fix ppshitu_lite bugs and fix README.md
dongshuilong
parent
96e3a20877
commit
267f6508f0
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@ -1,4 +1,4 @@
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# Paddle-Lite端侧部署
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# PP-ShiTu在Paddle-Lite端侧部署
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本教程将介绍基于[Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) 在移动端部署PaddleDetection模型的详细步骤。
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@ -125,30 +125,71 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括
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#### 2.1.3 转换示例
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下面以PaddleDetection中的 `PicoDet` 模型为例，介绍使用`paddle_lite_opt`完成预训练模型到inference模型，再到Paddle-Lite优化模型的转换。
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下面介绍使用`paddle_lite_opt`完成主体检测模型和识别模型的预训练模型，转成inference模型，最终转换成Paddle-Lite的优化模型的过程。
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##### 2.1.3.1 转换主体检测模型
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```shell
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# 当前目录为 $PaddleClas/deploy/lite_shitu
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# $code_path需替换成相应的运行目录,可以根据需要，将$code_path设置成需要的目录
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export $code_path=~
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cd $code_path
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git clone https://github.com/PaddlePaddle/PaddleDetection.git
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# 进入PaddleDetection根目录
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cd PaddleDetection_root_path
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cd PaddleDetection
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# 将预训练模型导出为inference模型
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python tools/export_model.py -c configs/picodet/picodet_s_320_coco.yml \
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-o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams --output_dir=output_inference
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python tools/export_model.py -c configs/picodet/application/mainbody_detection/picodet_lcnet_x2_5_640_mainbody.yml -o weights=https://paddledet.bj.bcebos.com/models/picodet_lcnet_x2_5_640_mainbody.pdparams --output_dir=inference
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# 将inference模型转化为Paddle-Lite优化模型
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# FP32
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paddle_lite_opt --valid_targets=arm --model_file=output_inference/picodet_s_320_coco/model.pdmodel --param_file=output_inference/picodet_s_320_coco/model.pdiparams --optimize_out=output_inference/picodet_s_320_coco/model
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# FP16
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paddle_lite_opt --valid_targets=arm --model_file=output_inference/picodet_s_320_coco/model.pdmodel --param_file=output_inference/picodet_s_320_coco/model.pdiparams --optimize_out=output_inference/picodet_s_320_coco/model --enable_fp16=true
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paddle_lite_opt --model_file=inference/picodet_lcnet_x2_5_640_mainbody/model.pdmodel --param_file=inference/picodet_lcnet_x2_5_640_mainbody/model.pdiparams --optimize_out=inference/picodet_lcnet_x2_5_640_mainbody/mainbody_det
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# 将inference模型配置转化为json格式
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python deploy/lite/convert_yml_to_json.py output_inference/picodet_s_320_coco/infer_cfg.yml
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# 将转好的模型复制到lite_shitu目录下
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cd $PaddleClas/deploy/lite_shitu
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mkdir models
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cp $code_path/PaddleDetection/inference/picodet_lcnet_x2_5_640_mainbody/mainbody_det.nb $PaddleClas/deploy/lite_shitu/models
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```
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最终在output_inference/picodet_s_320_coco/文件夹下生成`model.nb` 和 `infer_cfg.json`的文件。
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##### 2.1.3.2 转换识别模型
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```shell
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# 转换inference model
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待补充,生成的inference model存储在PaddleClas/inference下，同时生成label.txt，也存在此文件夹下
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# 转换为Paddle-Lite模型
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paddle_lite_opt --model_file=inference/inference.pdmodel --param_file=inference/inference.pdiparams --optimize_out=inference/rec
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# 将模型、label文件拷贝到lite_shitu下
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cp inference/rec.nb deploy/lite_shitu/models/
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cp inference/label.txt deploy/lite_shitu/models/
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cd deploy/lite_shitu
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```
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**注意**：`--optimize_out` 参数为优化后模型的保存路径，无需加后缀`.nb`；`--model_file` 参数为模型结构信息文件的路径，`--param_file` 参数为模型权重信息文件的路径，请注意文件名。
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##### 2.1.3.3 准备测试图像
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```shell
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mkdir images
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# 根据需要准备测试图像，可以在images文件夹中存放多张图像
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cp ../images/wangzai.jpg images/
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```
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##### 2.1.3.4 将yaml文件转换成json文件
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```shell
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# 如果测试单张图像
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python generate_json_config.py --det_model_path models/mainbody_det.nb --rec_model_path models/rec.nb --rec_label_path models/label.txt --img_path images/wangzai.jpg
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# or
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# 如果测试多张图像
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python generate_json_config.py --det_model_path models/mainbody_det.nb --rec_model_path models/rec.nb --rec_label_path models/label.txt --img_dir images
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# 执行完成后，会在lit_shitu下生成shitu_config.json配置文件
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```
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### 2.2 与手机联调
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首先需要进行一些准备工作。
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@ -183,41 +224,28 @@ List of devices attached
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4. 编译lite部署代码生成移动端可执行文件
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```shell
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cd {PadddleDetection_Root}
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cd deploy/lite/
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cd $PaddleClas/deploy/lite_shitu
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inference_lite_path=/{lite prediction library path}/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv/
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mkdir $inference_lite_path/demo/cxx/lite
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mkdir $inference_lite_path/demo/cxx/ppshitu_lite
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cp -r Makefile src/ include/ *runtime_config.json $inference_lite_path/demo/cxx/lite
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cp -r Makefile src/ include/ *.json models/ images/ $inference_lite_path/demo/cxx/ppshitu_lite
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cd $inference_lite_path/demo/cxx/lite
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cd $inference_lite_path/demo/cxx/ppshitu_lite
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# 执行编译，等待完成后得到可执行文件main
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make ARM_ABI=arm8
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#如果是arm7，则执行 make ARM_ABI = arm7 (或者在Makefile中修改该项)
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```
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5. 准备优化后的模型、预测库文件、测试图像。
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```shell
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mkdir deploy
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cp main *runtime_config.json deploy/
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mv models deploy/
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mv images deploy/
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cp pp_shitu deploy/
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cd deploy
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mkdir model_det
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mkdir model_keypoint
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# 将优化后的模型、预测库文件、测试图像放置在预测库中的demo/cxx/detection文件夹下
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cp {PadddleDetection_Root}/output_inference/picodet_s_320_coco/model.nb ./model_det/
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cp {PadddleDetection_Root}/output_inference/picodet_s_320_coco/infer_cfg.json ./model_det/
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# 如果需要关键点模型，则只需操作：
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cp {PadddleDetection_Root}/output_inference/hrnet_w32_256x192/model.nb ./model_keypoint/
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cp {PadddleDetection_Root}/output_inference/hrnet_w32_256x192/infer_cfg.json ./model_keypoint/
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# 将测试图像复制到deploy文件夹中
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cp [your_test_img].jpg ./demo.jpg
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# 将C++预测动态库so文件复制到deploy文件夹中
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cp ../../../cxx/lib/libpaddle_light_api_shared.so ./
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@ -227,45 +255,19 @@ cp ../../../cxx/lib/libpaddle_light_api_shared.so ./
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```
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deploy/
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|-- model_det/
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| |--model.nb 优化后的检测模型文件
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| |--infer_cfg.json 检测器模型配置文件
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|-- model_keypoint/
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| |--model.nb 优化后的关键点模型文件
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| |--infer_cfg.json 关键点模型配置文件
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|-- main 生成的移动端执行文件
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|-- det_runtime_config.json 目标检测执行时参数配置文件
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|-- keypoint_runtime_config.json 关键点检测执行时参数配置文件
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|-- models/
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| |--mainbody_det.nb 优化后的主体检测模型文件
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| |--rec.nb 优化后的识别模型文件
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| |--label.txt 识别模型的label文件
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|-- images/
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| ... 图片文件
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|-- pp_shitu 生成的移动端执行文件
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|-- shitu_config.json 执行时参数配置文件
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|-- libpaddle_light_api_shared.so Paddle-Lite库文件
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```
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**注意：**
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* `det_runtime_config.json` 包含了目标检测的超参数，请按需进行修改：
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```shell
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{
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"model_dir_det": "./model_det/", #检测器模型路径
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"batch_size_det": 1, #检测预测时batchsize
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"threshold_det": 0.5, #检测器输出阈值
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"image_file": "demo.jpg", #测试图片
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"image_dir": "", #测试图片文件夹
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"run_benchmark": true, #性能测试开关
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"cpu_threads": 4 #线程数
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}
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```
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* `keypoint_runtime_config.json` 包含了关键点检测的超参数，请按需进行修改：
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```shell
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{
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"model_dir_keypoint": "./model_keypoint/", #关键点模型路径（不使用需为空字符）
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"batch_size_keypoint": 8, #关键点预测时batchsize
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"threshold_keypoint": 0.5, #关键点输出阈值
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"image_file": "demo.jpg", #测试图片
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"image_dir": "", #测试图片文件夹
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"run_benchmark": true, #性能测试开关
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"cpu_threads": 4 #线程数
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}
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```
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* `shitu_config.json` 包含了目标检测的超参数，请按需进行修改
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6. 启动调试，上述步骤完成后就可以使用ADB将文件夹 `deploy/` push到手机上运行，步骤如下：
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@ -278,23 +280,20 @@ cd /data/local/tmp/deploy
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export LD_LIBRARY_PATH=/data/local/tmp/deploy:$LD_LIBRARY_PATH
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# 修改权限为可执行
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chmod 777 main
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# 以检测为例，执行程序
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./main det_runtime_config.json
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chmod 777 pp_shitu
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# 执行程序
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./pp_shitu shitu_config.json
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```
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如果对代码做了修改，则需要重新编译并push到手机上。
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运行效果如下：
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<div align="center">
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<img src="../../docs/images/lite_demo.jpg" width="600">
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</div>
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## FAQ
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Q1：如果想更换模型怎么办，需要重新按照流程走一遍吗？
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Q1：如果想更换模型怎么办，需要重新按照流程走一遍吗？
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A1：如果已经走通了上述步骤，更换模型只需要替换 `.nb` 模型文件即可，同时要注意修改下配置文件中的 `.nb` 文件路径以及类别映射文件（如有必要）。
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Q2：换一个图测试怎么做？
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A2：替换 deploy 下的测试图像为你想要测试的图像，使用 ADB 再次 push 到手机上即可。
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Q2：换一个图测试怎么做？
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A2：替换 deploy 下的测试图像为你想要测试的图像，并重新生成json配置文件（或者直接修改图像路径），使用 ADB 再次 push 到手机上即可。
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@ -7,40 +7,63 @@ import yaml
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--yaml_path',
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type=str,
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default='../configs/inference_drink.yaml')
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parser.add_argument('--img_dir', type=str, default=None,
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help='The dir path for inference images')
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parser.add_argument('--det_model_path',
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type=str, default='./det.nb',
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help="The model path for mainbody detection")
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parser.add_argument('--rec_model_path', type=str, default='./rec.nb', help="The rec model path")
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parser.add_argument('--rec-label-path', type=str, default='./label.txt', help='The rec model label')
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parser.add_argument('--arch',
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type=str,
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default='GFL',
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help='The model structure for detection model')
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parser.add_argument('--fpn-stride',
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type=list,
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default=[8, 16, 32, 64],
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help="The fpn strid for detection model")
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parser.add_argument('--keep_top_k',
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type=int,
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default=100,
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help='The params for nms(postprocess for detection)')
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parser.add_argument('--nms-name',
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type=str,
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default='MultiClassNMS',
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help='The nms name for postprocess of detection model')
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parser.add_argument('--nms_threshold',
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type=float,
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default=0.5,
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help='The nms nms_threshold for detection postprocess')
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parser.add_argument('--nms_top_k',
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type=int,
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default=1000,
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help='The nms_top_k in postprocess of detection model')
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parser.add_argument(
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'--yaml_path', type=str, default='../configs/inference_drink.yaml')
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parser.add_argument(
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'--img_dir',
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type=str,
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default=None,
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help='The dir path for inference images')
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parser.add_argument(
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'--img_path',
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type=str,
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default=None,
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help='The dir path for inference images')
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parser.add_argument(
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'--det_model_path',
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type=str,
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default='./det.nb',
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help="The model path for mainbody detection")
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parser.add_argument(
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'--rec_model_path',
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type=str,
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default='./rec.nb',
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help="The rec model path")
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parser.add_argument(
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'--rec_label_path',
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type=str,
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default='./label.txt',
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help='The rec model label')
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parser.add_argument(
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'--arch',
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type=str,
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default='PicoDet',
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help='The model structure for detection model')
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parser.add_argument(
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'--fpn-stride',
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type=list,
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default=[8, 16, 32, 64],
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help="The fpn strid for detection model")
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parser.add_argument(
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'--keep_top_k',
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type=int,
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default=100,
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help='The params for nms(postprocess for detection)')
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parser.add_argument(
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'--nms-name',
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type=str,
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default='MultiClassNMS',
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help='The nms name for postprocess of detection model')
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parser.add_argument(
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'--nms_threshold',
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type=float,
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default=0.5,
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help='The nms nms_threshold for detection postprocess')
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parser.add_argument(
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'--nms_top_k',
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type=int,
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default=1000,
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help='The nms_top_k in postprocess of detection model')
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parser.add_argument(
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'--score_threshold',
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type=float,
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@ -55,18 +78,24 @@ def main():
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config_yaml = yaml.safe_load(open(args.yaml_path))
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config_json = {}
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config_json["Global"] = {}
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config_json["Global"]["infer_imgs"] = config_yaml["Global"]["infer_imgs"]
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config_json["Global"]["infer_imgs_dir"] = args.img_dir
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config_json["Global"][
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"infer_imgs"] = args.img_path if args.img_path else config_yaml[
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"Global"]["infer_imgs"]
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if args.img_dir is not None:
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config_json["Global"]["infer_imgs_dir"] = args.img_dir
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config_json["Global"]["infer_imgs"] = None
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else:
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config_json["Global"][
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"infer_imgs"] = args.img_path if args.img_path else config_yaml[
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"Global"]["infer_imgs"]
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config_json["Global"]["batch_size"] = config_yaml["Global"]["batch_size"]
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config_json["Global"]["cpu_num_threads"] = config_yaml["Global"][
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"cpu_num_threads"]
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config_json["Global"]["cpu_num_threads"] = min(
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config_yaml["Global"]["cpu_num_threads"], 4)
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config_json["Global"]["image_shape"] = config_yaml["Global"]["image_shape"]
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config_json["Global"][
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"det_model_path"] = args.det_model_path
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config_json["Global"][
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"rec_model_path"] = args.rec_model_path
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config_json["Global"]["det_model_path"] = args.det_model_path
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config_json["Global"]["rec_model_path"] = args.rec_model_path
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config_json["Global"]["rec_label_path"] = args.rec_label_path
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config_json["Global"]["labe_list"] = config_yaml["Global"]["labe_list"]
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config_json["Global"]["label_list"] = config_yaml["Global"]["labe_list"]
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config_json["Global"]["rec_nms_thresold"] = config_yaml["Global"][
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"rec_nms_thresold"]
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config_json["Global"]["max_det_results"] = config_yaml["Global"][
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@ -60,7 +60,7 @@ class ConfigPaser {
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// Get label_list for visualization
|
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if (config["Global"].isMember("label_list")) {
|
||||
label_list_.clear();
|
||||
for (auto item : config["label_list"]) {
|
||||
for (auto item : config["Global"]["label_list"]) {
|
||||
label_list_.emplace_back(item.as<std::string>());
|
||||
}
|
||||
} else {
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@
|
|||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
#include "paddle_api.h" // NOLINT
|
||||
#include "json/json.h"
|
||||
#include <arm_neon.h>
|
||||
|
|
@ -54,8 +55,10 @@ public:
|
|||
}
|
||||
LoadLabel(config_file["Global"]["rec_label_path"].as<std::string>());
|
||||
SetPreProcessParam(config_file["RecPreProcess"]["transform_ops"]);
|
||||
if (!config_file["Global"].isMember("return_k"))
|
||||
if (!config_file["Global"].isMember("return_k")){
|
||||
this->topk = config_file["Global"]["return_k"].as<int>();
|
||||
}
|
||||
printf("rec model create!\n");
|
||||
}
|
||||
|
||||
void LoadLabel(std::string path) {
|
||||
|
|
|
|||
|
|
@ -14,13 +14,14 @@
|
|||
|
||||
#pragma once
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include <memory>
|
||||
#include <utility>
|
||||
#include <ctime>
|
||||
#include <numeric>
|
||||
#include <algorithm>
|
||||
#include <ctime>
|
||||
#include <include/recognition.h>
|
||||
#include <memory>
|
||||
#include <numeric>
|
||||
#include <string>
|
||||
#include <utility>
|
||||
#include <vector>
|
||||
|
||||
namespace PPShiTu {
|
||||
|
||||
|
|
@ -32,8 +33,11 @@ struct ObjectResult {
|
|||
int class_id;
|
||||
// Confidence of detected object
|
||||
float confidence;
|
||||
|
||||
// RecModel result
|
||||
std::vector<RESULT> rec_result;
|
||||
};
|
||||
|
||||
void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold);
|
||||
void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold, bool rec_nms=false);
|
||||
|
||||
} // namespace PPShiTu
|
||||
} // namespace PPShiTu
|
||||
|
|
|
|||
|
|
@ -73,17 +73,10 @@ void DetPredictImage(const std::vector<cv::Mat> &batch_imgs,
|
|||
std::vector<double> det_t = {0, 0, 0};
|
||||
int steps = ceil(float(batch_imgs.size()) / batch_size_det);
|
||||
for (int idx = 0; idx < steps; idx++) {
|
||||
std::vector<cv::Mat> batch_imgs;
|
||||
int left_image_cnt = batch_imgs.size() - idx * batch_size_det;
|
||||
if (left_image_cnt > batch_size_det) {
|
||||
left_image_cnt = batch_size_det;
|
||||
}
|
||||
/* for (int bs = 0; bs < left_image_cnt; bs++) { */
|
||||
/* std::string image_file_path = all_img_paths.at(idx * batch_size_det +
|
||||
* bs); */
|
||||
/* cv::Mat im = cv::imread(image_file_path, 1); */
|
||||
/* batch_imgs.insert(batch_imgs.end(), im); */
|
||||
/* } */
|
||||
// Store all detected result
|
||||
std::vector<PPShiTu::ObjectResult> result;
|
||||
std::vector<int> bbox_num;
|
||||
|
|
@ -108,32 +101,7 @@ void DetPredictImage(const std::vector<cv::Mat> &batch_imgs,
|
|||
}
|
||||
detect_num += 1;
|
||||
im_result.push_back(item);
|
||||
/* if (item.rect.size() > 6) { */
|
||||
/* is_rbox = true; */
|
||||
/* printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
|
||||
*/
|
||||
/* item.class_id, */
|
||||
/* item.confidence, */
|
||||
/* item.rect[0], */
|
||||
/* item.rect[1], */
|
||||
/* item.rect[2], */
|
||||
/* item.rect[3], */
|
||||
/* item.rect[4], */
|
||||
/* item.rect[5], */
|
||||
/* item.rect[6], */
|
||||
/* item.rect[7]); */
|
||||
/* } else { */
|
||||
/* printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n", */
|
||||
/* item.class_id, */
|
||||
/* item.confidence, */
|
||||
/* item.rect[0], */
|
||||
/* item.rect[1], */
|
||||
/* item.rect[2], */
|
||||
/* item.rect[3]); */
|
||||
/* } */
|
||||
}
|
||||
/* std::cout << all_img_paths.at(idx * batch_size_det + i) */
|
||||
/* << " The number of detected box: " << detect_num << std::endl; */
|
||||
item_start_idx = item_start_idx + bbox_num[i];
|
||||
}
|
||||
|
||||
|
|
@ -144,14 +112,13 @@ void DetPredictImage(const std::vector<cv::Mat> &batch_imgs,
|
|||
}
|
||||
|
||||
void PrintResult(const std::string &image_path,
|
||||
std::vector<PPShiTu::ObjectResult> &det_result,
|
||||
std::vector<std::vector<PPShiTu::RESULT>> &rec_results) {
|
||||
std::vector<PPShiTu::ObjectResult> &det_result) {
|
||||
printf("%s:\n", image_path.c_str());
|
||||
for (int i = 0; i < det_result.size(); ++i) {
|
||||
printf("\tresult%d: bbox[%d, %d, %d, %d], score: %f, label: %s\n", i,
|
||||
det_result[i].rect[0], det_result[i].rect[1], det_result[i].rect[2],
|
||||
det_result[i].rect[3], rec_results[i][0].score,
|
||||
rec_results[i][0].class_name.c_str());
|
||||
det_result[i].rect[3], det_result[i].rec_result[0].score,
|
||||
det_result[i].rec_result[0].class_name.c_str());
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -163,37 +130,33 @@ int main(int argc, char **argv) {
|
|||
return -1;
|
||||
}
|
||||
std::string config_path = argv[1];
|
||||
std::string img_path = "";
|
||||
std::string img_dir = "";
|
||||
|
||||
if (argc >= 3) {
|
||||
img_path = argv[2];
|
||||
img_dir = argv[2];
|
||||
}
|
||||
// Parsing command-line
|
||||
PPShiTu::load_jsonf(config_path, RT_Config);
|
||||
if (RT_Config["Global"]["det_inference_model_dir"]
|
||||
.as<std::string>()
|
||||
.empty()) {
|
||||
std::cout << "Please set [det_inference_model_dir] in " << config_path
|
||||
<< std::endl;
|
||||
if (RT_Config["Global"]["det_model_path"].as<std::string>().empty()) {
|
||||
std::cout << "Please set [det_model_path] in " << config_path << std::endl;
|
||||
return -1;
|
||||
}
|
||||
if (RT_Config["Global"]["infer_imgs"].as<std::string>().empty() &&
|
||||
img_path.empty()) {
|
||||
img_dir.empty()) {
|
||||
std::cout << "Please set [infer_imgs] in " << config_path
|
||||
<< " Or use command: <" << argv[0] << " [shitu_config]"
|
||||
<< " [image_dir]>" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
if (!img_path.empty()) {
|
||||
if (!img_dir.empty()) {
|
||||
std::cout << "Use image_dir in command line overide the path in config file"
|
||||
<< std::endl;
|
||||
RT_Config["Global"]["infer_imgs_dir"] = img_path;
|
||||
RT_Config["Global"]["infer_imgs_dir"] = img_dir;
|
||||
RT_Config["Global"]["infer_imgs"] = "";
|
||||
}
|
||||
// Load model and create a object detector
|
||||
PPShiTu::ObjectDetector det(
|
||||
RT_Config,
|
||||
RT_Config["Global"]["det_inference_model_dir"].as<std::string>(),
|
||||
RT_Config, RT_Config["Global"]["det_model_path"].as<std::string>(),
|
||||
RT_Config["Global"]["cpu_num_threads"].as<int>(),
|
||||
RT_Config["Global"]["batch_size"].as<int>());
|
||||
// create rec model
|
||||
|
|
@ -202,7 +165,6 @@ int main(int argc, char **argv) {
|
|||
|
||||
std::vector<PPShiTu::ObjectResult> det_result;
|
||||
std::vector<cv::Mat> batch_imgs;
|
||||
std::vector<std::vector<PPShiTu::RESULT>> rec_results;
|
||||
double rec_time;
|
||||
if (!RT_Config["Global"]["infer_imgs"].as<std::string>().empty() ||
|
||||
!RT_Config["Global"]["infer_imgs_dir"].as<std::string>().empty()) {
|
||||
|
|
@ -239,7 +201,7 @@ int main(int argc, char **argv) {
|
|||
|
||||
// add the whole image for recognition to improve recall
|
||||
PPShiTu::ObjectResult result_whole_img = {
|
||||
{0, 0, srcimg.cols - 1, srcimg.rows - 1}, 0, 1.0};
|
||||
{0, 0, srcimg.cols, srcimg.rows}, 0, 1.0};
|
||||
det_result.push_back(result_whole_img);
|
||||
|
||||
// get rec result
|
||||
|
|
@ -250,13 +212,14 @@ int main(int argc, char **argv) {
|
|||
cv::Mat crop_img = srcimg(rect);
|
||||
std::vector<PPShiTu::RESULT> result =
|
||||
rec.RunRecModel(crop_img, rec_time);
|
||||
rec_results.push_back(result);
|
||||
det_result[j].rec_result.assign(result.begin(), result.end());
|
||||
}
|
||||
PrintResult(img_path, det_result, rec_results);
|
||||
|
||||
// rec nms
|
||||
PPShiTu::nms(det_result,
|
||||
RT_Config["Global"]["rec_nms_thresold"].as<float>(), true);
|
||||
PrintResult(img_path, det_result);
|
||||
batch_imgs.clear();
|
||||
det_result.clear();
|
||||
rec_results.clear();
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
|
|
|
|||
|
|
@ -95,7 +95,7 @@ cv::Mat VisualizeResult(const cv::Mat& img,
|
|||
void ObjectDetector::Preprocess(const cv::Mat& ori_im) {
|
||||
// Clone the image : keep the original mat for postprocess
|
||||
cv::Mat im = ori_im.clone();
|
||||
cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
|
||||
// cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
|
||||
preprocessor_.Run(&im, &inputs_);
|
||||
}
|
||||
|
||||
|
|
@ -235,7 +235,7 @@ void ObjectDetector::Predict(const std::vector<cv::Mat>& imgs,
|
|||
auto postprocess_start = std::chrono::steady_clock::now();
|
||||
// Get output tensor
|
||||
output_data_list_.clear();
|
||||
int num_class = 80;
|
||||
int num_class = 1;
|
||||
int reg_max = 7;
|
||||
auto output_names = predictor_->GetOutputNames();
|
||||
// TODO: Unified model output.
|
||||
|
|
@ -281,6 +281,7 @@ void ObjectDetector::Predict(const std::vector<cv::Mat>& imgs,
|
|||
out_bbox_num_data_.data());
|
||||
}
|
||||
// Postprocessing result
|
||||
|
||||
result->clear();
|
||||
if (config_.arch_ == "PicoDet") {
|
||||
PPShiTu::PicoDetPostProcess(
|
||||
|
|
|
|||
|
|
@ -127,11 +127,11 @@ void TopDownEvalAffine::Run(cv::Mat* im, ImageBlob* data) {
|
|||
|
||||
// Preprocessor op running order
|
||||
const std::vector<std::string> Preprocessor::RUN_ORDER = {"InitInfo",
|
||||
"TopDownEvalAffine",
|
||||
"Resize",
|
||||
"NormalizeImage",
|
||||
"PadStride",
|
||||
"Permute"};
|
||||
"DetTopDownEvalAffine",
|
||||
"DetResize",
|
||||
"DetNormalizeImage",
|
||||
"DetPadStride",
|
||||
"DetPermute"};
|
||||
|
||||
void Preprocessor::Run(cv::Mat* im, ImageBlob* data) {
|
||||
for (const auto& name : RUN_ORDER) {
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ std::vector<RESULT> Recognition::RunRecModel(const cv::Mat &img,
|
|||
|
||||
// Get output and post process
|
||||
std::unique_ptr<const Tensor> output_tensor(
|
||||
std::move(this->predictor->GetOutput(0)));
|
||||
std::move(this->predictor->GetOutput(1)));
|
||||
auto *output_data = output_tensor->data<float>();
|
||||
auto end = std::chrono::system_clock::now();
|
||||
auto duration =
|
||||
|
|
|
|||
|
|
@ -16,14 +16,23 @@
|
|||
|
||||
namespace PPShiTu {
|
||||
|
||||
void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold) {
|
||||
std::sort(input_boxes.begin(),
|
||||
input_boxes.end(),
|
||||
[](ObjectResult a, ObjectResult b) { return a.confidence > b.confidence; });
|
||||
void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold,
|
||||
bool rec_nms) {
|
||||
if (!rec_nms) {
|
||||
std::sort(input_boxes.begin(), input_boxes.end(),
|
||||
[](ObjectResult a, ObjectResult b) {
|
||||
return a.confidence > b.confidence;
|
||||
});
|
||||
} else {
|
||||
std::sort(input_boxes.begin(), input_boxes.end(),
|
||||
[](ObjectResult a, ObjectResult b) {
|
||||
return a.rec_result[0].score > b.rec_result[0].score;
|
||||
});
|
||||
}
|
||||
std::vector<float> vArea(input_boxes.size());
|
||||
for (int i = 0; i < int(input_boxes.size()); ++i) {
|
||||
vArea[i] = (input_boxes.at(i).rect[2] - input_boxes.at(i).rect[0] + 1)
|
||||
* (input_boxes.at(i).rect[3] - input_boxes.at(i).rect[1] + 1);
|
||||
vArea[i] = (input_boxes.at(i).rect[2] - input_boxes.at(i).rect[0] + 1) *
|
||||
(input_boxes.at(i).rect[3] - input_boxes.at(i).rect[1] + 1);
|
||||
}
|
||||
for (int i = 0; i < int(input_boxes.size()); ++i) {
|
||||
for (int j = i + 1; j < int(input_boxes.size());) {
|
||||
|
|
@ -36,14 +45,13 @@ void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold) {
|
|||
float inter = w * h;
|
||||
float ovr = inter / (vArea[i] + vArea[j] - inter);
|
||||
if (ovr >= nms_threshold) {
|
||||
input_boxes.erase(input_boxes.begin() + j);
|
||||
vArea.erase(vArea.begin() + j);
|
||||
}
|
||||
else {
|
||||
j++;
|
||||
input_boxes.erase(input_boxes.begin() + j);
|
||||
vArea.erase(vArea.begin() + j);
|
||||
} else {
|
||||
j++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace PPShiTu
|
||||
} // namespace PPShiTu
|
||||
|
|
|
|||
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|
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Reference in New Issue