diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md index f9437d07..cd2de78f 100644 --- a/doc/doc_ch/inference.md +++ b/doc/doc_ch/inference.md @@ -128,24 +128,32 @@ python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_mo 超轻量中文检测模型推理，可以执行如下命令： ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" +# 下载超轻量中文检测模型： +wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar +tar xf ch_ppocr_mobile_v2.0_det_infer.tar +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/22.jpg" --det_model_dir="./ch_ppocr_mobile_v2.0_det_infer/" ``` 可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下： -![](../imgs_results/det_res_2.jpg) +![](../imgs_results/det_res_22.jpg) -通过参数`limit_type`和`det_limit_side_len`来对图片的尺寸进行限制限，`limit_type=max`为限制长边长度<`det_limit_side_len`，`limit_type=min`为限制短边长度>`det_limit_side_len`, -图片不满足限制条件时(`limit_type=max`时长边长度>`det_limit_side_len`或`limit_type=min`时短边长度<`det_limit_side_len`)，将对图片进行等比例缩放。 -该参数默认设置为`limit_type='max',det_max_side_len=960`。 如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以执行如下命令： +通过参数`limit_type`和`det_limit_side_len`来对图片的尺寸进行限制， +`litmit_type`可选参数为[`max`, `min`]， +`det_limit_size_len` 为正整数，一般设置为32 的倍数，比如960。 +参数默认设置为`limit_type='max', det_limit_side_len=960`。表示网络输入图像的最长边不能超过960， +如果超过这个值，会对图像做等宽比的resize操作，确保最长边为`det_limit_side_len`。 +设置为`limit_type='min', det_limit_side_len=960` 则表示限制图像的最短边为960。 + +如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以设置det_limit_side_len 为想要的值，比如1216： ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1200 +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216 ``` 如果想使用CPU进行预测，执行命令如下 ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False ``` @@ -165,7 +173,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_ 可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下： -![](../imgs_results/det_res_img_10_db.jpg) +![](../imgs_results/det_res_22.jpg) **注意**：由于ICDAR2015数据集只有1000张训练图像，且主要针对英文场景，所以上述模型对中文文本图像检测效果会比较差。 diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md index 826aad69..012c6c7e 100644 --- a/doc/doc_en/inference_en.md +++ b/doc/doc_en/inference_en.md @@ -134,24 +134,33 @@ Because EAST and DB algorithms are very different, when inference, it is necessa For lightweight Chinese detection model inference, you can execute the following commands: ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" +# download DB text detection inference model +wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar +tar xf ch_ppocr_mobile_v2.0_det_infer.tar +# predict +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/22.jpg" --det_model_dir="./inference/det_db/" ``` The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with'det_res'. Examples of results are as follows: -![](../imgs_results/det_res_2.jpg) +![](../imgs_results/det_res_22.jpg) -The size of the image is limited by the parameters `limit_type` and `det_limit_side_len`, `limit_type=max` is to limit the length of the long side <`det_limit_side_len`, and `limit_type=min` is to limit the length of the short side>`det_limit_side_len`, -When the picture does not meet the restriction conditions (for `limit_type=max`and long side >`det_limit_side_len` or for `min` and short side <`det_limit_side_len`), the image will be scaled proportionally. -This parameter is set to `limit_type='max', det_max_side_len=960` by default. If the resolution of the input picture is relatively large, and you want to use a larger resolution prediction, you can execute the following command: +You can use the parameters `limit_type` and `det_limit_side_len` to limit the size of the input image, +The optional parameters of `litmit_type` are [`max`, `min`], and +`det_limit_size_len` is a positive integer, generally set to a multiple of 32, such as 960. +The default setting of the parameters is `limit_type='max', det_limit_side_len=960`. Indicates that the longest side of the network input image cannot exceed 960, +If this value is exceeded, the image will be resized with the same width ratio to ensure that the longest side is `det_limit_side_len`. +Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest side of the image is limited to 960. + +If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216: ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1200 +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2s.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216 ``` If you want to use the CPU for prediction, execute the command as follows ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2s.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False ``` diff --git a/doc/imgs_results/det_res_22.jpg b/doc/imgs_results/det_res_22.jpg new file mode 100644 index 00000000..d1255f49 Binary files /dev/null and b/doc/imgs_results/det_res_22.jpg differ