# Automatic Grayscale Color Tracking Example
#
# This example shows off single color automatic grayscale color tracking using the CanMV Cam.
import time, os, gc, sys, math

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

DETECT_WIDTH = 640
DETECT_HEIGHT = 480

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()

    frame_count = 0
    threshold = [128, 128] # Middle grayscale values.
    # Capture the color thresholds for whatever was in the center of the image.
    r = [(DETECT_WIDTH//2)-(50//2), (DETECT_HEIGHT//2)-(50//2), 50, 50] # 50x50 center of QVGA.

    while True:
        fps.tick()

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

        if frame_count < 60:
            if frame_count == 0:
                print("Letting auto algorithms run. Don't put anything in front of the camera!")
                print("Auto algorithms done. Hold the object you want to track in front of the camera in the box.")
                print("MAKE SURE THE COLOR OF THE OBJECT YOU WANT TO TRACK IS FULLY ENCLOSED BY THE BOX!")
            img.draw_rectangle([v for v in r])
            frame_count = frame_count + 1
        elif frame_count < 120:
            if frame_count == 60:
                print("Learning thresholds...")
            elif frame_count == 119:
                print("Thresholds learned...")
                print("Tracking colors...")
            hist = img.get_histogram(roi=r)
            lo = hist.get_percentile(0.01) # Get the CDF of the histogram at the 1% range (ADJUST AS NECESSARY)!
            hi = hist.get_percentile(0.99) # Get the CDF of the histogram at the 99% range (ADJUST AS NECESSARY)!
            # Average in percentile values.
            threshold[0] = (threshold[0] + lo.value()) // 2
            threshold[1] = (threshold[1] + hi.value()) // 2
            for blob in img.find_blobs([threshold], pixels_threshold=100, area_threshold=100, merge=True, margin=10):
                img.draw_rectangle([v for v in blob.rect()])
                img.draw_cross(blob.cx(), blob.cy())
                img.draw_rectangle([v for v in r])
            frame_count = frame_count + 1
            del hist
        else:
            for blob in img.find_blobs([threshold], pixels_threshold=100, area_threshold=100, merge=True, margin=10):
                img.draw_rectangle([v for v in blob.rect()])
                img.draw_cross(blob.cx(), blob.cy())

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

        if frame_count >= 120:
            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)
