358 lines
20 KiB
Markdown
358 lines
20 KiB
Markdown
# 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
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pip install "paddleocr>=2.0.1" # 推荐使用2.0.1+版本
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```
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本地构建并安装
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```bash
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python3 setup.py bdist_wheel
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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
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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 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/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
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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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img_path = 'PaddleOCR/doc/imgs/11.jpg'
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result = ocr.ocr(img_path)
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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/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
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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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* 方向分类器+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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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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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
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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="../imgs_results/whl/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
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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 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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* 单独执行方向分类器
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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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### 2.2 通过命令行使用
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查看帮助信息
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```bash
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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
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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
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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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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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* 方向分类器+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 --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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```
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结果是一个list，每个item只包含文本框
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```bash
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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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* 单独执行识别
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```bash
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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
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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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```bash
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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
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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。
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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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### 3.1 代码使用
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```python
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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}', rec_char_dict_path='{your_rec_char_dict_path}', 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, 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/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
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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
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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 = '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 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/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
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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
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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 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/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 参数说明
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| 字段 | 说明 | 默认值 |
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|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|
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| use_gpu | 是否使用GPU | TRUE |
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| gpu_mem | 初始化占用的GPU内存大小 | 8000M |
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| image_dir | 通过命令行调用时执行预测的图片或文件夹路径 | |
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| det_algorithm | 使用的检测算法类型 | DB |
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| 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 |
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| 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 |
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| det_db_thresh | DB模型输出预测图的二值化阈值 | 0.3 |
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| 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 |
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| det_db_unclip_ratio | DB模型输出框扩大的比例 | 2 |
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| det_east_score_thresh | EAST模型输出预测图的二值化阈值 | 0.8 |
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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，中英文(ch)、英文(en)、法语(french)、德语(german)、韩语(korean)、日语(japan) | 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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| 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 |
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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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| 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 |
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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 (命令行模式下使用use_angle_cls控制前向是否启动分类) | FALSE |
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