[PaddlePaddle Hackathon 2] task 62 PP-TinyPose Demo #250
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| 在 Android 上实现实时的Pose关键点的检测功能 | ||
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| ## 如何运行 Demo |
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这块内容辛苦补充下，并展示运行效果示意图
| 'dest': 'OpenCV' | ||
| ], | ||
| [ | ||
| 'src' : 'https://paddlelite-demo.bj.bcebos.com/models/ultra_light_fast_generic_face_detector_1mb_640_for_cpu_v2_10_rc.tar.gz', |
| ${OpenCV_LIBS} | ||
| GLESv2 | ||
| EGL | ||
| ${log-lib}) |
| # CMake builds them for you. Gradle automatically packages shared libraries with | ||
| # your APK. | ||
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| set(PaddleLite_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../../PaddleLite-2.11-rc") |
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建议目录名由PaddleLite-2.11-rc 改为PaddleLite
| if (tensor_name == "image") { | ||
| auto inputTensor = predictor_->GetInputByName(tensor_name); | ||
| // Set the data of input image | ||
| // auto inputTensor = predictor_->GetInput(0); |
| fontThickness); | ||
| } | ||
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| // void Pipeline::Action_Process(cv::Mat *rgbaImage, |
| VisualizeKptsResults(results, results_kpts, &rgbaImage, false); | ||
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| // Visualize the status(performance data) to the origin image | ||
| // VisualizeStatus(readGLFBOTime, writeGLTextureTime, preprocessTime+preprocessTime_kpts, |
| double postprocessTime, cv::Mat *rgbaImage, | ||
| std::vector<RESULT_KEYPOINT> &results_kpts, | ||
| std::vector<RESULT> &results); | ||
| // void Action_Process(cv::Mat *rgbaImage, |
| } | ||
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| void NHWC3ToNC3HW_bn(const float *src, float *dst, const float *mean, | ||
| const float *std, int width, int height) { |
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这个函数可以和NHWC3ToNC3HW 合并，如果没有归一化处理，mean 是0，scale 是1 就行
(din - mean) / scale
| *(dst_c1++) = *(src++); | ||
| *(dst_c2++) = *(src++); | ||
| } | ||
| // for (; i < size; i++) { |
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在shell 目录下，补充下shell demo（参考：https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/develop/object_detection/android/shell/cxx/ssd_mobilenetv1_detection），并加入ci 测试(在tools/ci_tools/android_ci_demo.sh）https://github.com/PaddlePaddle/Paddle-Lite-Demo/blob/develop/tools/ci_tools/android_demo_ci.sh#L71 处添加shell demo 单测
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另外，你的代码格式没有通过检测
建议安装 pre-commit，例如：pip install pre-commit 然后：pre-commit run -a
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另外，readme 文档格式请参考已有的readme 文档，辛苦重新更新下
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@chenjiaoAngel 感谢评审。后面会一一修改。 |
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