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