OpenCV / Python - Найти угловые координаты прямоугольника с рисунком в оттенках серого по серому изображению?

Если вы ищете код python для достижения намного большего, вы можете найти его на этом репо...

https://github.com/DevashishPrasad/Angle-Distance

Итак, чтобы решить вашу проблему, этот код может быть полезен -

# import the necessary packages
from imutils import perspective
from imutils import contours
import numpy as np
import imutils
import cv2

# load the image, convert it to grayscale, and blur it slightly
image = cv2.imread("test.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)

# perform edge detection, then perform a dilation + erosion to
# close gaps in between object edges
edged = cv2.Canny(gray, 50, 100)
edged = cv2.dilate(edged, None, iterations=1)
edged = cv2.erode(edged, None, iterations=1)

# find contours in the edge map
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
    cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]

# loop over the contours individually
for c in cnts:
    # This is to ignore that small hair countour which is not big enough
    if cv2.contourArea(c) < 1000:
        continue

    # compute the rotated bounding box of the contour
    box = cv2.minAreaRect(c)
    box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
    box = np.array(box, dtype="int")

    # order the points in the contour such that they appear
    # in top-left, top-right, bottom-right, and bottom-left
    # order, then draw the outline of the rotated bounding
    # box
    box = perspective.order_points(box)
    # draw the contours on the image
    orig = image.copy()
    cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 5)

    # loop over the original points
    for (xA, yA) in list(box):
        # draw circles corresponding to the current points and
        cv2.circle(orig, (int(xA), int(yA)), 9, (0,0,255), -1)
        cv2.putText(orig, "({},{})".format(xA, yA), (int(xA - 50), int(yA - 10) - 20),
            cv2.FONT_HERSHEY_SIMPLEX, 1.8, (255,0,0), 5)

        # show the output image, resize it as per your requirements
        cv2.imshow("Image", cv2.resize(orig,(800,600))) 

    cv2.waitKey(0)

Комментарии объясняют все это

Выход -