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123 lines
4.4 KiB
Python
123 lines
4.4 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 copy
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import math
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import json
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from typing import List
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import cv2
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import numpy as np
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from utils.utils import OrtInferSession
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class ClsPostProcess(object):
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"""Convert between text-label and text-index"""
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def __init__(self, label_list):
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super(ClsPostProcess, self).__init__()
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self.label_list = label_list
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def __call__(self, preds, label=None):
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pred_idxs = preds.argmax(axis=1)
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decode_out = [
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(self.label_list[idx], preds[i, idx]) for i, idx in enumerate(pred_idxs)
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]
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if label is None:
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return decode_out
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label = [(self.label_list[idx], 1.0) for idx in label]
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return decode_out, label
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class TextClassifier(object):
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def __init__(self, path, config):
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self.cls_batch_num = config['batch_size']
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self.cls_thresh = config['score_thresh']
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session_instance = OrtInferSession(path)
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self.session = session_instance.session
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metamap = self.session.get_modelmeta().custom_metadata_map
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self.cls_image_shape = json.loads(metamap['shape'])
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labels = json.loads(metamap['labels'])
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self.postprocess_op = ClsPostProcess(labels)
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self.input_name = session_instance.get_input_name()
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def resize_norm_img(self, img):
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img_c, img_h, img_w = self.cls_image_shape
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h, w = img.shape[:2]
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ratio = w / float(h)
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if math.ceil(img_h * ratio) > img_w:
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resized_w = img_w
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else:
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resized_w = int(math.ceil(img_h * ratio))
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resized_image = cv2.resize(img, (resized_w, img_h))
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resized_image = resized_image.astype('float32')
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if img_c == 1:
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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else:
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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((img_c, img_h, img_w), dtype=np.float32)
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padding_im[:, :, :resized_w] = resized_image
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return padding_im
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def __call__(self, img_list: List[np.ndarray]):
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if isinstance(img_list, np.ndarray):
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img_list = [img_list]
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img_list = copy.deepcopy(img_list)
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# Calculate the aspect ratio of all text bars
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width_list = [img.shape[1] / float(img.shape[0]) for img in img_list]
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# Sorting can speed up the cls process
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indices = np.argsort(np.array(width_list))
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img_num = len(img_list)
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cls_res = [['', 0.0]] * img_num
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batch_num = self.cls_batch_num
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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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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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norm_img_batch = []
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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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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).astype(np.float32)
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onnx_inputs = {self.input_name: norm_img_batch}
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prob_out = self.session.run(None, onnx_inputs)[0]
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cls_result = self.postprocess_op(prob_out)
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for rno in range(len(cls_result)):
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label, score = cls_result[rno]
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cls_res[indices[beg_img_no + rno]] = [label, score]
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if label == '180' and score > self.cls_thresh:
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img_list[indices[beg_img_no + rno]] = cv2.rotate(
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img_list[indices[beg_img_no + rno]], 1
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)
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return img_list, cls_res
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