Python cv2.groupRectangles() Examples
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code examples of cv2.groupRectangles().
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Example #1
Source File: facerec_train.py From deepvisualminer with MIT License | 6 votes |
def detect(img_file, detector_xml_path, dest_img_file): img = cv2.imread(img_file) gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) detector = cv2.CascadeClassifier(detector_xml_path) min_size = (min(50, gray_img.shape[0] // 10), min(50, gray_img.shape[1] // 10)) hits = detector.detectMultiScale(gray_img, 1.1, 4, 0, min_size) #cv2.groupRectangles(hits, 2) print(hits) hits_img = np.copy(img) for (x,y,w,h) in hits: cv2.rectangle(hits_img, (x,y), (x+w, y+h), (0,0,255), 2) cv2.imwrite(dest_img_file, hits_img)
Example #2
Source File: proposal_helpers.py From raster-deep-learning with Apache License 2.0 | 5 votes |
def filterRois(rects, img_w, img_h, roi_min_area, roi_max_area, roi_min_side, roi_max_side, roi_max_aspect_ratio): filteredRects = [] filteredRectsSet = set() for rect in rects: if tuple(rect) in filteredRectsSet: # excluding rectangles with same co-ordinates continue x, y, x2, y2 = rect w = x2 - x h = y2 - y assert(w>=0 and h>=0) # apply filters if h == 0 or w == 0 or \ x2 > img_w or y2 > img_h or \ w < roi_min_side or h < roi_min_side or \ w > roi_max_side or h > roi_max_side or \ w * h < roi_min_area or w * h > roi_max_area or \ w / h > roi_max_aspect_ratio or h / w > roi_max_aspect_ratio: continue filteredRects.append(rect) filteredRectsSet.add(tuple(rect)) # could combine rectangles using non-maximum surpression or with similar co-ordinates # groupedRectangles, weights = cv2.groupRectangles(np.asanyarray(rectsInput, np.float).tolist(), 1, 0.3) # groupedRectangles = nms_python(np.asarray(rectsInput, np.float), 0.5) assert(len(filteredRects) > 0) return filteredRects
Example #3
Source File: facerec_train.py From deepvisualminer with MIT License | 5 votes |
def detectvideo(vid_file, detector_xml_path, dest_img_dir): if not os.path.exists(dest_img_dir): os.makedirs(dest_img_dir) detector = cv2.CascadeClassifier(detector_xml_path) vid = imageio.get_reader(vid_file, 'ffmpeg') # If size and source_size are not equal, then device was probably # rotated (like a mobile) and we should compensate for the rotation. # Images will have 'source_size' dimensions but we need 'size'. metadata = vid.get_meta_data() rotate = False if metadata['source_size'] != metadata['size']: print('Rotating') rotate = True for i, img in enumerate(vid): if rotate: #img = np.transpose(img, axes=(1, 0, 2)).copy() img = np.rot90(img).copy() print('Frame ',i, img.shape) gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) min_size = (min(20, gray_img.shape[0] // 10), min(20, gray_img.shape[1] // 10)) hits = detector.detectMultiScale(gray_img, 1.1, 3, 0, min_size) #cv2.groupRectangles(hits, 2) print(len(hits), ' hits') hits_img = np.copy(img) if len(hits) > 0: for (x,y,w,h) in hits: cv2.rectangle(hits_img, (x,y), (x+w, y+h), (0,0,255), 2) cv2.imwrite(os.path.join(dest_img_dir, 'frame-%d.png'%(i)), hits_img)
Example #4
Source File: read_image.py From image_text_reader with MIT License | 5 votes |
def get_text_with_location(boxed_image, contours, img): image_text_dict = {} for contour in contours: # get rectangle bounding contour [x, y, w, h] = cv2.boundingRect(contour) # cv2.groupRectangles # draw rectangle around contour on original image # if w < 20 or h < 20: # continue if w > 300: continue cv2.rectangle( boxed_image, (x, y), (x + w + 10, y + h + 10), thickness=2, color=(0, 123, 123)) """This writes the bounding box on image. """ box_read = extract_image_from_location(img, x, y, w, h) box_read = box_read.strip() image_text_dict[(x, y)] = box_read return image_text_dict
Example #5
Source File: tracking.py From OpenCV-Computer-Vision-Projects-with-Python with MIT License | 4 votes |
def advance_frame(self, frame, proto_objects_map): """Advances the algorithm by a single frame This method tracks all objects via the following steps: - adds all bounding boxes from saliency map as potential targets - finds bounding boxes from previous frame in current frame via mean-shift tracking - combines the two lists by removing duplicates certain targets are discarded: - targets that are too small - targets that don't move :param frame: New input RGB frame :param proto_objects_map: corresponding proto-objects map of the frame :returns: frame annotated with bounding boxes around all objects that are being tracked """ self.tracker = copy.deepcopy(frame) # build a list of all bounding boxes box_all = [] # append to the list all bounding boxes found from the # current proto-objects map box_all = self._append_boxes_from_saliency(proto_objects_map, box_all) # find all bounding boxes extrapolated from last frame # via mean-shift tracking box_all = self._append_boxes_from_meanshift(frame, box_all) # only keep those that are both salient and in mean shift if len(self.object_roi) == 0: group_thresh = 0 # no previous frame: keep all form saliency else: group_thresh = 1 # previous frame + saliency box_grouped, _ = cv2.groupRectangles(box_all, group_thresh, 0.1) # update mean-shift bookkeeping for remaining boxes self._update_mean_shift_bookkeeping(frame, box_grouped) # draw remaining boxes for (x, y, w, h) in box_grouped: cv2.rectangle(self.tracker, (x, y), (x + w, y + h), (0, 255, 0), 2) return self.tracker