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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 tools.infer.utility as utility |
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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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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 ppstructure.utility import parse_args |
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from picodet_postprocess import PicoDetPostProcess |
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logger = get_logger() |
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class LayoutPredictor(object): |
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def __init__(self, args): |
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pre_process_list = [{ |
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'Resize': { |
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'size': [800, 608] |
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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'] |
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} |
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}] |
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postprocess_params = { |
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'name': 'PicoDetPostProcess', |
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"layout_dict_path": args.layout_dict_path, |
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"score_threshold": args.layout_score_threshold, |
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"nms_threshold": args.layout_nms_threshold, |
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} |
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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 = \ |
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utility.create_predictor(args, 'layout', logger) |
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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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data = transform(data, self.preprocess_op) |
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img = data[0] |
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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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img = img.copy() |
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preds, elapse = 0, 1 |
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starttime = time.time() |
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self.input_tensor.copy_from_cpu(img) |
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self.predictor.run() |
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np_score_list, np_boxes_list = [], [] |
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output_names = self.predictor.get_output_names() |
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num_outs = int(len(output_names) / 2) |
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for out_idx in range(num_outs): |
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np_score_list.append( |
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self.predictor.get_output_handle(output_names[out_idx]) |
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.copy_to_cpu()) |
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np_boxes_list.append( |
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self.predictor.get_output_handle(output_names[ |
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out_idx + num_outs]).copy_to_cpu()) |
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preds = dict(boxes=np_score_list, boxes_num=np_boxes_list) |
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post_preds = self.postprocess_op(ori_im, img, preds) |
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elapse = time.time() - starttime |
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return post_preds, elapse |
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def main(args): |
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image_file_list = get_image_file_list(args.image_dir) |
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layout_predictor = LayoutPredictor(args) |
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count = 0 |
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total_time = 0 |
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repeats = 50 |
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for image_file in image_file_list: |
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img, flag, _ = check_and_read(image_file) |
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if not flag: |
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img = cv2.imread(image_file) |
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if img is None: |
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logger.info("error in loading image:{}".format(image_file)) |
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continue |
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layout_res, elapse = layout_predictor(img) |
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logger.info("result: {}".format(layout_res)) |
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if count > 0: |
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total_time += elapse |
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count += 1 |
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logger.info("Predict time of {}: {}".format(image_file, elapse)) |
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if __name__ == "__main__": |
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main(parse_args()) |
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