diff options
Diffstat (limited to 'scan.py')
| -rw-r--r-- | scan.py | 105 |
1 files changed, 74 insertions, 31 deletions
@@ -17,49 +17,92 @@ def order_points(pts): def four_point_transform(image, pts): rect = order_points(pts) (tl, tr, br, bl) = rect - widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) - widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) - maxWidth = max(int(widthA), int(widthB)) + 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)) - heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) - heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) - maxHeight = max(int(heightA), int(heightB)) + 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], - [maxWidth - 1, 0], - [maxWidth - 1, maxHeight - 1], - [0, maxHeight - 1]], dtype="float32") + [max_width - 1, 0], + [max_width - 1, max_height - 1], + [0, max_height - 1]], dtype="float32") - M = cv2.getPerspectiveTransform(rect, dst) - warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) + transformation = cv2.getPerspectiveTransform(rect, dst) + warped = cv2.warpPerspective(image, transformation, (max_width, max_height)) return warped -image = cv2.imread("example.jpg") -ratio = image.shape[0] / 500.0 -orig = image.copy() +def align_images(im1, im2): + im1_gra = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY) + im2_gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY) -gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) -gray = cv2.GaussianBlur(gray, (5, 5), 0) -edged = cv2.Canny(gray, 75, 200) + orb = cv2.ORB_create(500) + keypoints1, descriptors1 = orb.detectAndCompute(im1_gra, None) + keypoints2, descriptors2 = orb.detectAndCompute(im2_gray, None) -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] + matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING) + matches = matcher.match(descriptors1, descriptors2, None) -for c in cnts: - peri = cv2.arcLength(c, True) - approx = cv2.approxPolyDP(c, 0.02 * peri, True) - if len(approx) == 4: - screenCnt = approx - break + matches.sort(key=lambda x: x.distance, reverse=False) -cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2) + num_good_matches = int(len(matches) * 0.15) + matches = matches[:num_good_matches] -warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio) + im_matches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None) + cv2.imwrite("matches.jpg", im_matches) -cv2.imshow("Original", imutils.resize(orig, height=650)) -cv2.imshow("Scanned", imutils.resize(warped, height=650)) -cv2.waitKey(0) + 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 + + +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) + + # cv2.imshow("Original", imutils.resize(orig, height=650)) + # cv2.imshow("Scanned", imutils.resize(warped, height=650)) + # cv2.waitKey(0) + + 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) |