332 lines
12 KiB
Python
332 lines
12 KiB
Python
# Copyright (c) 2022 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 yaml
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import glob
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from functools import reduce
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import time
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import cv2
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import numpy as np
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import math
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import paddle
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import sys
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 3)))
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sys.path.insert(0, parent_path)
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from python.infer import get_test_images
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from python.preprocess import preprocess, NormalizeImage, Permute, Resize_Mult32
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from pipeline.ppvehicle.vehicle_plateutils import create_predictor, get_infer_gpuid, get_rotate_crop_image, draw_boxes
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from pipeline.ppvehicle.vehicleplate_postprocess import build_post_process
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from pipeline.cfg_utils import merge_cfg, print_arguments, argsparser
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class PlateDetector(object):
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def __init__(self, args, cfg):
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self.args = args
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self.pre_process_list = {
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'Resize_Mult32': {
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'limit_side_len': cfg['det_limit_side_len'],
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'limit_type': cfg['det_limit_type'],
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},
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'NormalizeImage': {
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225],
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'is_scale': True,
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},
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'Permute': {}
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}
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postprocess_params = {}
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postprocess_params['name'] = 'DBPostProcess'
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postprocess_params["thresh"] = 0.3
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postprocess_params["box_thresh"] = 0.6
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postprocess_params["max_candidates"] = 1000
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postprocess_params["unclip_ratio"] = 1.5
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postprocess_params["use_dilation"] = False
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postprocess_params["score_mode"] = "fast"
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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 = create_predictor(
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args, cfg, 'det')
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def preprocess(self, im_path):
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preprocess_ops = []
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for op_type, new_op_info in self.pre_process_list.items():
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preprocess_ops.append(eval(op_type)(**new_op_info))
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input_im_lst = []
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input_im_info_lst = []
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im, im_info = preprocess(im_path, preprocess_ops)
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input_im_lst.append(im)
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input_im_info_lst.append(im_info['im_shape'] / im_info['scale_factor'])
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return np.stack(input_im_lst, axis=0), input_im_info_lst
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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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diff = np.diff(pts, axis=1)
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rect[1] = pts[np.argmin(diff)]
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rect[3] = pts[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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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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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 predict_image(self, img_list):
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st = time.time()
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dt_batch_boxes = []
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for image in img_list:
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img, shape_list = self.preprocess(image)
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if img is None:
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return None, 0
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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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preds = {}
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preds['maps'] = outputs[0]
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#self.predictor.try_shrink_memory()
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post_result = self.postprocess_op(preds, shape_list)
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# print("post_result length:{}".format(len(post_result)))
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org_shape = image.shape
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dt_boxes = post_result[0]['points']
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dt_boxes = self.filter_tag_det_res(dt_boxes, org_shape)
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dt_batch_boxes.append(dt_boxes)
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et = time.time()
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return dt_batch_boxes, et - st
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class TextRecognizer(object):
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def __init__(self, args, cfg, use_gpu=True):
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self.rec_image_shape = cfg['rec_image_shape']
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self.rec_batch_num = cfg['rec_batch_num']
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word_dict_path = cfg['word_dict_path']
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use_space_char = True
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postprocess_params = {
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'name': 'CTCLabelDecode',
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"character_dict_path": word_dict_path,
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"use_space_char": use_space_char
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}
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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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create_predictor(args, cfg, 'rec')
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self.use_onnx = False
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def resize_norm_img(self, img, max_wh_ratio):
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imgC, imgH, imgW = self.rec_image_shape
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assert imgC == img.shape[2]
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imgW = int((imgH * max_wh_ratio))
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if self.use_onnx:
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w = self.input_tensor.shape[3:][0]
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if w is not None and w > 0:
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imgW = w
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h, w = img.shape[:2]
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype('float32')
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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def predict_text(self, img_list):
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img_num = len(img_list)
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# Calculate the aspect ratio of all text bars
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width_list = []
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for img in img_list:
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width_list.append(img.shape[1] / float(img.shape[0]))
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# Sorting can speed up the recognition process
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indices = np.argsort(np.array(width_list))
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rec_res = [['', 0.0]] * img_num
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batch_num = self.rec_batch_num
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st = time.time()
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for beg_img_no in range(0, img_num, batch_num):
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end_img_no = min(img_num, beg_img_no + batch_num)
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norm_img_batch = []
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imgC, imgH, imgW = self.rec_image_shape
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max_wh_ratio = imgW / imgH
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# max_wh_ratio = 0
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for ino in range(beg_img_no, end_img_no):
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h, w = img_list[indices[ino]].shape[0:2]
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wh_ratio = w * 1.0 / h
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max_wh_ratio = max(max_wh_ratio, wh_ratio)
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for ino in range(beg_img_no, end_img_no):
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norm_img = self.resize_norm_img(img_list[indices[ino]],
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max_wh_ratio)
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norm_img = norm_img[np.newaxis, :]
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norm_img_batch.append(norm_img)
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norm_img_batch = np.concatenate(norm_img_batch)
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norm_img_batch = norm_img_batch.copy()
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if self.use_onnx:
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input_dict = {}
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input_dict[self.input_tensor.name] = norm_img_batch
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outputs = self.predictor.run(self.output_tensors, input_dict)
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preds = outputs[0]
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else:
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self.input_tensor.copy_from_cpu(norm_img_batch)
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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 len(outputs) != 1:
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preds = outputs
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else:
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preds = outputs[0]
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rec_result = self.postprocess_op(preds)
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for rno in range(len(rec_result)):
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rec_res[indices[beg_img_no + rno]] = rec_result[rno]
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return rec_res, time.time() - st
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class PlateRecognizer(object):
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def __init__(self, args, cfg):
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use_gpu = args.device.lower() == "gpu"
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self.platedetector = PlateDetector(args, cfg)
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self.textrecognizer = TextRecognizer(args, cfg, use_gpu=use_gpu)
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def get_platelicense(self, image_list):
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plate_text_list = []
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plateboxes, det_time = self.platedetector.predict_image(image_list)
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for idx, boxes_pcar in enumerate(plateboxes):
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plate_pcar_list = []
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for box in boxes_pcar:
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plate_images = get_rotate_crop_image(image_list[idx], box)
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plate_texts = self.textrecognizer.predict_text([plate_images])
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plate_pcar_list.append(plate_texts)
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plate_text_list.append(plate_pcar_list)
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return self.check_plate(plate_text_list)
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def check_plate(self, text_list):
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plate_all = {"plate": []}
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for text_pcar in text_list:
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platelicense = ""
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for text_info in text_pcar:
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text = text_info[0][0][0]
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if len(text) > 2 and len(text) < 10:
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platelicense = self.replace_cn_code(text)
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plate_all["plate"].append(platelicense)
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return plate_all
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def replace_cn_code(self, text):
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simcode = {
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'浙': 'ZJ-',
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'粤': 'GD-',
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'京': 'BJ-',
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'津': 'TJ-',
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'冀': 'HE-',
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'晋': 'SX-',
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'蒙': 'NM-',
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'辽': 'LN-',
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'黑': 'HLJ-',
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'沪': 'SH-',
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'吉': 'JL-',
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'苏': 'JS-',
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'皖': 'AH-',
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'赣': 'JX-',
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'鲁': 'SD-',
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'豫': 'HA-',
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'鄂': 'HB-',
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'湘': 'HN-',
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'桂': 'GX-',
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'琼': 'HI-',
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'渝': 'CQ-',
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'川': 'SC-',
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'贵': 'GZ-',
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'云': 'YN-',
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'藏': 'XZ-',
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'陕': 'SN-',
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'甘': 'GS-',
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'青': 'QH-',
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'宁': 'NX-',
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'闽': 'FJ-',
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'·': ' '
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}
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for _char in text:
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if _char in simcode:
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text = text.replace(_char, simcode[_char])
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return text
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def main():
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cfg = merge_cfg(FLAGS)
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print_arguments(cfg)
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vehicleplate_cfg = cfg['VEHICLE_PLATE']
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detector = PlateRecognizer(FLAGS, vehicleplate_cfg)
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# predict from image
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img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
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for img in img_list:
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image = cv2.imread(img)
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results = detector.get_platelicense([image])
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print(results)
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if __name__ == '__main__':
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paddle.enable_static()
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parser = argsparser()
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FLAGS = parser.parse_args()
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FLAGS.device = FLAGS.device.upper()
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assert FLAGS.device in ['CPU', 'GPU', 'XPU', 'NPU'
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], "device should be CPU, GPU, NPU or XPU"
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main()
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