def initial_finetune(self, img, detection_box): self.stopwatch.start('initial_finetune') t = time.time() # generate samples pos_num, neg_num = ADNetConf.g()['initial_finetune'][ 'pos_num'], ADNetConf.g()['initial_finetune']['neg_num'] pos_boxes, neg_boxes = detection_box.get_posneg_samples(self.imgwh, pos_num, neg_num, use_whole=True) pos_lb_action = BoundingBox.get_action_labels(pos_boxes, detection_box) feats = self._get_features([ commons.extract_region(img, box) for i, box in enumerate(pos_boxes) ]) for box, feat in zip(pos_boxes, feats): box.feat = feat feats = self._get_features([ commons.extract_region(img, box) for i, box in enumerate(neg_boxes) ]) for box, feat in zip(neg_boxes, feats): box.feat = feat # train_fc_finetune_hem self._finetune_fc(img, pos_boxes, neg_boxes, pos_lb_action, ADNetConf.get()['initial_finetune']['learning_rate'], ADNetConf.get()['initial_finetune']['iter']) self.histories.append((pos_boxes, neg_boxes, pos_lb_action, np.copy(img), self.iteration)) _logger.info('ADNetRunner.initial_finetune t=%.3f' % t) self.stopwatch.stop('initial_finetune')
def initial_finetune(self, img, detection_box): # print("Start initial_finetune1") # generate samples pos_num, neg_num = ADNetConf.g()['initial_finetune'][ 'pos_num'], ADNetConf.g()['initial_finetune']['neg_num'] # print("Ending initial_finetune1") pos_boxes, neg_boxes = detection_box.get_posneg_samples(self.imgwh, pos_num, neg_num, use_whole=True) # print("Ending initial_finetune133") pos_lb_action = BoundingBox.get_action_labels(pos_boxes, detection_box) # print("Ending initial_finetune44") feats = self._get_features([ commons.extract_region(img, box) for i, box in enumerate(pos_boxes) ]) for box, feat in zip(pos_boxes, feats): box.feat = feat feats = self._get_features([ commons.extract_region(img, box) for i, box in enumerate(neg_boxes) ]) for box, feat in zip(neg_boxes, feats): box.feat = feat # print("Ending initial_finetune2") # train_fc_finetune_hem self._finetune_fc(img, pos_boxes, neg_boxes, pos_lb_action, ADNetConf.get()['initial_finetune']['learning_rate'], ADNetConf.get()['initial_finetune']['iter']) self.histories.append((pos_boxes, neg_boxes, pos_lb_action, np.copy(img), self.iteration))
def tracking(self, img, curr_bbox): self.iteration += 1 is_tracked = True boxes = [] self.latest_score = -1 self.stopwatch.start('tracking.do_action') for track_i in range(ADNetConf.get()['predict']['num_action']): patch = commons.extract_region(img, curr_bbox) # forward with image & action history actions, classes = self.persistent_sess.run( [self.adnet.layer_actions, self.adnet.layer_scores], feed_dict={ self.adnet.input_tensor: [patch], self.adnet.action_history_tensor: [commons.onehot_flatten(self.action_histories)], self.tensor_is_training: False }) latest_score = classes[0][1] if latest_score < ADNetConf.g()['predict']['thresh_fail']: is_tracked = False self.action_histories_old = np.copy(self.action_histories) self.action_histories = np.insert(self.action_histories, 0, 12)[:-1] break else: self.failed_cnt = 0 self.latest_score = latest_score # move box action_idx = np.argmax(actions[0]) self.action_histories = np.insert(self.action_histories, 0, action_idx)[:-1] prev_bbox = curr_bbox curr_bbox = curr_bbox.do_action(self.imgwh, action_idx) if action_idx != ADNetwork.ACTION_IDX_STOP: if prev_bbox == curr_bbox: print('action idx', action_idx) print(prev_bbox) print(curr_bbox) raise Exception('box not moved.') # oscillation check if action_idx != ADNetwork.ACTION_IDX_STOP and curr_bbox in boxes: action_idx = ADNetwork.ACTION_IDX_STOP if action_idx == ADNetwork.ACTION_IDX_STOP: break boxes.append(curr_bbox) self.stopwatch.stop('tracking.do_action') # redetection when tracking failed new_score = 0.0 if not is_tracked: self.failed_cnt += 1 # run redetection callback function new_box, new_score = self.callback_redetection(curr_bbox, img) if new_box is not None: curr_bbox = new_box patch = commons.extract_region(img, curr_bbox) _logger.debug('redetection success=%s' % (str(new_box is not None))) # save samples if is_tracked or new_score > ADNetConf.g( )['predict']['thresh_success']: self.stopwatch.start('tracking.save_samples.roi') imgwh = Coordinate.get_imgwh(img) pos_num, neg_num = ADNetConf.g( )['finetune']['pos_num'], ADNetConf.g()['finetune']['neg_num'] pos_boxes, neg_boxes = curr_bbox.get_posneg_samples( imgwh, pos_num, neg_num, use_whole=False, pos_thresh=ADNetConf.g()['finetune']['pos_thresh'], neg_thresh=ADNetConf.g()['finetune']['neg_thresh'], uniform_translation_f=2, uniform_scale_f=5) self.stopwatch.stop('tracking.save_samples.roi') self.stopwatch.start('tracking.save_samples.feat') feats = self._get_features([ commons.extract_region(img, box) for i, box in enumerate(pos_boxes) ]) for box, feat in zip(pos_boxes, feats): box.feat = feat feats = self._get_features([ commons.extract_region(img, box) for i, box in enumerate(neg_boxes) ]) for box, feat in zip(neg_boxes, feats): box.feat = feat pos_lb_action = BoundingBox.get_action_labels(pos_boxes, curr_bbox) self.histories.append((pos_boxes, neg_boxes, pos_lb_action, np.copy(img), self.iteration)) # clear old ones self.histories = self.histories[-ADNetConf.g( )['finetune']['long_term']:] self.stopwatch.stop('tracking.save_samples.feat') # online finetune if self.iteration % ADNetConf.g( )['finetune']['interval'] == 0 or is_tracked is False: img_pos, img_neg = [], [] pos_boxes, neg_boxes, pos_lb_action = [], [], [] pos_term = 'long_term' if is_tracked else 'short_term' for i in range(ADNetConf.g()['finetune'][pos_term]): if i >= len(self.histories): break pos_boxes.extend(self.histories[-(i + 1)][0]) pos_lb_action.extend(self.histories[-(i + 1)][2]) img_pos.extend([self.histories[-(i + 1)][3]] * len(self.histories[-(i + 1)][0])) for i in range(ADNetConf.g()['finetune']['short_term']): if i >= len(self.histories): break neg_boxes.extend(self.histories[-(i + 1)][1]) img_neg.extend([self.histories[-(i + 1)][3]] * len(self.histories[-(i + 1)][1])) self.stopwatch.start('tracking.online_finetune') self._finetune_fc((img_pos, img_neg), pos_boxes, neg_boxes, pos_lb_action, ADNetConf.get()['finetune']['learning_rate'], ADNetConf.get()['finetune']['iter']) _logger.debug('finetuned') self.stopwatch.stop('tracking.online_finetune') visualizer.image('patch', patch) # cv2.imshow('patch', patch) return curr_bbox