Linux в кармане — на службе у фотографа

# The distance between green marks shuold be at least 4a, where "a" is the width of the square.
# It's easier to draw the area around the central point between them.

import cv2
import numpy as np

cap = cv2.VideoCapture(0)

def nothing(x):
    pass

cv2.namedWindow('mask')

# Create sliders for range adjusting
cv2.createTrackbar('Hue_low', 'mask', 40, 255, nothing)
cv2.createTrackbar('Hue_high', 'mask', 76, 255, nothing)
cv2.createTrackbar('Saturation_low', 'mask', 81, 255, nothing)
cv2.createTrackbar('Saturation_high', 'mask', 255, 255, nothing)

while True:
    # flashing the counter
    centrIndex = 0
    # init list
    centroidListX = []
    centroidListY = []

    # Take each frame
    _, frame = cap.read()

    # Convert BGR to HSV
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # Get current slider position
    Hue_low = cv2.getTrackbarPos('Hue_low', 'mask')
    Hue_high = cv2.getTrackbarPos('Hue_high', 'mask')
    Sat_low = cv2.getTrackbarPos('Saturation_low', 'mask')
    Sat_high = cv2.getTrackbarPos('Saturation_high', 'mask')

    # define range of green color in HSV
    lower_green = np.array([Hue_low, Sat_low, 40])
    upper_green = np.array([Hue_high, Sat_high, 250])

    # Threshold the HSV image to get only blue colors
    mask = cv2.inRange(hsv, lower_green, upper_green)

    #Erode disabled to speedup. Works nice without it.
    mask = cv2.erode(mask, None, iterations = 1)
    mask = cv2.dilate(mask, None, iterations = 1)

    # Bitwise-AND mask and original image
    image_result = cv2.bitwise_and(frame, frame, mask=mask)

    # Detecting edges
    image_mask_blur = cv2.medianBlur(mask, 3)
    image_canny = cv2.Canny(image_mask_blur, 30, 150)

    # Closing gaps
    kernel_canny = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
    image_canny_closed = cv2.morphologyEx(image_canny, cv2.MORPH_CLOSE, kernel_canny)

    # Finding contours
    (cnts, _) = cv2.findContours(image_canny_closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    for c in cnts:

        # approximate the contour
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.03 * peri, True)
        # get the bounding box with it's coordinates and aspect ratio
        (x,y,w,h) = cv2.boundingRect(approx)
        aspectRatio = w / float(h)

        # initialize bool variables
        boolRectangle = False
        boolPeri = False
        boolAspect = False

        if len(approx) >= 4 and len(approx) <= 6:
            boolRectangle = True
        if peri >= 50:
            boolPeri = True
        if aspectRatio > 0.9 and aspectRatio < 1.1:
            boolAspect = True

        if boolRectangle and boolPeri and boolAspect:
            cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)

            # get centroid of rectangle
            centroidX = x + w/2
            centroidY = y + h/2
            centroidListX.append(centroidX)
            centroidListY.append(centroidY)

            cv2.circle(frame, (centroidListX[centrIndex], centroidListY[centrIndex]), 1,(0,0,255),3)
            centrIndex += 1

            if centrIndex == 4:
                centroidMainX = 0
                centroidMainY = 0
                for loopIndex in range(0, 3):
                    centroidMainX = centroidMainX + centroidListX[loopIndex]
                    centroidMainY = centroidMainY + centroidListY[loopIndex]
                centroidMainX = centroidMainX/4
                centroidMainY = centroidMainY/4
                cv2.circle(frame, (centroidMainX, centroidMainY), 5, (0, 0, 255),3)
                cv2.rectangle(frame, (centroidMainX - int(peri),centroidMainY + int(peri)), (centroidMainX + int(peri), centroidMainY - int(peri)), (0,255,255), 2)
        cv2.imshow('frame', frame)

    # Visualization
    cv2.imshow('mask', image_result)
    #cv2.imshow('canny_closed', image_canny_closed)
    #cv2.imshow('canny', image_canny)
    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break

cv2.destroyAllWindows()