator caused the accumulator to cross 0.1. This is useful to determine min (with 0.1) and max (with 0.9) of a color distribution without outlier effects ruining your results for adaptive color tracking.

  get_threshold()

    Uses Otsu’s Method to compute the optimal threshold values that split the histogram into two halves for each channel of the histogram. This method returns a `image.threshold` object. This method is particularly useful for determining optimal `Image.binary()` thresholds.

  get_statistics()

    Computes the mean, median, mode, standard deviation, min, max, lower quartile, and upper quartile of each color channel in the histogram and returns a `statistics` object.

    You may also use `histogram.statistics()` and `histogram.get_stats()` as aliases for this method.

## class Percentile – Percentile Object

The percentile object is returned by `histogram.get_percentile()`.

Grayscale percentiles have one channel. Use the non `l_*`, `a_*`, and `b_*` method.

RGB565 percentiles have three channels. Use the `l_*`, `a_*`, and `b_*` methods.

class image.percentile

  Please call `histogram.get_percentile()` to create this object.

  value()

    Return the grayscale percentile value (between 0 and 255).

    You may also get this value doing `[0]` on the object.

  l_value()

    Return the RGB565 LAB L channel percentile value (between 0 and 100).

    You may also get this value doing `[0]` on the object.

  a_value()

    Return the RGB565 LAB A channel percentile value (between -128 and 127).

    You may also get this value doing `[1]` on the object.

  b_value()

    Return the RGB565 LAB B channel percentile value (between -128 and 127).

    You may also get this value doing `[2]` on the object.

## class Threshold – Threshold Object

The threshold object is returned by `histogram.get_threshold()`.

Grayscale thresholds have one channel. Use the non `l_*`, `a_*`, and `b_*` method.

RGB565 thresholds have three channels. Use the `l_*`, `a_*`, and `b_*` methods.

class image.threshold

  Please call `histogram.get_threshold()` to create this object.

  value()

    Return the grayscale threshold value (between 0 and 255).

    You may also get this value doing `[0]` on the object.

  l_value()

    Return the RGB565 LAB L channel threshold value (between 0 and 100).

    You may also get this value doing `[0]` on the object.

  a_value()

    Return the RGB565 LAB A channel threshold value (between -128 and 127).

    You may also get this value doing `[1]` on the object.

  b_value()

    Return the RGB565 LAB B channel threshold value (between -128 and 127).

    You may also get this value doing `[2]` on the object.

## class Statistics – Statistics Object

The percentile object is returned by `histogram.get_statistics()` or `Image.get_statistics()`.

Grayscale statistics have one channel. Use the non `l_*`, `a_*`, and `b_*` method.

RGB565 statistics have three channels. Use the `l_*`, `a_*`, and `b_*` methods.

class image.statistics

  Please call `histogram.get_statistics()` or `Image.get_statistics()` to create this object.

  mean()

    Returns the grayscale mean (0-255) (int).

    You may also get this value doing `[0]` on the object.

  median()

    Returns the grayscale median (0-255) (int).

    You may also get this value doing `[1]` on the object.

  mode()

    Returns the grayscale mode (0-255) (int).

    You may also get this value doing `[2]` on the object.

  stdev()

    Returns the grayscale standard deviation (0-255) (int).

    You may also get this value doing `[3]` on the object.

  min()

    Returns the grayscale min (0-255) (int).

    You may also get this value doing `[4]` on the object.

  max()

    Returns the grayscale max (0-255) (int).

    You may also get this value doing `[5]` on the object.

  lq()

    Returns the grayscale lower quartile (0-255) (int).

    You may also get this value doing `[6]` on the object.

  uq()

    Returns the grayscale upper quartile (0-255) (int).

    You may also get this value doing `[7]` on the object.

  l_mean()

    Returns the RGB565 LAB L mean (0-255) (int).

    You may also get this value doing `[0]` on the object.

  l_median()

    Returns the RGB565 LAB L median (0-255) (int).

    You may also get this value doing `[1]` on the object.

  l_mode()

    Returns the RGB565 LAB L mode (0-255) (int).

    You may also get this value doing `[2]` on the object.

  l_stdev()

    Returns the RGB565 LAB L standard deviation (0-255) (int).

    You may also get this value doing `[3]` on the object.

  l_min()

    Returns the RGB565 LAB L min (0-255) (int).

    You may also get this value doing `[4]` on the object.

  l_max()

    Returns the RGB565 LAB L max (0-255) (int).

    You may also get this value doing `[5]` on the object.

  l_lq()

    Returns the RGB565 LAB L lower quartile (0-255) (int).

    You may also get this value doing `[6]` on the object.

  l_uq()

    Returns the RGB565 LAB L upper quartile (0-255) (int).

    You may also get this value doing `[7]` on the object.

  a_mean()

    Returns the RGB565 LAB A mean (0-255) (int).

    You may also get this value doing `[8]` on the object.

  a_median()

    Returns the RGB565 LAB A median (0-255) (int).

    You may also get this value doing `[9]` on the object.

  a_mode()

    Returns the RGB565 LAB A mode (0-255) (int).

    You may also get this value doing `[10]` on the object.

  a_stdev()

    Returns the RGB565 LAB A standard deviation (0-255) (int).

    You may also get this value doing `[11]` on the object.

  a_min()

    Returns the RGB565 LAB A min (0-255) (int).

    You may also get this value doing `[12]` on the object.

  a_max()

    Returns the RGB565 LAB A max (0-255) (int).

    You may also get this value doing `[13]` on the object.

  a_lq()

    Returns the RGB565 LAB A lower quartile (0-255) (int).

    You may also get this value doing `[14]` on the object.

  a_uq()

    Returns the RGB565 LAB A upper quartile (0-255) (int).

    You may also get this value doing `[15]` on the object.

  b_mean()

    Returns the RGB565 LAB B mean (0-255) (int).

    You may also get this value doing `[16]` on the object.

  b_median()

    Returns the RGB565 LAB B median (0-255) (int).

    You may also get this value doing `[17]` on the object.

  b_mode()

    Returns the RGB565 LAB B mode (0-255) (int).

    You may also get this value doing `[18]` on the object.

  b_stdev()

    Returns the RGB565 LAB B standard deviation (0-255) (int).

    You may also get this value doing `[19]` on the object.

  b_min()

    Returns the RGB565 LAB B min (0-255) (int).

    You may also get this value doing `[20]` on the object.

  b_max()

    Returns the RGB565 LAB B max (0-255) (int).

    You may also get this value doing `[21]` on the object.

  b_lq()

    Returns the RGB565 LAB B lower quartile (0-255) (int).

    You may also get this value doing `[22]` on the object.

  b_uq()

    Returns the RGB565 LAB B upper quartile (0-255) (int).

    You may also get this value doing `[23]` on the object.

## class Blob – Blob object

The blob object is returned by `Image.find_blobs()`.

class image.blob

  Please call `Image.find_blobs()` to create this object.

  corners()

    Returns a list of 4 (x,y) tuples of the 4 corners of the object. Corners are always returned in sorted clock-wise order starting from the top left.

  min_corners()

    Returns a list of 4 (x,y) tuples of the 4 corners than bound the min area rectangle of the blob. Unlike `blob.corners()` the min area rectangle corners do not necessarily lie on the blob.

  rect()

    Returns a rectangle tuple (x, y, w, h) for use with other `image` methods like `Image.draw_rectangle()` of the blob’s bounding box.

  x()

    Returns the blob’s bounding box x coordinate (int).

    You may also get this value doing `[0]` on the object.

  y()

    Returns the blob’s bounding box y coordinate (int).

    You may also get this value doing `[1]` on the object.

  w()

    Returns the blob’s bounding box w coordinate (int).

    You may also get this value doing `[2]` on the object.

  h()

    Returns the blob’s bounding box h coordinate (int).

    You may also get this value doing `[3]` on the object.

  pixels()

    Returns the number of pixels that are part of this blob (int).

    You may also get this value doing `[4]` on the object.

  cx()

    Returns the centroid x position of the blob (int).

    You may also get this value doing `[5]` on the object.

  cxf()

    Returns the centroid x position of the blob (float).

  cy()

    Returns the centroid y position of the blob (int).

    You may also get this value doing `[6]` on the object.

  cyf()

    Returns the centroid y position of the blob (float).

  rotation()

    Returns the rotation of the blob in radians (float). If the blob is like a pencil or pen this value will be unique for 0-180 degrees. If the blob is round this value is not useful.

    You may also get this value doing `[7]` on the object.

  rotation_deg()

    Returns the rotation of the blob in degrees.

  rotation_rad()

    Returns the rotation of the blob in radians. This method is more descriptive than just `blob.rotation()`.

  code()

    Returns a 32-bit binary number with a bit set in it for each color threshold that’s part of this blob. For example, if you passed `Image.find_blobs()` three color thresholds to look for then bits 0/1/2 may be set for this blob. Note that only one bit will be set for each blob unless `Image.find_blobs()` was called with `merge=True`. Then its possible for multiple blobs with different color thresholds to be merged together. You can use this method along with multiple thresholds to implement color code tracking.

    You may also get this value doing `[8]` on the object.

  count()

    Returns the number of blobs merged into this blob. This is 1 unless you called `Image.find_blobs()` with `merge=True`.

    You may also get this value doing `[9]` on the object.

  perimeter()

    Returns the number of pixels on this blob’s perimeter.

  roundness()

    Returns a value between 0 and 1 representing how round the object is. A circle would be a 1.

  elongation()

    Returns a value between 0 and 1 representing how long (not round) the object is. A line would be a 1.

  area()

    Returns the area of the bounding box around the blob. (w * h).

  density()

    Returns the density ratio of the blob. This is the number of pixels in the blob over its bounding box area. A low density ratio means in general that the lock on the object isn’t very good. The result is between 0 and 1.

  extent()

    Alias for `blob.density()`.

  compactness()

    Like `blob.density()`, but, uses the perimeter of the blob instead to measure the objects density and is thus more accurate. The result is between 0 and 1.

  solidity()

    Like `blob.density()` but, uses the minimum area rotated rectangle versus the bounding rectangle to measure density. The result is between 0 and 1.

  convexity()

    Returns a value between 0 and 1 representing how convex the object is. A square would be 1.

  x_hist_bins()

    Returns a histogram of the x axis of all columns in a blob. Bin values are scaled between 0 and 1.

  y_hist_bins()

    Returns a histogram of the y axis of all the rows in a blob. Bin values are scaled between 0 and 1.

  major_axis_line()

    Returns a line tuple (x1, y1, x2, y2) that can be drawn with `Image.draw_line()` of the major axis of the blob (the line going through the longest side of the min area rectangle).

  minor_axis_line()

    Returns a line tuple (x1, y1, x2, y2) that can be drawn with `Image.draw_line()` of the minor axis of the blob (the line going through the shortest side of the min area rectangle).

  enclosing_circle()

    Returns a circle tuple (x, y, r) that can be drawn with `Image.draw_circle()` of the circle that encloses the min area rectangle of a blob.

  enclosed_ellipse()

    Returns an ellipse tuple (x, y, rx, ry, rotation) that can be drawn with `Image.draw_ellipse()` of the ellipse that fits inside of the min area rectangle of a blob.

## class Line – Line object

The line object is returned by `Image.find_lines()`, `Image.find_line_segments()`, or `Image.get_regression()`.

class image.line

  Please call `Image.find_lines()`, `Image.find_line_segments()`, or `Image.get_regression()` to create this object.

  line()

    Returns a line tuple (x1, y1, x2, y2) for use with other `image` methods like `Image.draw_line()`.

  x1()

    Returns the line’s p1 x component.

    You may also get this value doing `[0]` on the object.

  y1()

    Returns the line’s p1 y component.

    You may also get this value doing `[1]` on the object.

  x2()

    Returns the line’s p2 x component.

    You may also get this value doing `[2]` on the object.

  y2()

    Returns the line’s p2 y component.

    You may also get this value doing `[3]` on the object.

  length()

    Returns the line’s length: sqrt(((x2-x1)^2) + ((y2-y1)^2).

    You may also get this value doing `[4]` on the object.

  magnitude()

    Returns the magnitude of the line from the hough transform.

    You may also get this value doing `[5]` on the object.

  theta()

    Returns the angle of the line from the hough transform - (0 - 179) degrees.

    You may also get this value doing `[7]` on the object.

  rho()

    Returns the the rho value for the line from the hough transform.

    You may also get this value doing `[8]` on the object.

## class Circle – Circle object

The circle object is returned by `Image.find_circles()`.

class image.circle

  Please call `Image.find_circles()` to create this object.

  x()

    Returns the circle’s x position.

    You may also get this value doing `[0]` on the object.

  y()

    Returns the circle’s y position.

    You may also get this value doing `[1]` on the object.

  r()

    Returns the circle’s radius.

    You may also get this value doing `[2]` on the object.

  magnitude()

    Returns the circle’s magnitude.

    You may also get this value doing `[3]` on the object.

## class Rect – Rectangle Object

The rect object is returned by `Image.find_rects()`.

class image.rect

  Please call `Image.find_rects()` to create this object.

  corners()

    Returns a list of 4 (x,y) tuples of the 4 corners of the object. Corners are always returned in sorted clock-wise order starting from the top left.

  rect()

    Returns a rectangle tuple (x, y, w, h) for use with other `image` methods like `Image.draw_rectangle()` of the rect’s bounding box.

  x()

    Returns the rectangle’s top left corner’s x position.

    You may also get this value doing `[0]` on the object.

  y()

    Returns the rectangle’s top left corner’s y position.

    You may also get this value doing `[1]` on the object.

  w()

    Returns the rectangle’s width.

    You may also get this value doing `[2]` on the object.

  h()

    Returns the rectangle’s height.

    You may also get this value doing `[3]` on the object.

  magnitude()

    Returns the rectangle’s magnitude.

    You may also get this value doing `[4]` on the object.

## class QRCode – QRCode object

The qrcode object is returned by `Image.find_qrcodes()`.

class image.qrcode

  Please call `Image.find_qrcodes()` to create this object.

  corners()

    Returns a list of 4 (x,y) tuples of the 4 corners of the object. Corners are always returned in sorted clock-wise order starting from the top left.

  rect()

    Returns a rectangle tuple (x, y, w, h) for use with other `image` methods like `Image.draw_rectangle()` of the qrcode’s bounding box.

  x()

    Returns the qrcode’s bounding box x coordinate (int).

    You may also get this value doing `[0]` on the object.

  y()

    Returns the qrcode’s bounding box y coordinate (int).

    You may also get this value doing `[1]` on the object.

  w()

    Returns the qrcode’s bounding box w coordinate (int).

    You may also get this value doing `[2]` on the object.

  h()

    Returns the qrcode’s bounding box h coordinate (int).

    You may also get this value doing `[3]` on the object.

  payload()

    Returns the payload string of the qrcode. E.g. the URL.

    You may also get this value doing `[4]` on the object.

  version()

    Returns the version number of the qrcode (int).

    You may also get this value doing `[5]` on the object.

  ecc_level()

    Returns the ecc_level of the qrcode (int).

    You may also get this value doing `[6]` on the object.

  mask()

    Returns the mask of the qrcode (int).

    You may also get this value doing `[7]` on the object.

  data_type()

    Returns the data type of the qrcode (int).

    You may also get this value doing `[8]` on the object.

  eci()

    Returns the eci of the qrcode (int). The eci stores the encoding of data bytes in the QR Code. If you plan to handling QR Codes that contain more than just standard ASCII text you will need to look at this value.

    You may also get this value doing `[9]` on the object.

  is_numeric()

    Returns True if the data_type of the qrcode is numeric.

  is_alphanumeric()

    Returns True if the data_type of the qrcode is alpha numeric.

  is_binary()

    Returns True if the data_type of the qrcode is binary. If you are serious about handling all types of text you need to check the eci if this is True to determine the text encoding of the data. Usually, it’s just standard ASCII, but, it could be UTF8 that has some 2-byte characters in it.

  is_kanji()

    Returns True if the data_type of the qrcode is alpha Kanji. If this is True then you’ll need to decode the string yourself as Kanji symbols are 10-bits per character and MicroPython has no support to parse this kind of text. The payload in this case must be treated as just a large byte array.

## class AprilTag – AprilTag object

The apriltag object is returned by `Image.find_apriltags()`.

class image.apriltag

  Please call `Image.find_apriltags()` to create this object.

  corners()

    Returns a list of 4 (x,y) tuples of the 4 corners of the object. Corners are always returned in sorted clock-wise order starting from the top left.

  rect()

    Returns a rectangle tuple (x, y, w, h) for use with other `image` methods like `Image.draw_rectangle()` of the apriltag’s bounding box.

  x()

    Returns the apriltag’s bounding box x coordinate (int).

    You may also get this value doing `[0]` on the object.

  y()

    Returns the apriltag’s bounding box y coordinate (int).

    You may also get this value doing `[1]` on the object.

  w()

    Returns the apriltag’s bounding box w coordinate (int).

    You may also get this value doing `[2]` on the object.

  h()

    Returns the apriltag’s bounding box h coordinate (int).

    You may also get this value doing `[3]` on the object.

  id()

    Returns the numeric id of the apriltag.

    > - TAG16H5 -> 0 to 29

    > - TAG25H7 -> 0 to 241

    > - TAG25H9 -> 0 to 34

    > - TAG36H10 -> 0 to 2319

    > - TAG36H11 -> 0 to 586

    > - ARTOOLKIT -> 0 to 511

    You may also get this value doing `[4]` on the object.

  family()

    Returns the numeric family of the apriltag.

    > - image.TAG16H5

    > - image.TAG25H7

    > - image.TAG25H9

    > - image.TAG36H10

    > - image.TAG36H11

    > - image.ARTOOLKIT

    You may also get this value doing `[5]` on the object.

  cx()

    Returns the centroid x position of the apriltag (int).

  cxf()

    Returns the centroid x position of the apriltag (float).

    You may also get this value doing `[6]` on the object.

  cy()

    Returns the centroid y position of the apriltag (int).

  cyf()

    Returns the centroid y position of the apriltag (float).

    You may also get this value doing `[7]` on the object.

  rotation()

    Returns the rotation of the apriltag in radians (float).

    You may also get this value doing `[8]` on the object.

  decision_margin()

    Returns the quality of the apriltag match (0.0 - 1.0) where 1.0 is the best.

    You may also get this value doing `[9]` on the object.

  hamming()

    Returns the number of accepted bit errors for this tag.

    > - TAG16H5 -> 0 bit errors will be accepted

    > - TAG25H7 -> up to 1 bit error may be accepted

    > - TAG25H9 -> up to 3 bit errors may be accepted

    > - TAG36H10 -> up to 3 bit errors may be accepted

    > - TAG36H11 -> up to 4 bit errors may be accepted

    > - ARTOOLKIT -> 0 bit errors will be accepted

    You may also get this value doing `[10]` on the object.

  goodness()

    Returns the quality of the apriltag image (0.0 - 1.0) where 1.0 is the best.

    Note

    This value is always 0.0 for now. We may enable a feature called “tag refinement” in the future which will allow detection of small apriltags. However, this feature currently drops the frame rate to less than 1 FPS.

    You may also get this value doing `[11]` on the object.

  x_translation()

    Returns the translation in unknown units from the camera in the X direction.

    This method is useful for determining the apriltag’s location away from the camera. However, the size of the apriltag, the lens you are using, etc. all come into play as to actually determining what the X units are in. For ease of use we recommend you use a lookup table to convert the output of this method to something useful for your application.

    Note that this is the left-to-right direction.

    You may also get this value doing `[12]` on the object.

  y_translation()

    Returns the translation in unknown units from the camera in the Y direction.

    This method is useful for determining the apriltag’s location away from the camera. However, the size of the apriltag, the lens you are using, etc. all come into play as to actually determining what the Y units are in. For ease of use we recommend you use a lookup table to convert the output of this method to something useful for your application.

    Note that this is the up-to-down direction.

    You may also get this value doing `[13]` on the object.

  z_translation()

    Returns the translation in unknown units from the camera in the Z direction.

    This method is useful for determining the apriltag’s location away from the camera. However, the size of the apriltag, the lens you are using, etc. all come into play as to actually determining what the Z units are in. For ease of use we recommend you use a lookup table to convert the output of this method to something useful for your application.

    Note that this is the front-to-back direction.

    You may also get this value doing `[14]` on the object.

  x_rotation()

    Returns the rotation in radians of the apriltag in the X plane. E.g. moving the camera left-to-right while looking at the tag.

    You may also get this value doing `[15]` on the object.

  y_rotation()

    Returns the rotation in radians of the apriltag in the Y plane. E.g. moving the camera up-to-down while looking at the tag.

    You may also get this value doing `[16]` on the object.

  z_rotation()

    Returns the rotation in radians of the apriltag in the Z plane. E.g. rotating the camera while looking directly at the tag.

    Note that this is just a renamed version of `apriltag.rotation()`.

    You may also get this value doing `[17]` on the object.

## class DataMatrix – DataMatrix object

The datamatrix object is returned by `Image.find_datamatrices()`.

class image.datamatrix

  Please call `Image.find_datamatrices()` to create this object.

  corners()

    Returns a list of 4 (x,y) tuples of the 4 corners of the object. Corners are always returned in sorted clock-wise order starting from the top left.

  rect()

    Returns a rectangle tuple (x, y, w, h) for use with other `image` methods like `Image.draw_rectangle()` of the datamatrix’s bounding box.

  x()

    Returns the datamatrix’s bounding box x coordinate (int).

    You may also get this value doing `[0]` on the object.

  y()

    Returns the datamatrix’s bounding box y coordinate (int).

    You may also get this value doing `[1]` on the object.

  w()

    Returns the datamatrix’s bounding box w coordinate (int).

    You may also get this value doing `[2]` on the object.

  h()

    Returns the datamatrix’s bounding box h coordinate (int).

    You may also get this value doing `[3]` on the object.

  payload()

    Returns the payload string of the datamatrix. E.g. The string.

    You may also get this value doing `[4]` on the object.

  rotation()

    Returns the rotation of the datamatrix in radians (float).

    You may also get this value doing `[5]` on the object.

  rows()

    Returns the number of rows in the data matrix (int).

    You may also get this value doing `[6]` on the object.

  columns()

    Returns the number of columns in the data matrix (int).

    You may also get this value doing `[7]` on the object.

  capacity()

    Returns how many characters could fit in this data matrix.

    You may also get this value doing `[8]` on the object.

  padding()

    Returns how many unused characters are in this data matrix.

    You may also get this value doing `[9]` on the object.

## class BarCode – BarCode object

The barcode object is returned by `Image.find_barcodes()`.

class image.barcode

  Please call `Image.find_barcodes()` to create this object.

  corners()

    Returns a list of 4 (x,y) tuples of the 4 corners of the object. Corners are always returned in sorted clock-wise order starting from the top left.

  rect()

    Returns a rectangle tuple (x, y, w, h) for use with other `image` methods like `Image.draw_rectangle()` of the barcode’s bounding box.

  x()

    Returns the barcode’s bounding box x coordinate (int).

    You may also get this value doing `[0]` on the object.

  y()

    Returns the barcode’s bounding box y coordinate (int).

    You may also get this value doing `[1]` on the object.

  w()

    Returns the barcode’s bounding box w coordinate (int).

    You may also get this value doing `[2]` on the object.

  h()

    Returns the barcode’s bounding box h coordinate (int).

    You may also get this value doing `[3]` on the object.

  payload()

    Returns the payload string of the barcode. E.g. The number.

    You may also get this value doing `[4]` on the object.

  type()

    Returns the type enumeration of the barcode (int).

    You may also get this value doing `[5]` on the object.

    > - image.EAN2

    > - image.EAN5

    > - image.EAN8

    > - image.UPCE

    > - image.ISBN10

    > - image.UPCA

    > - image.EAN13

    > - image.ISBN13

    > - image.I25

    > - image.DATABAR

    > - image.DATABAR_EXP

    > - image.CODABAR

    > - image.CODE39

    > - image.PDF417 - Future (e.g. doesn’t work right now).

    > - image.CODE93

    > - image.CODE128

  rotation()

    Returns the rotation of the barcode in radians (float).

    You may also get this value doing `[6]` on the object.

  quality()

    Returns the number of times this barcode was detected in the image (int).

    When scanning a barcode each new scanline can decode the same barcode. This value increments for a barcode each time that happens…

    You may also get this value doing `[7]` on the object.

## class Displacement – Displacement object

The displacement object is returned by `Image.find_displacement()`.

class image.displacement

  Please call `Image.find_displacement()` to create this object.

  x_translation()

    Returns the x translation in pixels between two images. This is sub pixel accurate so it’s a float.

    You may also get this value doing `[0]` on the object.

  y_translation()

    Returns the y translation in pixels between two images. This is sub pixel accurate so it’s a float.

    You may also get this value doing `[1]` on the object.

  rotation()

    Returns the rotation in radians between two images.

    You may also get this value doing `[2]` on the object.

  scale()

    Returns the scale change between two images.

    You may also get this value doing `[3]` on the object.

  response()

    Returns the quality of the results of displacement matching between two images. Between 0-1. A `displacement` object with a response less than 0.1 is likely noise.

    You may also get this value doing `[4]` on the object.

## class kptmatch – Keypoint Object

The kptmatch object is returned by `image.match_descriptor()` for keypoint matches.

class image.kptmatch

  Please call `image.match_descriptor()` to create this object.

  rect()

    Returns a rectangle tuple (x, y, w, h) for use with other `image` methods like `Image.draw_rectangle()` of the kptmatch’s bounding box.

  cx()

    Returns the centroid x position of the kptmatch (int).

    You may also get this value doing `[0]` on the object.

  cy()

    Returns the centroid y position of the kptmatch (int).

    You may also get this value doing `[1]` on the object.

  x()

    Returns the kptmatch’s bounding box x coordinate (int).

    You may also get this value doing `[2]` on the object.

  y()

    Returns the kptmatch’s bounding box y coordinate (int).

    You may also get this value doing `[3]` on the object.

  w()

    Returns the kptmatch’s bounding box w coordinate (int).

    You may also get this value doing `[4]` on the object.

  h()

    Returns the kptmatch’s bounding box h coordinate (int).

    You may also get this value doing `[5]` on the object.

  count()

    Returns the number of keypoints matched (int).

    You may also get this value doing `[6]` on the object.

  theta()

    Returns the estimated angle of rotation for the keypoint (int).

    You may also get this value doing `[7]` on the object.

  match()

    Returns the list of (x,y) tuples of matching keypoints.

    You may also get this value doing `[8]` on the object.

## class ImageIO – ImageIO Object

The ImageIO object allows you to read/write OpenMV Image objects in their native form to disk or to memory. This class provides fast read/write random access for loading/storing images.

class image.ImageIO(path, mode)

  Creates an ImageIO object.

  If `path` is a file name on disk then that file will be opened for reading if `mode` is `'r'` or writing if `mode` is `'w'`.

  `path` may also be a 3-value tuple (w, h, bpp) for in-memory storage of images. `mode` in this case is then the number of image buffers to store in memory. Note that the in-memory storage buffer is not allowed to grow in size after being allocated. Use a `bpp` value of 0 for binary images, 1 for grayscale images, and 2 for rgb565 images.

  type()

    Returns if the `ImageIO` object is a `FILE_STREAM` or `MEMORY_STREAM`.

  is_closed()

    Returns if the `ImageIO` object is closed and can no longer be used.

  count()

    Returns the number of frames stored.

  offset()

    Returns the image index offset.

  version()

    Returns the version of the object if it’s `FILE_STREAM`. `MEMORY_STREAM` versions are `none`.

  buffer_size()

    Returns the size allocated by the object for a frame in a single buffer.

    `buffer_size() * count() == size()`

  size()

    Returns the number of bytes on disk or memory used by the ImageIO object.

  write(img)

    Writes a new image `img` to the ImageIO object. For on disk ImageIO objects the file will grow as new images are added. For in-memory ImageIO objects this just writes an image to the current pre-allocated slot before advancing to the next slot.

    Returns the ImageIO object.

  read([copy_to_fb=False[, loop=True[, pause=True]]])

    Returns an image object from the ImageIO object. If `copy_to_fb` is False then the new image is allocated on the MicroPython heap. However, the MicroPython heap is limited and may not have space to store the new image if exhausted. Instead, set `copy_to_fb` to True to set the frame buffer to the new image making this function work just like `sensor.snapshot()`.

    `loop` if True automatically causes the ImageIO object to seek to the beginning at the end of the stream of images.

    `pause` if True causes this method to pause for a previously recorded number of milliseconds by write in-order to match the original frame rate that captured the image data.

    `copy_to_fb` may also be another image object if you want to replace that image object’s memory buffer, type, width, and height with new image data.

    Note

    Any use of `copy_to_fb` invalidates the previous image object it overwrites. Do not use any references to previous image objects anymore it overwrites. Either for an image object referencing the frame buffer, frame buffer stack, or an image on the MicroPython heap.

  seek(offset)

    Seeks to the image slot number `offset` in the ImageIO object.

    Works for on disk or in-memory objects.

  sync()

    Writes out all data pending for on-disk ImageIO objects.

  close()

    Closes the ImageIO object. For in-memory objects this free’s the allocated space and for on-disk files this closes the file and writes out all meta-data.

  FILE_STREAM

    ImageIO object was opened on a file.

  MEMORY_STREAM

    iamgeIO object was opened in memory.

## class Image – Image object

The image object is the basic object for machine vision operations.

class image.Image(path[, copy_to_fb=False])

  Creates a new image object from a file at `path`. Alternatively, you may pass a width, height, and either `sensor.BINARY`, `sensor.GRAYSCALE`, or `sensor.RGB565` to create new blank image object (initialized to 0 - black).

  Supports bmp/pgm/ppm/jpg/jpeg image files.

  `copy_to_fb` if True the image is loaded directly into the frame buffer allowing you to load up large images. If False, the image is loaded into MicroPython’s heap which is much smaller than the frame buffer.

  You may also set `copy_to_fb` equal to another image object and that buffer will then be overwritten with the copied image changing the passed image object’s pixel format and resolution.

  Note

  If `copy_to_fb` is set to True or another image object do not continue using the old image object passed to this method or the frame buffer image object. Use the new returned image object reference from this method as the old references are now stale.

  Images support “[]” notation. Do `image[index] = 8/16-bit value` to assign an image pixel or `image[index]` to get an image pixel which will be either an 8-bit value for grayscale images of a 16-bit RGB565 value for RGB images.

  For JPEG images the “[]” allows you to access the compressed JPEG image blob as a byte-array. Reading and writing to the data array is opaque however as JPEG images are compressed byte streams.

  Images also support read buffer operations. You can pass images to all sorts of MicroPython functions like as if the image were a byte-array object. In particular, if you’d like to transmit an image you can just pass it to the UART/SPI/I2C write functions to be transmitted automatically.

  width()

    Returns the image width in pixels.

  height()

    Returns the image height in pixels.

  format()

    Returns `sensor.GRAYSCALE` for grayscale images, `sensor.RGB565` for RGB565 images, `sensor.BAYER` for bayer pattern images, and `sensor.JPEG` for JPEG images.

  size()

    Returns the image size in bytes.

  bytearray()

    Returns a `bytearray` object that points to the image data for byte-level read/write access.

    Note

    Image objects are automatically cast as `bytes` objects when passed to MicroPython driver that requires a `bytes` like object. This is read-only access. Call `bytearray()` to get read/write access.

  get_pixel(x, y[, rgbtuple])

    For grayscale images: Returns the grayscale pixel value at location (x, y). For RGB565 images: Returns the RGB888 pixel tuple (r, g, b) at location (x, y). For bayer pattern images: Returns the the pixel value at the location (x, y).

    Returns None if `x` or `y` is outside of the image.

    `x` and `y` may either be passed independently or as a tuple.

    `rgbtuple` if True causes this method to return an RGB888 tuple. Otherwise, this method returns the integer value of the underlying pixel. I.e. for RGB565 images this method returns a RGB565 value. Defaults to True for RGB565 images and False otherwise.

    Not supported on compressed images.

    Note

    `Image.get_pixel()` and `Image.set_pixel()` are the only methods that allow you to manipulate bayer pattern images. Bayer pattern images are literal images where pixels in the image are R/G/R/G/etc. for even rows and G/B/G/B/etc. for odd rows. Each pixel is 8-bits. If you call this method with `rgbtuple` set then `Image.get_pixel()` will debayer the source image at that pixel location and return a valid RGB888 tuple for the pixel location.

  set_pixel(x, y, pixel)

    For grayscale images: Sets the pixel at location (x, y) to the grayscale value `pixel`. For RGB565 images: Sets the pixel at location (x, y) to the RGB888 tuple (r, g, b) `pixel`. For bayer pattern images: Sets the pixel value at the location (x, y) to the value `pixel`.

    Returns the image object so you can call another method using `.` notation.

    `x` and `y` may either be passed independently or as a tuple.

    `pixel` may either be an RGB888 tuple (r, g, b) or the underlying pixel value (i.e. a RGB565 value for RGB565 images or an 8-bit value for grayscale images.

    Not supported on compressed images.

    Note

    `Image.get_pixel()` and `Image.set_pixel()` are the only methods that allow you to manipulate bayer pattern images. Bayer pattern images are literal images where pixels in the image are R/G/R/G/etc. for even rows and G/B/G/B/etc. for odd rows. Each pixel is 8-bits. If you call this method with an RGB888 tuple the grayscale value of that RGB888 tuple is extracted and set to the pixel location.

  mean_pool(x_div, y_div)

    Finds the mean of `x_div` * `y_div` squares in the image and returns the modified image composed of the mean of each square.

    This method allows you to shrink an image down very quickly in-place.

    Not supported on compressed images or bayer images.

  mean_pooled(x_div, y_div)

    Finds the mean of `x_div` * `y_div` squares in the image and returns a new image composed of the mean of each square.

    This method allows you to create a shrunken down image copy.

    Not supported on compressed images or bayer images.

  midpoint_pool(x_div, y_div[, bias=0.5])

    Finds the midpoint of `x_div` * `y_div` squares in the image and returns the modified image composed of the midpoint of each square.

    A `bias` of 0.0 returns the min of each area while a `bias` of 1.0 returns the max of each area.

    This method allows you to shrink an image down very quickly in-place.

    Not supported on compressed images or bayer images.

  midpoint_pooled(x_div, y_div[, bias=0.5])

    Finds the midpoint of `x_div` * `y_div` squares in the image and returns a new image composed of the midpoint of each square.

    A `bias` of 0.0 returns the min of each area while a `bias` of 1.0 returns the max of each area.

    This method allows you to create a shrunken down image copy.

    Not supported on compressed images or bayer images.

  to_bitmap([x_scale=1.0[, y_scale=1.0[, roi=None[, rgb_channel=-1[, alpha=256[, color_palette=None[, alpha_palette=None[, hint=0[, x_size=None[, y_size=None[, copy=False]]]]]]]]]]])

    Converts an image to a bitmap image (1 bit per pixel). If `copy` is False this method will try to modify the image in-place. If `copy` is True then this method will return a new image copy allocated on the heap.

    `copy` may also be another image object, which in this case this method will try to re-use that image objects storage space and will return a new image object that uses the previous image objects storage space. After doing this do not use any references to the old image object anymore as they will be stale.

    `x_scale` controls how much the drawn image is scaled by in the x direction (float). If this value is negative the image will be flipped horizontally.

    `y_scale` controls how much the drawn image is scaled by in the y direction (float). If this value is negative the image will be flipped vertically.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h) of the source image to draw. This allows you to extract just the pixels in the ROI to scale and draw on the destination image.

    `rgb_channel` is the RGB channel (0=R, G=1, B=2) to extract from an RGB565 image (if passed) and to render onto the destination image. For example, if you pass `rgb_channel=1` this will extract the green channel of the source RGB565 image and draw that in grayscale on the destination image.

    `alpha` controls how much of the source image to blend into the destination image. A value of 256 draws an opaque source image while a value lower than 256 produces a blend between the source and destination image. 0 results in no modification to the destination image.

    `color_palette` if not `-1` can be `sensor.PALETTE_RAINBOW`, `sensor.PALETTE_IRONBOW`, or a 256 pixel in total RGB565 image to use as a color lookup table on the grayscale value of whatever the source image is. This is applied after `rgb_channel` extraction if used.

    `alpha_palette` if not `-1` can be a 256 pixel in total GRAYSCALE image to use as a alpha palette which modulates the `alpha` value of the source image being drawn at a pixel pixel level allowing you to precisely control the alpha value of pixels based on their grayscale value. A pixel value of 255 in the alpha lookup table is opaque which anything less than 255 becomes more transparent until 0. This is applied after `rgb_channel` extraction if used.

    `hint` can be a logical OR of the flags:

    > - `image.AREA`: Use area scaling when downscaling versus the default of nearest neighbor.

    > - `image.BILINEAR`: Use bilinear scaling versus the default of nearest neighbor scaling.

    > - `image.BICUBIC`: Use bicubic scaling versus the default of nearest neighbor scaling.

    > - `image.CENTER`: Center the image image being draw on (x, y).

    > - `image.EXTRACT_RGB_CHANNEL_FIRST`: Do rgb_channel extraction before scaling.

    > - `image.APPLY_COLOR_PALETTE_FIRST`: Apply color palette before scaling.

    > - `image.BLACK_BACKGROUND`: Assume the destination image is black. This speeds up drawing.

    `x_size` may be passed if `x_scale` is not passed to specify the size of the image to draw and `x_scale` will automatically be determined passed on the input image size. If neither `y_scale` or `y_size` are specified then `y_scale` internally will be set to be equal to `x_size` to maintain the aspect-ratio.

    `y_size` may be passed if `y_scale` is not passed to specify the size of the image to draw and `y_scale` will automatically be determined passed on the input image size. If neither `x_scale` or `x_size` are specified then `x_scale` internally will be set to be equal to `y_size` to maintain the aspect-ratio.

    Note

    Bitmap images are like grayscale images with only two pixels values - 0 and 1. Additionally, bitmap images are packed such that they only store 1 bit per pixel making them very small. The OpenMV image library allows bitmap images to be used in all places `sensor.GRAYSCALE` and `sensor.RGB565` images can be used. However, many operations when applied on bitmap images don’t make any sense becuase bitmap images only have 2 values. OpenMV recommends using bitmap images for `mask` values in operations and such as they fit on the MicroPython heap quite easily. Finally, bitmap image pixel values 0 and 1 are interpreted as black and white when being applied to `sensor.GRAYSCALE` or `sensor.RGB565` images. The library automatically handles conversion.

    Returns the image object so you can call another method using `.` notation.

  to_grayscale([x_scale=1.0[, y_scale=1.0[, roi=None[, rgb_channel=-1[, alpha=256[, color_palette=None[, alpha_palette=None[, hint=0[, x_size=None[, y_size=None[, copy=False]]]]]]]]]]])

    Converts an image to a grayscale image (8-bits per pixel). If `copy` is False this method will try to modify the image in-place. If `copy` is True then this method will return a new image copy allocated on the heap.

    `copy` may also be another image object, which in this case this method will try to re-use that image objects storage space and will return a new image object that uses the previous image objects storage space. After doing this do not use any references to the old image object anymore as they will be stale.

    `x_scale` controls how much the drawn image is scaled by in the x direction (float). If this value is negative the image will be flipped horizontally.

    `y_scale` controls how much the drawn image is scaled by in the y direction (float). If this value is negative the image will be flipped vertically.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h) of the source image to draw. This allows you to extract just the pixels in the ROI to scale and draw on the destination image.

    `rgb_channel` is the RGB channel (0=R, G=1, B=2) to extract from an RGB565 image (if passed) and to render onto the destination image. For example, if you pass `rgb_channel=1` this will extract the green channel of the source RGB565 image and draw that in grayscale on the destination image.

    `alpha` controls how much of the source image to blend into the destination image. A value of 256 draws an opaque source image while a value lower than 256 produces a blend between the source and destination image. 0 results in no modification to the destination image.

    `color_palette` if not `-1` can be `sensor.PALETTE_RAINBOW`, `sensor.PALETTE_IRONBOW`, or a 256 pixel in total RGB565 image to use as a color lookup table on the grayscale value of whatever the source image is. This is applied after `rgb_channel` extraction if used.

    `alpha_palette` if not `-1` can be a 256 pixel in total GRAYSCALE image to use as a alpha palette which modulates the `alpha` value of the source image being drawn at a pixel pixel level allowing you to precisely control the alpha value of pixels based on their grayscale value. A pixel value of 255 in the alpha lookup table is opaque which anything less than 255 becomes more transparent until 0. This is applied after `rgb_channel` extraction if used.

    `hint` can be a logical OR of the flags:

    > - `image.AREA`: Use area scaling when downscaling versus the default of nearest neighbor.

    > - `image.BILINEAR`: Use bilinear scaling versus the default of nearest neighbor scaling.

    > - `image.BICUBIC`: Use bicubic scaling versus the default of nearest neighbor scaling.

    > - `image.CENTER`: Center the image image being draw on (x, y).

    > - `image.EXTRACT_RGB_CHANNEL_FIRST`: Do rgb_channel extraction before scaling.

    > - `image.APPLY_COLOR_PALETTE_FIRST`: Apply color palette before scaling.

    > - `image.BLACK_BACKGROUND`: Assume the destination image is black. This speeds up drawing.

    `x_size` may be passed if `x_scale` is not passed to specify the size of the image to draw and `x_scale` will automatically be determined passed on the input image size. If neither `y_scale` or `y_size` are specified then `y_scale` internally will be set to be equal to `x_size` to maintain the aspect-ratio.

    `y_size` may be passed if `y_scale` is not passed to specify the size of the image to draw and `y_scale` will automatically be determined passed on the input image size. If neither `x_scale` or `x_size` are specified then `x_scale` internally will be set to be equal to `y_size` to maintain the aspect-ratio.

    Returns the image object so you can call another method using `.` notation.

  to_rgb565([x_scale=1.0[, y_scale=1.0[, roi=None[, rgb_channel=-1[, alpha=256[, color_palette=None[, alpha_palette=None[, hint=0[, x_size=None[, y_size=None[, copy=False]]]]]]]]]]])

    Converts an image to an RGB565 image (16-bits per pixel). If `copy` is False this method will try to modify the image in-place. If `copy` is True then this method will return a new image copy allocated on the heap.

    `copy` may also be another image object, which in this case this method will try to re-use that image objects storage space and will return a new image object that uses the previous image objects storage space. After doing this do not use any references to the old image object anymore as they will be stale.

    `x_scale` controls how much the drawn image is scaled by in the x direction (float). If this value is negative the image will be flipped horizontally.

    `y_scale` controls how much the drawn image is scaled by in the y direction (float). If this value is negative the image will be flipped vertically.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h) of the source image to draw. This allows you to extract just the pixels in the ROI to scale and draw on the destination image.

    `rgb_channel` is the RGB channel (0=R, G=1, B=2) to extract from an RGB565 image (if passed) and to render onto the destination image. For example, if you pass `rgb_channel=1` this will extract the green channel of the source RGB565 image and draw that in grayscale on the destination image.

    `alpha` controls how much of the source image to blend into the destination image. A value of 256 draws an opaque source image while a value lower than 256 produces a blend between the source and destination image. 0 results in no modification to the destination image.

    `color_palette` if not `-1` can be `sensor.PALETTE_RAINBOW`, `sensor.PALETTE_IRONBOW`, or a 256 pixel in total RGB565 image to use as a color lookup table on the grayscale value of whatever the source image is. This is applied after `rgb_channel` extraction if used.

    `alpha_palette` if not `-1` can be a 256 pixel in total GRAYSCALE image to use as a alpha palette which modulates the `alpha` value of the source image being drawn at a pixel pixel level allowing you to precisely control the alpha value of pixels based on their grayscale value. A pixel value of 255 in the alpha lookup table is opaque which anything less than 255 becomes more transparent until 0. This is applied after `rgb_channel` extraction if used.

    `hint` can be a logical OR of the flags:

    > - `image.AREA`: Use area scaling when downscaling versus the default of nearest neighbor.

    > - `image.BILINEAR`: Use bilinear scaling versus the default of nearest neighbor scaling.

    > - `image.BICUBIC`: Use bicubic scaling versus the default of nearest neighbor scaling.

    > - `image.CENTER`: Center the image image being draw on (x, y).

    > - `image.EXTRACT_RGB_CHANNEL_FIRST`: Do rgb_channel extraction before scaling.

    > - `image.APPLY_COLOR_PALETTE_FIRST`: Apply color palette before scaling.

    > - `image.BLACK_BACKGROUND`: Assume the destination image is black. This speeds up drawing.

    `x_size` may be passed if `x_scale` is not passed to specify the size of the image to draw and `x_scale` will automatically be determined passed on the input image size. If neither `y_scale` or `y_size` are specified then `y_scale` internally will be set to be equal to `x_size` to maintain the aspect-ratio.

    `y_size` may be passed if `y_scale` is not passed to specify the size of the image to draw and `y_scale` will automatically be determined passed on the input image size. If neither `x_scale` or `x_size` are specified then `x_scale` internally will be set to be equal to `y_size` to maintain the aspect-ratio.

    Returns the image object so you can call another method using `.` notation.

  to_rainbow([x_scale=1.0[, y_scale=1.0[, roi=None[, rgb_channel=-1[, alpha=256[, color_palette=sensor.PALETTE_RAINBOW[, alpha_palette=None[, hint=0[, x_size=None[, y_size=None[, copy=False]]]]]]]]]]])

    Converts an image to an RGB565 rainbow image (16-bits per pixel). If `copy` is False this method will try to modify the image in-place. If `copy` is True then this method will return a new image copy allocated on the heap.

    `copy` may also be another image object, which in this case this method will try to re-use that image objects storage space and will return a new image object that uses the previous image objects storage space. After doing this do not use any references to the old image object anymore as they will be stale.

    `x_scale` controls how much the drawn image is scaled by in the x direction (float). If this value is negative the image will be flipped horizontally.

    `y_scale` controls how much the drawn image is scaled by in the y direction (float). If this value is negative the image will be flipped vertically.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h) of the source image to draw. This allows you to extract just the pixels in the ROI to scale and draw on the destination image.

    `rgb_channel` is the RGB channel (0=R, G=1, B=2) to extract from an RGB565 image (if passed) and to render onto the destination image. For example, if you pass `rgb_channel=1` this will extract the green channel of the source RGB565 image and draw that in grayscale on the destination image.

    `alpha` controls how much of the source image to blend into the destination image. A value of 256 draws an opaque source image while a value lower than 256 produces a blend between the source and destination image. 0 results in no modification to the destination image.

    `color_palette` if not `-1` can be `sensor.PALETTE_RAINBOW`, `sensor.PALETTE_IRONBOW`, or a 256 pixel in total RGB565 image to use as a color lookup table on the grayscale value of whatever the source image is. This is applied after `rgb_channel` extraction if used.

    `alpha_palette` if not `-1` can be a 256 pixel in total GRAYSCALE image to use as a alpha palette which modulates the `alpha` value of the source image being drawn at a pixel pixel level allowing you to precisely control the alpha value of pixels based on their grayscale value. A pixel value of 255 in the alpha lookup table is opaque which anything less than 255 becomes more transparent until 0. This is applied after `rgb_channel` extraction if used.

    `hint` can be a logical OR of the flags:

    > - `image.AREA`: Use area scaling when downscaling versus the default of nearest neighbor.

    > - `image.BILINEAR`: Use bilinear scaling versus the default of nearest neighbor scaling.

    > - `image.BICUBIC`: Use bicubic scaling versus the default of nearest neighbor scaling.

    > - `image.CENTER`: Center the image image being draw on (x, y).

    > - `image.EXTRACT_RGB_CHANNEL_FIRST`: Do rgb_channel extraction before scaling.

    > - `image.APPLY_COLOR_PALETTE_FIRST`: Apply color palette before scaling.

    > - `image.BLACK_BACKGROUND`: Assume the destination image is black. This speeds up drawing.

    `x_size` may be passed if `x_scale` is not passed to specify the size of the image to draw and `x_scale` will automatically be determined passed on the input image size. If neither `y_scale` or `y_size` are specified then `y_scale` internally will be set to be equal to `x_size` to maintain the aspect-ratio.

    `y_size` may be passed if `y_scale` is not passed to specify the size of the image to draw and `y_scale` will automatically be determined passed on the input image size. If neither `x_scale` or `x_size` are specified then `x_scale` internally will be set to be equal to `y_size` to maintain the aspect-ratio.

    Returns the image object so you can call another method using `.` notation.

  to_ironbow([x_scale=1.0[, y_scale=1.0[, roi=None[, rgb_channel=-1[, alpha=256[, color_palette=sensor.PALETTE_IRONBOW[, alpha_palette=None[, hint=0[, x_size=None[, y_size=None[, copy=False]]]]]]]]]]])

    Converts an image to an RGB565 ironbow image (16-bits per pixel). If `copy` is False this method will try to modify the image in-place. If `copy` is True then this method will return a new image copy allocated on the heap.

    `copy` may also be another image object, which in this case this method will try to re-use that image objects storage space and will return a new image object that uses the previous image objects storage space. After doing this do not use any references to the old image object anymore as they will be stale.

    `x_scale` controls how much the drawn image is scaled by in the x direction (float). If this value is negative the image will be flipped horizontally.

    `y_scale` controls how much the drawn image is scaled by in the y direction (float). If this value is negative the image will be flipped vertically.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h) of the source image to draw. This allows you to extract just the pixels in the ROI to scale and draw on the destination image.

    `rgb_channel` is the RGB channel (0=R, G=1, B=2) to extract from an RGB565 image (if passed) and to render onto the destination image. For example, if you pass `rgb_channel=1` this will extract the green channel of the source RGB565 image and draw that in grayscale on the destination image.

    `alpha` controls how much of the source image to blend into the destination image. A value of 256 draws an opaque source image while a value lower than 256 produces a blend between the source and destination image. 0 results in no modification to the destination image.

    `color_palette` if not `-1` can be `sensor.PALETTE_RAINBOW`, `sensor.PALETTE_IRONBOW`, or a 256 pixel in total RGB565 image to use as a color lookup table on the grayscale value of whatever the source image is. This is applied after `rgb_channel` extraction if used.

    `alpha_palette` if not `-1` can be a 256 pixel in total GRAYSCALE image to use as a alpha palette which modulates the `alpha` value of the source image being drawn at a pixel pixel level allowing you to precisely control the alpha value of pixels based on their grayscale value. A pixel value of 255 in the alpha lookup table is opaque which anything less than 255 becomes more transparent until 0. This is applied after `rgb_channel` extraction if used.

    `hint` can be a logical OR of the flags:

    > - `image.AREA`: Use area scaling when downscaling versus the default of nearest neighbor.

    > - `image.BILINEAR`: Use bilinear scaling versus the default of nearest neighbor scaling.

    > - `image.BICUBIC`: Use bicubic scaling versus the default of nearest neighbor scaling.

    > - `image.CENTER`: Center the image image being draw on (x, y).

    > - `image.EXTRACT_RGB_CHANNEL_FIRST`: Do rgb_channel extraction before scaling.

    > - `image.APPLY_COLOR_PALETTE_FIRST`: Apply color palette before scaling.

    > - `image.BLACK_BACKGROUND`: Assume the destination image is black. This speeds up drawing.

    `x_size` may be passed if `x_scale` is not passed to specify the size of the image to draw and `x_scale` will automatically be determined passed on the input image size. If neither `y_scale` or `y_size` are specified then `y_scale` internally will be set to be equal to `x_size` to maintain the aspect-ratio.

    `y_size` may be passed if `y_scale` is not passed to specify the size of the image to draw and `y_scale` will automatically be determined passed on the input image size. If neither `x_scale` or `x_size` are specified then `x_scale` internally will be set to be equal to `y_size` to maintain the aspect-ratio.

    Returns the image object so you can call another method using `.` notation.

  to_jpeg([x_scale=1.0[, y_scale=1.0[, roi=None[, rgb_channel=-1[, alpha=256[, color_palette=None[, alpha_palette=None[, hint=0[, x_size=None[, y_size=None[, copy=False]]]]]]]]]]])

    Converts an image to a JPEG image. If `copy` is False this method will try to modify the image in-place. If `copy` is True then this method will return a new image copy allocated on the heap.

    `copy` may also be another image object, which in this case this method will try to re-use that image objects storage space and will return a new image object that uses the previous image objects storage space. After doing this do not use any references to the old image object anymore as they will be stale.

    `x_scale` controls how much the drawn image is scaled by in the x direction (float). If this value is negative the image will be flipped horizontally.

    `y_scale` controls how much the drawn image is scaled by in the y direction (float). If this value is negative the image will be flipped vertically.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h) of the source image to draw. This allows you to extract just the pixels in the ROI to scale and draw on the destination image.

    `rgb_channel` is the RGB channel (0=R, G=1, B=2) to extract from an RGB565 image (if passed) and to render onto the destination image. For example, if you pass `rgb_channel=1` this will extract the green channel of the source RGB565 image and draw that in grayscale on the destination image.

    `alpha` controls how much of the source image to blend into the destination image. A value of 256 draws an opaque source image while a value lower than 256 produces a blend between the source and destination image. 0 results in no modification to the destination image.

    `color_palette` if not `-1` can be `sensor.PALETTE_RAINBOW`, `sensor.PALETTE_IRONBOW`, or a 256 pixel in total RGB565 image to use as a color lookup table on the grayscale value of whatever the source image is. This is applied after `rgb_channel` extraction if used.

    `alpha_palette` if not `-1` can be a 256 pixel in total GRAYSCALE image to use as a alpha palette which modulates the `alpha` value of the source image being drawn at a pixel pixel level allowing you to precisely control the alpha value of pixels based on their grayscale value. A pixel value of 255 in the alpha lookup table is opaque which anything less than 255 becomes more transparent until 0. This is applied after `rgb_channel` extraction if used.

    `hint` can be a logical OR of the flags:

    > - `image.AREA`: Use area scaling when downscaling versus the default of nearest neighbor.

    > - `image.BILINEAR`: Use bilinear scaling versus the default of nearest neighbor scaling.

    > - `image.BICUBIC`: Use bicubic scaling versus the default of nearest neighbor scaling.

    > - `image.CENTER`: Center the image image being draw on (x, y).

    > - `image.EXTRACT_RGB_CHANNEL_FIRST`: Do rgb_channel extraction before scaling.

    > - `image.APPLY_COLOR_PALETTE_FIRST`: Apply color palette before scaling.

    > - `image.BLACK_BACKGROUND`: Assume the destination image is black. This speeds up drawing.

    `x_size` may be passed if `x_scale` is not passed to specify the size of the image to draw and `x_scale` will automatically be determined passed on the input image size. If neither `y_scale` or `y_size` are specified then `y_scale` internally will be set to be equal to `x_size` to maintain the aspect-ratio.

    `y_size` may be passed if `y_scale` is not passed to specify the size of the image to draw and `y_scale` will automatically be determined passed on the input image size. If neither `x_scale` or `x_size` are specified then `x_scale` internally will be set to be equal to `y_size` to maintain the aspect-ratio.

    Returns the image object so you can call another method using `.` notation.

  to_png([x_scale=1.0[, y_scale=1.0[, roi=None[, rgb_channel=-1[, alpha=256[, color_palette=None[, alpha_palette=None[, hint=0[, x_size=None[, y_size=None[, copy=False]]]]]]]]]]])

    Converts an image to a PNG image. If `copy` is False this method will try to modify the image in-place. If `copy` is True then this method will return a new image copy allocated on the heap.

    `copy` may also be another image object, which in this case this method will try to re-use that image objects storage space and will return a new image object that uses the previous image objects storage space. After doing this do not use any references to the old image object anymore as they will be stale.

    `x_scale` controls how much the drawn image is scaled by in the x direction (float). If this value is negative the image will be flipped horizontally.

    `y_scale` controls how much the drawn image is scaled by in the y direction (float). If this value is negative the image will be flipped vertically.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h) of the source image to draw. This allows you to extract just the pixels in the ROI to scale and draw on the destination image.

    `rgb_channel` is the RGB channel (0=R, G=1, B=2) to extract from an RGB565 image (if passed) and to render onto the destination image. For example, if you pass `rgb_channel=1` this will extract the green channel of the source RGB565 image and draw that in grayscale on the destination image.

    `alpha` controls how much of the source image to blend into the destination image. A value of 256 draws an opaque source image while a value lower than 256 produces a blend between the source and destination image. 0 results in no modification to the destination image.

    `color_palette` if not `-1` can be `sensor.PALETTE_RAINBOW`, `sensor.PALETTE_IRONBOW`, or a 256 pixel in total RGB565 image to use as a color lookup table on the grayscale value of whatever the source image is. This is applied after `rgb_channel` extraction if used.

    `alpha_palette` if not `-1` can be a 256 pixel in total GRAYSCALE image to use as a alpha palette which modulates the `alpha` value of the source image being drawn at a pixel pixel level allowing you to precisely control the alpha value of pixels based on their grayscale value. A pixel value of 255 in the alpha lookup table is opaque which anything less than 255 becomes more transparent until 0. This is applied after `rgb_channel` extraction if used.

    `hint` can be a logical OR of the flags:

    > - `image.AREA`: Use area scaling when downscaling versus the default of nearest neighbor.

    > - `image.BILINEAR`: Use bilinear scaling versus the default of nearest neighbor scaling.

    > - `image.BICUBIC`: Use bicubic scaling versus the default of nearest neighbor scaling.

    > - `image.CENTER`: Center the image image being draw on (x, y).

    > - `image.EXTRACT_RGB_CHANNEL_FIRST`: Do rgb_channel extraction before scaling.

    > - `image.APPLY_COLOR_PALETTE_FIRST`: Apply color palette before scaling.

    > - `image.BLACK_BACKGROUND`: Assume the destination image is black. This speeds up drawing.

    `x_size` may be passed if `x_scale` is not passed to specify the size of the image to draw and `x_scale` will automatically be determined passed on the input image size. If neither `y_scale` or `y_size` are specified then `y_scale` internally will be set to be equal to `x_size` to maintain the aspect-ratio.

    `y_size` may be passed if `y_scale` is not passed to specify the size of the image to draw and `y_scale` will automatically be determined passed on the input image size. If neither `x_scale` or `x_size` are specified then `x_scale` internally will be set to be equal to `y_size` to maintain the aspect-ratio.

    Returns the image object so you can call another method using `.` notation.

  compress([quality=90])

    JPEG compresses the image in place. Use this method versus `Image.compressed()` to save heap space and to use a higher `quality` for compression at the cost of destroying the original image.

    Returns the image object so you can call another method using `.` notation.

    `quality` is the compression quality (0-100) (int).

    Returns the compressed image if called on a compressed image.

  compress_for_ide([quality=90])

    JPEG compresses the image in place. Use this method versus `Image.compressed()` to save heap space and to use a higher `quality` for compression at the cost of destroying the original image.

    This method JPEG compresses the image and then formats the JPEG data for transmission to OpenMV IDE to display by encoding every 6-bits as a byte valued between 128-191. This is done to prevent JPEG data from being misinterpreted as other text data in the byte stream.

    You need to use this method to format image data for display to terminal windows created via “Open Terminal” in OpenMV IDE.

    Returns the image object so you can call another method using `.` notation.

    `quality` is the compression quality (0-100) (int).

    Returns the image compressed for the IDE if called on a compressed image. Do not call this on an image already compressed for the IDE.

  compressed([quality=90])

    Returns a JPEG compressed image - the original image is untouched. However, this method requires a somewhat large allocation of heap space so the image compression quality must be lower and the image resolution must be lower than what you could do with `Image.compress()`.

    `quality` is the compression quality (0-100) (int).

    Returns a compressed image copy if called on a compressed image.

  compressed_for_ide([quality=90])

    Returns a JPEG compressed image - the original image is untouched. However, this method requires a somewhat large allocation of heap space so the image compression quality must be lower and the image resolution must be lower than what you could do with `Image.compress()`.

    This method JPEG compresses the image and then formats the JPEG data for transmission to OpenMV IDE to display by encoding every 6-bits as a byte valued between 128-191. This is done to prevent JPEG data from being misinterpreted as other text data in the byte stream.

    You need to use this method to format image data for display to terminal windows created via “Open Terminal” in OpenMV IDE.

    `quality` is the compression quality (0-100) (int).

    Returns a image compressed for the IDE copy if called on a compressed image. Do not call this on an image already compressed for the IDE.

  copy([x_scale=1.0[, y_scale=1.0[, roi=None[, rgb_channel=-1[, alpha=256[, color_palette=None[, alpha_palette=None[, hint=0[, x_size=None[, y_size=None[, copy_to_fb=False]]]]]]]]]]])

    Creates a deep copy of the image object. If `copy_to_fb` is False then the new image is allocated on the MicroPython heap. However, the MicroPython heap is limited and may not have space to store the new image if exhausted. Instead, set `copy_to_fb` to True to set the frame buffer to the new image making this function work just like `sensor.snapshot()`.

    `x_scale` controls how much the drawn image is scaled by in the x direction (float). If this value is negative the image will be flipped horizontally.

    `y_scale` controls how much the drawn image is scaled by in the y direction (float). If this value is negative the image will be flipped vertically.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h) of the source image to draw. This allows you to extract just the pixels in the ROI to scale and draw on the destination image.

    `rgb_channel` is the RGB channel (0=R, G=1, B=2) to extract from an RGB565 image (if passed) and to render onto the destination image. For example, if you pass `rgb_channel=1` this will extract the green channel of the source RGB565 image and draw that in grayscale on the destination image.

    `alpha` controls how much of the source image to blend into the destination image. A value of 256 draws an opaque source image while a value lower than 256 produces a blend between the source and destination image. 0 results in no modification to the destination image.

    `color_palette` if not `-1` can be `sensor.PALETTE_RAINBOW`, `sensor.PALETTE_IRONBOW`, or a 256 pixel in total RGB565 image to use as a color lookup table on the grayscale value of whatever the source image is. This is applied after `rgb_channel` extraction if used.

    `alpha_palette` if not `-1` can be a 256 pixel in total GRAYSCALE image to use as a alpha palette which modulates the `alpha` value of the source image being drawn at a pixel pixel level allowing you to precisely control the alpha value of pixels based on their grayscale value. A pixel value of 255 in the alpha lookup table is opaque which anything less than 255 becomes more transparent until 0. This is applied after `rgb_channel` extraction if used.

    `hint` can be a logical OR of the flags:

    > - `image.AREA`: Use area scaling when downscaling versus the default of nearest neighbor.

    > - `image.BILINEAR`: Use bilinear scaling versus the default of nearest neighbor scaling.

    > - `image.BICUBIC`: Use bicubic scaling versus the default of nearest neighbor scaling.

    > - `image.CENTER`: Center the image image being draw on (x, y).

    > - `image.EXTRACT_RGB_CHANNEL_FIRST`: Do rgb_channel extraction before scaling.

    > - `image.APPLY_COLOR_PALETTE_FIRST`: Apply color palette before scaling.

    > - `image.BLACK_BACKGROUND`: Assume the destination image is black. This speeds up drawing.

    `x_size` may be passed if `x_scale` is not passed to specify the size of the image to draw and `x_scale` will automatically be determined passed on the input image size. If neither `y_scale` or `y_size` are specified then `y_scale` internally will be set to be equal to `x_size` to maintain the aspect-ratio.

    `y_size` may be passed if `y_scale` is not passed to specify the size of the image to draw and `y_scale` will automatically be determined passed on the input image size. If neither `x_scale` or `x_size` are specified then `x_scale` internally will be set to be equal to `y_size` to maintain the aspect-ratio.

    `copy_to_fb` may also be another image object if you want to replace that image object’s memory buffer, type, width, and height with new image data.

    Note

    Any use of `copy_to_fb` invalidates the previous image object it overwrites. Do not use any references to previous image objects anymore it overwrites. Either for an image object referencing the frame buffer, frame buffer stack, or an image on the MicroPython heap.

    Returns the new image object.

    Not supported on compressed images.

  crop([x_scale=1.0[, y_scale=1.0[, roi=None[, rgb_channel=-1[, alpha=256[, color_palette=None[, alpha_palette=None[, hint=0[, x_size=None[, y_size=None[, copy=False]]]]]]]]]]])

    Modifies an image in-place without changing the underlying image type. If `copy` is False this method will try to modify the image in-place. If `copy` is True then this method will return a new image copy allocated on the heap.

    `copy` may also be another image object, which in this case this method will try to re-use that image objects storage space and will return a new image object that uses the previous image objects storage space. After doing this do not use any references to the old image object anymore as they will be stale.

    `x_scale` controls how much the drawn image is scaled by in the x direction (float). If this value is negative the image will be flipped horizontally.

    `y_scale` controls how much the drawn image is scaled by in the y direction (float). If this value is negative the image will be flipped vertically.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h) of the source image to draw. This allows you to extract just the pixels in the ROI to scale and draw on the destination image.

    `rgb_channel` is the RGB channel (0=R, G=1, B=2) to extract from an RGB565 image (if passed) and to render onto the destination image. For example, if you pass `rgb_channel=1` this will extract the green channel of the source RGB565 image and draw that in grayscale on the destination image.

    `alpha` controls how much of the source image to blend into the destination image. A value of 256 draws an opaque source image while a value lower than 256 produces a blend between the source and destination image. 0 results in no modification to the destination image.

    `color_palette` if not `-1` can be `sensor.PALETTE_RAINBOW`, `sensor.PALETTE_IRONBOW`, or a 256 pixel in total RGB565 image to use as a color lookup table on the grayscale value of whatever the source image is. This is applied after `rgb_channel` extraction if used.

    `alpha_palette` if not `-1` can be a 256 pixel in total GRAYSCALE image to use as a alpha palette which modulates the `alpha` value of the source image being drawn at a pixel pixel level allowing you to precisely control the alpha value of pixels based on their grayscale value. A pixel value of 255 in the alpha lookup table is opaque which anything less than 255 becomes more transparent until 0. This is applied after `rgb_channel` extraction if used.

    `hint` can be a logical OR of the flags:

    > - `image.AREA`: Use area scaling when downscaling versus the default of nearest neighbor.

    > - `image.BILINEAR`: Use bilinear scaling versus the default of nearest neighbor scaling.

    > - `image.BICUBIC`: Use bicubic scaling versus the default of nearest neighbor scaling.

    > - `image.CENTER`: Center the image image being draw on (x, y).

    > - `image.EXTRACT_RGB_CHANNEL_FIRST`: Do rgb_channel extraction before scaling.

    > - `image.APPLY_COLOR_PALETTE_FIRST`: Apply color palette before scaling.

    > - `image.BLACK_BACKGROUND`: Assume the destination image is black. This speeds up drawing.

    `x_size` may be passed if `x_scale` is not passed to specify the size of the image to draw and `x_scale` will automatically be determined passed on the input image size. If neither `y_scale` or `y_size` are specified then `y_scale` internally will be set to be equal to `x_size` to maintain the aspect-ratio.

    `y_size` may be passed if `y_scale` is not passed to specify the size of the image to draw and `y_scale` will automatically be determined passed on the input image size. If neither `x_scale` or `x_size` are specified then `x_scale` internally will be set to be equal to `y_size` to maintain the aspect-ratio.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images.

  scale([x_scale=1.0[, y_scale=1.0[, roi=None[, rgb_channel=-1[, alpha=256[, color_palette=None[, alpha_palette=None[, hint=0[, x_size=None[, y_size=None[, copy=False]]]]]]]]]]])

    Modifies an image in-place without changing the underlying image type. If `copy` is False this method will try to modify the image in-place. If `copy` is True then this method will return a new image copy allocated on the heap.

    `copy` may also be another image object, which in this case this method will try to re-use that image objects storage space and will return a new image object that uses the previous image objects storage space. After doing this do not use any references to the old image object anymore as they will be stale.

    `x_scale` controls how much the drawn image is scaled by in the x direction (float). If this value is negative the image will be flipped horizontally.

    `y_scale` controls how much the drawn image is scaled by in the y direction (float). If this value is negative the image will be flipped vertically.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h) of the source image to draw. This allows you to extract just the pixels in the ROI to scale and draw on the destination image.

    `rgb_channel` is the RGB channel (0=R, G=1, B=2) to extract from an RGB565 image (if passed) and to render onto the destination image. For example, if you pass `rgb_channel=1` this will extract the green channel of the source RGB565 image and draw that in grayscale on the destination image.

    `alpha` controls how much of the source image to blend into the destination image. A value of 256 draws an opaque source image while a value lower than 256 produces a blend between the source and destination image. 0 results in no modification to the destination image.

    `color_palette` if not `-1` can be `sensor.PALETTE_RAINBOW`, `sensor.PALETTE_IRONBOW`, or a 256 pixel in total RGB565 image to use as a color lookup table on the grayscale value of whatever the source image is. This is applied after `rgb_channel` extraction if used.

    `alpha_palette` if not `-1` can be a 256 pixel in total GRAYSCALE image to use as a alpha palette which modulates the `alpha` value of the source image being drawn at a pixel pixel level allowing you to precisely control the alpha value of pixels based on their grayscale value. A pixel value of 255 in the alpha lookup table is opaque which anything less than 255 becomes more transparent until 0. This is applied after `rgb_channel` extraction if used.

    `hint` can be a logical OR of the flags:

    > - `image.AREA`: Use area scaling when downscaling versus the default of nearest neighbor.

    > - `image.BILINEAR`: Use bilinear scaling versus the default of nearest neighbor scaling.

    > - `image.BICUBIC`: Use bicubic scaling versus the default of nearest neighbor scaling.

    > - `image.CENTER`: Center the image image being draw on (x, y).

    > - `image.EXTRACT_RGB_CHANNEL_FIRST`: Do rgb_channel extraction before scaling.

    > - `image.APPLY_COLOR_PALETTE_FIRST`: Apply color palette before scaling.

    > - `image.BLACK_BACKGROUND`: Assume the destination image is black. This speeds up drawing.

    `x_size` may be passed if `x_scale` is not passed to specify the size of the image to draw and `x_scale` will automatically be determined passed on the input image size. If neither `y_scale` or `y_size` are specified then `y_scale` internally will be set to be equal to `x_size` to maintain the aspect-ratio.

    `y_size` may be passed if `y_scale` is not passed to specify the size of the image to draw and `y_scale` will automatically be determined passed on the input image size. If neither `x_scale` or `x_size` are specified then `x_scale` internally will be set to be equal to `y_size` to maintain the aspect-ratio.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images.

  save(path[, roi[, quality=50]])

    Saves a copy of the image to the filesystem at `path`.

    Supports bmp/pgm/ppm/jpg/jpeg image files. Note that you cannot save jpeg compressed images to an uncompressed format.

    `roi` is the region-of-interest rectangle (x, y, w, h) to save from. If not specified, it is equal to the image rectangle which copies the entire image. This argument is not applicable for JPEG images.

    `quality` is the jpeg compression quality to use to save the image to jpeg format if the image is not already compressed (0-100) (int).

    Returns the image object so you can call another method using `.` notation.

  flush()

    Updates the frame buffer in the IDE with the image in the frame buffer on the camera.

  clear([mask])

    Sets all pixels in the image to zero (very fast).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images.

  draw_line(x0, y0, x1, y1[, color[, thickness=1]])

    Draws a line from (x0, y0) to (x1, y1) on the image. You may either pass x0, y0, x1, y1 separately or as a tuple (x0, y0, x1, y1).

    `color` is an RGB888 tuple for Grayscale or RGB565 images. Defaults to white. However, you may also pass the underlying pixel value (0-255) for grayscale images or a RGB565 value for RGB565 images.

    `thickness` controls how thick the line is in pixels.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  draw_rectangle(x, y, w, h[, color[, thickness=1[, fill=False]]])

    Draws a rectangle on the image. You may either pass x, y, w, h separately or as a tuple (x, y, w, h).

    `color` is an RGB888 tuple for Grayscale or RGB565 images. Defaults to white. However, you may also pass the underlying pixel value (0-255) for grayscale images or a RGB565 value for RGB565 images.

    `thickness` controls how thick the lines are in pixels.

    Pass `fill` set to True to fill the rectangle.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  draw_circle(x, y, radius[, color[, thickness=1[, fill=False]]])

    Draws a circle on the image. You may either pass x, y, radius separately or as a tuple (x, y, radius).

    `color` is an RGB888 tuple for Grayscale or RGB565 images. Defaults to white. However, you may also pass the underlying pixel value (0-255) for grayscale images or a RGB565 value for RGB565 images.

    `thickness` controls how thick the edges are in pixels.

    Pass `fill` set to True to fill the circle.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  draw_ellipse(cx, cy, rx, ry, rotation[, color[, thickness=1[, fill=False]]])

    Draws an ellipse on the image. You may either pass cx, cy, rx, ry, and the rotation (in degrees) separately or as a tuple (cx, yc, rx, ry, rotation).

    `color` is an RGB888 tuple for Grayscale or RGB565 images. Defaults to white. However, you may also pass the underlying pixel value (0-255) for grayscale images or a RGB565 value for RGB565 images.

    `thickness` controls how thick the edges are in pixels.

    Pass `fill` set to True to fill the ellipse.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  draw_string(x, y, text[, color[, scale=1[, x_spacing=0[, y_spacing=0[, mono_space=True[, char_rotation=0[, char_hmirror=False[, char_vflip=False[, string_rotation=0[, string_hmirror=False[, string_vflip=False]]]]]]]]]]])

    Draws 8x10 text starting at location (x, y) in the image. You may either pass x, y separately or as a tuple (x, y).

    `text` is a string to write to the image. `\n`, `\r`, and `\r\n` line endings move the cursor to the next line.

    `color` is an RGB888 tuple for Grayscale or RGB565 images. Defaults to white. However, you may also pass the underlying pixel value (0-255) for grayscale images or a RGB565 value for RGB565 images.

    `scale` may be increased to increase/decrease the size of the text on the image. You can pass greater than 0 integer or floating point values.

    `x_spacing` allows you to add (if positive) or subtract (if negative) x pixels between cahracters.

    `y_spacing` allows you to add (if positive) or subtract (if negative) y pixels between cahracters (for multi-line text).

    `mono_space` defaults to True which forces text to be fixed spaced. For large text scales this looks terrible. Set the False to get non-fixed width character spacing which looks A LOT better.

    `char_rotation` may be 0, 90, 180, 270 to rotate each character in the string by this amount.

    `char_hmirror` if True horizontally mirrors all characters in the string.

    `char_vflip` if True vertically flips all characters in the string.

    `string_rotation` may be 0, 90, 180, 270 to rotate the string by this amount.

    `string_hmirror` if True horizontally mirrors the string.

    `string_vflip` if True vertically flips the string.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  draw_cross(x, y[, color[, size=5[, thickness=1]]])

    Draws a cross at location x, y. You may either pass x, y separately or as a tuple (x, y).

    `color` is an RGB888 tuple for Grayscale or RGB565 images. Defaults to white. However, you may also pass the underlying pixel value (0-255) for grayscale images or a RGB565 value for RGB565 images.

    `size` controls how long the lines of the cross extend.

    `thickness` controls how thick the edges are in pixels.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  draw_arrow(x0, y0, x1, y1[, color[, thickness=1]])

    Draws an arrow from (x0, y0) to (x1, y1) on the image. You may either pass x0, y0, x1, y1 separately or as a tuple (x0, y0, x1, y1).

    `color` is an RGB888 tuple for Grayscale or RGB565 images. Defaults to white. However, you may also pass the underlying pixel value (0-255) for grayscale images or a RGB565 value for RGB565 images.

    `thickness` controls how thick the line is in pixels.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  draw_edges(image, corners[, color[, size=0[, thickness=1[, fill=False]]]])

    Draws line edges between a corner list returned by methods like `blob.corners`. Coners is a four valued tuple of two valued x/y tuples. E.g. [(x1,y1),(x2,y2),(x3,y3),(x4,y4)].

    `color` is an RGB888 tuple for Grayscale or RGB565 images. Defaults to white. However, you may also pass the underlying pixel value (0-255) for grayscale images or a RGB565 value for RGB565 images.

    `size` if greater than 0 causes the corners to be drawn as circles of radius `size`.

    `thickness` controls how thick the line is in pixels.

    Pass `fill` set to True to fill the corner circles if drawn.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  draw_image(image, x, y[, x_scale=1.0[, y_scale=1.0[, roi=None[, rgb_channel=-1[, alpha=256[, color_palette=None[, alpha_palette=None[, hint=0[, x_size=None[, y_size=None]]]]]]]]]])

    Draws an `image` whose top-left corner starts at location x, y. You may either pass x, y separately or as a tuple (x, y). This method automatically handles rendering the image passed into the correct pixel format for the destination image while also handling clipping seamlessly.

    `x_scale` controls how much the drawn image is scaled by in the x direction (float). If this value is negative the image will be flipped horizontally.

    `y_scale` controls how much the drawn image is scaled by in the y direction (float). If this value is negative the image will be flipped vertically.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h) of the source image to draw. This allows you to extract just the pixels in the ROI to scale and draw on the destination image.

    `rgb_channel` is the RGB channel (0=R, G=1, B=2) to extract from an RGB565 image (if passed) and to render onto the destination image. For example, if you pass `rgb_channel=1` this will extract the green channel of the source RGB565 image and draw that in grayscale on the destination image.

    `alpha` controls how much of the source image to blend into the destination image. A value of 256 draws an opaque source image while a value lower than 256 produces a blend between the source and destination image. 0 results in no modification to the destination image.

    `color_palette` if not `-1` can be `sensor.PALETTE_RAINBOW`, `sensor.PALETTE_IRONBOW`, or a 256 pixel in total RGB565 image to use as a color lookup table on the grayscale value of whatever the source image is. This is applied after `rgb_channel` extraction if used.

    `alpha_palette` if not `-1` can be a 256 pixel in total GRAYSCALE image to use as a alpha palette which modulates the `alpha` value of the source image being drawn at a pixel pixel level allowing you to precisely control the alpha value of pixels based on their grayscale value. A pixel value of 255 in the alpha lookup table is opaque which anything less than 255 becomes more transparent until 0. This is applied after `rgb_channel` extraction if used.

    `hint` can be a logical OR of the flags:

    > - `image.AREA`: Use area scaling when downscaling versus the default of nearest neighbor.

    > - `image.BILINEAR`: Use bilinear scaling versus the default of nearest neighbor scaling.

    > - `image.BICUBIC`: Use bicubic scaling versus the default of nearest neighbor scaling.

    > - `image.CENTER`: Center the image image being draw on (x, y).

    > - `image.EXTRACT_RGB_CHANNEL_FIRST`: Do rgb_channel extraction before scaling.

    > - `image.APPLY_COLOR_PALETTE_FIRST`: Apply color palette before scaling.

    > - `image.BLACK_BACKGROUND`: Assume the destination image is black. This speeds up drawing.

    `x_size` may be passed if `x_scale` is not passed to specify the size of the image to draw and `x_scale` will automatically be determined passed on the input image size. If neither `y_scale` or `y_size` are specified then `y_scale` internally will be set to be equal to `x_size` to maintain the aspect-ratio.

    `y_size` may be passed if `y_scale` is not passed to specify the size of the image to draw and `y_scale` will automatically be determined passed on the input image size. If neither `x_scale` or `x_size` are specified then `x_scale` internally will be set to be equal to `y_size` to maintain the aspect-ratio.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images.

  draw_keypoints(keypoints[, color[, size=10[, thickness=1[, fill=False]]]])

    Draws the keypoints of a keypoints object on the image. You may also pass a list of three value tuples containing the (x, y, rotation_angle_in_degrees) to re-use this method for drawing keypoint glyphs which are a cirle with a line pointing in a particular direction.

    `color` is an RGB888 tuple for Grayscale or RGB565 images. Defaults to white. However, you may also pass the underlying pixel value (0-255) for grayscale images or a RGB565 value for RGB565 images.

    `size` controls how large the keypoints are.

    `thickness` controls how thick the line is in pixels.

    Pass `fill` set to True to fill the keypoints.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  flood_fill(x, y[, seed_threshold=0.05[, floating_threshold=0.05[, color[, invert=False[, clear_background=False[, mask=None]]]]]])

    Flood fills a region of the image starting from location x, y. You may either pass x, y separately or as a tuple (x, y).

    `seed_threshold` controls how different any pixel in the fill area may be from the original starting pixel.

    `floating_threshold` controls how different any pixel in the fill area may be from any neighbor pixels.

    `color` is an RGB888 tuple for Grayscale or RGB565 images. Defaults to white. However, you may also pass the underlying pixel value (0-255) for grayscale images or a RGB565 value for RGB565 images.

    Pass `invert` as True to re-color everything outside of the flood-fill connected area.

    Pass `clear_background` as True to zero the rest of the pixels that flood-fill did not re-color.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are evaluated when flood filling.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  mask_rectange([x, y, w, h])

    Zeros a rectangular part of the image. If no arguments are supplied this method zeros the center of the image.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  mask_circle([x, y, radius])

    Zeros a circular part of the image. If no arguments are supplied this method zeros the center of the image.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  mask_ellipse([x, y, radius_x, radius_y, rotation_angle_in_degrees])

    Zeros an ellipsed shaped part of the image. If no arguments are supplied this method zeros the center of the image.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  binary(thresholds[, invert=False[, zero=False[, mask=None[, to_bitmap=False[, copy=False]]]]])

    Sets all pixels in the image to black or white depending on if the pixel is inside of a threshold in the threshold list `thresholds` or not.

    `thresholds` must be a list of tuples `[(lo, hi), (lo, hi), ..., (lo, hi)]` defining the ranges of color you want to track. For grayscale images each tuple needs to contain two values - a min grayscale value and a max grayscale value. Only pixel regions that fall between these thresholds will be considered. For RGB565 images each tuple needs to have six values (l_lo, l_hi, a_lo, a_hi, b_lo, b_hi) - which are minimums and maximums for the LAB L, A, and B channels respectively. For easy usage this function will automatically fix swapped min and max values. Additionally, if a tuple is larger than six values the rest are ignored. Conversely, if the tuple is too short the rest of the thresholds are assumed to be at maximum range.

    Note

    To get the thresholds for the object you want to track just select (click and drag) on the object you want to track in the IDE frame buffer. The histogram will then update to just be in that area. Then just write down where the color distribution starts and falls off in each histogram channel. These will be your low and high values for `thresholds`. It’s best to manually determine the thresholds versus using the upper and lower quartile statistics because they are too tight.

    You may also determine color thresholds by going into `Tools->Machine Vision->Threshold Editor` in OpenMV IDE and selecting thresholds from the GUI slider window.

    `invert` inverts the thresholding operation such that instead of matching pixels inside of some known color bounds pixels are matched that are outside of the known color bounds.

    Set `zero` to True to instead zero thresholded pixels and leave pixels not in the threshold list untouched.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    `to_bitmap` turns the image data into a binary bitmap image where each pixel is stored in 1 bit. For very small images the new bitmap image may not fit inside of the original image requiring an out-of-place operation using `copy`.

    `copy` if True creates a copy of the binarized image on the heap versus modifying the source image.

    Note

    Bitmap images are like grayscale images with only two pixels values - 0 and 1. Additionally, bitmap images are packed such that they only store 1 bit per pixel making them very small. The OpenMV image library allows bitmap images to be used in all places `sensor.GRAYSCALE` and `sensor.RGB565` images can be used. However, many operations when applied on bitmap images don’t make any sense becuase bitmap images only have 2 values. OpenMV recommends using bitmap images for `mask` values in operations and such as they fit on the MicroPython heap quite easily. Finally, bitmap image pixel values 0 and 1 are interpreted as black and white when being applied to `sensor.GRAYSCALE` or `sensor.RGB565` images. The library automatically handles conversion.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  invert()

    Flips (binary inverts) all pixels values in a binary image very quickly.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  b_and(image[, mask=None])

    Logically ANDs this image with another image.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  b_nand(image[, mask=None])

    Logically NANDs this image with another image.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  b_or(image[, mask=None])

    Logically ORs this image with another image.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  b_nor(image[, mask=None])

    Logically NORs this image with another image.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  b_xor(image[, mask=None])

    Logically XORs this image with another image.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  b_xnor(image[, mask=None])

    Logically XNORs this image with another image.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  erode(size[, threshold[, mask=None]])

    Removes pixels from the edges of segmented areas.

    This method works by convolving a kernel of ((size*2)+1)x((size*2)+1) pixels across the image and zeroing the center pixel of the kernel if the sum of the neighbour pixels set is not greater than `threshold`.

    This method works like the standard erode method if threshold is not set. If `threshold` is set then you can specify erode to only erode pixels that have, for example, less than 2 pixels set around them with a threshold of 2.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  dilate(size[, threshold[, mask=None]])

    Adds pixels to the edges of segmented areas.

    This method works by convolving a kernel of ((size*2)+1)x((size*2)+1) pixels across the image and setting the center pixel of the kernel if the sum of the neighbour pixels set is greater than `threshold`.

    This method works like the standard dilate method if threshold is not set. If `threshold` is set then you can specify dilate to only dilate pixels that have, for example, more than 2 pixels set around them with a threshold of 2.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  open(size[, threshold[, mask=None]])

    Performs erosion and dilation on an image in order. Please see `Image.erode()` and `Image.dilate()` for more information.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  close(size[, threshold[, mask=None]])

    Performs dilation and erosion on an image in order. Please see `Image.dilate()` and `Image.erode()` for more information.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  top_hat(size[, threshold[, mask=None]])

    Returns the image difference of the image and `Image.open()`’ed image.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Not supported on compressed images or bayer images.

  black_hat(size[, threshold[, mask=None]])

    Returns the image difference of the image and `Image.close()`’ed image.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Not supported on compressed images or bayer images.

  awb(max=False)

    Performs automatic white balance on the image using the gray-world algorithm. This method operates on RAW Bayer Images so that you can improve image quality before converting to RGB565 or passing the RAW Bayer Image to an image processing funciton. You may also call this on a RGB565. This method has no affect on binary/grayscale images.

    `max` if True uses the white-patch algorithm instead.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images.

  ccm(matrix)

    Multiples the passed (3x3) or (4x3) floating-point color-correction-matrix with the image.

    For example, if you pass a 3x3 matrix the method will do this to every pixel the image:

           1x3         1x4         4x3
        [R, G, B] = [r, g, b][1, 0, 0,
                              0, 1, 0,
                              0, 0, 1]

    Note that the sum of each column in the 3x3 matrix should generally be 1. Weights may either be positive or negative but, again, should generally sum to 1 per column.

    If you pass a 4x3 matrix the method will do this to every pixel the image:

           1x3         1x4         4x3
        [R, G, B] = [r, g, b, 1][1, 0, 0,
                                 0, 1, 0,
                                 0, 0, 1,
                                 0, 0, 0]

    Where the last row of the 4x3 matrix is an offset per color channel. If you add an offset you may wish to make the weights sum to less than 1 to account for the offset.

    You want to use this method to eliminate systemic cross talk between color channels. However, this should generally be never.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  gamma([gamma=1.0, [contrast=1.0, [brightness=0.0])

    Quickly changes the image gamma, contrast, and brightness. Please use this method instead of `Image.mul` or `Image.div` which are meant for blending to adjust pixels values.

    `gamma` with values greater than 1.0 makes the image darker in a non-linear manner while less than 1.0 makes the image brighter. The gamma value is applied to the image by scaling all pixel color channels to be between [0:1) and then doing a remapping of `pow(pixel, 1/gamma)` on all pixels before scaling back.

    `contrast` with values greater than 1.0 makes the image brighter in a linear manner while less than 1.0 makes the image darker. The contrast value is applied to the image by scaling all pixel color channels to be between [0:1) and then doing a remapping of `pixel * contrast` on all pixels before scaling back.

    `brightness` with values greater than 0.0 makes the image brighter in a constant manner while less than 0.0 makes the image darker. The brightness value is applied to the image by scaling all pixel color channels to be between [0:1) and then doing a remapping of `pixel + brightness` on all pixels before scaling back.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images.

  gamma_corr([gamma=1.0, [contrast=1.0, [brightness=0.0])

    Alias for `Image.gamma`.

  negate()

    Flips (numerically inverts) all pixels values in an image very quickly. E.g. for GRAYSCALE images this method changes all pixels from `pixel` to `255 - pixel`.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  replace(image[, hmirror=False[, vflip=False[, transpose=False[, mask=None]]]])

    Replaces all pixels in the image object with a new image.

    `image` can either be another image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    Set `hmirror` to True to horizontally mirror the replacing image.

    Set `vflip` to True to vertically flip the replacing image.

    Set `transpose` to True to flip the image along the diagonal (this changes the image image width/height if the image is non-square).

    If you want to rotate an image by multiples of 90 degrees pass the following:

    > - vflip=False, hmirror=False, transpose=False -> 0 degree rotation

    > - vflip=True, hmirror=False, transpose=True -> 90 degree rotation

    > - vflip=True, hmirror=True, transpose=False -> 180 degree rotation

    > - vflip=False, hmirror=True, transpose=True -> 270 degree rotation

    Note

    If you don’t pass an `image` this method will operate on the underlying image that you were going to replace by applying the `hmirror`, `vflip`, and `transpose` options to rotate the image around. E.g. if you want to do `img.replace(img, etc...)` you just need to do `img.replace(etc..)`.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified. Note that the mask is applied on the image before hmirror/vflip/transpose so the mask should be the same width/height of the initial unmodifed image.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  assign(image[, hmirror=False[, vflip=False[, transpose=False[, mask=None]]]])

    Alias for `Image.replace`.

  set(image[, hmirror=False[, vflip=False[, transpose=False[, mask=None]]]])

    Alias for `Image.replace`.

  add(image[, mask=None])

    Adds an image pixel-wise to this one.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  sub(image[, reverse=False[, mask=None]])

    Subtracts an image pixel-wise to this one.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    Set `reverse` to True to reverse the subtraction operation from `this_image-image` to `image-this_image`.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  mul(image[, invert=False[, mask=None]])

    Multiplies two images pixel-wise with each other.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    Set `invert` to True to change the multiplication operation from `a*b` to `1/((1/a)*(1/b))`. In particular, this lightens the image instead of darkening it (e.g. multiply versus burn operations).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

    Note

    This method is meant for image blending and cannot multiply the pixels in the image by a scalar like `2`. Use `Image.gamma_corr` for that.

  div(image[, invert=False[, mod=False[, mask=None]]])

    Divides this image by another one.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    Set `invert` to True to change the division direction from `a/b` to `b/a`.

    Set `mod` to True to change the division operation to the modulus operation.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

    Note

    This method is meant for image blending and cannot divide the pixels in the image by a scalar like `2`. Use `Image.gamma_corr` for that.

  min(image[, mask=None])

    Returns the minimum image of two images pixel-wise.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  max(image[, mask=None])

    Returns the maximum image of two images pixel-wise.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  difference(image[, mask=None])

    Returns the absolute difference image between two images (e.g. ||a-b||).

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  blend(image[, alpha=128[, mask=None]])

    Alpha blends two images with each other.

    `image` can either be an image object, a path to an uncompressed image file (bmp/pgm/ppm), or a scalar value. If a scalar value the value can either be an RGB888 tuple or the underlying pixel value (e.g. an 8-bit grayscale for grayscale images or a RGB565 value for RGB images).

    `alpha` controls how much of the other image to blend into this image. `alpha` should be an integer value between 0 and 256. A value closer to zero blends more of the other image into this image and a value closer to 256 does the opposite.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  histeq([adaptive=False[, clip_limit=-1[, mask=None]]])

    Runs the histogram equalization algorithm on the image. Histogram equalization normalizes the contrast and brightness in the image.

    If you pass `adaptive` as True then an adaptive histogram equalization method will be run on the image instead which as generally better results than non-adaptive histogram qualization but a longer run time.

    `clip_limit` provides a way to limit the contrast of the adaptive histogram qualization. Use a small value for this, like 10, to produce good histogram equalized contrast limited images.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  mean(size, [threshold=False, [offset=0, [invert=False, [mask=None]]]]])

    Standard mean blurring filter using a box filter.

    `size` is the kernel size. Use 1 (3x3 kernel), 2 (5x5 kernel), etc.

    If you’d like to adaptive threshold the image on the output of the filter you can pass `threshold=True` which will enable adaptive thresholding of the image which sets pixels to one or zero based on a pixel’s brightness in relation to the brightness of the kernel of pixels around them. A negative `offset` value sets more pixels to 1 as you make it more negative while a positive value only sets the sharpest contrast changes to 1. Set `invert` to invert the binary image resulting output.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  median(size[, percentile=0.5[, threshold=False[, offset=0[, invert=False[, mask=None]]]]])

    Runs the median filter on the image. The median filter is the best filter for smoothing surfaces while preserving edges but it is very slow.

    `size` is the kernel size. Use 1 (3x3 kernel), 2 (5x5 kernel), etc.

    `percentile` controls the percentile of the value used in the kernel. By default each pixel is replaced with the 50th percentile (center) of its neighbors. You can set this to 0 for a min filter, 0.25 for a lower quartile filter, 0.75 for an upper quartile filter, and 1.0 for a max filter.

    If you’d like to adaptive threshold the image on the output of the filter you can pass `threshold=True` which will enable adaptive thresholding of the image which sets pixels to one or zero based on a pixel’s brightness in relation to the brightness of the kernel of pixels around them. A negative `offset` value sets more pixels to 1 as you make it more negative while a positive value only sets the sharpest contrast changes to 1. Set `invert` to invert the binary image resulting output.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  mode(size, [threshold=False, [offset=0, [invert=False, [mask=None]]]]])

    Runs the mode filter on the image by replacing each pixel with the mode of their neighbors. This method works great on grayscale images. However, on RGB images it creates a lot of artifacts on edges because of the non-linear nature of the operation.

    `size` is the kernel size. Use 1 (3x3 kernel), 2 (5x5 kernel), etc.

    If you’d like to adaptive threshold the image on the output of the filter you can pass `threshold=True` which will enable adaptive thresholding of the image which sets pixels to one or zero based on a pixel’s brightness in relation to the brightness of the kernel of pixels around them. A negative `offset` value sets more pixels to 1 as you make it more negative while a positive value only sets the sharpest contrast changes to 1. Set `invert` to invert the binary image resulting output.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  midpoint(size[, bias=0.5[, threshold=False[, offset=0[, invert=False[, mask=None]]]]])

    Runs the midpoint filter on the image. This filter finds the midpoint ((max-min)/2) of each pixel neighborhood in the image.

    `size` is the kernel size. Use 1 (3x3 kernel), 2 (5x5 kernel), etc.

    `bias` controls the min/max mixing. 0 for min filtering only, 1.0 for max filtering only. By using the `bias` you can min/max filter the image.

    If you’d like to adaptive threshold the image on the output of the filter you can pass `threshold=True` which will enable adaptive thresholding of the image which sets pixels to one or zero based on a pixel’s brightness in relation to the brightness of the kernel of pixels around them. A negative `offset` value sets more pixels to 1 as you make it more negative while a positive value only sets the sharpest contrast changes to 1. Set `invert` to invert the binary image resulting output.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  morph(size, kernel[, mul[, add=0[, threshold=False[, offset=0[, invert=False[, mask=None]]]]]])

    Convolves the image by a filter kernel. This allows you to do general purpose convolutions on an image.

    `size` controls the size of the kernel which must be ((size*2)+1)x((size*2)+1) elements big.

    `kernel` is the kernel to convolve the image by. It can either be a tuple or a list of integer values.

    `mul` is number to multiply the convolution pixel results by. When not set it defaults to a value that will prevent scaling in the convolution output.

    `add` is a value to add to each convolution pixel result.

    `mul` basically allows you to do a global contrast adjustment and `add` allows you to do a global brightness adjustment. Pixels that go outside of the image mins and maxes for color channels will be clipped.

    If you’d like to adaptive threshold the image on the output of the filter you can pass `threshold=True` which will enable adaptive thresholding of the image which sets pixels to one or zero based on a pixel’s brightness in relation to the brightness of the kernel of pixels around them. A negative `offset` value sets more pixels to 1 as you make it more negative while a positive value only sets the sharpest contrast changes to 1. Set `invert` to invert the binary image resulting output.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  gaussian(size[, unsharp=False[, mul[, add=0[, threshold=False[, offset=0[, invert=False[, mask=None]]]]]]])

    Convolves the image by a smoothing guassian kernel.

    `size` is the kernel size. Use 1 (3x3 kernel), 2 (5x5 kernel), etc.

    If `unsharp` is set to the True then instead of doing just a guassian filtering operation this method will perform an unsharp mask operation which improves image sharpness on edges.

    `mul` is number to multiply the convolution pixel results by. When not set it defaults to a value that will prevent scaling in the convolution output.

    `add` is a value to add to each convolution pixel result.

    `mul` basically allows you to do a global contrast adjustment and `add` allows you to do a global brightness adjustment. Pixels that go outside of the image mins and maxes for color channels will be clipped.

    If you’d like to adaptive threshold the image on the output of the filter you can pass `threshold=True` which will enable adaptive thresholding of the image which sets pixels to one or zero based on a pixel’s brightness in relation to the brightness of the kernel of pixels around them. A negative `offset` value sets more pixels to 1 as you make it more negative while a positive value only sets the sharpest contrast changes to 1. Set `invert` to invert the binary image resulting output.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  laplacian(size[, sharpen=False[, mul[, add=0[, threshold=False[, offset=0[, invert=False[, mask=None]]]]]]])

    Convolves the image by a edge detecting laplacian kernel.

    `size` is the kernel size. Use 1 (3x3 kernel), 2 (5x5 kernel), etc.

    If `sharpen` is set to the True then instead of just outputting an unthresholded edge detection image this method will instead sharpen the image. Increase the kernel size then to increase the image sharpness.

    `mul` is number to multiply the convolution pixel results by. When not set it defaults to a value that will prevent scaling in the convolution output.

    `add` is a value to add to each convolution pixel result.

    `mul` basically allows you to do a global contrast adjustment and `add` allows you to do a global brightness adjustment. Pixels that go outside of the image mins and maxes for color channels will be clipped.

    If you’d like to adaptive threshold the image on the output of the filter you can pass `threshold=True` which will enable adaptive thresholding of the image which sets pixels to one or zero based on a pixel’s brightness in relation to the brightness of the kernel of pixels around them. A negative `offset` value sets more pixels to 1 as you make it more negative while a positive value only sets the sharpest contrast changes to 1. Set `invert` to invert the binary image resulting output.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  bilateral(size[, color_sigma=0.1[, space_sigma=1[, threshold=False[, offset=0[, invert=False[, mask=None]]]]]])

    Convolves the image by a bilateral filter. The bilateral filter smooths the image while keeping edges in the image.

    `size` is the kernel size. Use 1 (3x3 kernel), 2 (5x5 kernel), etc.

    `color_sigma` controls how closely colors are matched using the bilateral filter. Increase this to increase color blurring.

    `space_sigma` controls how closely pixels space-wise are blurred with each other. Increase this to increase pixel blurring.

    If you’d like to adaptive threshold the image on the output of the filter you can pass `threshold=True` which will enable adaptive thresholding of the image which sets pixels to one or zero based on a pixel’s brightness in relation to the brightness of the kernel of pixels around them. A negative `offset` value sets more pixels to 1 as you make it more negative while a positive value only sets the sharpest contrast changes to 1. Set `invert` to invert the binary image resulting output.

    `mask` is another image to use as a pixel level mask for the operation. The mask should be an image with just black or white pixels and should be the same size as the image being operated on. Only pixels set in the mask are modified.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  linpolar([reverse=False])

    Re-project’s and image from cartessian coordinates to linear polar coordinates.

    Set `reverse=True` to re-project in the opposite direction.

    Linear polar re-projection turns rotation of an image into x-translation.

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  logpolar([reverse=False])

    Re-project’s and image from cartessian coordinates to log polar coordinates.

    Set `reverse=True` to re-project in the opposite direction.

    Log polar re-projection turns rotation of an image into x-translation and scaling/zooming into y-translation.

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  lens_corr([strength=1.8[, zoom=1.0[, x_corr=0.0[, y_corr=0.0]]]])

    Performs lens correction to un-fisheye the image due to the lens distortion.

    `strength` is a float defining how much to un-fisheye the image. Try 1.8 out by default and then increase or decrease from there until the image looks good.

    `zoom` is the amount to zoom in on the image by. 1.0 by default.

    `x_corr` floating point pixel offset from center. Can be negative or positive.

    `y_corr` floating point pixel offset from center. Can be negative or positive.

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

  img.rotation_corr([x_rotation=0.0[, y_rotation=0.0[, z_rotation=0.0[, x_translation=0.0[, y_translation=0.0[, zoom=1.0[, fov=60.0[, corners]]]]]]]])

    Corrects perspective issues in the image by doing a 3D rotation of the frame buffer.

    `x_rotation` is the number of degrees to rotation the image in the frame buffer around the x axis (i.e. this spins the image up and down).

    `y_rotation` is the number of degrees to rotation the image in the frame buffer around the y axis (i.e. this spins the image left and right).

    `z_rotation` is the number of degrees to rotation the image in the frame buffer around the z axis (i.e. this spins the image in place).

    `x_translation` is the number of units to move the image to the left or right after rotation. Because this translation is applied in 3D space the units aren’t pixels…

    `y_translation` is the number of units to move the image to the up or down after rotation. Because this translation is applied in 3D space the units aren’t pixels…

    `zoom` is the amount to zoom in on the image by. 1.0 by default.

    `fov` is the field-of-view to use internally when doing 2D->3D projection before rotating the image in 3D space. As this value approaches 0 the image is placed at infinity away from the viewport. As this value approaches 180 the image is placed within the viewport. Typically, you should not change this value but you can modify it to change the 2D->3D mapping effect.

    `corners` is a list of four (x,y) tuples representing four corners used to create a 4-point correspondence homography that will map the first corner to (0, 0), the second corner to (image_width-1, 0), the third corner to (image_width-1, image_height-1), and the fourth corner to (0, image_height-1). The 3D rotation is then applied after the image is re-mapped. This argument lets you use `rotation_corr` to do things like birds-eye-view transforms. E.g:

        top_tilt = 10 # if the difference between top/bottom_tilt become to large this method will stop working
        bottom_tilt = 0

        points = [(tilt, 0), (img.width()-tilt, 0), (img.width()-1-bottom_tilt, img.height()-1), (bottom_tilt, img.height()-1)]

        img.rotation_corr(corners=points)

    Returns the image object so you can call another method using `.` notation.

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  get_histogram([thresholds[, invert=False[, roi[, bins[, l_bins[, a_bins[, b_bins[, difference]]]]]]]])

    Computes the normalized histogram on all color channels for an `roi` and returns a `image.histogram` object. Please see the `image.histogram` object for more information. You can also invoke this method by using `Image.get_hist()` or `Image.histogram()`. If you pass a list of `thresholds` then the histogram information will only be computed from pixels within the threshold list.

    `thresholds` must be a list of tuples `[(lo, hi), (lo, hi), ..., (lo, hi)]` defining the ranges of color you want to track. For grayscale images each tuple needs to contain two values - a min grayscale value and a max grayscale value. Only pixel regions that fall between these thresholds will be considered. For RGB565 images each tuple needs to have six values (l_lo, l_hi, a_lo, a_hi, b_lo, b_hi) - which are minimums and maximums for the LAB L, A, and B channels respectively. For easy usage this function will automatically fix swapped min and max values. Additionally, if a tuple is larger than six values the rest are ignored. Conversely, if the tuple is too short the rest of the thresholds are assumed to be at maximum range.

    Note

    To get the thresholds for the object you want to track just select (click and drag) on the object you want to track in the IDE frame buffer. The histogram will then update to just be in that area. Then just write down where the color distribution starts and falls off in each histogram channel. These will be your low and high values for `thresholds`. It’s best to manually determine the thresholds versus using the upper and lower quartile statistics because they are too tight.

    You may also determine color thresholds by going into `Tools->Machine Vision->Threshold Editor` in OpenMV IDE and selecting thresholds from the GUI slider window.

    `invert` inverts the thresholding operation such that instead of matching pixels inside of some known color bounds pixels are matched that are outside of the known color bounds.

    Unless you need to do something advanced with color statistics just use the `Image.get_statistics()` method instead of this method for looking at pixel areas in an image.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    `bins` and others are the number of bins to use for the histogram channels. For grayscale images use `bins` and for RGB565 images use the others for each channel. The bin counts must be greater than 2 for each channel. Additionally, it makes no sense to set the bin count larger than the number of unique pixel values for each channel. By default, the historgram will have the maximum number of bins per channel.

    `difference` may be set to an image object to cause this method to operate on the difference image between the current image and the `difference` image object. This saves having to use a separate buffer.

    Not supported on compressed images or bayer images.

  get_statistics([thresholds[, invert=False[, roi[, bins[, l_bins[, a_bins[, b_bins[, difference]]]]]]]])

    Computes the mean, median, mode, standard deviation, min, max, lower quartile, and upper quartile for all color channels for an `roi` and returns a `image.statistics` object. Please see the `image.statistics` object for more information. You can also invoke this method by using `Image.get_stats()` or `Image.statistics()`. If you pass a list of `thresholds` then the histogram information will only be computed from pixels within the threshold list.

    `thresholds` must be a list of tuples `[(lo, hi), (lo, hi), ..., (lo, hi)]` defining the ranges of color you want to track. For grayscale images each tuple needs to contain two values - a min grayscale value and a max grayscale value. Only pixel regions that fall between these thresholds will be considered. For RGB565 images each tuple needs to have six values (l_lo, l_hi, a_lo, a_hi, b_lo, b_hi) - which are minimums and maximums for the LAB L, A, and B channels respectively. For easy usage this function will automatically fix swapped min and max values. Additionally, if a tuple is larger than six values the rest are ignored. Conversely, if the tuple is too short the rest of the thresholds are assumed to be at maximum range.

    Note

    To get the thresholds for the object you want to track just select (click and drag) on the object you want to track in the IDE frame buffer. The histogram will then update to just be in that area. Then just write down where the color distribution starts and falls off in each histogram channel. These will be your low and high values for `thresholds`. It’s best to manually determine the thresholds versus using the upper and lower quartile statistics because they are too tight.

    You may also determine color thresholds by going into `Tools->Machine Vision->Threshold Editor` in OpenMV IDE and selecting thresholds from the GUI slider window.

    `invert` inverts the thresholding operation such that instead of matching pixels inside of some known color bounds pixels are matched that are outside of the known color bounds.

    You’ll want to use this method any time you need to get information about the values of an area of pixels in an image. For example, after if you’re trying to detect motion using frame differencing you’ll want to use this method to determine a change in the color channels of the image to trigger your motion detection threshold.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    `bins` and others are the number of bins to use for the histogram channels. For grayscale images use `bins` and for RGB565 images use the others for each channel. The bin counts must be greater than 2 for each channel. Additionally, it makes no sense to set the bin count larger than the number of unique pixel values for each channel. By default, the historgram will have the maximum number of bins per channel.

    `difference` may be set to an image object to cause this method to operate on the difference image between the current image and the `difference` image object. This saves having to use a separate buffer.

    Not supported on compressed images or bayer images.

  get_regression(thresholds[, invert=False[, roi[, x_stride=2[, y_stride=1[, area_threshold=10[, pixels_threshold=10[, robust=False]]]]]]])

    Computes a linear regression on all the thresholded pixels in the image. The linear regression is computed using least-squares normally which is fast but cannot handle any outliers. If `robust` is True then the Theil–Sen linear regression is used instead which computes the median of all slopes between all thresholded pixels in the image. This is an N^2 operation which may drops your FPS down to below 5 even on an 80x60 image if too many pixels are set after thresholding. However, as long as the number of pixels set after thresholding remains low the linear regression will be valid even in the case of up to 30% of the thresholded pixels being outliers (e.g. it’s robust).

    This method returns a `image.line` object. See this blog post on how to use the line object easily: https://openmv.io/blogs/news/linear-regression-line-following

    `thresholds` must be a list of tuples `[(lo, hi), (lo, hi), ..., (lo, hi)]` defining the ranges of color you want to track. For grayscale images each tuple needs to contain two values - a min grayscale value and a max grayscale value. Only pixel regions that fall between these thresholds will be considered. For RGB565 images each tuple needs to have six values (l_lo, l_hi, a_lo, a_hi, b_lo, b_hi) - which are minimums and maximums for the LAB L, A, and B channels respectively. For easy usage this function will automatically fix swapped min and max values. Additionally, if a tuple is larger than six values the rest are ignored. Conversely, if the tuple is too short the rest of the thresholds are assumed to be at maximum range.

    Note

    To get the thresholds for the object you want to track just select (click and drag) on the object you want to track in the IDE frame buffer. The histogram will then update to just be in that area. Then just write down where the color distribution starts and falls off in each histogram channel. These will be your low and high values for `thresholds`. It’s best to manually determine the thresholds versus using the upper and lower quartile statistics because they are too tight.

    You may also determine color thresholds by going into `Tools->Machine Vision->Threshold Editor` in OpenMV IDE and selecting thresholds from the GUI slider window.

    `invert` inverts the thresholding operation such that instead of matching pixels inside of some known color bounds pixels are matched that are outside of the known color bounds.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    `x_stride` is the number of x pixels to skip over when evaluating the image.

    `y_stride` is the number of y pixels to skip over when evaluating the image.

    If the regression’s bounding box area is less than `area_threshold` then None is returned.

    If the regression’s pixel count is less than `pixels_threshold` then None is returned.

    Not supported on compressed images or bayer images.

  find_blobs(thresholds[, invert=False[, roi[, x_stride=2[, y_stride=1[, area_threshold=10[, pixels_threshold=10[, merge=False[, margin=0[, threshold_cb=None[, merge_cb=None[, x_hist_bins_max=0[, y_hist_bins_max=0]]]]]]]]]]]])

    Finds all blobs (connected pixel regions that pass a threshold test) in the image and returns a list of `image.blob` objects which describe each blob. Please see the `image.blob` object more more information.

    `thresholds` must be a list of tuples `[(lo, hi), (lo, hi), ..., (lo, hi)]` defining the ranges of color you want to track. You may pass up to 32 threshold tuples in one call. For grayscale images each tuple needs to contain two values - a min grayscale value and a max grayscale value. Only pixel regions that fall between these thresholds will be considered. For RGB565 images each tuple needs to have six values (l_lo, l_hi, a_lo, a_hi, b_lo, b_hi) - which are minimums and maximums for the LAB L, A, and B channels respectively. For easy usage this function will automatically fix swapped min and max values. Additionally, if a tuple is larger than six values the rest are ignored. Conversely, if the tuple is too short the rest of the thresholds are assumed to be at maximum range.

    Note

    To get the thresholds for the object you want to track just select (click and drag) on the object you want to track in the IDE frame buffer. The histogram will then update to just be in that area. Then just write down where the color distribution starts and falls off in each histogram channel. These will be your low and high values for `thresholds`. It’s best to manually determine the thresholds versus using the upper and lower quartile statistics because they are too tight.

    You may also determine color thresholds by going into `Tools->Machine Vision->Threshold Editor` in OpenMV IDE and selecting thresholds from the GUI slider window.

    `invert` inverts the thresholding operation such that instead of matching pixels inside of some known color bounds pixels are matched that are outside of the known color bounds.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    `x_stride` is the number of x pixels to skip when searching for a blob. Once a blob is found the line fill algorithm will be pixel accurate. Increase `x_stride` to speed up finding blobs if blobs are known to be large.

    `y_stride` is the number of y pixels to skip when searching for a blob. Once a blob is found the line fill algorithm will be pixel accurate. Increase `y_stride` to speed up finding blobs if blobs are known to be large.

    If a blob’s bounding box area is less than `area_threshold` it is filtered out.

    If a blob’s pixel count is less than `pixels_threshold` it is filtered out.

    `merge` if True merges all not filtered out blobs whos bounding rectangles intersect each other. `margin` can be used to increase or decrease the size of the bounding rectangles for blobs during the intersection test. For example, with a margin of 1 blobs whos bounding rectangles are 1 pixel away from each other will be merged.

    Merging blobs allows you to implement color code tracking. Each blob object has a `code` value which is a bit vector made up of 1s for each color threshold. For example, if you pass `Image.find_blobs` two color thresholds then the first threshold has a code of 1 and the second 2 (a third threshold would be 4 and a fourth would be 8 and so on). Merged blobs logically OR all their codes together so that you know what colors produced them. This allows you to then track two colors if you get a blob object back with two colors then you know it might be a color code.

    You might also want to merge blobs if you are using tight color bounds which do not fully track all the pixels of an object you are trying to follow.

    Finally, if you want to merge blobs, but, don’t want two color thresholds to be merged then just call `Image.find_blobs` twice with separate thresholds so that blobs aren’t merged.

    `threshold_cb` may be set to the function to call on every blob after its been thresholded to filter it from the list of blobs to be merged. The call back function will receive one argument - the blob object to be filtered. The call back then must return True to keep the blob and False to filter it.

    `merge_cb` may be set to the function to call on every two blobs about to be merged to prevent or allow the merge. The call back function will receive two arguments - the two blob objects to be merged. The call back then must return True to merge the blobs or False to prevent merging the blobs.

    `x_hist_bins_max` if set to non-zero populates a histogram buffer in each blob object with an x_histogram projection of all columns in the object. This value then sets the number of bins for that projection.

    `y_hist_bins_max` if set to non-zero populates a histogram buffer in each blob object with an y_histogram projection of all rows in the object. This value then sets the number of bins for that projection.

    Not supported on compressed images or bayer images.

  find_lines([roi[, x_stride=2[, y_stride=1[, threshold=1000[, theta_margin=25[, rho_margin=25]]]]]])

    Finds all infinite lines in the image using the hough transform. Returns a list of `image.line` objects.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    `x_stride` is the number of x pixels to skip when doing the hough transform. Only increase this if lines you are searching for are large and bulky.

    `y_stride` is the number of y pixels to skip when doing the hough transform. Only increase this if lines you are searching for are large and bulky.

    `threshold` controls what lines are detected from the hough transform. Only lines with a magnitude greater than or equal to `threshold` are returned. The right value of `threshold` for your application is image dependent. Note that the magnitude of a line is the sum of all sobel filter magnitudes of pixels that make up that line.

    `theta_margin` controls the merging of detected lines. Lines which are `theta_margin` degrees apart and `rho_margin` rho apart are merged.

    `rho_margin` controls the merging of detected lines. Lines which are `theta_margin` degrees apart and `rho_margin` rho apart are merged.

    This method working by running the sobel filter over the image and taking the magnitude and gradient responses from the sobel filter to feed a hough transform. It does not require any preprocessing on the image first. However, my cleaning up the image using filtering you may get more stable results.

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  find_line_segments([roi[, merge_distance=0[, max_theta_difference=15]]])

    Finds line segments in the image using the hough transform. Returns a list of `image.line` objects .

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    `merge_distance` specifies the maximum number of pixels two line segements can be seperated by each other (at any point on one line) to be merged.

    `max_theta_difference` is the maximum theta difference in degrees two line segements that are `merge_distance` apart to be merged.

    This method uses the LSD library (also used by OpenCV) to find line segements in the image. It’s somewhat slow but very accurate and lines don’t jump around.

    This method is not available on the OpenMV Cam M4.

  find_circles([roi[, x_stride=2[, y_stride=1[, threshold=2000[, x_margin=10[, y_margin=10[, r_margin=10[, r_min=2[, r_max[, r_step=2]]]]]]]]]])

    Finds circles in the image using the hough transform. Returns a list of `image.circle` objects.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    `x_stride` is the number of x pixels to skip when doing the hough transform. Only increase this if circles you are searching for are large and bulky.

    `y_stride` is the number of y pixels to skip when doing the hough transform. Only increase this if circles you are searching for are large and bulky.

    `threshold` controls what circles are detected from the hough transform. Only circles with a magnitude greater than or equal to `threshold` are returned. The right value of `threshold` for your application is image dependent. Note that the magnitude of a circle is the sum of all sobel filter magnitudes of pixels that make up that circle.

    `x_margin` controls the merging of detected circles. Circles which are `x_margin`, `y_margin`, and `r_margin` pixels apart are merged.

    `y_margin` controls the merging of detected circles. Circles which are `x_margin`, `y_margin`, and `r_margin` pixels apart are merged.

    `r_margin` controls the merging of detected circles. Circles which are `x_margin`, `y_margin`, and `r_margin` pixels apart are merged.

    `r_min` controls the minimum circle radius detected. Increase this to speed up the algorithm. Defaults to 2.

    `r_max` controls the maximum circle radius detected. Decrease this to speed up the algorithm. Defaults to min(roi.w/2, roi.h/2).

    `r_step` controls how to step the radius detection by. Defaults to 2.

    This method is not available on the OpenMV Cam M4.

  find_rects([roi=Auto[, threshold=10000]])

    Find rectangles in the image using the same quad detection algorithm used to find apriltags. Works best of rectangles that have good contrast against the background. The apriltag quad detection algorithm can handle any scale/rotation/shear on rectangles. Returns a list of `image.rect` objects.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    Rectangles which have an edge magnitude (which is computed by sliding the sobel operator across all pixels on the edges of the rectangle and summing their values) less than `threshold` are filtered out of the returned list. The correct value of `threshold` is depended on your application/scene.

    This method is not available on the OpenMV Cam M4.

  find_qrcodes([roi])

    Finds all qrcodes within the `roi` and returns a list of `image.qrcode` objects. Please see the `image.qrcode` object for more information.

    QR Codes need to be relatively flat in the image for this method to work. You can achieve a flatter image that is not effected by lens distortion by either using the `sensor.set_windowing()` function to zoom in the on the center of the lens, `Image.lens_corr()` to undo lens barrel distortion, or by just changing out the lens for something with a narrower fields of view. There are machine vision lenses available which do not cause barrel distortion but they are much more expensive to than the standard lenses supplied by OpenMV.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    This method is not available on the OpenMV Cam M4.

  find_apriltags([roi[, families=image.TAG36H11[, fx[, fy[, cx[, cy]]]]]])

    Finds all apriltags within the `roi` and returns a list of `image.apriltag` objects. Please see the `image.apriltag` object for more information.

    Unlike QR Codes, AprilTags can be detected at much farther distances, worse lighting, in warped images, etc. AprilTags are robust too all kinds of image distortion issues that QR Codes are not to. That said, AprilTags can only encode a numeric ID as their payload.

    AprilTags can also be used for localization purposes. Each `image.apriltag` object returns its translation and rotation from the camera. The units of the translation are determined by `fx`, `fy`, `cx`, and `cy` which are the focal lengths and center points of the image in the X and Y directions respectively.

    Note

    To create AprilTags use the tag generator tool built-in to OpenMV IDE. The tag generator can create printable 8.5”x11” AprilTags.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    `families` is bitmask of tag families to decode. It is the logical OR of:

    > - `image.TAG16H5`

    > - `image.TAG25H7`

    > - `image.TAG25H9`

    > - `image.TAG36H10`

    > - `image.TAG36H11`

    > - `image.ARTOOLKIT`

    By default it is just `image.TAG36H11` which is the best tag family to use. Note that `Image.find_apriltags()` slows down per enabled tag family.

    `fx` is the camera X focal length in pixels. For the standard OpenMV Cam this is (2.8 / 3.984) * 656. Which is the lens focal length in mm, divided by the camera sensor length in the X direction multiplied by the number of camera sensor pixels in the X direction (for the OV7725 camera).

    `fx` is the camera Y focal length in pixels. For the standard OpenMV Cam this is (2.8 / 2.952) * 488. Which is the lens focal length in mm, divided by the camera sensor length in the Y direction multiplied by the number of camera sensor pixels in the Y direction (for the OV7725 camera).

    `cx` is the image center which is just `image.width()/2`. This is not `roi.w()/2`.

    `cy` is the image center which is just `image.height()/2`. This is not `roi.h()/2`.

    Not supported on compressed images.

    This method is not available on the OpenMV Cam M4.

  find_datamatrices([roi[, effort=200]])

    Finds all datamatrices within the `roi` and returns a list of `image.datamatrix` objects. Please see the `image.datamatrix` object for more information.

    Data Matrices need to be relatively flat in the image for this method to work. You can achieve a flatter image that is not effected by lens distortion by either using the `sensor.set_windowing()` function to zoom in the on the center of the lens, `Image.lens_corr()` to undo lens barrel distortion, or by just changing out the lens for something with a narrower fields of view. There are machine vision lenses available which do not cause barrel distortion but they are much more expensive to than the standard lenses supplied by OpenMV.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    `effort` controls how much time to spend trying to find data matrix matches. The default value of 200 should be good for all use-cases. However, you may increase the effort, at a cost of the frame rate, to increase detection. You may also lower the effort to increase the frame rate, but, at a cost of detections… note that when `effort` is set to below 160 or so you will not detect anything anymore. Also note that you can make `effort` as high as you like. But, any values above 240 or so do not result in much increase in the detection rate.

    This method is not available on the OpenMV Cam M4.

  find_barcodes([roi])

    Finds all 1D barcodes within the `roi` and returns a list of `image.barcode` objects. Please see the `image.barcode` object for more information.

    For best results use a 640 by 40/80/160 window. The lower the vertical res the faster everything will run. Since bar codes are linear 1D images you just need a lot of resolution in one direction and just a little resolution in the other direction. Note that this function scans both horizontally and vertically so you can use a 40/80/160 by 480 window if you want. Finally, make sure to adjust your lens so that the bar code is positioned where the focal length produces the sharpest image. Blurry bar codes can’t be decoded.

    This function supports all these 1D barcodes (basically all barcodes):

    > - `image.EAN2`

    > - `image.EAN5`

    > - `image.EAN8`

    > - `image.UPCE`

    > - `image.ISBN10`

    > - `image.UPCA`

    > - `image.EAN13`

    > - `image.ISBN13`

    > - `image.I25`

    > - `image.DATABAR` (RSS-14)

    > - `image.DATABAR_EXP` (RSS-Expanded)

    > - `image.CODABAR`

    > - `image.CODE39`

    > - `image.PDF417`

    > - `image.CODE93`

    > - `image.CODE128`

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    This method is not available on the OpenMV Cam M4.

  find_displacement(template[, roi[, template_roi[, logpolar=False]]])

    Find the translation offset of the this image from the template. This method can be used to do optical flow. This method returns a `image.displacement` object with the results of the displacement calculation using phase correlation.

    `roi` is the region-of-interest rectangle (x, y, w, h) to work in. If not specified, it is equal to the image rectangle.

    `template_roi` is the region-of-interest rectangle (x, y, w, h) to work in. If not specified, it is equal to the image rectangle.

    `roi` and `template` roi must have the same w/h but may have any x/y location in the image. You may slide smaller rois arround a larger image to get an optical flow gradient image…

    `Image.find_displacement()` normally computes the x/y translation between two images. However, if you pass `logpolar=True` it will instead find rotation and scale changes between the two images. The same `image.displacement` object result encodes both possible repsonses.

    Not supported on compressed images or bayer images.

    Note

    Please use this method on power-of-2 image sizes (e.g. `sensor.B64X64`).

    Not supported on compressed images or bayer images.

    This method is not available on the OpenMV Cam M4.

  find_template(template, threshold[, roi[, step=2[, search=image.SEARCH_EX]]])

    Tries to find the first location in the image where template matches using Normalized Cross Correlation. Returns a bounding box tuple (x, y, w, h) for the matching location otherwise None.

    `template` is a small image object that is matched against this image object. Note that both images must be grayscale.

    `threshold` is floating point number (0.0-1.0) where a higher threshold prevents false positives while lowering the detection rate while a lower threshold does the opposite.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    `step` is the number of pixels to skip past while looking for the template. Skipping pixels considerably speeds the algorithm up. This only affects the algorithm in SERACH_EX mode.

    `search` can be either `image.SEARCH_DS` or `image.SEARCH_EX`. `image.SEARCH_DS` searches for the template using as faster algorithm than `image.SEARCH_EX` but may not find the template if it’s near the edges of the image. `image.SEARCH_EX` does an exhaustive search for the image but can be much slower than `image.SEARCH_DS`.

    Only works on grayscale images.

  find_features(cascade[, threshold=0.5[, scale=1.5[, roi]]])

    This method searches the image for all areas that match the passed in Haar Cascade and returns a list of bounding box rectangles tuples (x, y, w, h) around those features. Returns an empty list if no features are found.

    `cascade` is a Haar Cascade object. See `image.HaarCascade()` for more details.

    `threshold` is a threshold (0.0-1.0) where a smaller value increase the detection rate while raising the false positive rate. Conversely, a higher value decreases the detection rate while lowering the false positive rate.

    `scale` is a float that must be greater than 1.0. A higher scale factor will run faster but will have much poorer image matches. A good value is between 1.35 and 1.5.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

  find_eye(roi)

    Searches for the pupil in a region-of-interest (x, y, w, h) tuple around an eye. Returns a tuple with the (x, y) location of the pupil in the image. Returns (0,0) if no pupils are found.

    To use this function first use `Image.find_features()` with the `frontalface` HaarCascade to find someone’s face. Then use `Image.find_features()` with the `eye` HaarCascade to find the eyes on the face. Finally, call this method on the eye ROI returned by `Image.find_features()` to get the pupil coordinates.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    Only works on grayscale images.

  find_lbp(roi)

    Extracts LBP (local-binary-patterns) keypoints from the region-of-interest (x, y, w, h) tuple. You can then use then use the `image.match_descriptor()` function to compare two sets of keypoints to get the matching distance.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    Only works on grayscale images.

  find_keypoints([roi[, threshold=20[, normalized=False[, scale_factor=1.5[, max_keypoints=100[, corner_detector=image.CORNER_AGAST]]]]]])

    Extracts ORB keypoints from the region-of-interest (x, y, w, h) tuple. You can then use then use the `image.match_descriptor()` function to compare two sets of keypoints to get the matching areas. Returns None if no keypoints were found.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    `threshold` is a number (between 0 - 255) which controls the number of extracted corners. For the default AGAST corner detector this should be around 20. FOr the FAST corner detector this should be around 60-80. The lower the threshold the more extracted corners you get.

    `normalized` is a boolean value that if True turns off extracting keypoints at multiple resolutions. Set this to true if you don’t care about dealing with scaling issues and want the algorithm to run faster.

    `scale_factor` is a float that must be greater than 1.0. A higher scale factor will run faster but will have much poorer image matches. A good value is between 1.35 and 1.5.

    `max_keypoints` is the maximum number of keypoints a keypoint object may hold. If keypoint objects are too big and causing out of RAM issues then decrease this value.

    `corner_detector` is the corner detector algorithm to use which extracts keypoints from the image. It can be either `image.CORNER_FAST` or `image.CORNER_AGAST`. The FAST corner detector is faster but much less accurate.

    Only works on grayscale images.

  find_edges(edge_type[, threshold])

    Turns the image to black and white leaving only the edges as white pixels.

    > - image.EDGE_SIMPLE - Simple thresholded high pass filter algorithm.

    > - image.EDGE_CANNY - Canny edge detection algorithm.

    `threshold` is a two valued tuple containing a low threshold and high threshold. You can control the quality of edges by adjusting these values. It defaults to (100, 200).

    Only works on grayscale images.

  find_hog([roi[, size=8]])

    Replaces the pixels in the ROI with HOG (histogram of orientated graidients) lines.

    `roi` is the region-of-interest rectangle tuple (x, y, w, h). If not specified, it is equal to the image rectangle. Only pixels within the `roi` are operated on.

    Only works on grayscale images.

    This method is not available on the OpenMV Cam M4.

  stero_disparity([reversed=False[, max_disparity=64[, threshold=64]]])

    Takes a double wide grayscale image that contains the output of two camera sensors side-by-side and replaces one of the images int he double wide image with the stero-disparity image where each pixel reprsents depth. E.g. if you have two 320x240 cameras then this method takes a 640x240 image.

    `reversed` By default the left image is compared to the right image and the right image is then replaced. Pass true to compare the right image to the left image and replace the left image.

    Note

    The algorithm only works comparing left->right or right->left. If your camrea setup does not match this then you will get useless results.

    `max_disparity` is the maximum distance to search for a matching pixel block using the sum-of-absolute differences algorith. Larger values take exponentially longer to search with but result in higher quality images. Lower values make the algorithm run faster but may result in nonsense output.

    `threshold` if the sum-of-absolute differences between two blocks is less than or equal to this threshold they are considered to be matching.

    This method is only available on the Arduino Portenta.

    Note

    Even with our best SIMD effort this algorithm is not real-time on the Cortex-M7 processor. This is just a toy example algorithm showing off stero-disparity.

## Constants

image.BINARY

  BINARY (bitmap) pixel format. Each pixel is 1-bit.

image.GRAYSCALE

  GRAYSCALE pixel format. Each pixel is 8-bits, 1-byte.

image.RGB565

  RGB565 pixel format. Each pixel is 16-bits, 2-bytes. 5-bits are used for red, 6-bits are used for green, and 5-bits are used for blue.

image.BAYER

  RAW BAYER image pixel format. If you try to make the frame size too big to fit in the frame buffer your OpenMV Cam will set the pixel format to BAYER so that you can capture images but no image processing methods will be operational.

image.YUV422

  A pixel format that is very easy to jpeg compress. Each pixel is stored as a grayscale 8-bit Y value followed by alternating 8-bit U/V color values that are shared between two Y values (8-bits Y1, 8-bits U, 8-bits Y2, 8-bits V, etc.). Only some image processing methods work with YUV422.

image.JPEG

  A JPEG image.

image.PNG

  A PNG image.

image.AREA

  Use area scaling when downscaling an image (Nearest Neighbor is used for upscaling).

  You should use area scaling when downscaling for the highest visual quality.

image.BILINEAR

  Use bilinear scaling when upscaling an image. This produces a good quality scaled image output and is fast.

  When downscaling an image this method will subsample the input image to produce the downscaled image. Use `image.AREA` for the higest quality downscaling if speed is not an issue.

image.BICUBIC

  Use bicubic scaling when upscaling an image. This produces a high quality scaled image output, but is slow.

  When downscaling an image this method will subsample the input image to produce the downscaled image. Use `image.AREA` for the higest quality downscaling if speed is not an issue.

image.CENTER

  Anchor the image being drawn with `draw_image` in the center versus the top left hand corner.

image.EXTRACT_RGB_CHANNEL_FIRST

  When extracting an RGB channel from an RGB image using `draw_image` extract the channel first before scaling versus afterwards to prevent any artifacts.

image.APPLY_COLOR_PALETTE_FIRST

  When applying a color lookup table to an image using `draw_image` apply the color look table first before scaling versus afterwards to prevent any artifacts.

image.BLACK_BACKGROUND

  Speeds up `draw_image` when drawing on a black destination image when using alpha effects that require reading both source and destination pixels. This skips reading the destination pixel.

image.SEARCH_EX

  Exhaustive template matching search.

image.SEARCH_DS

  Faster template matching search.

image.EDGE_CANNY

  Use the canny edge detection algorithm for doing edge detection on an image.

image.EDGE_SIMPLE

  Use a simple thresholded high pass filter algorithm for doing edge detection on an image.

image.CORNER_FAST

  Faster and less accurate corner detection algorithm for ORB keypoints.

image.CORNER_AGAST

  Slower and more accurate corner detection algorithm for ORB keypoints.

image.TAG16H5

  TAG1H5 tag family bit mask enum. Used for AprilTags.

image.TAG25H7

  TAG25H7 tag family bit mask enum. Used for AprilTags.

image.TAG25H9

  TAG25H9 tag family bit mask enum. Used for AprilTags.

image.TAG36H10

  TAG36H10 tag family bit mask enum. Used for AprilTags.

image.TAG36H11

  TAG36H11 tag family bit mask enum. Used for AprilTags.

image.ARTOOLKIT

  ARTOOLKIT tag family bit mask enum. Used for AprilTags.

image.EAN2

  EAN2 barcode type enum.

image.EAN5

  EAN5 barcode type enum.

image.EAN8

  EAN8 barcode type enum.

image.UPCE

  UPCE barcode type enum.

image.ISBN10

  ISBN10 barcode type enum.

image.UPCA

  UPCA barcode type enum.

image.EAN13

  EAN13 barcode type enum.

image.ISBN13

  ISBN13 barcode type enum.

image.I25

  I25 barcode type enum.

image.DATABAR

  DATABAR barcode type enum.

image.DATABAR_EXP

  DATABAR_EXP barcode type enum.

image.CODABAR

  CODABAR barcode type enum.

image.CODE39

  CODE39 barcode type enum.

image.PDF417

  PDF417 barcode type enum - Future (e.g. doesn’t work right now).

image.CODE93

  CODE93 barcode type enum.

image.CODE128

  CODE128 barcode type enum.

Language and External Links

Language
  English
  中文

External links
  openmv.io
  forums.openmv.io
  github.com/openmv/openmv
  micropython.org
  forum.micropython.org
  github.com/micropython/micropython