我们从Python开源项目中,提取了以下12个代码示例,用于说明如何使用cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS。
def _extract_spots(self) -> None: # Dilate and Erode to 'clean' the spot (nb that this harms the number itself, so we only do it to extract spots) kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) img = cv2.dilate(self._img, kernel, iterations=1) img = cv2.erode(img, kernel, iterations=2) img = cv2.dilate(img, kernel, iterations=1) # Perform a simple blob detect params = cv2.SimpleBlobDetector_Params() params.filterByArea = True params.minArea = 20 # The dot in 20pt font has area of about 30 params.filterByCircularity = True params.minCircularity = 0.7 params.filterByConvexity = True params.minConvexity = 0.8 params.filterByInertia = True params.minInertiaRatio = 0.4 detector = cv2.SimpleBlobDetector_create(params) self.spot_keypoints = detector.detect(img) # Log intermediate image img_with_keypoints = cv2.drawKeypoints(img, self.spot_keypoints, outImage=np.array([]), color=(0, 0, 255), flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) self.intermediate_images.append(NamedImage(img_with_keypoints, 'Spot Detection Image'))
def add_blobs(crop_frame): frame=cv2.GaussianBlur(crop_frame, (3, 3), 0) # Convert BGR to HSV hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # define range of green color in HSV lower_green = np.array([70,50,50]) upper_green = np.array([85,255,255]) # Threshold the HSV image to get only blue colors mask = cv2.inRange(hsv, lower_green, upper_green) mask = cv2.erode(mask, None, iterations=1) mask = cv2.dilate(mask, None, iterations=1) # Bitwise-AND mask and original image res = cv2.bitwise_and(frame,frame, mask= mask) detector = cv2.SimpleBlobDetector_create(params) # Detect blobs. reversemask=255-mask keypoints = detector.detect(reversemask) if keypoints: print "found blobs" if len(keypoints) > 4: keypoints.sort(key=(lambda s: s.size)) keypoints=keypoints[0:3] # Draw detected blobs as red circles. # cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob im_with_keypoints = cv2.drawKeypoints(frame, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) else: print "no blobs" im_with_keypoints=crop_frame return im_with_keypoints #, max_blob_dist, blob_center, keypoint_in_orders
def execute_BlobDetector(proxy,obj): try: img=obj.sourceObject.Proxy.img.copy() except: img=cv2.imread(__dir__+'/icons/freek.png') im = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) im=255-im im2 = img params = cv2.SimpleBlobDetector_Params() params.filterByArea = True params.minArea = obj.Area params.filterByConvexity = True params.minConvexity = obj.Convexity/200 # Set up the detector with default parameters. detector = cv2.SimpleBlobDetector_create(params) # Detect blobs. keypoints = detector.detect(im) # Draw detected blobs as red circles. # cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob if not obj.showBlobs: im_with_keypoints = cv2.drawKeypoints(im, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) obj.Proxy.img = im_with_keypoints for k in keypoints: (x,y)=k.pt x=int(round(x)) y=int(round(y)) # cv2.circle(im,(x,y),4,0,5) cv2.circle(im,(x,y),4,255,5) cv2.circle(im,(x,y),4,0,5) im[y,x]=255 im[y,x]=0 obj.Proxy.img = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) else: for k in keypoints: (x,y)=k.pt x=int(round(x)) y=int(round(y)) cv2.circle(im2,(x,y),4,(255,0,0),5) cv2.circle(im2,(x,y),4,(0,0,0),5) im2[y,x]=(255,0,0) im2[y,x]=(0,0,0) obj.Proxy.img = im2
def find_blobs(img): # Setup SimpleBlobDetector parameters. params = cv2.SimpleBlobDetector_Params() # Change thresholds params.minThreshold = 100; params.maxThreshold = 5000; # Filter by Area. params.filterByArea = True params.minArea = 200 # Filter by Circularity params.filterByCircularity = False params.minCircularity = 0.785 # Filter by Convexity params.filterByConvexity = False params.minConvexity = 0.87 # Filter by Inertia #params.filterByInertia = True #params.minInertiaRatio = 0.01 # Set up the detector with default parameters. detector = cv2.SimpleBlobDetector(params) # Detect blobs. keypoints = detector.detect(img) print keypoints # Draw detected blobs as red circles. # cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob im_with_keypoints = cv2.drawKeypoints(img, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) cv2.imwrite("blobs.jpg", im_with_keypoints);
def draw_keypoints(self, im, keypoints, filename="keypoints.jpg"): self._log("drawing keypoints into '%s'..." % filename) rows, cols = im.shape def to_cv2_kp(kp): # assert kp = [<row>, <col>, <ori>, <octave_ind>, <layer_ind>] ratio = get_size_ratio_by_octave(kp[3]) scale = get_scale_by_ind(kp[3], kp[4]) return cv2.KeyPoint(kp[1] / ratio, kp[0] / ratio, 10, kp[2] / PI * 180) kp_for_draw = list(map(to_cv2_kp, keypoints)) im_kp = cv2.drawKeypoints(im, kp_for_draw, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) cv2.imwrite(filename, im_kp)
def cv2_match(im1, im2): mysift = SIFT() sift = cv2.SIFT() bf = cv2.BFMatcher() kp1, dp1 = sift.detectAndCompute(im1, None) kp2, dp2 = sift.detectAndCompute(im2, None) matches_ = bf.knnMatch(dp1, dp2, k=2) print(len(matches_)) good = [] for m, n in matches_: if m.distance < 0.90 * n.distance: good.append(m) print(len(good)) pos1 = [(int(kp.pt[1]), int(kp.pt[0])) for kp in kp1] pos2 = [(int(kp.pt[1]), int(kp.pt[0])) for kp in kp2] matches = [(m.queryIdx, m.trainIdx, 0.15) for m in good] cv2.imwrite("cvkp1.jpg", cv2.drawKeypoints(im, kp1, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)) cv2.imwrite("cvkp2.jpg", cv2.drawKeypoints(imm, kp2, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)) mysift.draw_matches(im, pos1, imm, pos2, matches, 'ckmatch.jpg')
def visualize_keypoints(image, keypoints): kp_image = np.array([]) kp_image = cv2.drawKeypoints(image, keypoints, kp_image, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) cv2.imshow(PROJ_WIN, kp_image) wait()
def draw_image_with_keypoints(img: np.ndarray, keypoints, window_title: str ="Image with keypoints") -> None: """An apparently unused method which is actually quite useful when debugging!""" # cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob img_with_keypoints = cv2.drawKeypoints(img, keypoints, outImage=np.array([]), color=(0, 0, 255), flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) draw_image(img_with_keypoints, window_title)
def sift_thread(): sift = cv2.xfeatures2d.SIFT_create() (kps, descs) = sift.detectAndCompute(gray, None) cv2.drawKeypoints(gray, kps, img, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) cv2.imshow('SIFT Algorithm', img)
def surf_thread(): surf = cv2.xfeatures2d.SURF_create() (kps2, descs2) = surf.detectAndCompute(gray, None) cv2.drawKeypoints(gray, kps2, img2, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) cv2.imshow('SURF Algorithm', img2)
def fast_thread(): fast = cv2.FastFeatureDetector_create() kps3 = fast.detect(gray, None) cv2.drawKeypoints(gray, kps3, img3, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) cv2.imshow('FAST Algorithm', img3)
def orb_thread(): orb = cv2.ORB_create() kps4 = orb.detect(gray, None) (kps4, des4) = orb.compute(gray, kps4) cv2.drawKeypoints(gray, kps4, img4, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) cv2.imshow('ORB Algorithm', img4)