491 lines
19 KiB
Python
491 lines
19 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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class TextDetector(object):
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def __init__(self, args, logger=None):
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if logger is None:
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logger = get_logger()
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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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{
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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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{
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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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{"KeepKeys": {"keep_keys": ["image", "shape"]}},
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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": [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": {"resize_long": args.det_limit_side_len}
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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] = {"DetResizeForTest": {"rescale_img": [1080, 736]}}
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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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(
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self.predictor,
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self.input_tensor,
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self.output_tensors,
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self.config,
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) = utility.create_predictor(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 isinstance(img_h, str) or isinstance(img_w, str):
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pass
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elif 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": {"image_shape": [img_h, img_w]}
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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, # not used if logger is not 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=["preprocess_time", "inference_time", "postprocess_time"],
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warmup=2,
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logger=logger,
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)
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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 pad_polygons(self, polygon, max_points):
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padding_size = max_points - len(polygon)
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if padding_size == 0:
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return polygon
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last_point = polygon[-1]
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padding = np.repeat([last_point], padding_size, axis=0)
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return np.vstack([polygon, padding])
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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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if len(dt_boxes_new) > 0:
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max_points = max(len(polygon) for polygon in dt_boxes_new)
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dt_boxes_new = [
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self.pad_polygons(polygon, max_points) for polygon in dt_boxes_new
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]
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dt_boxes = np.array(dt_boxes_new)
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return dt_boxes
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def predict(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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def __call__(self, img, use_slice=False):
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# For image like poster with one side much greater than the other side,
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# splitting recursively and processing with overlap to enhance performance.
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MIN_BOUND_DISTANCE = 50
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dt_boxes = np.zeros((0, 4, 2), dtype=np.float32)
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elapse = 0
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if (
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img.shape[0] / img.shape[1] > 2
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and img.shape[0] > self.args.det_limit_side_len
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and use_slice
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):
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start_h = 0
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end_h = 0
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while end_h <= img.shape[0]:
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end_h = start_h + img.shape[1] * 3 // 4
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subimg = img[start_h:end_h, :]
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if len(subimg) == 0:
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break
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sub_dt_boxes, sub_elapse = self.predict(subimg)
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offset = start_h
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# To prevent text blocks from being cut off, roll back a certain buffer area.
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if (
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len(sub_dt_boxes) == 0
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or img.shape[1] - max([x[-1][1] for x in sub_dt_boxes])
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> MIN_BOUND_DISTANCE
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):
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start_h = end_h
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else:
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sorted_indices = np.argsort(sub_dt_boxes[:, 2, 1])
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sub_dt_boxes = sub_dt_boxes[sorted_indices]
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bottom_line = (
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0
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if len(sub_dt_boxes) <= 1
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else int(np.max(sub_dt_boxes[:-1, 2, 1]))
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)
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if bottom_line > 0:
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start_h += bottom_line
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sub_dt_boxes = sub_dt_boxes[
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sub_dt_boxes[:, 2, 1] <= bottom_line
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]
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else:
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start_h = end_h
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if len(sub_dt_boxes) > 0:
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if dt_boxes.shape[0] == 0:
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dt_boxes = sub_dt_boxes + np.array(
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[0, offset], dtype=np.float32
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)
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else:
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dt_boxes = np.append(
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dt_boxes,
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sub_dt_boxes + np.array([0, offset], dtype=np.float32),
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axis=0,
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)
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elapse += sub_elapse
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elif (
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img.shape[1] / img.shape[0] > 3
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and img.shape[1] > self.args.det_limit_side_len * 3
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and use_slice
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):
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start_w = 0
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end_w = 0
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while end_w <= img.shape[1]:
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end_w = start_w + img.shape[0] * 3 // 4
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subimg = img[:, start_w:end_w]
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if len(subimg) == 0:
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break
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sub_dt_boxes, sub_elapse = self.predict(subimg)
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offset = start_w
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if (
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len(sub_dt_boxes) == 0
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or img.shape[0] - max([x[-1][0] for x in sub_dt_boxes])
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> MIN_BOUND_DISTANCE
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):
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start_w = end_w
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else:
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sorted_indices = np.argsort(sub_dt_boxes[:, 2, 0])
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sub_dt_boxes = sub_dt_boxes[sorted_indices]
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right_line = (
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0
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if len(sub_dt_boxes) <= 1
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else int(np.max(sub_dt_boxes[:-1, 1, 0]))
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)
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if right_line > 0:
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start_w += right_line
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sub_dt_boxes = sub_dt_boxes[sub_dt_boxes[:, 1, 0] <= right_line]
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else:
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start_w = end_w
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if len(sub_dt_boxes) > 0:
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if dt_boxes.shape[0] == 0:
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dt_boxes = sub_dt_boxes + np.array(
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[offset, 0], dtype=np.float32
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)
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else:
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dt_boxes = np.append(
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dt_boxes,
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sub_dt_boxes + np.array([offset, 0], dtype=np.float32),
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axis=0,
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)
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elapse += sub_elapse
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else:
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dt_boxes, elapse = self.predict(img)
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return dt_boxes, elapse
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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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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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# logger
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log_file = args.save_log_path
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if os.path.isdir(args.save_log_path) or (
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not os.path.exists(args.save_log_path) and args.save_log_path.endswith("/")
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):
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log_file = os.path.join(log_file, "benchmark_detection.log")
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logger = get_logger(log_file=log_file)
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# create text detector
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text_detector = TextDetector(args, logger)
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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 = (
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os.path.basename(image_file)
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+ "_"
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+ str(index)
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+ "\t"
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+ str(json.dumps([x.tolist() for x in dt_boxes]))
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+ "\n"
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)
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else:
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save_pred = (
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os.path.basename(image_file)
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+ "\t"
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+ str(json.dumps([x.tolist() for x in dt_boxes]))
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+ "\n"
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)
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save_results.append(save_pred)
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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()
|