class PaddleOCR(predict_system.TextSystem): NameError: name 'predict_system' is not defined 加了import可以运行了 不知道需不需要改
y_predict = paddle.layer.fc(input=x,size=1,act=paddle.activation.Linear()) # Data layer y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1)) # sum of square error cost: # # 参数: # # name (basestring) – The name of this layer. It is optional. # input (...
下面开始调用tools/infer/predict_system.py 完成图像文本识别,共需要传入三个参数: image_dir: 指定要测试的图像 det_model_dir: 指定轻量检测模型的inference model rec_model_dir: 指定轻量识别模型的inference model cls_model_dir: 指定轻量方向分类器模型的inference model In [5] # 快速运行 !python3 ~...
识别地址就是http://127.0.0.1:9000/predict/ocr_system 测试没问题,到这里运行镜像就构建好了 最后将容器内无用文件删除,减小容器的体积 rm -rf /root/.cache/* \ && rm -rf /var/lib/apt/lists/* \ && rm -rf /app/test/pg/* 保存并推送到对应厂商的容器镜像仓库 ...
Well, As you can see the results are very poor. The detector wasn’t able to detect the text fields. The reason is because of training data. The data on which the default detector is trained on, contains all straight-line text. Even the neural network should predict curved bounding boxes...
{"modules_info": {"ocr_system": {"init_args": {"version":"1.0.0","use_gpu":true},"predict_args": { } } },"port":8868,"use_multiprocess":false,"workers":2} The configurable parameters ininit_argsare consistent with the_initializefunction interface inmodule.py. Among them,whenuse_...
# 将模型导出 python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml -o Global.pretrained_model=output/ch_db_res18/best_accuracy Global.save_inference_dir=output/ch_db_res18/ # 对测试集进行预测 python3 tools/infer/predict_system_tianchi.py --image_dir=...
visualize:Whether to visualize the results, the default value is False output:The floder to save Visualization result, default value is ./hubserving_result Eg. python tools/test_hubserving.py --server_url=http://127.0.0.1:8868/predict/ocr_system --image_dir=./doc/imgs/ --visualize=false`...
visualize:Whether to visualize the results, the default value is False output:The floder to save Visualization result, default value is./hubserving_result Eg. python tools/test_hubserving.py --server_url=http://127.0.0.1:8868/predict/ocr_system --image_dir=./doc/imgs/ --visualize=false` ...
采用教程编译了windows下的 ocr_system.exe(mkl数学库),测试发现,同一张图片有如下情况 同样的cpu_math_library_num_threads_=10情况下,use_mkldnn 选项打开耗时(1.85s) 关闭选项(1.6s) use_mkldnn 关闭,cpu_math_library_num_threads_=0时,耗时1.4s cpu_math_library_num_threads_=12时,耗时1.9s CPU Int...