def parse_rec(self, filename): """ Parse a PASCAL VOC xml file """ tree = ET.parse(filename) objects = [] for obj in tree.findall('object'): obj_struct = {} obj_struct['name'] = obj.find('name').text # obj_struct['pose'] = obj.find('pose').text # obj_struct['truncated'] = int(obj.find('truncated').text) # obj_struct['difficult'] = int(obj.find('difficult').text) obj_struct['difficult'] = 0 bbox = obj.find('bndbox') rbox = [ eval(bbox.find('x1').text), eval(bbox.find('y1').text), eval(bbox.find('x2').text), eval(bbox.find('y2').text), eval(bbox.find('x3').text), eval(bbox.find('y3').text), eval(bbox.find('x4').text), eval(bbox.find('y4').text) ] rbox = np.array([rbox], np.float32) rbox = coordinate_convert.backward_convert(rbox, with_label=False) obj_struct['bbox'] = rbox objects.append(obj_struct) return objects
def clip_image(file_idx, image, boxes_all, width, height, stride_w, stride_h): min_pixel = 5 print(file_idx) boxes_all_5 = backward_convert(boxes_all[:, :8], False) print(boxes_all[np.logical_or(boxes_all_5[:, 2] <= min_pixel, boxes_all_5[:, 3] <= min_pixel), :]) boxes_all = boxes_all[np.logical_and(boxes_all_5[:, 2] > min_pixel, boxes_all_5[:, 3] > min_pixel), :] if boxes_all.shape[0] > 0: shape = image.shape for start_h in range(0, shape[0], stride_h): for start_w in range(0, shape[1], stride_w): boxes = copy.deepcopy(boxes_all) box = np.zeros_like(boxes_all) start_h_new = start_h start_w_new = start_w if start_h + height > shape[0]: start_h_new = shape[0] - height if start_w + width > shape[1]: start_w_new = shape[1] - width top_left_row = max(start_h_new, 0) top_left_col = max(start_w_new, 0) bottom_right_row = min(start_h + height, shape[0]) bottom_right_col = min(start_w + width, shape[1]) subImage = image[top_left_row:bottom_right_row, top_left_col: bottom_right_col] box[:, 0] = boxes[:, 0] - top_left_col box[:, 2] = boxes[:, 2] - top_left_col box[:, 4] = boxes[:, 4] - top_left_col box[:, 6] = boxes[:, 6] - top_left_col box[:, 1] = boxes[:, 1] - top_left_row box[:, 3] = boxes[:, 3] - top_left_row box[:, 5] = boxes[:, 5] - top_left_row box[:, 7] = boxes[:, 7] - top_left_row box[:, 8] = boxes[:, 8] center_y = 0.25 * (box[:, 1] + box[:, 3] + box[:, 5] + box[:, 7]) center_x = 0.25 * (box[:, 0] + box[:, 2] + box[:, 4] + box[:, 6]) cond1 = np.intersect1d(np.where(center_y[:] >= 0)[0], np.where(center_x[:] >= 0)[0]) cond2 = np.intersect1d(np.where(center_y[:] <= (bottom_right_row - top_left_row))[0], np.where(center_x[:] <= (bottom_right_col - top_left_col))[0]) idx = np.intersect1d(cond1, cond2) if len(idx) > 0 and (subImage.shape[0] > 5 and subImage.shape[1] > 5): makedirs(os.path.join(save_dir, 'images')) img = os.path.join(save_dir, 'images', "%s_%04d_%04d.png" % (file_idx, top_left_row, top_left_col)) cv2.imwrite(img, subImage) makedirs(os.path.join(save_dir, 'labeltxt')) xml = os.path.join(save_dir, 'labeltxt', "%s_%04d_%04d.xml" % (file_idx, top_left_row, top_left_col)) save_to_xml(xml, subImage.shape[0], subImage.shape[1], box[idx, :], class_list)
def worker(self, gpu_id, images, det_net, result_queue): os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id) img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR img_batch = tf.cast(img_plac, tf.float32) if self.cfgs.NET_NAME in [ 'resnet152_v1d', 'resnet101_v1d', 'resnet50_v1d' ]: img_batch = (img_batch / 255 - tf.constant( self.cfgs.PIXEL_MEAN_)) / tf.constant(self.cfgs.PIXEL_STD) else: img_batch = img_batch - tf.constant(self.cfgs.PIXEL_MEAN) img_batch = tf.expand_dims(img_batch, axis=0) detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( input_img_batch=img_batch) init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) restorer, restore_ckpt = det_net.get_restorer() config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: sess.run(init_op) if not restorer is None: restorer.restore(sess, restore_ckpt) print('restore model %d ...' % gpu_id) for img_path in images: # if 'P0006' not in img_path: # continue img = cv2.imread(img_path) box_res_rotate = [] label_res_rotate = [] score_res_rotate = [] imgH = img.shape[0] imgW = img.shape[1] img_short_side_len_list = self.cfgs.IMG_SHORT_SIDE_LEN if isinstance( self.cfgs.IMG_SHORT_SIDE_LEN, list) else [self.cfgs.IMG_SHORT_SIDE_LEN] img_short_side_len_list = [ img_short_side_len_list[0] ] if not self.args.multi_scale else img_short_side_len_list if imgH < self.args.h_len: temp = np.zeros([self.args.h_len, imgW, 3], np.float32) temp[0:imgH, :, :] = img img = temp imgH = self.args.h_len if imgW < self.args.w_len: temp = np.zeros([imgH, self.args.w_len, 3], np.float32) temp[:, 0:imgW, :] = img img = temp imgW = self.args.w_len for hh in range(0, imgH, self.args.h_len - self.args.h_overlap): if imgH - hh - 1 < self.args.h_len: hh_ = imgH - self.args.h_len else: hh_ = hh for ww in range(0, imgW, self.args.w_len - self.args.w_overlap): if imgW - ww - 1 < self.args.w_len: ww_ = imgW - self.args.w_len else: ww_ = ww src_img = img[hh_:(hh_ + self.args.h_len), ww_:(ww_ + self.args.w_len), :] for short_size in img_short_side_len_list: max_len = self.cfgs.IMG_MAX_LENGTH if self.args.h_len < self.args.w_len: new_h, new_w = short_size, min( int(short_size * float(self.args.w_len) / self.args.h_len), max_len) else: new_h, new_w = min( int(short_size * float(self.args.h_len) / self.args.w_len), max_len), short_size img_resize = cv2.resize(src_img, (new_w, new_h)) resized_img, det_boxes_r_, det_scores_r_, det_category_r_ = \ sess.run( [img_batch, detection_boxes, detection_scores, detection_category], feed_dict={img_plac: img_resize[:, :, ::-1]} ) resized_h, resized_w = resized_img.shape[ 1], resized_img.shape[2] src_h, src_w = src_img.shape[0], src_img.shape[1] if len(det_boxes_r_) > 0: det_boxes_r_ = forward_convert( det_boxes_r_, False) det_boxes_r_[:, 0::2] *= (src_w / resized_w) det_boxes_r_[:, 1::2] *= (src_h / resized_h) for ii in range(len(det_boxes_r_)): box_rotate = det_boxes_r_[ii] box_rotate[0::2] = box_rotate[0::2] + ww_ box_rotate[1::2] = box_rotate[1::2] + hh_ box_res_rotate.append(box_rotate) label_res_rotate.append( det_category_r_[ii]) score_res_rotate.append(det_scores_r_[ii]) if self.args.flip_img: det_boxes_r_flip, det_scores_r_flip, det_category_r_flip = \ sess.run( [detection_boxes, detection_scores, detection_category], feed_dict={img_plac: cv2.flip(img_resize, flipCode=1)[:, :, ::-1]} ) if len(det_boxes_r_flip) > 0: det_boxes_r_flip = forward_convert( det_boxes_r_flip, False) det_boxes_r_flip[:, 0::2] *= (src_w / resized_w) det_boxes_r_flip[:, 1::2] *= (src_h / resized_h) for ii in range(len(det_boxes_r_flip)): box_rotate = det_boxes_r_flip[ii] box_rotate[0::2] = ( src_w - box_rotate[0::2]) + ww_ box_rotate[ 1::2] = box_rotate[1::2] + hh_ box_res_rotate.append(box_rotate) label_res_rotate.append( det_category_r_flip[ii]) score_res_rotate.append( det_scores_r_flip[ii]) det_boxes_r_flip, det_scores_r_flip, det_category_r_flip = \ sess.run( [detection_boxes, detection_scores, detection_category], feed_dict={img_plac: cv2.flip(img_resize, flipCode=0)[:, :, ::-1]} ) if len(det_boxes_r_flip) > 0: det_boxes_r_flip = forward_convert( det_boxes_r_flip, False) det_boxes_r_flip[:, 0::2] *= (src_w / resized_w) det_boxes_r_flip[:, 1::2] *= (src_h / resized_h) for ii in range(len(det_boxes_r_flip)): box_rotate = det_boxes_r_flip[ii] box_rotate[ 0::2] = box_rotate[0::2] + ww_ box_rotate[1::2] = ( src_h - box_rotate[1::2]) + hh_ box_res_rotate.append(box_rotate) label_res_rotate.append( det_category_r_flip[ii]) score_res_rotate.append( det_scores_r_flip[ii]) box_res_rotate = np.array(box_res_rotate) label_res_rotate = np.array(label_res_rotate) score_res_rotate = np.array(score_res_rotate) box_res_rotate_ = [] label_res_rotate_ = [] score_res_rotate_ = [] threshold = { 'roundabout': 0.1, 'tennis-court': 0.3, 'swimming-pool': 0.1, 'storage-tank': 0.2, 'soccer-ball-field': 0.3, 'small-vehicle': 0.2, 'ship': 0.2, 'plane': 0.3, 'large-vehicle': 0.1, 'helicopter': 0.2, 'harbor': 0.0001, 'ground-track-field': 0.3, 'bridge': 0.0001, 'basketball-court': 0.3, 'baseball-diamond': 0.3 } for sub_class in range(1, self.cfgs.CLASS_NUM + 1): index = np.where(label_res_rotate == sub_class)[0] if len(index) == 0: continue tmp_boxes_r = box_res_rotate[index] tmp_label_r = label_res_rotate[index] tmp_score_r = score_res_rotate[index] tmp_boxes_r_ = backward_convert(tmp_boxes_r, False) try: inx = nms_rotate.nms_rotate_cpu( boxes=np.array(tmp_boxes_r_), scores=np.array(tmp_score_r), iou_threshold=threshold[ self.label_name_map[sub_class]], max_output_size=5000) except: tmp_boxes_r_ = np.array(tmp_boxes_r_) tmp = np.zeros( [tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1]) tmp[:, 0:-1] = tmp_boxes_r_ tmp[:, -1] = np.array(tmp_score_r) # Note: the IoU of two same rectangles is 0, which is calculated by rotate_gpu_nms jitter = np.zeros( [tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1]) jitter[:, 0] += np.random.rand( tmp_boxes_r_.shape[0], ) / 1000 inx = rotate_gpu_nms( np.array(tmp, np.float32) + np.array(jitter, np.float32), float(threshold[self.label_name_map[sub_class]]), 0) box_res_rotate_.extend(np.array(tmp_boxes_r)[inx]) score_res_rotate_.extend(np.array(tmp_score_r)[inx]) label_res_rotate_.extend(np.array(tmp_label_r)[inx]) result_dict = { 'boxes': np.array(box_res_rotate_), 'scores': np.array(score_res_rotate_), 'labels': np.array(label_res_rotate_), 'image_id': img_path } result_queue.put_nowait(result_dict)
def test_dota(self, det_net, real_test_img_list, txt_name): save_path = os.path.join('./test_dota', self.cfgs.VERSION) nr_records = len(real_test_img_list) pbar = tqdm(total=nr_records) gpu_num = len(self.args.gpus.strip().split(',')) nr_image = math.ceil(nr_records / gpu_num) result_queue = Queue(500) procs = [] for i, gpu_id in enumerate(self.args.gpus.strip().split(',')): start = i * nr_image end = min(start + nr_image, nr_records) split_records = real_test_img_list[start:end] proc = Process(target=self.worker, args=(int(gpu_id), split_records, det_net, result_queue)) print('process:%d, start:%d, end:%d' % (i, start, end)) proc.start() procs.append(proc) for i in range(nr_records): res = result_queue.get() if self.args.show_box: nake_name = res['image_id'].split('/')[-1] tools.makedirs(os.path.join(save_path, 'dota_img_vis')) draw_path = os.path.join(save_path, 'dota_img_vis', nake_name) draw_img = np.array(cv2.imread(res['image_id']), np.float32) detected_boxes = backward_convert(res['boxes'], with_label=False) detected_indices = res['scores'] >= self.cfgs.VIS_SCORE detected_scores = res['scores'][detected_indices] detected_boxes = detected_boxes[detected_indices] detected_categories = res['labels'][detected_indices] drawer = DrawBox(self.cfgs) final_detections = drawer.draw_boxes_with_label_and_scores( draw_img, boxes=detected_boxes, labels=detected_categories, scores=detected_scores, method=1, is_csl=True, in_graph=False) cv2.imwrite(draw_path, final_detections) else: CLASS_DOTA = self.name_label_map.keys() write_handle = {} tools.makedirs(os.path.join(save_path, 'dota_res')) for sub_class in CLASS_DOTA: if sub_class == 'back_ground': continue write_handle[sub_class] = open( os.path.join(save_path, 'dota_res', 'Task1_%s.txt' % sub_class), 'a+') for i, rbox in enumerate(res['boxes']): command = '%s %.3f %.1f %.1f %.1f %.1f %.1f %.1f %.1f %.1f\n' % ( res['image_id'].split('/')[-1].split('.')[0], res['scores'][i], rbox[0], rbox[1], rbox[2], rbox[3], rbox[4], rbox[5], rbox[6], rbox[7], ) write_handle[self.label_name_map[res['labels'][i]]].write( command) for sub_class in CLASS_DOTA: if sub_class == 'back_ground': continue write_handle[sub_class].close() fw = open(txt_name, 'a+') fw.write('{}\n'.format(res['image_id'].split('/')[-1])) fw.close() pbar.set_description("Test image %s" % res['image_id'].split('/')[-1]) pbar.update(1) for p in procs: p.join()
def eval_with_plac(self, img_dir, det_net, image_ext): os.environ["CUDA_VISIBLE_DEVICES"] = self.args.gpu # 1. preprocess img img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR img_batch = tf.cast(img_plac, tf.float32) pretrain_zoo = PretrainModelZoo() if self.cfgs.NET_NAME in pretrain_zoo.pth_zoo or self.cfgs.NET_NAME in pretrain_zoo.mxnet_zoo: img_batch = (img_batch / 255 - tf.constant( self.cfgs.PIXEL_MEAN_)) / tf.constant(self.cfgs.PIXEL_STD) else: img_batch = img_batch - tf.constant(self.cfgs.PIXEL_MEAN) img_batch = tf.expand_dims(img_batch, axis=0) detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( input_img_batch=img_batch) init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) restorer, restore_ckpt = det_net.get_restorer() config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: sess.run(init_op) if not restorer is None: restorer.restore(sess, restore_ckpt) print('restore model') all_boxes_r = [] imgs = os.listdir(img_dir) pbar = tqdm(imgs) for a_img_name in pbar: a_img_name = a_img_name.split(image_ext)[0] raw_img = cv2.imread( os.path.join(img_dir, a_img_name + image_ext)) raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] det_boxes_r_all, det_scores_r_all, det_category_r_all = [], [], [] img_short_side_len_list = self.cfgs.IMG_SHORT_SIDE_LEN if isinstance( self.cfgs.IMG_SHORT_SIDE_LEN, list) else [self.cfgs.IMG_SHORT_SIDE_LEN] img_short_side_len_list = [ img_short_side_len_list[0] ] if not self.args.multi_scale else img_short_side_len_list for short_size in img_short_side_len_list: max_len = self.cfgs.IMG_MAX_LENGTH if raw_h < raw_w: new_h, new_w = short_size, min( int(short_size * float(raw_w) / raw_h), max_len) else: new_h, new_w = min( int(short_size * float(raw_h) / raw_w), max_len), short_size img_resize = cv2.resize(raw_img, (new_w, new_h)) resized_img, detected_boxes, detected_scores, detected_categories = \ sess.run( [img_batch, detection_boxes, detection_scores, detection_category], feed_dict={img_plac: img_resize[:, :, ::-1]} ) if detected_boxes.shape[0] == 0: continue resized_h, resized_w = resized_img.shape[ 1], resized_img.shape[2] detected_boxes = forward_convert(detected_boxes, False) detected_boxes[:, 0::2] *= (raw_w / resized_w) detected_boxes[:, 1::2] *= (raw_h / resized_h) det_boxes_r_all.extend(detected_boxes) det_scores_r_all.extend(detected_scores) det_category_r_all.extend(detected_categories) det_boxes_r_all = np.array(det_boxes_r_all) det_scores_r_all = np.array(det_scores_r_all) det_category_r_all = np.array(det_category_r_all) box_res_rotate_ = [] label_res_rotate_ = [] score_res_rotate_ = [] if det_scores_r_all.shape[0] != 0: for sub_class in range(1, self.cfgs.CLASS_NUM + 1): index = np.where(det_category_r_all == sub_class)[0] if len(index) == 0: continue tmp_boxes_r = det_boxes_r_all[index] tmp_label_r = det_category_r_all[index] tmp_score_r = det_scores_r_all[index] if self.args.multi_scale: tmp_boxes_r_ = backward_convert(tmp_boxes_r, False) # try: # inx = nms_rotate.nms_rotate_cpu(boxes=np.array(tmp_boxes_r_), # scores=np.array(tmp_score_r), # iou_threshold=self.cfgs.NMS_IOU_THRESHOLD, # max_output_size=5000) # except: tmp_boxes_r_ = np.array(tmp_boxes_r_) tmp = np.zeros([ tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1 ]) tmp[:, 0:-1] = tmp_boxes_r_ tmp[:, -1] = np.array(tmp_score_r) # Note: the IoU of two same rectangles is 0, which is calculated by rotate_gpu_nms jitter = np.zeros([ tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1 ]) jitter[:, 0] += np.random.rand( tmp_boxes_r_.shape[0], ) / 1000 inx = rotate_gpu_nms( np.array(tmp, np.float32) + np.array(jitter, np.float32), float(self.cfgs.NMS_IOU_THRESHOLD), 0) else: inx = np.arange(0, tmp_score_r.shape[0]) box_res_rotate_.extend(np.array(tmp_boxes_r)[inx]) score_res_rotate_.extend(np.array(tmp_score_r)[inx]) label_res_rotate_.extend(np.array(tmp_label_r)[inx]) if len(box_res_rotate_) == 0: all_boxes_r.append(np.array([])) continue det_boxes_r_ = np.array(box_res_rotate_) det_scores_r_ = np.array(score_res_rotate_) det_category_r_ = np.array(label_res_rotate_) if self.args.draw_imgs: detected_indices = det_scores_r_ >= self.cfgs.VIS_SCORE detected_scores = det_scores_r_[detected_indices] detected_boxes = det_boxes_r_[detected_indices] detected_categories = det_category_r_[detected_indices] detected_boxes = backward_convert(detected_boxes, False) drawer = DrawBox(self.cfgs) det_detections_r = drawer.draw_boxes_with_label_and_scores( raw_img[:, :, ::-1], boxes=detected_boxes, labels=detected_categories, scores=detected_scores, method=1, in_graph=True) save_dir = os.path.join('test_hrsc', self.cfgs.VERSION, 'hrsc2016_img_vis') tools.makedirs(save_dir) cv2.imwrite(save_dir + '/{}.jpg'.format(a_img_name), det_detections_r[:, :, ::-1]) det_boxes_r_ = backward_convert(det_boxes_r_, False) x_c, y_c, w, h, theta = det_boxes_r_[:, 0], det_boxes_r_[:, 1], det_boxes_r_[:, 2], \ det_boxes_r_[:, 3], det_boxes_r_[:, 4] boxes_r = np.transpose(np.stack([x_c, y_c, w, h, theta])) dets_r = np.hstack((det_category_r_.reshape(-1, 1), det_scores_r_.reshape(-1, 1), boxes_r)) all_boxes_r.append(dets_r) pbar.set_description("Eval image %s" % a_img_name) # fw1 = open(cfgs.VERSION + '_detections_r.pkl', 'wb') # pickle.dump(all_boxes_r, fw1) return all_boxes_r
def worker(self, gpu_id, images, det_net, result_queue): os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id) # 1. preprocess img img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR img_batch = tf.cast(img_plac, tf.float32) if self.cfgs.NET_NAME in ['resnet152_v1d', 'resnet101_v1d', 'resnet50_v1d', 'resnet152_v1b', 'resnet101_v1b', 'resnet50_v1b', 'resnet34_v1b', 'resnet18_v1b']: img_batch = (img_batch / 255 - tf.constant(self.cfgs.PIXEL_MEAN_)) / tf.constant(self.cfgs.PIXEL_STD) else: img_batch = img_batch - tf.constant(self.cfgs.PIXEL_MEAN) img_batch = tf.expand_dims(img_batch, axis=0) detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( input_img_batch=img_batch, gtboxes_batch_h=None, gtboxes_batch_r=None, gpu_id=0) init_op = tf.group( tf.global_variables_initializer(), tf.local_variables_initializer() ) restorer, restore_ckpt = det_net.get_restorer() config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: sess.run(init_op) if not restorer is None: restorer.restore(sess, restore_ckpt) print('restore model %d ...' % gpu_id) for a_img in images: raw_img = cv2.imread(a_img) raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] det_boxes_r_all, det_scores_r_all, det_category_r_all = [], [], [] img_short_side_len_list = self.cfgs.IMG_SHORT_SIDE_LEN if isinstance(self.cfgs.IMG_SHORT_SIDE_LEN, list) else [ self.cfgs.IMG_SHORT_SIDE_LEN] img_short_side_len_list = [img_short_side_len_list[0]] if not self.args.multi_scale else img_short_side_len_list for short_size in img_short_side_len_list: max_len = self.cfgs.IMG_MAX_LENGTH if raw_h < raw_w: new_h, new_w = short_size, min(int(short_size * float(raw_w) / raw_h), max_len) else: new_h, new_w = min(int(short_size * float(raw_h) / raw_w), max_len), short_size img_resize = cv2.resize(raw_img, (new_w, new_h)) resized_img, detected_boxes, detected_scores, detected_categories = \ sess.run( [img_batch, detection_boxes, detection_scores, detection_category], feed_dict={img_plac: img_resize[:, :, ::-1]} ) detected_indices = detected_scores >= self.cfgs.VIS_SCORE detected_scores = detected_scores[detected_indices] detected_boxes = detected_boxes[detected_indices] detected_categories = detected_categories[detected_indices] if detected_boxes.shape[0] == 0: continue resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] detected_boxes = forward_convert(detected_boxes, False) detected_boxes[:, 0::2] *= (raw_w / resized_w) detected_boxes[:, 1::2] *= (raw_h / resized_h) det_boxes_r_all.extend(detected_boxes) det_scores_r_all.extend(detected_scores) det_category_r_all.extend(detected_categories) if self.args.flip_img: detected_boxes, detected_scores, detected_categories = \ sess.run( [detection_boxes, detection_scores, detection_category], feed_dict={img_plac: cv2.flip(img_resize, flipCode=1)[:, :, ::-1]} ) detected_indices = detected_scores >= self.cfgs.VIS_SCORE detected_scores = detected_scores[detected_indices] detected_boxes = detected_boxes[detected_indices] detected_categories = detected_categories[detected_indices] if detected_boxes.shape[0] == 0: continue resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] detected_boxes = forward_convert(detected_boxes, False) detected_boxes[:, 0::2] *= (raw_w / resized_w) detected_boxes[:, 0::2] = (raw_w - detected_boxes[:, 0::2]) detected_boxes[:, 1::2] *= (raw_h / resized_h) det_boxes_r_all.extend(sort_corners(detected_boxes)) det_scores_r_all.extend(detected_scores) det_category_r_all.extend(detected_categories) detected_boxes, detected_scores, detected_categories = \ sess.run( [detection_boxes, detection_scores, detection_category], feed_dict={img_plac: cv2.flip(img_resize, flipCode=0)[:, :, ::-1]} ) detected_indices = detected_scores >= self.cfgs.VIS_SCORE detected_scores = detected_scores[detected_indices] detected_boxes = detected_boxes[detected_indices] detected_categories = detected_categories[detected_indices] if detected_boxes.shape[0] == 0: continue resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] detected_boxes = forward_convert(detected_boxes, False) detected_boxes[:, 0::2] *= (raw_w / resized_w) detected_boxes[:, 1::2] *= (raw_h / resized_h) detected_boxes[:, 1::2] = (raw_h - detected_boxes[:, 1::2]) det_boxes_r_all.extend(sort_corners(detected_boxes)) det_scores_r_all.extend(detected_scores) det_category_r_all.extend(detected_categories) det_boxes_r_all = np.array(det_boxes_r_all) det_scores_r_all = np.array(det_scores_r_all) det_category_r_all = np.array(det_category_r_all) box_res_rotate_ = [] label_res_rotate_ = [] score_res_rotate_ = [] if det_scores_r_all.shape[0] != 0: for sub_class in range(1, self.cfgs.CLASS_NUM + 1): index = np.where(det_category_r_all == sub_class)[0] if len(index) == 0: continue tmp_boxes_r = det_boxes_r_all[index] tmp_label_r = det_category_r_all[index] tmp_score_r = det_scores_r_all[index] if self.args.multi_scale: tmp_boxes_r_ = backward_convert(tmp_boxes_r, False) # try: # inx = nms_rotate.nms_rotate_cpu(boxes=np.array(tmp_boxes_r_), # scores=np.array(tmp_score_r), # iou_threshold=self.cfgs.NMS_IOU_THRESHOLD, # max_output_size=5000) # except: tmp_boxes_r_ = np.array(tmp_boxes_r_) tmp = np.zeros([tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1]) tmp[:, 0:-1] = tmp_boxes_r_ tmp[:, -1] = np.array(tmp_score_r) # Note: the IoU of two same rectangles is 0, which is calculated by rotate_gpu_nms jitter = np.zeros([tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1]) jitter[:, 0] += np.random.rand(tmp_boxes_r_.shape[0], ) / 1000 inx = rotate_gpu_nms(np.array(tmp, np.float32) + np.array(jitter, np.float32), float(self.cfgs.NMS_IOU_THRESHOLD), 0) else: inx = np.arange(0, tmp_score_r.shape[0]) box_res_rotate_.extend(np.array(tmp_boxes_r)[inx]) score_res_rotate_.extend(np.array(tmp_score_r)[inx]) label_res_rotate_.extend(np.array(tmp_label_r)[inx]) box_res_rotate_ = np.array(box_res_rotate_) score_res_rotate_ = np.array(score_res_rotate_) label_res_rotate_ = np.array(label_res_rotate_) result_dict = {'scales': [1, 1], 'boxes': box_res_rotate_, 'scores': score_res_rotate_, 'labels': label_res_rotate_, 'image_id': a_img} result_queue.put_nowait(result_dict)
def test_icdar2015(self, det_net, real_test_img_list, txt_name): save_path = os.path.join('./test_icdar2015', self.cfgs.VERSION) tools.makedirs(save_path) nr_records = len(real_test_img_list) pbar = tqdm(total=nr_records) gpu_num = len(self.args.gpus.strip().split(',')) nr_image = math.ceil(nr_records / gpu_num) result_queue = Queue(500) procs = [] for i, gpu_id in enumerate(self.args.gpus.strip().split(',')): start = i * nr_image end = min(start + nr_image, nr_records) split_records = real_test_img_list[start:end] proc = Process(target=self.worker, args=(int(gpu_id), split_records, det_net, result_queue)) print('process:%d, start:%d, end:%d' % (i, start, end)) proc.start() procs.append(proc) for i in range(nr_records): res = result_queue.get() tools.makedirs(os.path.join(save_path, 'icdar2015_res')) if res['boxes'].shape[0] == 0: fw_txt_dt = open(os.path.join(save_path, 'icdar2015_res', 'res_{}.txt'.format(res['image_id'].split('/')[-1].split('.')[0])), 'w') fw_txt_dt.close() pbar.update(1) fw = open(txt_name, 'a+') fw.write('{}\n'.format(res['image_id'].split('/')[-1])) fw.close() continue x1, y1, x2, y2, x3, y3, x4, y4 = res['boxes'][:, 0], res['boxes'][:, 1], res['boxes'][:, 2], res['boxes'][:, 3],\ res['boxes'][:, 4], res['boxes'][:, 5], res['boxes'][:, 6], res['boxes'][:, 7] x1, y1 = x1 * res['scales'][0], y1 * res['scales'][1] x2, y2 = x2 * res['scales'][0], y2 * res['scales'][1] x3, y3 = x3 * res['scales'][0], y3 * res['scales'][1] x4, y4 = x4 * res['scales'][0], y4 * res['scales'][1] boxes = np.transpose(np.stack([x1, y1, x2, y2, x3, y3, x4, y4])) if self.args.show_box: boxes = backward_convert(boxes, False) nake_name = res['image_id'].split('/')[-1] tools.makedirs(os.path.join(save_path, 'icdar2015_img_vis')) draw_path = os.path.join(save_path, 'icdar2015_img_vis', nake_name) draw_img = np.array(cv2.imread(res['image_id']), np.float32) drawer = DrawBox(self.cfgs) final_detections = drawer.draw_boxes_with_label_and_scores(draw_img, boxes=boxes, labels=res['labels'], scores=res['scores'], method=1, in_graph=False) cv2.imwrite(draw_path, final_detections) else: fw_txt_dt = open(os.path.join(save_path, 'icdar2015_res', 'res_{}.txt'.format(res['image_id'].split('/')[-1].split('.')[0])), 'w') for box in boxes: line = '%d,%d,%d,%d,%d,%d,%d,%d\n' % (box[0], box[1], box[2], box[3], box[4], box[5], box[6], box[7]) fw_txt_dt.write(line) fw_txt_dt.close() fw = open(txt_name, 'a+') fw.write('{}\n'.format(res['image_id'].split('/')[-1])) fw.close() pbar.set_description("Test image %s" % res['image_id'].split('/')[-1]) pbar.update(1) for p in procs: p.join()
def clip_image(file_idx, image, boxes_all, width, height, w_overlap, h_overlap): print(file_idx) # fill useless boxes min_pixel = 5 boxes_all_5 = backward_convert(boxes_all[:, :8], False) small_boxes = boxes_all[np.logical_or(boxes_all_5[:, 2] <= min_pixel, boxes_all_5[:, 3] <= min_pixel), :] cv2.fillConvexPoly(image, np.reshape(small_boxes, [-1, 2]), color=(0, 0, 0)) different_boxes = boxes_all[boxes_all[:, 9] == 1] cv2.fillConvexPoly(image, np.reshape(different_boxes, [-1, 2]), color=(0, 0, 0)) boxes_all = boxes_all[np.logical_and(boxes_all_5[:, 2] > min_pixel, boxes_all_5[:, 3] > min_pixel), :] boxes_all = boxes_all[boxes_all[:, 9] == 0] if boxes_all.shape[0] > 0: imgH = image.shape[0] imgW = image.shape[1] if imgH < height: temp = np.zeros([height, imgW, 3], np.float32) temp[0:imgH, :, :] = image image = temp imgH = height if imgW < width: temp = np.zeros([imgH, width, 3], np.float32) temp[:, 0:imgW, :] = image image = temp imgW = width for hh in range(0, imgH, height - h_overlap): if imgH - hh - 1 < height: hh_ = imgH - height else: hh_ = hh for ww in range(0, imgW, width - w_overlap): if imgW - ww - 1 < width: ww_ = imgW - width else: ww_ = ww subimg = image[hh_:(hh_ + height), ww_:(ww_ + width), :] boxes = copy.deepcopy(boxes_all) box = np.zeros_like(boxes_all) top_left_row = max(hh_, 0) top_left_col = max(ww_, 0) bottom_right_row = min(hh_ + height, imgH) bottom_right_col = min(ww_ + width, imgW) box[:, :8:2] = boxes[:, :8:2] - top_left_col box[:, 1:8:2] = boxes[:, 1:8:2] - top_left_row box[:, 8:] = boxes[:, 8:] center_y = 0.25 * (box[:, 1] + box[:, 3] + box[:, 5] + box[:, 7]) center_x = 0.25 * (box[:, 0] + box[:, 2] + box[:, 4] + box[:, 6]) cond1 = np.intersect1d( np.where(center_y[:] >= 0)[0], np.where(center_x[:] >= 0)[0]) cond2 = np.intersect1d( np.where( center_y[:] <= (bottom_right_row - top_left_row))[0], np.where( center_x[:] <= (bottom_right_col - top_left_col))[0]) idx = np.intersect1d(cond1, cond2) if len(idx) > 0: makedirs(os.path.join(save_dir, 'images')) img = os.path.join( save_dir, 'images', "%s_%04d_%04d.png" % (file_idx, top_left_row, top_left_col)) cv2.imwrite(img, subimg) makedirs(os.path.join(save_dir, 'labeltxt')) xml = os.path.join( save_dir, 'labeltxt', "%s_%04d_%04d.xml" % (file_idx, top_left_row, top_left_col)) save_to_xml(xml, subimg.shape[0], subimg.shape[1], box[idx, :], class_list)