win10 paddleocr环境搭建与使用_paddleocr 环境变量

作者：从前慢现在也慢 | 2024-08-08 05:04:29

踩

paddleocr 环境变量

环境

win10系统下pycharm、Anaconda3、VS

VS环境搭建

1.下载vs工具路径
https://visualstudio.microsoft.com/visual-cpp-build-tools/

2.安装工具后，选择如下
在这里插入图片描述
其中window10的SDK根据自己电脑选择

3.更改环境变量
在环境变量中创建INCLUDE（如果已存在，则无需创建），新增路径“C:\Program Files (x86)\Windows Kits\10\Include\SDK版本\ucrt”。环境变量多个值之间，用英文分号“;”隔开。
环境变量中创建INCLUDE（如果已存在，则无需创建），新增路径“C:\Program Files (x86)\Windows Kits\10\Include\SDK版本\shared”。环境变量多个值之间，用英文分号“;”隔开。
环境变量中创建LIB（如果已存在，则无需创建），新增路径“C:\Program Files (x86)\Windows Kits\10\Lib\SDK版本\um\x64”
环境变量中创建LIB（如果已存在，则无需创建），新增路径“C:\Program Files (x86)\Windows Kits\10\Lib\SDK版本\ucrt\x64”
环境变量中创建Path（如果已存在，则无需创建），新增路径“C:\Program Files (x86)\Windows Kits\10\bin\SDK版本\x64”

4.配置完成后重启电脑

pycharm环境配置

1.使用Anaconda3创建一个环境，pycharm 3.6
2.创建一个pycharm工程，选择刚刚创建的环境
3.安装paddlepaddle，命令行输入

pip install paddlepaddle-gpu==2.4.1 -i https://mirror.baidu.com/pypi/simple
1

4.下载paddleocr源码，命令行输入

git clone https://gitee.com/paddlepaddle/PaddleOCR.git
1

5.进入PaddleOCR目录下找到requirements.txt，修改opencv版本。默认安装新版会有问题
在这里插入图片描述

6.安装PyMuPDF，默认安装会有问题
PyMuPDF-1.18.4百度网盘连接如下
链接：https://pan.baidu.com/s/1l58nnT0tidqYjgn1Xb2C0g?pwd=rz4p
提取码：rz4p

下载到pycharm工程同级目录后，命令行输入安装

 pip install PyMuPDF-1.18.4-cp36-cp36m-win_amd64.whl
1

7.如有其他包安装报错可以去下面这里下载whl安装
https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely

8.安装paddleocr, 命令行输入

cd PaddleOCR
pip install -r requirements.txt
1
2

没有报错就是安装成功，如lanms-neo==1.0.2报错，是vs的环境没有搭建好

paddleocr训练模型

1.修改configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml文件如下

Global:
  debug: false
  use_gpu: true  #  是否使用gpu
  epoch_num: 100  # 训练步数
  log_smooth_window: 20  
  print_batch_step: 10
  save_model_dir: ./output/ch_PP-OCR_v3_det/  #  模型保存路径
  save_epoch_step: 10
  eval_batch_step:
  - 0
  - 100
  cal_metric_during_train: false
  pretrained_model: ./pretrain_models/ch_PP-OCRv3_det_distill_train/best_accuracy.pdparams  # 预加载模型路径
  checkpoints: null
  save_inference_dir: null
  use_visualdl: false
  infer_img: doc/imgs_en/img_10.jpg
  save_res_path: ./checkpoints/det_db/predicts_db.txt
  distributed: true

Architecture:
  name: DistillationModel
  algorithm: Distillation
  model_type: det
  Models:
    Student:
      pretrained:
      model_type: det
      algorithm: DB
      Transform: null
      Backbone:
        name: MobileNetV3
        scale: 0.5
        model_name: large
        disable_se: true
      Neck:
        name: RSEFPN
        out_channels: 96
        shortcut: True
      Head:
        name: DBHead
        k: 50
    Student2:
      pretrained:
      model_type: det
      algorithm: DB
      Transform: null
      Backbone:
        name: MobileNetV3
        scale: 0.5
        model_name: large
        disable_se: true
      Neck:
        name: RSEFPN
        out_channels: 96
        shortcut: True
      Head:
        name: DBHead
        k: 50
    Teacher:
      freeze_params: true
      return_all_feats: false
      model_type: det
      algorithm: DB
      Backbone:
        name: ResNet_vd
        in_channels: 3
        layers: 50
      Neck:
        name: LKPAN
        out_channels: 256
      Head:
        name: DBHead
        kernel_list: [7,2,2]
        k: 50

Loss:
  name: CombinedLoss
  loss_config_list:
  - DistillationDilaDBLoss:
      weight: 1.0
      model_name_pairs:
      - ["Student", "Teacher"]
      - ["Student2", "Teacher"]
      key: maps
      balance_loss: true
      main_loss_type: DiceLoss
      alpha: 5
      beta: 10
      ohem_ratio: 3
  - DistillationDMLLoss:
      model_name_pairs:
      - ["Student", "Student2"]
      maps_name: "thrink_maps"
      weight: 1.0
      model_name_pairs: ["Student", "Student2"]
      key: maps
  - DistillationDBLoss:
      weight: 1.0
      model_name_list: ["Student", "Student2"]
      balance_loss: true
      main_loss_type: DiceLoss
      alpha: 5
      beta: 10
      ohem_ratio: 3

Optimizer:
  name: Adam
  beta1: 0.9
  beta2: 0.999
  lr:
    name: Cosine
    learning_rate: 0.002
    warmup_epoch: 2
  regularizer:
    name: L2
    factor: 5.0e-05

PostProcess:
  name: DistillationDBPostProcess
  model_name: ["Student"]
  key: head_out
  thresh: 0.3
  box_thresh: 0.6
  max_candidates: 1000
  unclip_ratio: 1.5

Metric:
  name: DistillationMetric
  base_metric_name: DetMetric
  main_indicator: hmean
  key: "Student"

Train:
  dataset:
    name: SimpleDataSet
    data_dir: ./train_data/icdar2015/text_localization/
    label_file_list:
      - ./train_data/icdar2015/text_localization/train/Label.txt
    ratio_list: [1.0]
    transforms:
    - DecodeImage:
        img_mode: BGR
        channel_first: false
    - DetLabelEncode: null
    - CopyPaste:
    - IaaAugment:
        augmenter_args:
        - type: Fliplr
          args:
            p: 0.5
        - type: Affine
          args:
            rotate:
            - -10
            - 10
        - type: Resize
          args:
            size:
            - 0.5
            - 3
    - EastRandomCropData:
        size:
        - 960
        - 960
        max_tries: 50
        keep_ratio: true
    - MakeBorderMap:
        shrink_ratio: 0.4
        thresh_min: 0.3
        thresh_max: 0.7
    - MakeShrinkMap:
        shrink_ratio: 0.4
        min_text_size: 8
    - NormalizeImage:
        scale: 1./255.
        mean:
        - 0.485
        - 0.456
        - 0.406
        std:
        - 0.229
        - 0.224
        - 0.225
        order: hwc
    - ToCHWImage: null
    - KeepKeys:
        keep_keys:
        - image
        - threshold_map
        - threshold_mask
        - shrink_map
        - shrink_mask
  loader:
    shuffle: true
    drop_last: false
    batch_size_per_card: 2
    num_workers: 2
    use_shared_memory: False

Eval:
  dataset:
    name: SimpleDataSet
    data_dir: ./train_data/icdar2015/text_localization/
    label_file_list:
      - ./train_data/icdar2015/text_localization/test/Label.txt
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - DetLabelEncode: # Class handling label
      - DetResizeForTest:
      - NormalizeImage:
          scale: 1./255.
          mean: [0.485, 0.456, 0.406]
          std: [0.229, 0.224, 0.225]
          order: 'hwc'
      - ToCHWImage:
      - KeepKeys:
          keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
  loader:
    shuffle: False
    drop_last: False
    batch_size_per_card: 1 # must be 1
    num_workers: 2
    use_shared_memory: False
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226

2.其中预加载模型去飞浆官网下载
在这里插入图片描述
选择训练模型

3.训练模型

python tools/train.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml
1

参考 https://www.ngui.cc/el/837915.html?action=onClick
参考 https://blog.csdn.net/suiyingy/article/details/126682769
参考 https://blog.csdn.net/qq_49627063/article/details/119134847

声明：本文内容由网友自发贡献，不代表【wpsshop博客】立场，版权归原作者所有，本站不承担相应法律责任。如您发现有侵权的内容，请联系我们。转载请注明出处：https://www.wpsshop.cn/w/从前慢现在也慢/article/detail/946189