Python cv2.SimpleBlobDetector_create() Examples
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code examples of cv2.SimpleBlobDetector_create().
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Example #1
Source File: utils_nms.py From lighttrack with MIT License | 6 votes |
def init_blob_detector(): params = cv2.SimpleBlobDetector_Params() params.minThreshold = 1 params.maxThreshold = 255 params.filterByArea = True params.minArea = 1 params.filterByCircularity = False params.filterByConvexity = False params.filterByInertia = False #detector = cv2.SimpleBlobDetector(params) detector = cv2.SimpleBlobDetector_create(params) return detector
Example #2
Source File: webcam_track_blobs.py From pc-drone with MIT License | 6 votes |
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
Example #3
Source File: __init__.py From stytra with GNU General Public License v3.0 | 5 votes |
def _find_triangle(image, blob_params=None): """Finds the three dots for calibration in the image (of a 30 60 90 degree triangle) Parameters ---------- image : return: the three triangle points blob_params : (Default value = None) Returns ------- type the three triangle points """ if blob_params is None: blobdet = cv2.SimpleBlobDetector_create() else: blobdet = cv2.SimpleBlobDetector_create(blob_params) # TODO check if blob detection is robust scaled_im = 255 - (image.astype(np.float32) * 255 / np.max(image)).astype( np.uint8 ) keypoints = blobdet.detect(scaled_im) if len(keypoints) != 3: raise CalibrationException("3 points for calibration not found") kps = np.array([k.pt for k in keypoints]) # Find the angles between the points # and return the points sorted by the angles return kps[np.argsort(CircleCalibrator._find_angles(kps)), :]
Example #4
Source File: utils_nms.py From cvToolkit with MIT License | 5 votes |
def init_blob_detector(): params = cv2.SimpleBlobDetector_Params() params.minThreshold = 1 params.maxThreshold = 255 params.filterByArea = True params.minArea = 1 params.filterByCircularity = False params.filterByConvexity = False params.filterByInertia = False #detector = cv2.SimpleBlobDetector(params) detector = cv2.SimpleBlobDetector_create(params) return detector
Example #5
Source File: utils_nms.py From cvToolkit with MIT License | 5 votes |
def init_blob_detector(): params = cv2.SimpleBlobDetector_Params() params.minThreshold = 1 params.maxThreshold = 255 params.filterByArea = True params.minArea = 1 params.filterByCircularity = False params.filterByConvexity = False params.filterByInertia = False #detector = cv2.SimpleBlobDetector(params) detector = cv2.SimpleBlobDetector_create(params) return detector
Example #6
Source File: utils_nms.py From video-to-pose3D with MIT License | 5 votes |
def init_blob_detector(): params = cv2.SimpleBlobDetector_Params() params.minThreshold = 1 params.maxThreshold = 255 params.filterByArea = True params.minArea = 1 params.filterByCircularity = False params.filterByConvexity = False params.filterByInertia = False # detector = cv2.SimpleBlobDetector(params) detector = cv2.SimpleBlobDetector_create(params) return detector
Example #7
Source File: find_balloon.py From ardupilot-balloon-finder with GNU General Public License v3.0 | 5 votes |
def analyse_frame(self,frame): balloon_found = False balloon_x = 0 balloon_y = 0 balloon_radius = 0 # Convert BGR to HSV hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # Threshold the HSV image mask = cv2.inRange(hsv, self.filter_low, self.filter_high) # Erode erode_kernel = numpy.ones((3,3),numpy.uint8); eroded_img = cv2.erode(mask,erode_kernel,iterations = 1) # dilate dilate_kernel = numpy.ones((10,10),numpy.uint8); dilate_img = cv2.dilate(eroded_img,dilate_kernel,iterations = 1) # blog detector blob_params = cv2.SimpleBlobDetector_Params() blob_params.minDistBetweenBlobs = 50 blob_params.filterByInertia = False blob_params.filterByConvexity = False blob_params.filterByColor = True blob_params.blobColor = 255 blob_params.filterByCircularity = False blob_params.filterByArea = False #blob_params.minArea = 20 #blob_params.maxArea = 500 blob_detector = cv2.SimpleBlobDetector_create(blob_params) keypts = blob_detector.detect(dilate_img) # draw centers of all keypoints in new image #blob_img = cv2.drawKeypoints(frame, keypts, color=(0,255,0), flags=0) # find largest blob if len(keypts) > 0: kp_max = keypts[0] for kp in keypts: if kp.size > kp_max.size: kp_max = kp # draw circle around the largest blob cv2.circle(frame,(int(kp_max.pt[0]),int(kp_max.pt[1])),int(kp_max.size),(0,255,0),2) # set the balloon location balloon_found = True balloon_x = kp_max.pt[0] balloon_y = kp_max.pt[1] balloon_radius = kp_max.size # return results return balloon_found, balloon_x, balloon_y, balloon_radius # add_artificial_horizon - adds artificial horizon to an image using the vehicle's attitude