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502 lines
18 KiB
Markdown
502 lines
18 KiB
Markdown
---
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typora-copy-images-to: images
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comments: true
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---
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# paddleocr package使用说明
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## 1 快速上手
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### 1.1 安装whl包
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pip安装
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```bash linenums="1"
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pip install paddleocr
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```
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本地构建并安装
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```bash linenums="1"
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python3 -m build
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pip3 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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## 2 使用
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### 2.1 代码使用
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paddleocr whl包会自动下载ppocr轻量级模型作为默认模型，可以根据第3节**自定义模型**进行自定义更换。
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* 检测+方向分类器+识别全流程
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```python linenums="1"
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from paddleocr import PaddleOCR, draw_ocr
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# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
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# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
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ocr = PaddleOCR(use_angle_cls=True, lang="ch") # 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 idx in range(len(result)):
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res = result[idx]
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for line in res:
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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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result = result[0]
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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/fonts/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 linenums="1"
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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="./images/11_det_rec.jpg" width="800">
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</div>
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* 检测+识别
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```python linenums="1"
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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, cls=False)
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for idx in range(len(result)):
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res = result[idx]
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for line in res:
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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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result = result[0]
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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/fonts/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 linenums="1"
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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="./images/11_det_rec.jpg" width="800">
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</div>
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* 方向分类器+识别
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```python linenums="1"
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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 idx in range(len(result)):
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res = result[idx]
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for line in res:
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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 linenums="1"
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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 linenums="1"
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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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for idx in range(len(result)):
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res = result[idx]
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for line in res:
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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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result = result[0]
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image = Image.open(img_path).convert('RGB')
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im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/fonts/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只包含文本框
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```bash linenums="1"
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[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
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[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
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[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
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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="./images/11_det.jpg" width="800">
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</div>
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* 单独执行识别
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```python linenums="1"
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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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for idx in range(len(result)):
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res = result[idx]
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for line in res:
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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 linenums="1"
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['韩国小馆', 0.9907421]
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```
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* 单独执行方向分类器
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```python linenums="1"
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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 idx in range(len(result)):
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res = result[idx]
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for line in res:
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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 linenums="1"
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['0', 0.9999924]
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```
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### 2.2 通过命令行使用
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查看帮助信息
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```bash linenums="1"
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paddleocr -h
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```
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* 检测+方向分类器+识别全流程
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```bash linenums="1"
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paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_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 linenums="1"
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[[[28.0, 37.0], [302.0, 39.0], [302.0, 72.0], [27.0, 70.0]], ('纯臻营养护发素', 0.9658738374710083)]
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......
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```
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此外，paddleocr也支持输入pdf文件，并且可以通过指定参数`page_num`来控制推理前面几页，默认为0，表示推理所有页。
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```bash linenums="1"
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paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
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```
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* 检测+识别
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```bash linenums="1"
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paddleocr --image_dir PaddleOCR/doc/imgs/11.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 linenums="1"
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[[[28.0, 37.0], [302.0, 39.0], [302.0, 72.0], [27.0, 70.0]], ('纯臻营养护发素', 0.9658738374710083)]
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......
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```
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* 方向分类器+识别
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```bash linenums="1"
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paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_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 linenums="1"
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['韩国小馆', 0.994467]
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```
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* 单独执行检测
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```bash linenums="1"
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paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
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```
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结果是一个list，每个item只包含文本框
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```bash linenums="1"
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[[27.0, 459.0], [136.0, 459.0], [136.0, 479.0], [27.0, 479.0]]
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[[28.0, 429.0], [372.0, 429.0], [372.0, 445.0], [28.0, 445.0]]
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......
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```
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* 单独执行识别
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```bash linenums="1"
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paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --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 linenums="1"
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['韩国小馆', 0.994467]
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```
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* 单独执行方向分类器
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```bash linenums="1"
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paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_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 linenums="1"
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['0', 0.9999924]
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```
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## 3 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。 首先，参照[模型导出](../model_train/detection.md#4-模型导出与预测)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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### 3.1 代码使用
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```python linenums="1"
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from paddleocr import PaddleOCR, draw_ocr
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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}',
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rec_char_dict_path='{your_rec_char_dict_path}', cls_model_dir='{your_cls_model_dir}',
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use_angle_cls=True)
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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 idx in range(len(result)):
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res = result[idx]
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for line in res:
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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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result = result[0]
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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/fonts/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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### 3.2 通过命令行使用
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```bash linenums="1"
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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} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true
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```
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## 4 使用网络图片或者numpy数组作为输入
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### 4.1 网络图片
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* 代码使用
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```python linenums="1"
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from paddleocr import PaddleOCR, draw_ocr, download_with_progressbar
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# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
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# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
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ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
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img_path = 'http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg'
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result = ocr.ocr(img_path, cls=True)
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for idx in range(len(result)):
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res = result[idx]
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for line in res:
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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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result = result[0]
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download_with_progressbar(img_path, 'tmp.jpg')
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image = Image.open('tmp.jpg').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/fonts/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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* 命令行模式
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```bash linenums="1"
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paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true
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```
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### 4.2 numpy数组
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仅通过代码使用时支持numpy数组作为输入
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```python linenums="1"
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import cv2
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from paddleocr import PaddleOCR, draw_ocr
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# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
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# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
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ocr = PaddleOCR(use_angle_cls=True, lang="ch") # 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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img = cv2.imread(img_path)
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# img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY), 如果你自己训练的模型支持灰度图，可以将这句话的注释取消
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result = ocr.ocr(img, cls=True)
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for idx in range(len(result)):
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res = result[idx]
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for line in res:
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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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result = result[0]
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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/fonts/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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## 5 PDF文件作为输入
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* 命令行模式
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可以通过指定参数`page_num`来控制推理前面几页，默认为0，表示推理所有页。
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```bash linenums="1"
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paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
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```
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* 代码使用
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```python linenums="1"
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from paddleocr import PaddleOCR, draw_ocr
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# Paddleocr目前支持的多语言语种可以通过修改lang参数进行切换
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# 例如`ch`, `en`, `fr`, `german`, `korean`, `japan`
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ocr = PaddleOCR(use_angle_cls=True, lang="ch"， page_num=2) # need to run only once to download and load model into memory
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img_path = './xxx.pdf'
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result = ocr.ocr(img_path, cls=True)
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for idx in range(len(result)):
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res = result[idx]
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for line in res:
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print(line)
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# 显示结果
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import fitz
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from PIL import Image
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import cv2
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import numpy as np
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imgs = []
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with fitz.open(img_path) as pdf:
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for pg in range(0, pdf.pageCount):
|
||
page = pdf[pg]
|
||
mat = fitz.Matrix(2, 2)
|
||
pm = page.getPixmap(matrix=mat, alpha=False)
|
||
# if width or height > 2000 pixels, don't enlarge the image
|
||
if pm.width > 2000 or pm.height > 2000:
|
||
pm = page.getPixmap(matrix=fitz.Matrix(1, 1), alpha=False)
|
||
|
||
img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples)
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||
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
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||
imgs.append(img)
|
||
for idx in range(len(result)):
|
||
res = result[idx]
|
||
image = imgs[idx]
|
||
boxes = [line[0] for line in res]
|
||
txts = [line[1][0] for line in res]
|
||
scores = [line[1][1] for line in res]
|
||
im_show = draw_ocr(image, boxes, txts, scores, font_path='doc/fonts/simfang.ttf')
|
||
im_show = Image.fromarray(im_show)
|
||
im_show.save('result_page_{}.jpg'.format(idx))
|
||
```
|
||
|
||
## 6 参数说明
|
||
|
||
| 字段 | 说明| 默认值 |
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||
|-------------------------|----------|-------------------------|
|
||
| use_gpu| 是否使用GPU | TRUE |
|
||
| gpu_mem| 初始化占用的GPU内存大小 | 8000M |
|
||
| image_dir | 通过命令行调用时执行预测的图片或文件夹路径 |
|
||
| page_num | 当输入类型为pdf文件时有效，指定预测前面page_num页，默认预测所有页 | 0|
|
||
| det_algorithm | 使用的检测算法类型 | DB |
|
||
| det_model_dir | 检测模型所在文件夹。传参方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/det`；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 |
|
||
| det_max_side_len | 检测算法前向时图片长边的最大尺寸，当长边超出这个值时会将长边resize到这个大小，短边等比例缩放 | 960 |
|
||
| det_db_thresh | DB模型输出预测图的二值化阈值 | 0.3 |
|
||
| det_db_box_thresh | DB模型输出框的阈值，低于此值的预测框会被丢弃 | 0.5 |
|
||
| det_db_unclip_ratio | DB模型输出框扩大的比例 | 2 |
|
||
| det_db_score_mode | 计算检测框score的方式，有'fast'和'slow'，如果要检测的文字有弯曲，建议用'slow'，'slow'模式计算的box的score偏大，box不容易被过滤掉 | 'fast' |
|
||
| det_east_score_thresh | EAST模型输出预测图的二值化阈值 | 0.8 |
|
||
| 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 |
|
||
| det_east_nms_thresh | EAST模型输出框NMS的阈值 | 0.2 |
|
||
| rec_algorithm | 使用的识别算法类型 | CRNN |
|
||
| 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 |
|
||
| rec_image_shape | 识别算法的输入图片尺寸 | "3,32,320" |
|
||
| rec_batch_num | 进行识别时，同时前向的图片数 | 30 |
|
||
| max_text_length | 识别算法能识别的最大文字长度 | 25 |
|
||
| 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 |
|
||
| use_space_char | 是否识别空格 | TRUE |
|
||
| drop_score | 对输出按照分数(来自于识别模型)进行过滤，低于此分数的不返回 | 0.5 |
|
||
| use_angle_cls | 是否加载分类模型 | FALSE |
|
||
| 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 |
|
||
| cls_image_shape | 分类算法的输入图片尺寸 | "3, 48, 192" |
|
||
| label_list | 分类算法的标签列表 | ['0', '180'] |
|
||
| cls_batch_num | 进行分类时，同时前向的图片数 |30|
|
||
| enable_mkldnn | 是否启用mkldnn | FALSE |
|
||
| use_zero_copy_run | 是否通过zero_copy_run的方式进行前向| FALSE |
|
||
| lang | 模型语言类型,目前支持 目前支持中英文(ch)、英文(en)、法语(french)、德语(german)、韩语(korean)、日语(japan) | ch |
|
||
| det | 前向时使用启动检测 | TRUE |
|
||
| rec | 前向时是否启动识别 | TRUE |
|
||
| cls | 前向时是否启动分类 (命令行模式下使用use_angle_cls控制前向是否启动分类)| FALSE |
|
||
| show_log | 是否打印logger信息| FALSE |
|
||
| type | 执行ocr或者表格结构化, 值可选['ocr','structure'] | ocr |
|
||
| ocr_version | OCR模型版本，可选PP-OCRv3, PP-OCRv2, PP-OCR。PP-OCRv3 支持中、英文的检测、识别、多语种识别，方向分类器等模型；PP-OCRv2 目前仅支持中文的检测和识别模型；PP-OCR支持中文的检测，识别，多语种识别，方向分类器等模型 | PP-OCRv3 |
|