我们从Python开源项目中，提取了以下20个代码示例，用于说明如何使用cv2.RANSAC。
def match_outlines(self, orig_image, skewed_image): orig_image = np.array(orig_image) skewed_image = np.array(skewed_image) try: surf = cv2.xfeatures2d.SURF_create(400) except Exception: surf = cv2.SIFT(400) kp1, des1 = surf.detectAndCompute(orig_image, None) kp2, des2 = surf.detectAndCompute(skewed_image, None) FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(des1, des2, k=2) # store all the good matches as per Lowe's ratio test. good = [] for m, n in matches: if m.distance < 0.7 * n.distance: good.append(m) MIN_MATCH_COUNT = 10 if len(good) > MIN_MATCH_COUNT: src_pts = np.float32([kp1[m.queryIdx].pt for m in good ]).reshape(-1, 1, 2) dst_pts = np.float32([kp2[m.trainIdx].pt for m in good ]).reshape(-1, 1, 2) M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) # see https://ch.mathworks.com/help/images/examples/find-image-rotation-and-scale-using-automated-feature-matching.html for details ss = M[0, 1] sc = M[0, 0] scaleRecovered = math.sqrt(ss * ss + sc * sc) thetaRecovered = math.atan2(ss, sc) * 180 / math.pi self.log.info("MAP: Calculated scale difference: %.2f, " "Calculated rotation difference: %.2f" % (scaleRecovered, thetaRecovered)) #deskew image im_out = cv2.warpPerspective(skewed_image, np.linalg.inv(M), (orig_image.shape[1], orig_image.shape[0])) return im_out else: self.log.warn("MAP: Not enough matches are found - %d/%d" % (len(good), MIN_MATCH_COUNT)) return skewed_image
def bf_knnmatches( matches, img, kp1, kp2): MIN_MATCH_COUNT = 10 # store all the good matches as per Lowe's ratio test. good = [] dst = [] if len(matches[0]) == 2: for m, n in matches: if m.distance < 0.7*n.distance: good.append(m) if len(good) > MIN_MATCH_COUNT: src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2) dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2) M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) if M is not None: h, w = img.shape pts = np.float32([[0, 0], [0, h-1], [w-1, h-1], [w-1, 0]]).reshape(-1, 1, 2) dst = cv2.perspectiveTransform(pts, M) else: dst = [] else: dst = [] return dst
def extract_rect(im): imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) ret,thresh = cv2.threshold(imgray, 127, 255, 0) contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # finding contour with max area largest = None for cnt in contours: if largest == None or cv2.contourArea(cnt) > cv2.contourArea(largest): largest = cnt peri = cv2.arcLength(largest, True) appr = cv2.approxPolyDP(largest, 0.02 * peri, True) #cv2.drawContours(im, appr, -1, (0,255,0), 3) points_list = [[i[0][0], i[0][1]] for i in appr] left = sorted(points_list, key = lambda p: p[0])[0:2] right = sorted(points_list, key = lambda p: p[0])[2:4] print("l " + str(left)) print("r " + str(right)) lu = sorted(left, key = lambda p: p[1])[0] ld = sorted(left, key = lambda p: p[1])[1] ru = sorted(right, key = lambda p: p[1])[0] rd = sorted(right, key = lambda p: p[1])[1] print("lu " + str(lu)) print("ld " + str(ld)) print("ru " + str(ru)) print("rd " + str(rd)) lu_ = [ (lu[0] + ld[0])/2, (lu[1] + ru[1])/2 ] ld_ = [ (lu[0] + ld[0])/2, (ld[1] + rd[1])/2 ] ru_ = [ (ru[0] + rd[0])/2, (lu[1] + ru[1])/2 ] rd_ = [ (ru[0] + rd[0])/2, (ld[1] + rd[1])/2 ] print("lu_ " + str(lu_)) print("ld_ " + str(ld_)) print("ru_ " + str(ru_)) print("rd_ " + str(rd_)) src_pts = np.float32(np.array([lu, ru, rd, ld])) dst_pts = np.float32(np.array([lu_, ru_, rd_, ld_])) h,w,b = im.shape H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) print("H" + str(H)) imw = cv2.warpPerspective(im, H, (w, h)) return imw[lu_[1]:rd_[1], lu_[0]:rd_[0]] # cropping image
def checkAvailability(sift, tkp, tdes, matchimg): """ :param sift: :param tkp: :param tdes:sift feature object, template keypoints, and template descriptor :param matchimg: :return: """ qimg = cv2.imread(matchimg) qimggray = cv2.cvtColor(qimg,cv2.COLOR_BGR2GRAY) # kernel = np.ones((5,5), np.uint8) # qimggray = cv2.erode(qimggray, kernel, iterations=1) # ret,threshimg = cv2.threshold(qimggray,100,255,cv2.THRESH_BINARY) qkp,qdes = sift.detectAndCompute(qimggray, None) # plt.imshow(threshimg, 'gray'), plt.show() FLANN_INDEX_KDITREE=0 index_params=dict(algorithm=FLANN_INDEX_KDITREE,tree=5) # FLANN_INDEX_LSH = 6 # index_params = dict(algorithm=FLANN_INDEX_LSH, # table_number=12, # 12 # key_size=20, # 20 # multi_probe_level=2) # 2 search_params = dict(checks = 50) flann=cv2.FlannBasedMatcher(index_params,search_params) matches=flann.knnMatch(tdes,qdes,k=2) goodMatch=[] for m_n in matches: if len(m_n) != 2: continue m, n = m_n if(m.distance<0.75*n.distance): goodMatch.append(m) MIN_MATCH_COUNT = 30 if (len(goodMatch) >= MIN_MATCH_COUNT): tp = [] qp = [] for m in goodMatch: tp.append(tkp[m.queryIdx].pt) qp.append(qkp[m.trainIdx].pt) tp, qp = np.float32((tp, qp)) H, status = cv2.findHomography(tp, qp, cv2.RANSAC, 3.0) h = timg.shape[0] w = timg.shape[1] trainBorder = np.float32([[[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]]) queryBorder = cv2.perspectiveTransform(trainBorder, H) cv2.polylines(qimg, [np.int32(queryBorder)], True, (0, 255, 0), 5) cv2.imshow('result', qimg) plt.imshow(qimg, 'gray'), plt.show() return True else: print "Not Enough match found- %d/%d" % (len(goodMatch), MIN_MATCH_COUNT) return False # cv2.imshow('result', qimg) # if cv2.waitKey(10) == ord('q'): # cv2.destroyAllWindows()
def frame_homography(totalPts, homTh): """ Filter foreground points i.e. the outlier points found by fitting homography using RANSAC Input: totalPts: (numAllPoints, 4): x0, y0, x1, y1 fgPts: (numAllPoints, 4): x0, y0, x1, y1 """ if totalPts.ndim != 2 or totalPts.shape[0] < 8 or homTh < 0: return totalPts import cv2 p1 = totalPts[:, :2].astype('float') p2 = totalPts[:, 2:4].astype('float') _, status = cv2.findHomography( p1, p2, cv2.RANSAC, ransacReprojThreshold=homTh) fgPts = totalPts[status[:, 0] == 0, :] return fgPts
def find_correspondence_points(img1, img2): sift = cv2.xfeatures2d.SIFT_create() # find the keypoints and descriptors with SIFT kp1, des1 = sift.detectAndCompute( cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY), None) kp2, des2 = sift.detectAndCompute( cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY), None) # Find point matches FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(des1, des2, k=2) # Apply Lowe's SIFT matching ratio test good = [] for m, n in matches: if m.distance < 0.8 * n.distance: good.append(m) src_pts = np.asarray([kp1[m.queryIdx].pt for m in good]) dst_pts = np.asarray([kp2[m.trainIdx].pt for m in good]) # Constrain matches to fit homography retval, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 100.0) mask = mask.ravel() # We select only inlier points pts1 = src_pts[mask == 1] pts2 = dst_pts[mask == 1] return pts1.T, pts2.T
def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB): # compute the raw matches and initialize the list of actual # matches matcher = cv2.DescriptorMatcher_create("FlannBased") rawMatches = matcher.knnMatch(featuresA, featuresB, 2) matches = [] # loop over the raw matches for m in rawMatches: # ensure the distance is within a certain ratio of each # other (i.e. Lowe's ratio test) matches.append((m[0].trainIdx, m[0].queryIdx)) # computing a homography requires at least 4 matches if len(matches) > 4: ptsA = np.float32([kpsA[i] for (_, i) in matches]) ptsB = np.float32([kpsB[i] for (i, _) in matches]) # compute the homography between the two sets of points (H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, self.reprojThresh) # return the matches along with the homograpy matrix # and status of each matched point return (matches, H, status) # otherwise, no homograpy could be computed return None
def shot_homography(shotTracks, homTh): """ Filter foreground points i.e. the outlier points found by fitting homography using RANSAC Input: shotTracks: (numFrames, numAllPoints, 2) fgTracks: (numFrames, numForegroundPoints, 2) """ if shotTracks.ndim < 3 or shotTracks.shape[0] < 2 or homTh < 0: return shotTracks import cv2 status = 1 for i in range(1, shotTracks.shape[0]): if shotTracks[i - 1, 0, 2] > -1000: p1 = shotTracks[i - 1, :, 2:].astype('float') else: p1 = shotTracks[i - 1, :, :2].astype('float') p2 = shotTracks[i, :, :2].astype('float') _, new_status = cv2.findHomography( p1, p2, cv2.RANSAC, ransacReprojThreshold=homTh) status = new_status * status fgTracks = shotTracks[:, status[:, 0] == 0, :] print(shotTracks.shape[0], shotTracks.shape[1], fgTracks.shape[1]) return fgTracks
def homography(img1, img2, visualize=False): """ Finds Homography matrix from Image1 to Image2. Two images should be a plane and can change in viewpoint :param img1: Source image :param img2: Target image :param visualize: Flag to visualize the matched pixels and Homography warping :return: Homography matrix. (or) Homography matrix, Visualization image - if visualize is True """ sift = cv.xfeatures2d.SIFT_create() kp1, desc1 = sift.detectAndCompute(img1, None) kp2, desc2 = sift.detectAndCompute(img2, None) index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=INDEX_PARAM_TREES) # number of times the trees in the index should be recursively traversed # Higher values gives better precision, but also takes more time sch_params = dict(checks=SCH_PARAM_CHECKS) flann = cv.FlannBasedMatcher(index_params, sch_params) matches = flann.knnMatch(desc1, desc2, k=2) logging.debug('{} matches found'.format(len(matches))) # select good matches matches_arr = [] good_matches = [] for m, n in matches: if m.distance < GOOD_MATCH_THRESHOLD * n.distance: good_matches.append(m) matches_arr.append(m) if len(good_matches) < MIN_MATCH_COUNT: raise (Exception('Not enough matches found')) else: logging.debug('{} of {} are good matches'.format(len(good_matches), len(matches))) src_pts = [kp1[m.queryIdx].pt for m in good_matches] src_pts = np.array(src_pts, dtype=np.float32).reshape((-1, 1, 2)) dst_pts = [kp2[m.trainIdx].pt for m in good_matches] dst_pts = np.array(dst_pts, dtype=np.float32).reshape((-1, 1, 2)) homo, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 5) if visualize: res = visualize_homo(img1, img2, kp1, kp2, matches, homo, mask) return homo, res return homo
def getMatches(self, sceneImage): """ sceneImage: ?????array?? return dst: ???????? """ # Initiate SIFT detector sift = cv2.xfeatures2d.SIFT_create() # find the keypoints and descriptors with SIFT kp1, des1 = sift.detectAndCompute(self.MarkImage[:,:,0],None) kp2, des2 = sift.detectAndCompute(sceneImage[:,:,0],None) # create BFMatcher object FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict(checks = 50) flann = cv2.FlannBasedMatcher(index_params, search_params) # Match descriptors. matches = flann.knnMatch(des1,des2,k=2) # Sort them in the order of their distance. good = [] for m,n in matches: if m.distance < 0.7*n.distance: good.append(m) if len(good) < self.MIN_MATCH_COUNT: return None src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2) dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2) M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0) matchesMask = mask.ravel().tolist() h,w = self.MarkImage.shape[:2] pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2) dst = cv2.perspectiveTransform(pts,M) draw_params = dict(matchColor = (0,255,0), # draw matches in green color singlePointColor = None, matchesMask = matchesMask, # draw only inliers flags = 2) self.SceneImage = sceneImage self.DrawParams = draw_params self.KP1 = kp1 self.KP2 = kp2 self.GoodMatches = good return dst
def homographyGeneration(raw_image, image_path, dataPath, pairs_per_img): img = cv2.resize(cv2.imread(image_path, 0),(320,240)) with open(dataPath + 'homography_re' + '.txt', 'ab') as output_file_1, open(dataPath + 'homography_ab' + '.txt', 'ab') as output_file_2: random_list = [] i = 1 while i < pairs_per_img + 1: y_start = random.randint(32,80) y_end = y_start + 128 x_start = random.randint(32,160) x_end = x_start + 128 y_1 = y_start x_1 = x_start y_2 = y_end x_2 = x_start y_3 = y_end x_3 = x_end y_4 = y_start x_4 = x_end img_patch = img[y_start:y_end, x_start:x_end] # patch 1 y_1_offset = random.randint(-32,32) x_1_offset = random.randint(-32,32) y_2_offset = random.randint(-32,32) x_2_offset = random.randint(-32,32) y_3_offset = random.randint(-32,32) x_3_offset = random.randint(-32,32) y_4_offset = random.randint(-32,32) x_4_offset = random.randint(-32,32) y_1_p = y_1 + y_1_offset x_1_p = x_1 + x_1_offset y_2_p = y_2 + y_2_offset x_2_p = x_2 + x_2_offset y_3_p = y_3 + y_3_offset x_3_p = x_3 + x_3_offset y_4_p = y_4 + y_4_offset x_4_p = x_4 + x_4_offset pts_img_patch = np.array([[y_1,x_1],[y_2,x_2],[y_3,x_3],[y_4,x_4]]).astype(np.float32) pts_img_patch_perturb = np.array([[y_1_p,x_1_p],[y_2_p,x_2_p],[y_3_p,x_3_p],[y_4_p,x_4_p]]).astype(np.float32) h,status = cv2.findHomography(pts_img_patch, pts_img_patch_perturb, cv2.RANSAC) img_perburb = cv2.warpPerspective(img, h, (320, 240)) img_perburb_patch = img_perburb[y_start:y_end, x_start:x_end] # patch 2 if not [y_1,x_1,y_2,x_2,y_3,x_3,y_4,x_4] in random_list: random_list.append([y_1,x_1,y_2,x_2,y_3,x_3,y_4,x_4]) h_4pt_1 = np.array([y_1_offset,x_1_offset,y_2_offset,x_2_offset,y_3_offset,x_3_offset,y_4_offset,x_4_offset]) h_4pt_2 = np.array([y_1_p,x_1_p,y_2_p,x_2_p,y_3_p,x_3_p,y_4_p,x_4_p]) img_patch_path = os.path.join(dataPath, (raw_image.split('.')[0] + '_' + str(i) + '_1' +'.jpg')) cv2.imwrite(img_patch_path, img_patch) img_perburb_patch_path = os.path.join(dataPath, (raw_image.split('.')[0] + '_' + str(i) + '_2' +'.jpg')) cv2.imwrite(img_perburb_patch_path, img_perburb_patch) np.savetxt(output_file_1, h_4pt_1) np.savetxt(output_file_2, h_4pt_2) i += 1
def calc_pose(self, frame): """ Parameters ---------- frame : Returns ------- """ px_ref, px_cur = feature_tracking(self.last_frame, frame, self.px_ref) E, mask = cv2.findEssentialMat(px_cur, px_ref, focal=self.focal, pp=self.pp, method=cv2.RANSAC, prob=0.999, threshold=1.0) _, R, t, mask = cv2.recoverPose(E, px_cur, px_ref, focal=self.focal, pp=self.pp) return R, t, px_ref, px_cur
def generate_data_test(img_path): data = [] img = cv2.resize(cv2.imread(img_path, 0), (640, 480)) y_start = random.randint(64, 160) y_end = y_start + 256 x_start = random.randint(64, 320) x_end = x_start + 256 y_1 = y_start x_1 = x_start y_2 = y_end x_2 = x_start y_3 = y_end x_3 = x_end y_4 = y_start x_4 = x_end img_patch = img[y_start:y_end, x_start:x_end] # patch 1 y_1_offset = random.randint(-64, 64) x_1_offset = random.randint(-64, 64) y_2_offset = random.randint(-64, 64) x_2_offset = random.randint(-64, 64) y_3_offset = random.randint(-64, 64) x_3_offset = random.randint(-64, 64) y_4_offset = random.randint(-64, 64) x_4_offset = random.randint(-64, 64) y_1_p = y_1 + y_1_offset x_1_p = x_1 + x_1_offset y_2_p = y_2 + y_2_offset x_2_p = x_2 + x_2_offset y_3_p = y_3 + y_3_offset x_3_p = x_3 + x_3_offset y_4_p = y_4 + y_4_offset x_4_p = x_4 + x_4_offset pts_img_patch = np.array([[y_1,x_1],[y_2,x_2],[y_3,x_3],[y_4,x_4]]).astype(np.float32) pts_img_patch_perturb = np.array([[y_1_p,x_1_p],[y_2_p,x_2_p],[y_3_p,x_3_p],[y_4_p,x_4_p]]).astype(np.float32) h, status = cv2.findHomography(pts_img_patch, pts_img_patch_perturb, cv2.RANSAC) img_perburb = cv2.warpPerspective(img, h, (640, 480)) img_perburb_patch = img_perburb[y_start:y_end, x_start:x_end] # patch 2 data.append(img_patch) data.append(img_perburb_patch) h_4pt = np.array([y_1_offset,x_1_offset,y_2_offset,x_2_offset,y_3_offset,x_3_offset,y_4_offset,x_4_offset]) h1 = np.array([y_1,x_1,y_2,x_2,y_3,x_3,y_4,x_4]) return data, h_4pt, h1, img, img_perburb
def generate_data_train(img_path): data = [] img = cv2.resize(cv2.imread(img_path, 0), (320, 240)) y_start = random.randint(32, 80) y_end = y_start + 128 x_start = random.randint(32, 160) x_end = x_start + 128 y_1 = y_start x_1 = x_start y_2 = y_end x_2 = x_start y_3 = y_end x_3 = x_end y_4 = y_start x_4 = x_end img_patch = img[y_start:y_end, x_start:x_end] # patch 1 y_1_offset = random.randint(-32, 32) x_1_offset = random.randint(-32, 32) y_2_offset = random.randint(-32, 32) x_2_offset = random.randint(-32, 32) y_3_offset = random.randint(-32, 32) x_3_offset = random.randint(-32, 32) y_4_offset = random.randint(-32, 32) x_4_offset = random.randint(-32, 32) y_1_p = y_1 + y_1_offset x_1_p = x_1 + x_1_offset y_2_p = y_2 + y_2_offset x_2_p = x_2 + x_2_offset y_3_p = y_3 + y_3_offset x_3_p = x_3 + x_3_offset y_4_p = y_4 + y_4_offset x_4_p = x_4 + x_4_offset pts_img_patch = np.array([[y_1,x_1],[y_2,x_2],[y_3,x_3],[y_4,x_4]]).astype(np.float32) pts_img_patch_perturb = np.array([[y_1_p,x_1_p],[y_2_p,x_2_p],[y_3_p,x_3_p],[y_4_p,x_4_p]]).astype(np.float32) h, status = cv2.findHomography(pts_img_patch, pts_img_patch_perturb, cv2.RANSAC) img_perburb = cv2.warpPerspective(img, h, (320, 240)) img_perburb_patch = img_perburb[y_start:y_end, x_start:x_end] # patch 2 data.append(img_patch) data.append(img_perburb_patch) h_4pt = np.array([y_1_offset,x_1_offset,y_2_offset,x_2_offset,y_3_offset,x_3_offset,y_4_offset,x_4_offset]) h1 = np.array([y_1,x_1,y_2,x_2,y_3,x_3,y_4,x_4]) return data, h_4pt, h1, img, img_perburb
def generate_data(img_path): data_re = [] label_re = [] random_list = [] img = cv2.resize(cv2.imread(img_path, 0), (320, 240)) i = 1 while i < pairs_per_img + 1: data = [] label = [] y_start = random.randint(32, 80) y_end = y_start + 128 x_start = random.randint(32, 160) x_end = x_start + 128 y_1 = y_start x_1 = x_start y_2 = y_end x_2 = x_start y_3 = y_end x_3 = x_end y_4 = y_start x_4 = x_end img_patch = img[y_start:y_end, x_start:x_end] # patch 1 y_1_offset = random.randint(-32, 32) x_1_offset = random.randint(-32, 32) y_2_offset = random.randint(-32, 32) x_2_offset = random.randint(-32, 32) y_3_offset = random.randint(-32, 32) x_3_offset = random.randint(-32, 32) y_4_offset = random.randint(-32, 32) x_4_offset = random.randint(-32, 32) y_1_p = y_1 + y_1_offset x_1_p = x_1 + x_1_offset y_2_p = y_2 + y_2_offset x_2_p = x_2 + x_2_offset y_3_p = y_3 + y_3_offset x_3_p = x_3 + x_3_offset y_4_p = y_4 + y_4_offset x_4_p = x_4 + x_4_offset pts_img_patch = np.array([[y_1,x_1],[y_2,x_2],[y_3,x_3],[y_4,x_4]]).astype(np.float32) pts_img_patch_perturb = np.array([[y_1_p,x_1_p],[y_2_p,x_2_p],[y_3_p,x_3_p],[y_4_p,x_4_p]]).astype(np.float32) h,status = cv2.findHomography(pts_img_patch, pts_img_patch_perturb, cv2.RANSAC) img_perburb = cv2.warpPerspective(img, h, (320, 240)) img_perburb_patch = img_perburb[y_start:y_end, x_start:x_end] # patch 2 if not [y_1,x_1,y_2,x_2,y_3,x_3,y_4,x_4] in random_list: data.append(img_patch) data.append(img_perburb_patch) # [2, 128, 128] random_list.append([y_1,x_1,y_2,x_2,y_3,x_3,y_4,x_4]) h_4pt = np.array([y_1_offset,x_1_offset,y_2_offset,x_2_offset,y_3_offset,x_3_offset,y_4_offset,x_4_offset]) # h_4pt = np.array([y_1_p,x_1_p,y_2_p,x_2_p,y_3_p,x_3_p,y_4_p,x_4_p]) # labels label.append(h_4pt) # [1, 8] i += 1 data_re.append(data) # [4, 2, 128, 128] label_re.append(label) # [4, 1, 8] return data_re, label_re
def stackImagesKeypointMatching(file_list): orb = cv2.ORB_create() # disable OpenCL to because of bug in ORB in OpenCV 3.1 cv2.ocl.setUseOpenCL(False) stacked_image = None first_image = None first_kp = None first_des = None for file in file_list: print(file) image = cv2.imread(file,1) imageF = image.astype(np.float32) / 255 # compute the descriptors with ORB kp = orb.detect(image, None) kp, des = orb.compute(image, kp) # create BFMatcher object matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) if first_image is None: # Save keypoints for first image stacked_image = imageF first_image = image first_kp = kp first_des = des else: # Find matches and sort them in the order of their distance matches = matcher.match(first_des, des) matches = sorted(matches, key=lambda x: x.distance) src_pts = np.float32( [first_kp[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2) dst_pts = np.float32( [kp[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2) # Estimate perspective transformation M, mask = cv2.findHomography(dst_pts, src_pts, cv2.RANSAC, 5.0) w, h, _ = imageF.shape imageF = cv2.warpPerspective(imageF, M, (h, w)) stacked_image += imageF stacked_image /= len(file_list) stacked_image = (stacked_image*255).astype(np.uint8) return stacked_image # ===== MAIN ===== # Read all files in directory
def matchFeatures(queryFeature, trainFeature, matcher): """ match(...) function: match query image features and train image features. parameter: queryFeature: features of query image trainFeature: features of train image matcher: feature matcher queryImage: this is just for test to show the position of the found image which in the query image , input query image data which has processed by cv2.imread(). return: if found matched image ,return image name, otherwise return None. """ queryKeypoints = queryFeature[0] queryDescriptors = queryFeature[1] trainKeypoints = trainFeature[0] trainDescriptors = trainFeature[1] trainImgSize = trainFeature[2] trainImgHeight = trainImgSize[0] trainImgWidth = trainImgSize[1] corners=numpy.float32([[0, 0], [trainImgWidth, 0], [trainImgWidth, trainImgHeight], [0, trainImgHeight]]) raw_matches = matcher.knnMatch(trainDescriptors, queryDescriptors, 2) queryGoodPoints, trainGoodPoints = filter_matches(trainKeypoints, queryKeypoints, raw_matches) if len(queryGoodPoints) >= 4: H,status = cv2.findHomography(queryGoodPoints, trainGoodPoints, cv2.RANSAC, 5.0) else: H,status = None,None res=False obj_corners=None if H is not None: corners = corners.reshape(1, -1, 2) obj_corners = numpy.int32(cv2.perspectiveTransform(corners, H).reshape(-1, 2)) is_polygon = ispolygon(obj_corners) if is_polygon: res=True del queryKeypoints del queryDescriptors del trainKeypoints del trainDescriptors del trainImgSize del corners del raw_matches del queryGoodPoints del trainGoodPoints del obj_corners return res
def TestKptMatch(): img1=cv2.imread("E:\\DevProj\\Datasets\\VGGAffine\\bark\\img1.ppm",cv2.IMREAD_COLOR) img2=cv2.imread("E:\\DevProj\\Datasets\\VGGAffine\\bark\\img2.ppm",cv2.IMREAD_COLOR) gray1=cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY) gray2=cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY) gap_width=20 black_gap=npy.zeros((img1.shape[0],gap_width),dtype=npy.uint8) # objSIFT = cv2.SIFT(500) # kpt1,desc1 = objSIFT.detectAndCompute(gray1,None) # kpt2,desc2 = objSIFT.detectAndCompute(gray2,None) # objMatcher=cv2.BFMatcher(cv2.NORM_L2) # matches=objMatcher.knnMatch(desc1,desc2,k=2) objORB = cv2.ORB(500) kpt1,desc1 = objORB.detectAndCompute(gray1,None) kpt2,desc2 = objORB.detectAndCompute(gray2,None) objMatcher=cv2.BFMatcher(cv2.NORM_HAMMING) matches=objMatcher.knnMatch(desc1,desc2,k=2) goodMatches=[] for bm1,bm2 in matches: if bm1.distance < 0.7*bm2.distance: goodMatches.append(bm1) if len(goodMatches)>10: ptsFrom = npy.float32([kpt1[bm.queryIdx].pt for bm in goodMatches]).reshape(-1,1,2) ptsTo = npy.float32([kpt2[bm.trainIdx].pt for bm in goodMatches]).reshape(-1,1,2) matH, matchMask = cv2.findHomography(ptsFrom, ptsTo, cv2.RANSAC,5.0) imgcnb=npy.concatenate((gray1,black_gap,gray2),axis=1) plt.figure(1,figsize=(15,6)) plt.imshow(imgcnb,cmap="gray") idx=0 for bm in goodMatches: if 1==matchMask[idx]: kptFrom=kpt1[bm.queryIdx] kptTo=kpt2[bm.trainIdx] plt.plot(kptFrom.pt[0],kptFrom.pt[1],"rs", markerfacecolor="none",markeredgecolor="r",markeredgewidth=2) plt.plot(kptTo.pt[0]+img1.shape[1]+gap_width,kptTo.pt[1],"bo", markerfacecolor="none",markeredgecolor="b",markeredgewidth=2) plt.plot([kptFrom.pt[0],kptTo.pt[0]+img1.shape[1]+gap_width], [kptFrom.pt[1],kptTo.pt[1]],"g-",linewidth=2) idx+=1 plt.axis("off")
