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import cv2
import imutils
import numpy as np
def order_points(pts):
rect = np.zeros((4, 2), dtype="float32")
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
return rect
def four_point_transform(image, pts):
rect = order_points(pts)
(tl, tr, br, bl) = rect
width_a = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
width_b = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
max_width = max(int(width_a), int(width_b))
height_a = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
height_b = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
max_height = max(int(height_a), int(height_b))
dst = np.array([
[0, 0],
[max_width - 1, 0],
[max_width - 1, max_height - 1],
[0, max_height - 1]], dtype="float32")
transformation = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(image, transformation, (max_width, max_height))
return warped
def align_images(im1, im2):
im1_gra = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im2_gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
orb = cv2.ORB_create(500)
keypoints1, descriptors1 = orb.detectAndCompute(im1_gra, None)
keypoints2, descriptors2 = orb.detectAndCompute(im2_gray, None)
matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
matches = matcher.match(descriptors1, descriptors2, None)
matches.sort(key=lambda x: x.distance, reverse=False)
num_good_matches = int(len(matches) * 0.15)
matches = matches[:num_good_matches]
im_matches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)
cv2.imwrite("matches.jpg", im_matches)
points1 = np.zeros((len(matches), 2), dtype=np.float32)
points2 = np.zeros((len(matches), 2), dtype=np.float32)
for i, match in enumerate(matches):
points1[i, :] = keypoints1[match.queryIdx].pt
points2[i, :] = keypoints2[match.trainIdx].pt
h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
height, width, channels = im2.shape
im1_reg = cv2.warpPerspective(im1, h, (width, height))
return im1_reg, h
def color_difference(color1, color2):
percentage1 = (color1[0] / 255 * 100 + color1[1] / 255 * 100 + color1[2] / 255 * 100) / 3
percentage2 = (color2[0] / 255 * 100 + color2[1] / 255 * 100 + color2[2] / 255 * 100) / 3
return percentage1 - percentage2 if percentage1 - percentage2 > 0 else 100
if __name__ == '__main__':
image = cv2.imread("example.jpg")
ratio = image.shape[0] / 500.0
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx) == 4:
screenCnt = approx
break
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
cv2.imwrite("before.jpg", warped)
ref_filename = "mask.jpg"
im_reference = cv2.imread(ref_filename, cv2.IMREAD_COLOR)
warped, h = align_images(warped, im_reference)
print("Estimated homography : \n", h)
out_filename = "aligned.jpg"
cv2.imwrite(out_filename, warped)
# cv2.imshow("Warped", warped)
# cv2.waitKey(0)
color = warped[392, 54]
blue = [0x2A, 0xAB, 0xE1]
black = [0, 0, 0]
detected = [color[2], color[1], color[0]]
if color_difference(blue, detected) < 30:
print("Color is blue!")
elif color_difference(black, detected) < 30:
print("Color is black!")
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