contour-approximation-1.11.4-receipt.py

# import the necessary packages
import cv2
import imutils

# load the receipt image, convert it to grayscale, and detect
# edges
# image = cv2.imread("./images/receipt.png")
image = cv2.imread("./images/dog_contour.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edged = cv2.Canny(gray, 75, 200)

# show the original image and edged map
cv2.imshow("Original", image)
cv2.imshow("Edge Map", edged)

# find contours in the image and sort them from largest to smallest,
# keeping only the largest ones
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:7]

# loop over the contours
for c in cnts:
    # approximate the contour and initialize the contour color
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.1 * peri, True)

    # show the difference in number of vertices between the original
    # and approximated contours
    print("original: {}, approx: {}".format(len(c), len(approx)))

    # if the approximated contour has 4 vertices, then we have found
    # our rectangle
    if len(approx) == 4:
        # draw the outline on the image
        cv2.drawContours(image, [approx], -1, (0, 255, 0), 2)

# show the output image
cv2.imshow("Output", image)


cv2.waitKey()
cv2.destroyAllWindows()