# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..'))) os.environ["FLAGS_allocator_strategy"] = 'auto_growth' import cv2 import numpy as np import time import sys import tools.infer.utility as utility from ppocr.utils.logging import get_logger from ppocr.utils.utility import get_image_file_list, check_and_read from ppocr.data import create_operators, transform from ppocr.postprocess import build_post_process import json logger = get_logger() class TextDetector(object): def __init__(self, args): self.args = args self.det_algorithm = args.det_algorithm self.use_onnx = args.use_onnx pre_process_list = [{ 'DetResizeForTest': { 'limit_side_len': args.det_limit_side_len, 'limit_type': args.det_limit_type, } }, { 'NormalizeImage': { 'std': [0.229, 0.224, 0.225], 'mean': [0.485, 0.456, 0.406], 'scale': '1./255.', 'order': 'hwc' } }, { 'ToCHWImage': None }, { 'KeepKeys': { 'keep_keys': ['image', 'shape'] } }] postprocess_params = {} if self.det_algorithm == "DB": postprocess_params['name'] = 'DBPostProcess' postprocess_params["thresh"] = args.det_db_thresh postprocess_params["box_thresh"] = args.det_db_box_thresh postprocess_params["max_candidates"] = 1000 postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio postprocess_params["use_dilation"] = args.use_dilation postprocess_params["score_mode"] = args.det_db_score_mode postprocess_params["box_type"] = args.det_box_type elif self.det_algorithm == "DB++": postprocess_params['name'] = 'DBPostProcess' postprocess_params["thresh"] = args.det_db_thresh postprocess_params["box_thresh"] = args.det_db_box_thresh postprocess_params["max_candidates"] = 1000 postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio postprocess_params["use_dilation"] = args.use_dilation postprocess_params["score_mode"] = args.det_db_score_mode postprocess_params["box_type"] = args.det_box_type pre_process_list[1] = { 'NormalizeImage': { 'std': [1.0, 1.0, 1.0], 'mean': [0.48109378172549, 0.45752457890196, 0.40787054090196], 'scale': '1./255.', 'order': 'hwc' } } elif self.det_algorithm == "EAST": postprocess_params['name'] = 'EASTPostProcess' postprocess_params["score_thresh"] = args.det_east_score_thresh postprocess_params["cover_thresh"] = args.det_east_cover_thresh postprocess_params["nms_thresh"] = args.det_east_nms_thresh elif self.det_algorithm == "SAST": pre_process_list[0] = { 'DetResizeForTest': { 'resize_long': args.det_limit_side_len } } postprocess_params['name'] = 'SASTPostProcess' postprocess_params["score_thresh"] = args.det_sast_score_thresh postprocess_params["nms_thresh"] = args.det_sast_nms_thresh if args.det_box_type == 'poly': postprocess_params["sample_pts_num"] = 6 postprocess_params["expand_scale"] = 1.2 postprocess_params["shrink_ratio_of_width"] = 0.2 else: postprocess_params["sample_pts_num"] = 2 postprocess_params["expand_scale"] = 1.0 postprocess_params["shrink_ratio_of_width"] = 0.3 elif self.det_algorithm == "PSE": postprocess_params['name'] = 'PSEPostProcess' postprocess_params["thresh"] = args.det_pse_thresh postprocess_params["box_thresh"] = args.det_pse_box_thresh postprocess_params["min_area"] = args.det_pse_min_area postprocess_params["box_type"] = args.det_box_type postprocess_params["scale"] = args.det_pse_scale elif self.det_algorithm == "FCE": pre_process_list[0] = { 'DetResizeForTest': { 'rescale_img': [1080, 736] } } postprocess_params['name'] = 'FCEPostProcess' postprocess_params["scales"] = args.scales postprocess_params["alpha"] = args.alpha postprocess_params["beta"] = args.beta postprocess_params["fourier_degree"] = args.fourier_degree postprocess_params["box_type"] = args.det_box_type elif self.det_algorithm == "CT": pre_process_list[0] = {'ScaleAlignedShort': {'short_size': 640}} postprocess_params['name'] = 'CTPostProcess' else: logger.info("unknown det_algorithm:{}".format(self.det_algorithm)) sys.exit(0) self.preprocess_op = create_operators(pre_process_list) self.postprocess_op = build_post_process(postprocess_params) self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor( args, 'det', logger) if self.use_onnx: img_h, img_w = self.input_tensor.shape[2:] if img_h is not None and img_w is not None and img_h > 0 and img_w > 0: pre_process_list[0] = { 'DetResizeForTest': { 'image_shape': [img_h, img_w] } } self.preprocess_op = create_operators(pre_process_list) if args.benchmark: import auto_log pid = os.getpid() gpu_id = utility.get_infer_gpuid() self.autolog = auto_log.AutoLogger( model_name="det", model_precision=args.precision, batch_size=1, data_shape="dynamic", save_path=None, inference_config=self.config, pids=pid, process_name=None, gpu_ids=gpu_id if args.use_gpu else None, time_keys=[ 'preprocess_time', 'inference_time', 'postprocess_time' ], warmup=2, logger=logger) def order_points_clockwise(self, pts): rect = np.zeros((4, 2), dtype="float32") s = pts.sum(axis=1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0) diff = np.diff(np.array(tmp), axis=1) rect[1] = tmp[np.argmin(diff)] rect[3] = tmp[np.argmax(diff)] return rect def clip_det_res(self, points, img_height, img_width): for pno in range(points.shape[0]): points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1)) points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1)) return points def filter_tag_det_res(self, dt_boxes, image_shape): img_height, img_width = image_shape[0:2] dt_boxes_new = [] for box in dt_boxes: box = self.order_points_clockwise(box) box = self.clip_det_res(box, img_height, img_width) rect_width = int(np.linalg.norm(box[0] - box[1])) rect_height = int(np.linalg.norm(box[0] - box[3])) if rect_width <= 3 or rect_height <= 3: continue dt_boxes_new.append(box) dt_boxes = np.array(dt_boxes_new) return dt_boxes def filter_tag_det_res_only_clip(self, dt_boxes, image_shape): img_height, img_width = image_shape[0:2] dt_boxes_new = [] for box in dt_boxes: if type(box) is list: box = np.array(box) box = self.clip_det_res(box, img_height, img_width) dt_boxes_new.append(box) dt_boxes = np.array(dt_boxes_new) return dt_boxes def __call__(self, img): ori_im = img.copy() data = {'image': img} st = time.time() if self.args.benchmark: self.autolog.times.start() data = transform(data, self.preprocess_op) img, shape_list = data if img is None: return None, 0 img = np.expand_dims(img, axis=0) shape_list = np.expand_dims(shape_list, axis=0) img = img.copy() if self.args.benchmark: self.autolog.times.stamp() if self.use_onnx: input_dict = {} input_dict[self.input_tensor.name] = img outputs = self.predictor.run(self.output_tensors, input_dict) else: self.input_tensor.copy_from_cpu(img) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) if self.args.benchmark: self.autolog.times.stamp() preds = {} if self.det_algorithm == "EAST": preds['f_geo'] = outputs[0] preds['f_score'] = outputs[1] elif self.det_algorithm == 'SAST': preds['f_border'] = outputs[0] preds['f_score'] = outputs[1] preds['f_tco'] = outputs[2] preds['f_tvo'] = outputs[3] elif self.det_algorithm in ['DB', 'PSE', 'DB++']: preds['maps'] = outputs[0] elif self.det_algorithm == 'FCE': for i, output in enumerate(outputs): preds['level_{}'.format(i)] = output elif self.det_algorithm == "CT": preds['maps'] = outputs[0] preds['score'] = outputs[1] else: raise NotImplementedError post_result = self.postprocess_op(preds, shape_list) dt_boxes = post_result[0]['points'] if self.args.det_box_type == 'poly': dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape) else: dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape) if self.args.benchmark: self.autolog.times.end(stamp=True) et = time.time() return dt_boxes, et - st if __name__ == "__main__": args = utility.parse_args() image_file_list = get_image_file_list(args.image_dir) text_detector = TextDetector(args) total_time = 0 draw_img_save_dir = args.draw_img_save_dir os.makedirs(draw_img_save_dir, exist_ok=True) if args.warmup: img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8) for i in range(2): res = text_detector(img) save_results = [] for idx, image_file in enumerate(image_file_list): img, flag_gif, flag_pdf = check_and_read(image_file) if not flag_gif and not flag_pdf: img = cv2.imread(image_file) if not flag_pdf: if img is None: logger.debug("error in loading image:{}".format(image_file)) continue imgs = [img] else: page_num = args.page_num if page_num > len(img) or page_num == 0: page_num = len(img) imgs = img[:page_num] for index, img in enumerate(imgs): st = time.time() dt_boxes, _ = text_detector(img) elapse = time.time() - st total_time += elapse if len(imgs) > 1: save_pred = os.path.basename(image_file) + '_' + str( index) + "\t" + str( json.dumps([x.tolist() for x in dt_boxes])) + "\n" else: save_pred = os.path.basename(image_file) + "\t" + str( json.dumps([x.tolist() for x in dt_boxes])) + "\n" save_results.append(save_pred) logger.info(save_pred) if len(imgs) > 1: logger.info("{}_{} The predict time of {}: {}".format( idx, index, image_file, elapse)) else: logger.info("{} The predict time of {}: {}".format( idx, image_file, elapse)) src_im = utility.draw_text_det_res(dt_boxes, img) if flag_gif: save_file = image_file[:-3] + "png" elif flag_pdf: save_file = image_file.replace('.pdf', '_' + str(index) + '.png') else: save_file = image_file img_path = os.path.join( draw_img_save_dir, "det_res_{}".format(os.path.basename(save_file))) cv2.imwrite(img_path, src_im) logger.info("The visualized image saved in {}".format(img_path)) with open(os.path.join(draw_img_save_dir, "det_results.txt"), 'w') as f: f.writelines(save_results) f.close() if args.benchmark: text_detector.autolog.report()