363 lines
20 KiB
Markdown
363 lines
20 KiB
Markdown
# paddleocr package
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## Get started quickly
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### install package
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install by pypi
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```bash
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pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+
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```
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build own whl package and install
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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 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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# Paddleocr supports Chinese, English, French, German, Korean and Japanese.
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# You can set the parameter `lang` as `ch`, `en`, `french`, `german`, `korean`, `japan`
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# to switch the language model in order.
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ocr = PaddleOCR(use_angle_cls=True, lang='en') # 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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ocr = PaddleOCR(lang='en') # 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)
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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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* 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, lang='en') # 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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ocr = PaddleOCR() # 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,rec=False)
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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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im_show = draw_ocr(image, result, txts=None, scores=None, 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 only contains bounding box
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```bash
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[[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]]
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[[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]]
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[[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]]
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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.jpg" width="800">
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</div>
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* only recognition
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```python
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from paddleocr import PaddleOCR
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ocr = PaddleOCR(lang='en') # 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=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 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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```bash
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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 --lang en
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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 --lang en
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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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* 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 --det false --lang en
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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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```
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Output will be a list, each item only contains bounding box
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```bash
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[[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]]
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[[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]]
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[[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]]
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......
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```
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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 --lang en
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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 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 --det false --rec false
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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 custom model
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When the built-in model cannot meet the needs, you need to use your own trained model.
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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
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### 1. Use by code
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```python
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from paddleocr import PaddleOCR,draw_ocr
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# The path of detection and recognition model must contain model and params files
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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_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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### Use by command line
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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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### Use web images or numpy array as input
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1. Web image
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Use by code
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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, 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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# 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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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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Use by command line
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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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2. Numpy array
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Support numpy array as input only when used by code
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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, 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), If your own training model supports grayscale images, you can uncomment this line
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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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# 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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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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## Parameter Description
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| Parameter | Description | Default value |
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|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|
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| use_gpu | use GPU or not | TRUE |
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| gpu_mem | GPU memory size used for initialization | 8000M |
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| image_dir | The images path or folder path for predicting when used by the command line | |
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| det_algorithm | Type of detection algorithm selected | DB |
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| det_model_dir | the text detection inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/det`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None |
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| det_max_side_len | The maximum size of the long side of the image. When the long side exceeds this value, the long side will be resized to this size, and the short side will be scaled proportionally | 960 |
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| det_db_thresh | Binarization threshold value of DB output map | 0.3 |
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| det_db_box_thresh | The threshold value of the DB output box. Boxes score lower than this value will be discarded | 0.5 |
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| det_db_unclip_ratio | The expanded ratio of DB output box | 2 |
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| det_east_score_thresh | Binarization threshold value of EAST output map | 0.8 |
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| det_east_cover_thresh | The threshold value of the EAST output box. Boxes score lower than this value will be discarded | 0.1 |
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| det_east_nms_thresh | The NMS threshold value of EAST model output box | 0.2 |
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| rec_algorithm | Type of recognition algorithm selected | CRNN |
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| rec_model_dir | the text recognition inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/rec`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None |
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| rec_image_shape | image shape of recognition algorithm | "3,32,320" |
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| rec_char_type | Character type of recognition algorithm, Chinese (ch) or English (en) | ch |
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| rec_batch_num | When performing recognition, the batchsize of forward images | 30 |
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| max_text_length | The maximum text length that the recognition algorithm can recognize | 25 |
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| 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 |
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| use_space_char | Whether to recognize spaces | TRUE |
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| drop_score | Filter the output by score (from the recognition model), and those below this score will not be returned | 0.5 |
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| use_angle_cls | Whether to load classification model | FALSE |
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| 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 |
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| cls_image_shape | image shape of classification algorithm | "3,48,192" |
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| label_list | label list of classification algorithm | ['0','180'] |
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| cls_batch_num | When performing classification, the batchsize of forward images | 30 |
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| enable_mkldnn | Whether to enable mkldnn | FALSE |
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| use_zero_copy_run | Whether to forward by zero_copy_run | FALSE |
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| lang | The support language, now only Chinese(ch)、English(en)、French(french)、German(german)、Korean(korean)、Japanese(japan) are supported | ch |
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| det | Enable detction when `ppocr.ocr` func exec | TRUE |
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| rec | Enable recognition when `ppocr.ocr` func exec | TRUE |
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| cls | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction) | FALSE |
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