fix bug when inference with network img
This commit is contained in:
WenmuZhou
parent
b2debedb31
commit
a569496808
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@ -15,4 +15,4 @@ import paddleocr
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from .paddleocr import *
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__version__ = paddleocr.VERSION
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__all__ = ['PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res']
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__all__ = ['PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res','download_with_progressbar']
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@ -5,26 +5,32 @@
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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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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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@ -32,6 +38,7 @@ for line in result:
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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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@ -40,31 +47,36 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
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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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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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result = ocr.ocr(img_path, cls=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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boxes = [line[0] for line in result]
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txts = [line[1][0] for line in result]
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@ -73,38 +85,46 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
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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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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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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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@ -118,13 +138,16 @@ im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/Pa
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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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@ -133,29 +156,37 @@ im_show.save('result.jpg')
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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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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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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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@ -163,15 +194,19 @@ for line in result:
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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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@ -180,10 +215,13 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true
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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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@ -192,20 +230,25 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
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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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@ -214,34 +257,42 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
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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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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[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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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 line in result:
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@ -249,6 +300,7 @@ for line in result:
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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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@ -269,11 +321,13 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
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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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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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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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@ -281,7 +335,9 @@ for line in result:
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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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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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@ -289,19 +345,24 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
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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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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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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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@ -311,6 +372,7 @@ for line in result:
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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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@ -306,7 +306,7 @@ Support numpy array as input only when used by code
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```python
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import cv2
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from paddleocr import PaddleOCR, draw_ocr
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from paddleocr import PaddleOCR, draw_ocr, download_with_progressbar
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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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@ -317,7 +317,9 @@ for line in result:
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# show result
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from PIL import Image
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image = Image.open(img_path).convert('RGB')
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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]
|
||||
txts = [line[1][0] for line in result]
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scores = [line[1][1] for line in result]
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@ -33,7 +33,7 @@ from tools.infer.utility import draw_ocr, str2bool
|
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from ppstructure.utility import init_args, draw_structure_result
|
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from ppstructure.predict_system import OCRSystem, save_structure_res
|
||||
|
||||
__all__ = ['PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res']
|
||||
__all__ = ['PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res','download_with_progressbar']
|
||||
|
||||
model_urls = {
|
||||
'det': {
|
||||
|
|
|
|||
Loading…
Reference in New Issue