paddleocr whl adaptation dygraph
This commit is contained in:
parent
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@ -1,8 +1,7 @@
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include LICENSE.txt
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include README.md
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recursive-include ppocr/utils *.txt utility.py character.py check.py
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recursive-include ppocr/data/det *.py
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recursive-include ppocr/utils *.txt utility.py logging.py
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recursive-include ppocr/data/ *.py
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recursive-include ppocr/postprocess *.py
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recursive-include ppocr/postprocess/lanms *.*
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recursive-include tools/infer *.py
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@ -261,6 +261,61 @@ im_show.save('result.jpg')
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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 --cls true
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```
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### 使用网络图片或者numpy数组作为输入
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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/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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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_path, cls=True)
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for line in result:
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print(line)
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# 显示结果
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from PIL import Image
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image = Image.open(img_path).convert('RGB')
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boxes = [line[0] for line in result]
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txts = [line[1][0] for line in result]
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scores = [line[1][1] for line in result]
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im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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## 参数说明
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| 字段 | 说明 | 默认值 |
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@ -285,6 +340,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
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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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| lang | 模型语言类型,目前支持 中文(ch)和英文(en) | 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 | FALSE |
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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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@ -271,6 +271,59 @@ im_show.save('result.jpg')
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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 --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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| 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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| 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 | FALSE |
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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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238
paddleocr.py
238
paddleocr.py
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from tqdm import tqdm
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from tools.infer import predict_system
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from ppocr.utils.utility import initial_logger
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from ppocr.utils.logging import get_logger
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logger = initial_logger()
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logger = get_logger()
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from ppocr.utils.utility import check_and_read_gif, get_image_file_list
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__all__ = ['PaddleOCR']
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model_params = {
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'det': 'https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar',
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'rec':
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'https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar',
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model_urls = {
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'det':
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'https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar',
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'rec': {
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'ch': {
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'url':
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'https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar',
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'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
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},
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'en': {
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'url':
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'https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_infer.tar',
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'dict_path': './ppocr/utils/ic15_dict.txt'
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},
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'french': {
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'url':
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'https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/french_dict.txt'
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},
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'german': {
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'url':
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'https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/german_dict.txt'
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},
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'korean': {
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'url':
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'https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/korean_dict.txt'
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},
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'japan': {
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'url':
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'https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/japan_dict.txt'
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}
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},
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'cls':
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'https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar'
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}
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SUPPORT_DET_MODEL = ['DB']
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progress_bar.update(len(data))
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file.write(data)
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progress_bar.close()
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if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
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logger.error("ERROR, something went wrong")
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if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes:
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logger.error("Something went wrong while downloading models")
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sys.exit(0)
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# using custom model
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if not os.path.exists(os.path.join(
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model_storage_directory, 'model')) or not os.path.exists(
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os.path.join(model_storage_directory, 'params')):
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os.path.join(model_storage_directory, 'params')):
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tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
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print('download {} to {}'.format(url, tmp_path))
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os.makedirs(model_storage_directory, exist_ok=True)
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os.remove(tmp_path)
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def parse_args():
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def parse_args(mMain=True, add_help=True):
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import argparse
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def str2bool(v):
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return v.lower() in ("true", "t", "1")
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parser = argparse.ArgumentParser()
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# params for prediction engine
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parser.add_argument("--use_gpu", type=str2bool, default=True)
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parser.add_argument("--ir_optim", type=str2bool, default=True)
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parser.add_argument("--use_tensorrt", type=str2bool, default=False)
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parser.add_argument("--gpu_mem", type=int, default=8000)
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if mMain:
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parser = argparse.ArgumentParser(add_help=add_help)
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# params for prediction engine
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parser.add_argument("--use_gpu", type=str2bool, default=True)
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parser.add_argument("--ir_optim", type=str2bool, default=True)
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parser.add_argument("--use_tensorrt", type=str2bool, default=False)
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parser.add_argument("--gpu_mem", type=int, default=8000)
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# params for text detector
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parser.add_argument("--image_dir", type=str)
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parser.add_argument("--det_algorithm", type=str, default='DB')
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parser.add_argument("--det_model_dir", type=str, default=None)
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parser.add_argument("--det_max_side_len", type=float, default=960)
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# params for text detector
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parser.add_argument("--image_dir", type=str)
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parser.add_argument("--det_algorithm", type=str, default='DB')
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parser.add_argument("--det_model_dir", type=str, default=None)
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parser.add_argument("--det_limit_side_len", type=float, default=960)
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parser.add_argument("--det_limit_type", type=str, default='max')
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# DB parmas
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parser.add_argument("--det_db_thresh", type=float, default=0.3)
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parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
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parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0)
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# DB parmas
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parser.add_argument("--det_db_thresh", type=float, default=0.3)
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parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
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parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0)
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# EAST parmas
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parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
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parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
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parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
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# EAST parmas
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parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
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parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
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parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
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# params for text recognizer
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parser.add_argument("--rec_algorithm", type=str, default='CRNN')
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parser.add_argument("--rec_model_dir", type=str, default=None)
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parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
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parser.add_argument("--rec_char_type", type=str, default='ch')
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parser.add_argument("--rec_batch_num", type=int, default=30)
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parser.add_argument("--max_text_length", type=int, default=25)
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parser.add_argument(
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"--rec_char_dict_path",
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type=str,
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default="./ppocr/utils/ppocr_keys_v1.txt")
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parser.add_argument("--use_space_char", type=bool, default=True)
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parser.add_argument("--enable_mkldnn", type=bool, default=False)
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# params for text recognizer
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parser.add_argument("--rec_algorithm", type=str, default='CRNN')
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parser.add_argument("--rec_model_dir", type=str, default=None)
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parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
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parser.add_argument("--rec_char_type", type=str, default='ch')
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parser.add_argument("--rec_batch_num", type=int, default=30)
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parser.add_argument("--max_text_length", type=int, default=25)
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parser.add_argument("--rec_char_dict_path", type=str, default=None)
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parser.add_argument("--use_space_char", type=bool, default=True)
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parser.add_argument("--drop_score", type=float, default=0.5)
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parser.add_argument("--det", type=str2bool, default=True)
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parser.add_argument("--rec", type=str2bool, default=True)
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parser.add_argument("--use_zero_copy_run", type=bool, default=False)
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return parser.parse_args()
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# params for text classifier
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parser.add_argument("--cls_model_dir", type=str, default=None)
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parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
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parser.add_argument("--label_list", type=list, default=['0', '180'])
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parser.add_argument("--cls_batch_num", type=int, default=30)
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parser.add_argument("--cls_thresh", type=float, default=0.9)
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parser.add_argument("--enable_mkldnn", type=bool, default=False)
|
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parser.add_argument("--use_zero_copy_run", type=bool, default=False)
|
||||
parser.add_argument("--use_pdserving", type=str2bool, default=False)
|
||||
|
||||
parser.add_argument("--lang", type=str, default='ch')
|
||||
parser.add_argument("--det", type=str2bool, default=True)
|
||||
parser.add_argument("--rec", type=str2bool, default=True)
|
||||
parser.add_argument("--use_angle_cls", type=str2bool, default=False)
|
||||
return parser.parse_args()
|
||||
else:
|
||||
return argparse.Namespace(use_gpu=True,
|
||||
ir_optim=True,
|
||||
use_tensorrt=False,
|
||||
gpu_mem=8000,
|
||||
image_dir='',
|
||||
det_algorithm='DB',
|
||||
det_model_dir=None,
|
||||
det_limit_side_len=960,
|
||||
det_limit_type='max',
|
||||
det_db_thresh=0.3,
|
||||
det_db_box_thresh=0.5,
|
||||
det_db_unclip_ratio=2.0,
|
||||
det_east_score_thresh=0.8,
|
||||
det_east_cover_thresh=0.1,
|
||||
det_east_nms_thresh=0.2,
|
||||
rec_algorithm='CRNN',
|
||||
rec_model_dir=None,
|
||||
rec_image_shape="3, 32, 320",
|
||||
rec_char_type='ch',
|
||||
rec_batch_num=30,
|
||||
max_text_length=25,
|
||||
rec_char_dict_path=None,
|
||||
use_space_char=True,
|
||||
drop_score=0.5,
|
||||
cls_model_dir=None,
|
||||
cls_image_shape="3, 48, 192",
|
||||
label_list=['0', '180'],
|
||||
cls_batch_num=30,
|
||||
cls_thresh=0.9,
|
||||
enable_mkldnn=False,
|
||||
use_zero_copy_run=False,
|
||||
use_pdserving=False,
|
||||
lang='ch',
|
||||
det=True,
|
||||
rec=True,
|
||||
use_angle_cls=False
|
||||
)
|
||||
|
||||
|
||||
class PaddleOCR(predict_system.TextSystem):
|
||||
|
@ -140,18 +222,31 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
args:
|
||||
**kwargs: other params show in paddleocr --help
|
||||
"""
|
||||
postprocess_params = parse_args()
|
||||
postprocess_params = parse_args(mMain=False, add_help=False)
|
||||
postprocess_params.__dict__.update(**kwargs)
|
||||
self.use_angle_cls = postprocess_params.use_angle_cls
|
||||
lang = postprocess_params.lang
|
||||
assert lang in model_urls[
|
||||
'rec'], 'param lang must in {}, but got {}'.format(
|
||||
model_urls['rec'].keys(), lang)
|
||||
if postprocess_params.rec_char_dict_path is None:
|
||||
postprocess_params.rec_char_dict_path = model_urls['rec'][lang][
|
||||
'dict_path']
|
||||
|
||||
# init model dir
|
||||
if postprocess_params.det_model_dir is None:
|
||||
postprocess_params.det_model_dir = os.path.join(BASE_DIR, 'det')
|
||||
if postprocess_params.rec_model_dir is None:
|
||||
postprocess_params.rec_model_dir = os.path.join(BASE_DIR, 'rec')
|
||||
postprocess_params.rec_model_dir = os.path.join(
|
||||
BASE_DIR, 'rec/{}'.format(lang))
|
||||
if postprocess_params.cls_model_dir is None:
|
||||
postprocess_params.cls_model_dir = os.path.join(BASE_DIR, 'cls')
|
||||
print(postprocess_params)
|
||||
# download model
|
||||
maybe_download(postprocess_params.det_model_dir, model_params['det'])
|
||||
maybe_download(postprocess_params.rec_model_dir, model_params['rec'])
|
||||
maybe_download(postprocess_params.det_model_dir, model_urls['det'])
|
||||
maybe_download(postprocess_params.rec_model_dir,
|
||||
model_urls['rec'][lang]['url'])
|
||||
maybe_download(postprocess_params.cls_model_dir, model_urls['cls'])
|
||||
|
||||
if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL:
|
||||
logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL))
|
||||
|
@ -166,7 +261,7 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
# init det_model and rec_model
|
||||
super().__init__(postprocess_params)
|
||||
|
||||
def ocr(self, img, det=True, rec=True):
|
||||
def ocr(self, img, det=True, rec=True, cls=False):
|
||||
"""
|
||||
ocr with paddleocr
|
||||
args:
|
||||
|
@ -175,7 +270,16 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
rec: use text recognition or not, if false, only det will be exec. default is True
|
||||
"""
|
||||
assert isinstance(img, (np.ndarray, list, str))
|
||||
if isinstance(img, list) and det == True:
|
||||
logger.error('When input a list of images, det must be false')
|
||||
exit(0)
|
||||
|
||||
self.use_angle_cls = cls
|
||||
if isinstance(img, str):
|
||||
# download net image
|
||||
if img.startswith('http'):
|
||||
download_with_progressbar(img, 'tmp.jpg')
|
||||
img = 'tmp.jpg'
|
||||
image_file = img
|
||||
img, flag = check_and_read_gif(image_file)
|
||||
if not flag:
|
||||
|
@ -183,6 +287,8 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
if img is None:
|
||||
logger.error("error in loading image:{}".format(image_file))
|
||||
return None
|
||||
if isinstance(img, np.ndarray) and len(img.shape) == 2:
|
||||
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
|
||||
if det and rec:
|
||||
dt_boxes, rec_res = self.__call__(img)
|
||||
return [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)]
|
||||
|
@ -194,20 +300,34 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
else:
|
||||
if not isinstance(img, list):
|
||||
img = [img]
|
||||
if self.use_angle_cls:
|
||||
img, cls_res, elapse = self.text_classifier(img)
|
||||
if not rec:
|
||||
return cls_res
|
||||
rec_res, elapse = self.text_recognizer(img)
|
||||
return rec_res
|
||||
|
||||
|
||||
def main():
|
||||
# for com
|
||||
args = parse_args()
|
||||
image_file_list = get_image_file_list(args.image_dir)
|
||||
# for cmd
|
||||
args = parse_args(mMain=True)
|
||||
image_dir = args.image_dir
|
||||
if image_dir.startswith('http'):
|
||||
download_with_progressbar(image_dir, 'tmp.jpg')
|
||||
image_file_list = ['tmp.jpg']
|
||||
else:
|
||||
image_file_list = get_image_file_list(args.image_dir)
|
||||
if len(image_file_list) == 0:
|
||||
logger.error('no images find in {}'.format(args.image_dir))
|
||||
return
|
||||
ocr_engine = PaddleOCR()
|
||||
|
||||
ocr_engine = PaddleOCR(**(args.__dict__))
|
||||
for img_path in image_file_list:
|
||||
print(img_path)
|
||||
result = ocr_engine.ocr(img_path, det=args.det, rec=args.rec)
|
||||
for line in result:
|
||||
print(line)
|
||||
logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10))
|
||||
result = ocr_engine.ocr(img_path,
|
||||
det=args.det,
|
||||
rec=args.rec,
|
||||
cls=args.use_angle_cls)
|
||||
if result is not None:
|
||||
for line in result:
|
||||
logger.info(line)
|
||||
|
|
2
setup.py
2
setup.py
|
@ -32,7 +32,7 @@ setup(
|
|||
package_dir={'paddleocr': ''},
|
||||
include_package_data=True,
|
||||
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
|
||||
version='0.0.3',
|
||||
version='2.0',
|
||||
install_requires=requirements,
|
||||
license='Apache License 2.0',
|
||||
description='Awesome OCR toolkits based on PaddlePaddle (8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
|
||||
|
|
|
@ -13,6 +13,7 @@
|
|||
# limitations under the License.
|
||||
import os
|
||||
import sys
|
||||
|
||||
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(__dir__)
|
||||
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
|
||||
|
@ -30,12 +31,15 @@ from ppocr.utils.utility import get_image_file_list, check_and_read_gif
|
|||
from ppocr.utils.logging import get_logger
|
||||
from tools.infer.utility import draw_ocr_box_txt
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
||||
class TextSystem(object):
|
||||
def __init__(self, args):
|
||||
self.text_detector = predict_det.TextDetector(args)
|
||||
self.text_recognizer = predict_rec.TextRecognizer(args)
|
||||
self.use_angle_cls = args.use_angle_cls
|
||||
self.drop_score = args.drop_score
|
||||
if self.use_angle_cls:
|
||||
self.text_classifier = predict_cls.TextClassifier(args)
|
||||
|
||||
|
@ -81,7 +85,8 @@ class TextSystem(object):
|
|||
def __call__(self, img):
|
||||
ori_im = img.copy()
|
||||
dt_boxes, elapse = self.text_detector(img)
|
||||
logger.info("dt_boxes num : {}, elapse : {}".format(len(dt_boxes), elapse))
|
||||
logger.info("dt_boxes num : {}, elapse : {}".format(
|
||||
len(dt_boxes), elapse))
|
||||
if dt_boxes is None:
|
||||
return None, None
|
||||
img_crop_list = []
|
||||
|
@ -99,9 +104,16 @@ class TextSystem(object):
|
|||
len(img_crop_list), elapse))
|
||||
|
||||
rec_res, elapse = self.text_recognizer(img_crop_list)
|
||||
logger.info("rec_res num : {}, elapse : {}".format(len(rec_res), elapse))
|
||||
logger.info("rec_res num : {}, elapse : {}".format(
|
||||
len(rec_res), elapse))
|
||||
# self.print_draw_crop_rec_res(img_crop_list, rec_res)
|
||||
return dt_boxes, rec_res
|
||||
filter_boxes, filter_rec_res = [], []
|
||||
for box, rec_reuslt in zip(dt_boxes, rec_res):
|
||||
text, score = rec_reuslt
|
||||
if score >= self.drop_score:
|
||||
filter_boxes.append(box)
|
||||
filter_rec_res.append(rec_reuslt)
|
||||
return filter_boxes, filter_rec_res
|
||||
|
||||
|
||||
def sorted_boxes(dt_boxes):
|
||||
|
@ -117,8 +129,8 @@ def sorted_boxes(dt_boxes):
|
|||
_boxes = list(sorted_boxes)
|
||||
|
||||
for i in range(num_boxes - 1):
|
||||
if abs(_boxes[i+1][0][1] - _boxes[i][0][1]) < 10 and \
|
||||
(_boxes[i + 1][0][0] < _boxes[i][0][0]):
|
||||
if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
|
||||
(_boxes[i + 1][0][0] < _boxes[i][0][0]):
|
||||
tmp = _boxes[i]
|
||||
_boxes[i] = _boxes[i + 1]
|
||||
_boxes[i + 1] = tmp
|
||||
|
@ -143,12 +155,8 @@ def main(args):
|
|||
elapse = time.time() - starttime
|
||||
logger.info("Predict time of %s: %.3fs" % (image_file, elapse))
|
||||
|
||||
dt_num = len(dt_boxes)
|
||||
for dno in range(dt_num):
|
||||
text, score = rec_res[dno]
|
||||
if score >= drop_score:
|
||||
text_str = "%s, %.3f" % (text, score)
|
||||
logger.info(text_str)
|
||||
for text, score in rec_res:
|
||||
logger.info("{}, {:.3f}".format(text, score))
|
||||
|
||||
if is_visualize:
|
||||
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
|
||||
|
@ -174,5 +182,4 @@ def main(args):
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
logger = get_logger()
|
||||
main(utility.parse_args())
|
Loading…
Reference in New Issue