# Black Grayscale Line Following Example
#
# Making a line following robot requires a lot of effort. This example script
# shows how to do the machine vision part of the line following robot. You
# can use the output from this script to drive a differential drive robot to
# follow a line. This script just generates a single turn value that tells
# your robot to go left or right.
#
# For this script to work properly you should point the camera at a line at a
# 45 or so degree angle. Please make sure that only the line is within the
# camera's field of view.
import time, os, gc, sys, math

from media.sensor import *
from media.display import *
from media.media import *

DETECT_WIDTH = 640
DETECT_HEIGHT = 480

# Tracks a black line. Use [(128, 255)] for a tracking a white line.
GRAYSCALE_THRESHOLD = [(0, 64)]

# Each roi is (x, y, w, h). The line detection algorithm will try to find the
# centroid of the largest blob in each roi. The x position of the centroids
# will then be averaged with different weights where the most weight is assigned
# to the roi near the bottom of the image and less to the next roi and so on.
ROIS = [ # [ROI, weight]
        (0, 100, 160, 20, 0.7), # You'll need to tweak the weights for your app
        (0,  50, 160, 20, 0.3), # depending on how your robot is setup.
        (0,   0, 160, 20, 0.1)
       ]

# Compute the weight divisor (we're computing this so you don't have to make weights add to 1).
weight_sum = 0
for r in ROIS: weight_sum += r[4] # r[4] is the roi weight.

sensor = None

try:
    # construct a Sensor object with default configure
    sensor = Sensor(width = DETECT_WIDTH, height = DETECT_HEIGHT)
    # sensor reset
    sensor.reset()
    # set hmirror
    # sensor.set_hmirror(False)
    # sensor vflip
    # sensor.set_vflip(False)
    # set chn0 output size
    sensor.set_framesize(width = DETECT_WIDTH, height = DETECT_HEIGHT)
    # set chn0 output format
    sensor.set_pixformat(Sensor.GRAYSCALE)

    # use hdmi as display output, set to VGA
    # Display.init(Display.LT9611, width = 640, height = 480, to_ide = True)

    # use hdmi as display output, set to 1080P
    # Display.init(Display.LT9611, width = 1920, height = 1080, to_ide = True)

    # use lcd as display output
    # Display.init(Display.ST7701, to_ide = True)

    # use IDE as output
    Display.init(Display.VIRT, width = DETECT_WIDTH, height = DETECT_HEIGHT, fps = 60, to_ide = True)



    # sensor start run
    sensor.run()

    fps = time.clock()

    while True:
        fps.tick()

        # check if should exit.
        os.exitpoint()
        img = sensor.snapshot()

        centroid_sum = 0
        for r in ROIS:
            blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=r[0:4], merge=True) # r[0:4] is roi tuple.

            if blobs:
                # Find the blob with the most pixels.
                largest_blob = max(blobs, key=lambda b: b.pixels())

                # Draw a rect around the blob.
                img.draw_rectangle([v for v in largest_blob.rect()])
                img.draw_cross(largest_blob.cx(), largest_blob.cy())

                centroid_sum += largest_blob.cx() * r[4] # r[4] is the roi weight.

        center_pos = (centroid_sum / weight_sum) # Determine center of line.

        # Convert the center_pos to a deflection angle. We're using a non-linear
        # operation so that the response gets stronger the farther off the line we
        # are. Non-linear operations are good to use on the output of algorithms
        # like this to cause a response "trigger".
        deflection_angle = 0

        # The 80 is from half the X res, the 60 is from half the Y res. The
        # equation below is just computing the angle of a triangle where the
        # opposite side of the triangle is the deviation of the center position
        # from the center and the adjacent side is half the Y res. This limits
        # the angle output to around -45 to 45. (It's not quite -45 and 45).
        deflection_angle = -math.atan((center_pos-80)/60)

        # Convert angle in radians to degrees.
        deflection_angle = math.degrees(deflection_angle)

        # Now you have an angle telling you how much to turn the robot by which
        # incorporates the part of the line nearest to the robot and parts of
        # the line farther away from the robot for a better prediction.
        print("Turn Angle: %f" % deflection_angle)

        # draw result to screen
        Display.show_image(img)
        gc.collect()

        print(fps.fps())
except KeyboardInterrupt as e:
    print(f"user stop")
except BaseException as e:
    print(f"Exception '{e}'")
finally:
    # sensor stop run
    if isinstance(sensor, Sensor):
        sensor.stop()
    # deinit display
    Display.deinit()

    os.exitpoint(os.EXITPOINT_ENABLE_SLEEP)
    time.sleep_ms(100)
