whl包添加分类模型
This commit is contained in:
WenmuZhou
parent
ecba3f85d6
commit
bf60cd827b
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@ -12,11 +12,44 @@ pip install paddleocr
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本地构建并安装
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```bash
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python setup.py bdist_wheel
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pip install dist/paddleocr-0.0.3-py3-none-any.whl
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pip install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x是paddleocr的版本号
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```
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### 1. 代码使用
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* 检测+识别全流程
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* 检测+分类+识别全流程
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```python
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from paddleocr import PaddleOCR, draw_ocr
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ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs/11.jpg'
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result = ocr.ocr(img_path, cls=True)
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for line in result:
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print(line)
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# 显示结果
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from PIL import Image
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image = Image.open(img_path).convert('RGB')
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boxes = [line[0] for line in result]
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txts = [line[1][0] for line in result]
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scores = [line[1][1] for line in result]
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im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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结果是一个list，每个item包含了文本框，文字和识别置信度
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```bash
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[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
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[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
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[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
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......
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```
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结果可视化
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<div align="center">
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<img src="../imgs_results/whl/11_det_rec.jpg" width="800">
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</div>
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* 检测+识别
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```python
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from paddleocr import PaddleOCR, draw_ocr
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ocr = PaddleOCR() # need to run only once to download and load model into memory
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@ -48,12 +81,27 @@ im_show.save('result.jpg')
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<img src="../imgs_results/whl/11_det_rec.jpg" width="800">
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</div>
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* 分类+识别
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```python
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from paddleocr import PaddleOCR
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ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
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result = ocr.ocr(img_path, det=False, cls=True)
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for line in result:
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print(line)
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```
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结果是一个list，每个item只包含识别结果和识别置信度
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```bash
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['韩国小馆', 0.9907421]
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```
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* 单独执行检测
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```python
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from paddleocr import PaddleOCR, draw_ocr
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ocr = PaddleOCR() # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs/11.jpg'
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result = ocr.ocr(img_path,rec=False)
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result = ocr.ocr(img_path, rec=False)
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for line in result:
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print(line)
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@ -84,7 +132,7 @@ im_show.save('result.jpg')
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from paddleocr import PaddleOCR
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ocr = PaddleOCR() # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
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result = ocr.ocr(img_path,det=False)
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result = ocr.ocr(img_path, det=False)
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for line in result:
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print(line)
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```
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@ -93,6 +141,20 @@ for line in result:
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['韩国小馆', 0.9907421]
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```
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* 单独执行分类
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```python
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from paddleocr import PaddleOCR
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ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
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result = ocr.ocr(img_path, det=False, rec=False, cls=True)
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for line in result:
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print(line)
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```
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结果是一个list，每个item只包含分类结果和分类置信度
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```bash
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['0', 0.9999924]
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```
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### 通过命令行使用
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查看帮助信息
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@ -100,7 +162,19 @@ for line in result:
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paddleocr -h
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```
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* 检测+识别全流程
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* 检测+分类+识别全流程
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true --cls true
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```
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结果是一个list，每个item包含了文本框，文字和识别置信度
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```bash
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[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
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[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
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[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
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......
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```
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* 检测+识别
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
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```
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@ -112,6 +186,16 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
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......
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```
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* 分类+识别
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --cls true --det false
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```
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结果是一个list，每个item只包含识别结果和识别置信度
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```bash
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['韩国小馆', 0.9907421]
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```
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* 单独执行检测
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
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@ -134,17 +218,27 @@ paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false
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['韩国小馆', 0.9907421]
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```
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* 单独执行分类
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --cls true --det false --rec false
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```
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结果是一个list，每个item只包含分类结果和分类置信度
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```bash
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['0', 0.9999924]
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```
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## 自定义模型
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当内置模型无法满足需求时，需要使用到自己训练的模型。
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首先，参照[inference.md](./inference.md) 第一节转换将检测和识别模型转换为inference模型，然后按照如下方式使用
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首先，参照[inference.md](./inference.md) 第一节转换将检测、分类和识别模型转换为inference模型，然后按照如下方式使用
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### 代码使用
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```python
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from paddleocr import PaddleOCR, draw_ocr
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# 检测模型和识别模型路径下必须含有model和params文件
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ocr = PaddleOCR(det_model_dir='{your_det_model_dir}',rec_model_dir='{your_rec_model_dir}')
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# 模型路径下必须含有model和params文件
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ocr = PaddleOCR(det_model_dir='{your_det_model_dir}', rec_model_dir='{your_rec_model_dir}', cls_model_dir='{your_cls_model_dir}', use_angle_cls=True)
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img_path = 'PaddleOCR/doc/imgs/11.jpg'
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result = ocr.ocr(img_path)
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result = ocr.ocr(img_path, cls=True)
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for line in result:
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print(line)
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@ -162,7 +256,7 @@ im_show.save('result.jpg')
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### 通过命令行使用
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir}
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paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --cls_model_dir {your_cls_model_dir} --use_angle_cls true --cls true
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```
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## 参数说明
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@ -182,13 +276,20 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
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| det_east_cover_thresh | EAST模型输出框的阈值，低于此值的预测框会被丢弃 | 0.1 |
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| det_east_nms_thresh | EAST模型输出框NMS的阈值 | 0.2 |
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| rec_algorithm | 使用的识别算法类型 | CRNN |
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| rec_model_dir | 识别模型所在文件夹。传承那方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/rec`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件 | None |
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| rec_model_dir | 识别模型所在文件夹。传参方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/rec`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件 | None |
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| rec_image_shape | 识别算法的输入图片尺寸 | "3,32,320" |
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| rec_char_type | 识别算法的字符类型，中文(ch)或英文(en) | ch |
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| rec_batch_num | 进行识别时，同时前向的图片数 | 30 |
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| max_text_length | 识别算法能识别的最大文字长度 | 25 |
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| rec_char_dict_path | 识别模型字典路径，当rec_model_dir使用方式2传参时需要修改为自己的字典路径 | ./ppocr/utils/ppocr_keys_v1.txt |
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| use_space_char | 是否识别空格 | TRUE |
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| use_angle_cls | 是否加载分类模型 | FALSE |
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| cls_model_dir | 分类模型所在文件夹。传参方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/cls`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件 | None |
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| cls_image_shape | 分类算法的输入图片尺寸 | "3, 48, 192" |
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| label_list | 分类算法的标签列表 | ['0', '180'] |
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| cls_batch_num | 进行分类时，同时前向的图片数 |30 |
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| enable_mkldnn | 是否启用mkldnn | FALSE |
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| use_zero_copy_run | 是否通过zero_copy_run的方式进行前向 | FALSE |
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| det | 前向时使用启动检测 | TRUE |
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| rec | 前向时是否启动识别 | TRUE |
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| cls | 前向时是否启动分类 | FALSE |
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@ -10,10 +10,44 @@ pip install paddleocr
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build own whl package and install
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```bash
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python setup.py bdist_wheel
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pip install dist/paddleocr-0.0.3-py3-none-any.whl
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pip install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x is the version of paddleocr
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```
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### 1. Use by code
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* detection classification and recognition
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```python
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from paddleocr import PaddleOCR,draw_ocr
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ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
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result = ocr.ocr(img_path, cls=True)
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for line in result:
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print(line)
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# draw result
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from PIL import Image
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image = Image.open(img_path).convert('RGB')
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boxes = [line[0] for line in result]
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txts = [line[1][0] for line in result]
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scores = [line[1][1] for line in result]
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im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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Output will be a list, each item contains bounding box, text and recognition confidence
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```bash
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[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
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[[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
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[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
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......
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```
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Visualization of results
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<div align="center">
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<img src="../imgs_results/whl/12_det_rec.jpg" width="800">
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</div>
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* detection and recognition
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```python
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from paddleocr import PaddleOCR,draw_ocr
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@ -48,6 +82,21 @@ Visualization of results
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<img src="../imgs_results/whl/12_det_rec.jpg" width="800">
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</div>
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* classification and recognition
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```python
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from paddleocr import PaddleOCR
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ocr = PaddleOCR(use_angle_cls=True) # need to run only once to load model into memory
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img_path = 'PaddleOCR/doc/imgs_words_en/word_10.png'
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result = ocr.ocr(img_path, det=False, cls=True)
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for line in result:
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print(line)
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```
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Output will be a list, each item contains recognition text and confidence
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```bash
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['PAIN', 0.990372]
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```
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* only detection
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```python
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from paddleocr import PaddleOCR,draw_ocr
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@ -85,16 +134,31 @@ Visualization of results
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from paddleocr import PaddleOCR
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ocr = PaddleOCR() # need to run only once to load model into memory
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img_path = 'PaddleOCR/doc/imgs_words_en/word_10.png'
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result = ocr.ocr(img_path,det=False)
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result = ocr.ocr(img_path, det=False, cls=False)
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for line in result:
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print(line)
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```
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Output will be a list, each item contains text and recognition confidence
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Output will be a list, each item contains recognition text and confidence
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```bash
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['PAIN', 0.990372]
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```
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* only classification
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```python
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from paddleocr import PaddleOCR
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ocr = PaddleOCR(use_angle_cls=True) # need to run only once to load model into memory
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img_path = 'PaddleOCR/doc/imgs_words_en/word_10.png'
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result = ocr.ocr(img_path, det=False, rec=False, cls=True)
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for line in result:
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print(line)
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```
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Output will be a list, each item contains classification result and confidence
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```bash
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['0', 0.99999964]
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```
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### Use by command line
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show help information
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@ -102,6 +166,19 @@ show help information
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paddleocr -h
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```
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* detection classification and recognition
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --use_angle_cls true -cls true
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```
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Output will be a list, each item contains bounding box, text and recognition confidence
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```bash
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[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
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[[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
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[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
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......
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```
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* detection and recognition
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg
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@ -115,6 +192,16 @@ Output will be a list, each item contains bounding box, text and recognition con
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......
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```
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* classification and recognition
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true -cls true --det false
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```
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Output will be a list, each item contains text and recognition confidence
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```bash
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['PAIN', 0.990372]
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```
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* only detection
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --rec false
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@ -130,7 +217,7 @@ Output will be a list, each item only contains bounding box
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* only recognition
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false
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paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false --cls false
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```
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Output will be a list, each item contains text and recognition confidence
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@ -138,6 +225,16 @@ Output will be a list, each item contains text and recognition confidence
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['PAIN', 0.990372]
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```
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* only classification
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true -cls true --det false --rec false
|
||||
```
|
||||
|
||||
Output will be a list, each item contains classification result and confidence
|
||||
```bash
|
||||
['0', 0.99999964]
|
||||
```
|
||||
|
||||
## Use custom model
|
||||
When the built-in model cannot meet the needs, you need to use your own trained model.
|
||||
First, refer to the first section of [inference_en.md](./inference_en.md) to convert your det and rec model to inference model, and then use it as follows
|
||||
|
|
@ -147,9 +244,9 @@ First, refer to the first section of [inference_en.md](./inference_en.md) to con
|
|||
```python
|
||||
from paddleocr import PaddleOCR,draw_ocr
|
||||
# The path of detection and recognition model must contain model and params files
|
||||
ocr = PaddleOCR(det_model_dir='{your_det_model_dir}',rec_model_dir='{your_rec_model_dir}å')
|
||||
ocr = PaddleOCR(det_model_dir='{your_det_model_dir}', rec_model_dir='{your_rec_model_dir}', cls_model_dir='{your_cls_model_dir}', use_angle_cls=True)
|
||||
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
|
||||
result = ocr.ocr(img_path)
|
||||
result = ocr.ocr(img_path, cls=True)
|
||||
for line in result:
|
||||
print(line)
|
||||
|
||||
|
|
@ -167,7 +264,7 @@ im_show.save('result.jpg')
|
|||
### Use by command line
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir}
|
||||
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --cls_model_dir {your_cls_model_dir} --use_angle_cls true --cls true
|
||||
```
|
||||
|
||||
## Parameter Description
|
||||
|
|
@ -194,6 +291,13 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
|
|||
| max_text_length | The maximum text length that the recognition algorithm can recognize | 25 |
|
||||
| rec_char_dict_path | the alphabet path which needs to be modified to your own path when `rec_model_Name` use mode 2 | ./ppocr/utils/ppocr_keys_v1.txt |
|
||||
| use_space_char | Whether to recognize spaces | TRUE |
|
||||
| use_angle_cls | Whether to load classification model | FALSE |
|
||||
| cls_model_dir | the classification inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/cls`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None |
|
||||
| cls_image_shape | image shape of classification algorithm | "3,48,192" |
|
||||
| label_list | label list of classification algorithm | ['0','180'] |
|
||||
| cls_batch_num | When performing classification, the batchsize of forward images | 30 |
|
||||
| enable_mkldnn | Whether to enable mkldnn | FALSE |
|
||||
| use_zero_copy_run | Whether to forward by zero_copy_run | FALSE |
|
||||
| det | Enable detction when `ppocr.ocr` func exec | TRUE |
|
||||
| rec | Enable detction when `ppocr.ocr` func exec | TRUE |
|
||||
| rec | Enable recognition when `ppocr.ocr` func exec | TRUE |
|
||||
| cls | Enable classification when `ppocr.ocr` func exec | FALSE |
|
||||
|
|
|
|||
36
paddleocr.py
36
paddleocr.py
|
|
@ -37,6 +37,8 @@ model_params = {
|
|||
'det': 'https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar',
|
||||
'rec':
|
||||
'https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar',
|
||||
'cls':
|
||||
'https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_infer.tar'
|
||||
}
|
||||
|
||||
SUPPORT_DET_MODEL = ['DB']
|
||||
|
|
@ -125,11 +127,20 @@ def parse_args():
|
|||
type=str,
|
||||
default="./ppocr/utils/ppocr_keys_v1.txt")
|
||||
parser.add_argument("--use_space_char", type=bool, default=True)
|
||||
|
||||
# params for text classifier
|
||||
parser.add_argument("--use_angle_cls", type=str2bool, default=False)
|
||||
parser.add_argument("--cls_model_dir", type=str, default=None)
|
||||
parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
|
||||
parser.add_argument("--label_list", type=list, default=['0', '180'])
|
||||
parser.add_argument("--cls_batch_num", type=int, default=30)
|
||||
|
||||
parser.add_argument("--enable_mkldnn", type=bool, default=False)
|
||||
parser.add_argument("--use_zero_copy_run", type=bool, default=False)
|
||||
|
||||
parser.add_argument("--det", type=str2bool, default=True)
|
||||
parser.add_argument("--rec", type=str2bool, default=True)
|
||||
parser.add_argument("--use_zero_copy_run", type=bool, default=False)
|
||||
parser.add_argument("--cls", type=str2bool, default=False)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
|
|
@ -142,16 +153,22 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
"""
|
||||
postprocess_params = parse_args()
|
||||
postprocess_params.__dict__.update(**kwargs)
|
||||
self.use_angle_cls = postprocess_params.use_angle_cls
|
||||
|
||||
# init model dir
|
||||
if postprocess_params.det_model_dir is None:
|
||||
postprocess_params.det_model_dir = os.path.join(BASE_DIR, 'det')
|
||||
if postprocess_params.rec_model_dir is None:
|
||||
postprocess_params.rec_model_dir = os.path.join(BASE_DIR, 'rec')
|
||||
if postprocess_params.cls_model_dir is None:
|
||||
postprocess_params.cls_model_dir = os.path.join(BASE_DIR, 'cls')
|
||||
print(postprocess_params)
|
||||
# download model
|
||||
maybe_download(postprocess_params.det_model_dir, model_params['det'])
|
||||
maybe_download(postprocess_params.rec_model_dir, model_params['rec'])
|
||||
if self.use_angle_cls:
|
||||
maybe_download(postprocess_params.cls_model_dir,
|
||||
model_params['cls'])
|
||||
|
||||
if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL:
|
||||
logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL))
|
||||
|
|
@ -166,7 +183,7 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
# init det_model and rec_model
|
||||
super().__init__(postprocess_params)
|
||||
|
||||
def ocr(self, img, det=True, rec=True):
|
||||
def ocr(self, img, det=True, rec=True, cls=False):
|
||||
"""
|
||||
ocr with paddleocr
|
||||
args：
|
||||
|
|
@ -175,6 +192,10 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
rec: use text recognition or not, if false, only det will be exec. default is True
|
||||
"""
|
||||
assert isinstance(img, (np.ndarray, list, str))
|
||||
if cls and not self.use_angle_cls:
|
||||
print('cls should be false when use_angle_cls is false')
|
||||
exit(-1)
|
||||
self.use_angle_cls = cls
|
||||
if isinstance(img, str):
|
||||
image_file = img
|
||||
img, flag = check_and_read_gif(image_file)
|
||||
|
|
@ -194,6 +215,10 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
else:
|
||||
if not isinstance(img, list):
|
||||
img = [img]
|
||||
if self.use_angle_cls:
|
||||
img, cls_res, elapse = self.text_classifier(img)
|
||||
if not rec:
|
||||
return cls_res
|
||||
rec_res, elapse = self.text_recognizer(img)
|
||||
return rec_res
|
||||
|
||||
|
|
@ -208,6 +233,9 @@ def main():
|
|||
ocr_engine = PaddleOCR()
|
||||
for img_path in image_file_list:
|
||||
print(img_path)
|
||||
result = ocr_engine.ocr(img_path, det=args.det, rec=args.rec)
|
||||
result = ocr_engine.ocr(img_path,
|
||||
det=args.det,
|
||||
rec=args.rec,
|
||||
cls=args.cls)
|
||||
for line in result:
|
||||
print(line)
|
||||
print(line)
|
||||
|
|
|
|||
2
setup.py
2
setup.py
|
|
@ -32,7 +32,7 @@ setup(
|
|||
package_dir={'paddleocr': ''},
|
||||
include_package_data=True,
|
||||
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
|
||||
version='0.0.3',
|
||||
version='1.0.0',
|
||||
install_requires=requirements,
|
||||
license='Apache License 2.0',
|
||||
description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
|
||||
|
|
|
|||
Loading…
Reference in New Issue