From b3537ee78105a58bea6dc9217a111b994accd6ad Mon Sep 17 00:00:00 2001 From: = <=> Date: Tue, 26 Mar 2019 00:35:03 +0100 Subject: [PATCH] Messy code, loads of shit commented out, it actually computes stuff though --- IMG_2070.jpeg | Bin 0 -> 797030 bytes pls_bo33.jpg | Bin 0 -> 309481 bytes runner.py | 185 ++++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 185 insertions(+) create mode 100644 IMG_2070.jpeg create mode 100644 pls_bo33.jpg create mode 100644 runner.py diff --git a/IMG_2070.jpeg b/IMG_2070.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..49378506d4171e6017a205ca15a41523100758e8 GIT binary patch literal 797030 zcmeFYbyQW~7caUF-AH#RASKUx>GPu9)yB>>9GfDHhE0kB~B z00}~9Pzi$F`hzhc91f!ds89`qN&<}PUz`GC0my&&P%9YEA9-}>Ivd9O2NOfM7>Vd_ z8K`xzB|!Nj4?tT8(?1w(4C)-~fxW%88L(8du(LIz7lNb!Q21N+PtC*2$-~JD053O> zh$uIoD6arLkASFvkSLEZ0Q7H?{;>*4;mzlNv0BKT|5zBp{5NI+APRsROHsIad3mCc 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0), (0, 0), (0, out_width), (out_height, out_width)]) + + + +img = cv2.imread("IMG_2070.jpeg") +img2 = cv2.imread("pls_bo33.jpg") +img_tmp = img.copy() + +gray_tmp = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) +gray_tmp = np.float32(gray_tmp) + +''' +dst = cv2.cornerHarris(gray_tmp, 20, 3, 0.04) + +#result is dilated for marking the corners, not important +dst = cv2.dilate(dst,None) + +# Threshold for an optimal value, it may vary depending on the image. +img_tmp[dst>0.01*dst.max()]=[0,0,255] + +cv2.imwrite('fuck.jpg',img_tmp) + + + + + +img = cv2.GaussianBlur(img,(5,5),0) + +kernel = np.ones((3,3),np.float32) +kernel[0,1] = 0 +kernel[0,2] = -1 +kernel[1,0] = 3 +kernel[1,1] = 0 +kernel[1,2] = -3 +kernel[2,1] = 0 +kernel[2,2] = -1 +img = cv2.filter2D(img,-1,kernel) + +''' + + +img_tmp_tmp = img.copy() +gray_2 = cv2.cvtColor(img_tmp_tmp, cv2.COLOR_BGR2GRAY) +gray_3 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) + +MAX_FEATURES = 500 +GOOD_MATCH_PERCENT = 0.002 + + +cv2.imwrite('pls_lasse.jpg', img) + +img_tmp = img.copy() +gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + +sift = cv2.xfeatures2d.SIFT_create() +kp = sift.detect(gray_2, None) +kp2 = sift.detect(gray_3, None) + +kp, des = sift.compute(gray_2, kp) +kp2, des2 = sift.compute(gray_3, kp2) + +cv2.drawKeypoints(img_tmp_tmp, keypoints=kp, outImage=img_tmp_tmp) +cv2.imwrite('keypoints_img.jpg', img_tmp_tmp) + +matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_SL2) +matches = matcher.match(des, des2, None) + +# Sort matches by score +matches.sort(key=lambda x: x.distance, reverse=False) + +# Remove poor matches +numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT) +matches = matches[:numGoodMatches] + +# Draw the top matches +imMatches = cv2.drawMatches(img_tmp_tmp, kp, img2, kp2, matches, None) +cv2.imwrite("matches.jpg", imMatches) + +# Extract location of good 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, :] = kp[match.queryIdx].pt + points2[i, :] = kp2[match.trainIdx].pt + + +h, mask = cv2.findHomography(points1, points2, cv2.RANSAC) + +height, width, channels = img2.shape +im1Reg = cv2.warpPerspective(img_tmp_tmp, h, (width, height)) + +cv2.imwrite('homo_pls_fuck.jpg', im1Reg) + + +''' +gray_tmp = gray.copy() +gray_tmp = np.float32(gray_tmp) +dst = cv2.cornerHarris(gray_tmp,10,17,0.1) + +#result is dilated for marking the corners, not important +dst = cv2.dilate(dst,None) + +# Threshold for an optimal value, it may vary depending on the image. +img_tmp[dst>0.07*dst.max()]=[0,0,255] + +cv2.imwrite('fuck.jpg',img_tmp) +''' + + +''' +ret, corners = cv2.findChessboardCorners(gray, (3,3), None) + +imgpoints = [] + +print(ret) + +if ret == True: + corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria) + imgpoints.append(corners) + # Draw and display the corners + cv2.drawChessboardCorners(img, (3,3), corners2, ret) + cv2.imwrite('corners_chess.jpg', img) +''' + +''' +# Detect edges using Canny +canny_output = cv2.Canny(gray, 140, 160) + +cv2.imwrite('canny_out.jpg', canny_output) +''' +''' +ret, thresholded = cv2.threshold(gray, 80, 255, cv2.THRESH_BINARY) +cv2.imwrite('threshold_out.jpg', thresholded) + +lines = cv2.HoughLinesP(canny_output, 0.1, np.pi/60, 1, 30, 20) +for line in lines: + print(line) + cv2.line(img, (line[0][0], line[0][1]), (line[0][2], line[0][3]), (0, 0, 255), 2, 8) + +cv2.imwrite('lined_chess.jpg', img) + + + +_, contours, hierarchy = cv2.findContours(canny_output, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) +''' + +#cv2.drawContours(img, contours, -1, (255, 0, 0), 2) + + +''' +pls_square = [] +prev_max = -1 +for contour in contours: + approx = cv2.approxPolyDP(contour, 0.01 * cv2.arcLength(contour, True), True) + if len(approx) == 4: + point_set = [(x[0,0], x[0,1]) for x in approx] + max_x = max([x[0] for x in point_set]) + min_x = min([x[0] for x in point_set]) + + max_y = max([x[1] for x in point_set]) + min_y = min([x[1] for x in point_set]) + + print(((max_x - min_x) * (max_y - min_y))) + + pls_square.append(approx) + + #if ((max_x - min_x) * (max_y - min_y)) > prev_max: + # prev_max = ((max_x - min_x) * (max_y - min_y)) + # pls_square = approx + + +print(pls_square) +#h, mask = cv2.findHomography(pls_square, dstPoints, cv2.RANSAC) +#height, width, channels = img.shape +#warped = cv2.warpPerspective(img, h, (out_height, out_width)) + +cv2.drawContours(img, contours, -1, (255, 0, 0), 2) + +cv2.imwrite('contours_chess.jpg', img) +#cv2.imwrite('homo_img_chess.jpg', warped)''' \ No newline at end of file