From 4639b6f1508e519f6dbb3082762bb851af86d2b6 Mon Sep 17 00:00:00 2001 From: dongshuilong Date: Sat, 22 Jan 2022 12:17:59 +0000 Subject: [PATCH] add ppshitu lite --- deploy/lite_shitu/Makefile | 90 +++++ deploy/lite_shitu/README.md | 300 ++++++++++++++++ deploy/lite_shitu/generate_json_config.py | 109 ++++++ deploy/lite_shitu/include/config_parser.h | 92 +++++ deploy/lite_shitu/include/object_detector.h | 102 ++++++ .../lite_shitu/include/picodet_postprocess.h | 38 ++ deploy/lite_shitu/include/preprocess_op.h | 188 ++++++++++ deploy/lite_shitu/include/recognition.h | 110 ++++++ deploy/lite_shitu/include/utils.h | 39 +++ deploy/lite_shitu/src/config_parser.cc | 32 ++ deploy/lite_shitu/src/main.cc | 263 ++++++++++++++ deploy/lite_shitu/src/object_detector.cc | 328 ++++++++++++++++++ deploy/lite_shitu/src/picodet_postprocess.cc | 128 +++++++ deploy/lite_shitu/src/preprocess_op.cc | 185 ++++++++++ deploy/lite_shitu/src/recognition.cc | 143 ++++++++ deploy/lite_shitu/src/utils.cc | 49 +++ 16 files changed, 2196 insertions(+) create mode 100644 deploy/lite_shitu/Makefile create mode 100644 deploy/lite_shitu/README.md create mode 100644 deploy/lite_shitu/generate_json_config.py create mode 100644 deploy/lite_shitu/include/config_parser.h create mode 100644 deploy/lite_shitu/include/object_detector.h create mode 100644 deploy/lite_shitu/include/picodet_postprocess.h create mode 100644 deploy/lite_shitu/include/preprocess_op.h create mode 100644 deploy/lite_shitu/include/recognition.h create mode 100644 deploy/lite_shitu/include/utils.h create mode 100644 deploy/lite_shitu/src/config_parser.cc create mode 100644 deploy/lite_shitu/src/main.cc create mode 100644 deploy/lite_shitu/src/object_detector.cc create mode 100644 deploy/lite_shitu/src/picodet_postprocess.cc create mode 100644 deploy/lite_shitu/src/preprocess_op.cc create mode 100644 deploy/lite_shitu/src/recognition.cc create mode 100644 deploy/lite_shitu/src/utils.cc diff --git a/deploy/lite_shitu/Makefile b/deploy/lite_shitu/Makefile new file mode 100644 index 00000000..9c439382 --- /dev/null +++ b/deploy/lite_shitu/Makefile @@ -0,0 +1,90 @@ +ARM_ABI = arm8#[arm7/arm8] +export ARM_ABI + +ifeq ($(ARM_ABI), arm8) + ARM_PLAT=arm64-v8a +else + ARM_PLAT=armeabi-v7a +endif +${info ARM_ABI: ${ARM_ABI}} +${info ARM_PLAT: ${ARM_PLAT}; option[arm7/arm8]} + +include ../Makefile.def + +LITE_ROOT=../../../ +${info LITE_ROOT: $(abspath ${LITE_ROOT})} + +THIRD_PARTY_DIR=third_party +${info THIRD_PARTY_DIR: $(abspath ${THIRD_PARTY_DIR})} + + +OPENCV_VERSION=opencv4.1.0 +OPENCV_LIBS = ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/libs/libopencv_imgcodecs.a \ + ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/libs/libopencv_imgproc.a \ + ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/libs/libopencv_core.a \ + ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/libtegra_hal.a \ + ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/liblibjpeg-turbo.a \ + ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/liblibwebp.a \ + ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/liblibpng.a \ + ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/liblibjasper.a \ + ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/liblibtiff.a \ + ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/libIlmImf.a \ + ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/libtbb.a \ + ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/libcpufeatures.a + + +LITE_LIBS = -L${LITE_ROOT}/cxx/lib/ -lpaddle_light_api_shared +############################################################### +# How to use one of static libaray: # +# `libpaddle_api_full_bundled.a` # +# `libpaddle_api_light_bundled.a` # +############################################################### +# Note: default use lite's shared library. # +############################################################### +# 1. Comment above line using `libpaddle_light_api_shared.so` +# 2. Undo comment below line using `libpaddle_api_light_bundled.a` +# LITE_LIBS = ${LITE_ROOT}/cxx/lib/libpaddle_api_light_bundled.a + +CXX_LIBS = $(LITE_LIBS) ${OPENCV_LIBS} $(SYSTEM_LIBS) + +LOCAL_DIRSRCS=$(wildcard src/*.cc) +LOCAL_SRCS=$(notdir $(LOCAL_DIRSRCS)) +LOCAL_OBJS=$(patsubst %.cpp, %.o, $(patsubst %.cc, %.o, $(LOCAL_SRCS))) + +JSON_OBJS = json_reader.o json_value.o json_writer.o + +main: $(LOCAL_OBJS) $(JSON_OBJS) fetch_opencv + $(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) $(LOCAL_OBJS) $(JSON_OBJS) -o main $(CXX_LIBS) $(LDFLAGS) + +fetch_opencv: + @ test -d ${THIRD_PARTY_DIR} || mkdir ${THIRD_PARTY_DIR} + @ test -e ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz || \ + (echo "fetch opencv libs" && \ + wget -P ${THIRD_PARTY_DIR} https://paddle-inference-dist.bj.bcebos.com/${OPENCV_VERSION}.tar.gz) + @ test -d ${THIRD_PARTY_DIR}/${OPENCV_VERSION} || \ + tar -zxf ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz -C ${THIRD_PARTY_DIR} + +fetch_json_code: + @ test -d ${THIRD_PARTY_DIR} || mkdir ${THIRD_PARTY_DIR} + @ test -e ${THIRD_PARTY_DIR}/jsoncpp_code.tar.gz || \ + (echo "fetch jsoncpp_code.tar.gz" && \ + wget -P ${THIRD_PARTY_DIR} https://bj.bcebos.com/v1/paddledet/deploy/jsoncpp_code.tar.gz ) + @ test -d ${THIRD_PARTY_DIR}/jsoncpp_code || \ + tar -zxf ${THIRD_PARTY_DIR}/jsoncpp_code.tar.gz -C ${THIRD_PARTY_DIR} + +LOCAL_INCLUDES = -I./ -Iinclude +OPENCV_INCLUDE = -I${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/include +JSON_INCLUDE = -I${THIRD_PARTY_DIR}/jsoncpp_code/include +CXX_INCLUDES = ${LOCAL_INCLUDES} ${INCLUDES} ${OPENCV_INCLUDE} ${JSON_INCLUDE} -I$(LITE_ROOT)/cxx/include + + +$(LOCAL_OBJS): %.o: src/%.cc fetch_opencv fetch_json_code + $(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -c $< -o $@ + +$(JSON_OBJS): %.o: ${THIRD_PARTY_DIR}/jsoncpp_code/%.cpp fetch_json_code + $(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -c $< -o $@ + +.PHONY: clean fetch_opencv fetch_json_code +clean: + rm -rf $(LOCAL_OBJS) $(JSON_OBJS) + rm -f main diff --git a/deploy/lite_shitu/README.md b/deploy/lite_shitu/README.md new file mode 100644 index 00000000..e775b134 --- /dev/null +++ b/deploy/lite_shitu/README.md @@ -0,0 +1,300 @@ +# Paddle-Lite端侧部署 + +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。 + +Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理能力，并广泛整合跨平台硬件，为端侧部署及应用落地问题提供轻量化的部署方案。 + +## 1. 准备环境 + +### 运行准备 +- 电脑（编译Paddle Lite） +- 安卓手机（armv7或armv8） + +### 1.1 准备交叉编译环境 +交叉编译环境用于编译 Paddle Lite 和 PaddleDetection 的C++ demo。 +支持多种开发环境，不同开发环境的编译流程请参考对应文档，请确保安装完成Java jdk、Android NDK(R17以上)。 + +1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker) +2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux) +3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#mac-os) + +### 1.2 准备预测库 + +预测库有两种获取方式： +1. [**建议**]直接下载，预测库下载链接如下： + |平台| 架构 | 预测库下载链接| + |-|-|-| + |Android| arm7 | [inference_lite_lib](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv7.clang.c++_static.with_extra.with_cv.tar.gz) | + | Android | arm8 | [inference_lite_lib](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv.tar.gz) | + | Android | arm8(FP16) | [inference_lite_lib](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv8_clang_c++_static_with_extra_with_cv_with_fp16.tiny_publish_427e46.zip) | + +**注意**：1. 如果是从 Paddle-Lite [官方文档](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc)下载的预测库，注意选择`with_extra=ON，with_cv=ON`的下载链接。2. 目前只提供Android端demo，IOS端demo可以参考[Paddle-Lite IOS demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/master/PaddleLite-ios-demo) + + +2. 编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下： +```shell +git clone https://github.com/PaddlePaddle/Paddle-Lite.git +cd Paddle-Lite +# 如果使用编译方式，建议使用develop分支编译预测库 +git checkout develop +# FP32 +./lite/tools/build_android.sh --arch=armv8 --toolchain=clang --with_cv=ON --with_extra=ON +# FP16 +./lite/tools/build_android.sh --arch=armv8 --toolchain=clang --with_cv=ON --with_extra=ON --with_arm82_fp16=ON +``` + +**注意**：编译Paddle-Lite获得预测库时，需要打开`--with_cv=ON --with_extra=ON`两个选项，`--arch`表示`arm`版本，这里指定为armv8，更多编译命令介绍请参考[链接](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_andriod.html#id2)。 + +直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/`文件夹，通过编译Paddle-Lite得到的预测库位于`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。 +预测库的文件目录如下： + +``` +inference_lite_lib.android.armv8/ +|-- cxx C++ 预测库和头文件 +| |-- include C++ 头文件 +| | |-- paddle_api.h +| | |-- paddle_image_preprocess.h +| | |-- paddle_lite_factory_helper.h +| | |-- paddle_place.h +| | |-- paddle_use_kernels.h +| | |-- paddle_use_ops.h +| | `-- paddle_use_passes.h +| `-- lib C++预测库 +| |-- libpaddle_api_light_bundled.a C++静态库 +| `-- libpaddle_light_api_shared.so C++动态库 +|-- java Java预测库 +| |-- jar +| | `-- PaddlePredictor.jar +| |-- so +| | `-- libpaddle_lite_jni.so +| `-- src +|-- demo C++和Java示例代码 +| |-- cxx C++ 预测库demo +| `-- java Java 预测库demo +``` + +## 2 开始运行 + +### 2.1 模型优化 + +Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括量化、子图融合、混合调度、Kernel优选等方法，使用Paddle-Lite的`opt`工具可以自动对inference模型进行优化，目前支持两种优化方式，优化后的模型更轻量，模型运行速度更快。 + +**注意**：如果已经准备好了 `.nb` 结尾的模型文件，可以跳过此步骤。 + +#### 2.1.1 安装paddle_lite_opt工具 +安装`paddle_lite_opt`工具有如下两种方法： +1. [**建议**]pip安装paddlelite并进行转换 + ```shell + pip install paddlelite==2.10rc + ``` + +2. 源码编译Paddle-Lite生成`paddle_lite_opt`工具 + + 模型优化需要Paddle-Lite的`opt`可执行文件，可以通过编译Paddle-Lite源码获得，编译步骤如下： + ```shell + # 如果准备环境时已经clone了Paddle-Lite，则不用重新clone Paddle-Lite + git clone https://github.com/PaddlePaddle/Paddle-Lite.git + cd Paddle-Lite + git checkout develop + # 启动编译 + ./lite/tools/build.sh build_optimize_tool + ``` + + 编译完成后，`opt`文件位于`build.opt/lite/api/`下，可通过如下方式查看`opt`的运行选项和使用方式； + ```shell + cd build.opt/lite/api/ + ./opt + ``` + + `opt`的使用方式与参数与上面的`paddle_lite_opt`完全一致。 + +之后使用`paddle_lite_opt`工具可以进行inference模型的转换。`paddle_lite_opt`的部分参数如下： + +|选项|说明| +|-|-| +|--model_file|待优化的PaddlePaddle模型（combined形式）的网络结构文件路径| +|--param_file|待优化的PaddlePaddle模型（combined形式）的权重文件路径| +|--optimize_out_type|输出模型类型，目前支持两种类型：protobuf和naive_buffer，其中naive_buffer是一种更轻量级的序列化/反序列化实现，默认为naive_buffer| +|--optimize_out|优化模型的输出路径| +|--valid_targets|指定模型可执行的backend，默认为arm。目前可支持x86、arm、opencl、npu、xpu，可以同时指定多个backend(以空格分隔)，Model Optimize Tool将会自动选择最佳方式。如果需要支持华为NPU（Kirin 810/990 Soc搭载的达芬奇架构NPU），应当设置为npu, arm| + +更详细的`paddle_lite_opt`工具使用说明请参考[使用opt转化模型文档](https://paddle-lite.readthedocs.io/zh/latest/user_guides/opt/opt_bin.html) + +`--model_file`表示inference模型的model文件地址，`--param_file`表示inference模型的param文件地址；`optimize_out`用于指定输出文件的名称（不需要添加`.nb`的后缀）。直接在命令行中运行`paddle_lite_opt`，也可以查看所有参数及其说明。 + + +#### 2.1.3 转换示例 + +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。 + +```shell +# 进入PaddleDetection根目录 +cd PaddleDetection_root_path + +# 将预训练模型导出为inference模型 +python tools/export_model.py -c configs/picodet/picodet_s_320_coco.yml \ + -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams --output_dir=output_inference + +# 将inference模型转化为Paddle-Lite优化模型 +# FP32 +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 +# FP16 +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 + +# 将inference模型配置转化为json格式 +python deploy/lite/convert_yml_to_json.py output_inference/picodet_s_320_coco/infer_cfg.yml +``` + +最终在output_inference/picodet_s_320_coco/文件夹下生成`model.nb` 和 `infer_cfg.json`的文件。 + +**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。 + +### 2.2 与手机联调 + +首先需要进行一些准备工作。 +1. 准备一台arm8的安卓手机，如果编译的预测库是armv7，则需要arm7的手机，并修改Makefile中`ARM_ABI=arm7`。 +2. 电脑上安装ADB工具，用于调试。 ADB安装方式如下： + + 2.1. MAC电脑安装ADB: + + ```shell + brew cask install android-platform-tools + ``` + 2.2. Linux安装ADB + ```shell + sudo apt update + sudo apt install -y wget adb + ``` + 2.3. Window安装ADB + + win上安装需要去谷歌的安卓平台下载ADB软件包进行安装：[链接](https://developer.android.com/studio) + +3. 手机连接电脑后，开启手机`USB调试`选项，选择`文件传输`模式，在电脑终端中输入： + +```shell +adb devices +``` +如果有device输出，则表示安装成功，如下所示： +``` +List of devices attached +744be294 device +``` + +4. 编译lite部署代码生成移动端可执行文件 + +```shell +cd {PadddleDetection_Root} +cd deploy/lite/ + +inference_lite_path=/{lite prediction library path}/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv/ +mkdir $inference_lite_path/demo/cxx/lite + +cp -r Makefile src/ include/ *runtime_config.json $inference_lite_path/demo/cxx/lite + +cd $inference_lite_path/demo/cxx/lite + +# 执行编译，等待完成后得到可执行文件main +make ARM_ABI=arm8 +#如果是arm7，则执行 make ARM_ABI = arm7 (或者在Makefile中修改该项) + +``` + +5. 准备优化后的模型、预测库文件、测试图像。 + +```shell +mkdir deploy +cp main *runtime_config.json deploy/ +cd deploy +mkdir model_det +mkdir model_keypoint + +# 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 +cp {PadddleDetection_Root}/output_inference/picodet_s_320_coco/model.nb ./model_det/ +cp {PadddleDetection_Root}/output_inference/picodet_s_320_coco/infer_cfg.json ./model_det/ + +# 如果需要关键点模型，则只需操作： +cp {PadddleDetection_Root}/output_inference/hrnet_w32_256x192/model.nb ./model_keypoint/ +cp {PadddleDetection_Root}/output_inference/hrnet_w32_256x192/infer_cfg.json ./model_keypoint/ + +# 将测试图像复制到deploy文件夹中 +cp [your_test_img].jpg ./demo.jpg + +# 将C++预测动态库so文件复制到deploy文件夹中 +cp ../../../cxx/lib/libpaddle_light_api_shared.so ./ +``` + +执行完成后，deploy文件夹下将有如下文件格式： + +``` +deploy/ +|-- model_det/ +| |--model.nb 优化后的检测模型文件 +| |--infer_cfg.json 检测器模型配置文件 +|-- model_keypoint/ +| |--model.nb 优化后的关键点模型文件 +| |--infer_cfg.json 关键点模型配置文件 +|-- main 生成的移动端执行文件 +|-- det_runtime_config.json 目标检测执行时参数配置文件 +|-- keypoint_runtime_config.json 关键点检测执行时参数配置文件 +|-- libpaddle_light_api_shared.so Paddle-Lite库文件 +``` + +**注意：** +* `det_runtime_config.json` 包含了目标检测的超参数，请按需进行修改： + +```shell +{ + "model_dir_det": "./model_det/", #检测器模型路径 + "batch_size_det": 1, #检测预测时batchsize + "threshold_det": 0.5, #检测器输出阈值 + "image_file": "demo.jpg", #测试图片 + "image_dir": "", #测试图片文件夹 + "run_benchmark": true, #性能测试开关 + "cpu_threads": 4 #线程数 +} +``` + +* `keypoint_runtime_config.json` 包含了关键点检测的超参数，请按需进行修改： +```shell +{ + "model_dir_keypoint": "./model_keypoint/", #关键点模型路径（不使用需为空字符） + "batch_size_keypoint": 8, #关键点预测时batchsize + "threshold_keypoint": 0.5, #关键点输出阈值 + "image_file": "demo.jpg", #测试图片 + "image_dir": "", #测试图片文件夹 + "run_benchmark": true, #性能测试开关 + "cpu_threads": 4 #线程数 +} +``` + +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： + +```shell +# 将上述deploy文件夹push到手机上 +adb push deploy /data/local/tmp/ + +adb shell +cd /data/local/tmp/deploy +export LD_LIBRARY_PATH=/data/local/tmp/deploy:$LD_LIBRARY_PATH + +# 修改权限为可执行 +chmod 777 main +# 以检测为例，执行程序 +./main det_runtime_config.json +``` + +如果对代码做了修改，则需要重新编译并push到手机上。 + +运行效果如下： + +
+ +
+ + +## FAQ +Q1：如果想更换模型怎么办，需要重新按照流程走一遍吗？ +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）。 + +Q2：换一个图测试怎么做？ +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。 diff --git a/deploy/lite_shitu/generate_json_config.py b/deploy/lite_shitu/generate_json_config.py new file mode 100644 index 00000000..fb54685d --- /dev/null +++ b/deploy/lite_shitu/generate_json_config.py @@ -0,0 +1,109 @@ +import argparse +import json +import os + +import yaml + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--yaml_path', + type=str, + default='../configs/inference_drink.yaml') + parser.add_argument('--img_dir', type=str, default=None, + help='The dir path for inference images') + parser.add_argument('--det_model_path', + type=str, default='./det.nb', + help="The model path for mainbody detection") + parser.add_argument('--rec_model_path', type=str, default='./rec.nb', help="The rec model path") + parser.add_argument('--rec-label-path', type=str, default='./label.txt', help='The rec model label') + parser.add_argument('--arch', + type=str, + default='GFL', + help='The model structure for detection model') + parser.add_argument('--fpn-stride', + type=list, + default=[8, 16, 32, 64], + help="The fpn strid for detection model") + parser.add_argument('--keep_top_k', + type=int, + default=100, + help='The params for nms(postprocess for detection)') + parser.add_argument('--nms-name', + type=str, + default='MultiClassNMS', + help='The nms name for postprocess of detection model') + parser.add_argument('--nms_threshold', + type=float, + default=0.5, + help='The nms nms_threshold for detection postprocess') + parser.add_argument('--nms_top_k', + type=int, + default=1000, + help='The nms_top_k in postprocess of detection model') + parser.add_argument( + '--score_threshold', + type=float, + default=0.3, + help='The score_threshold for postprocess of detection') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + config_yaml = yaml.safe_load(open(args.yaml_path)) + config_json = {} + config_json["Global"] = {} + config_json["Global"]["infer_imgs"] = config_yaml["Global"]["infer_imgs"] + config_json["Global"]["infer_imgs_dir"] = args.img_dir + config_json["Global"]["batch_size"] = config_yaml["Global"]["batch_size"] + config_json["Global"]["cpu_num_threads"] = config_yaml["Global"][ + "cpu_num_threads"] + config_json["Global"]["image_shape"] = config_yaml["Global"]["image_shape"] + config_json["Global"][ + "det_model_path"] = args.det_model_path + config_json["Global"][ + "rec_model_path"] = args.rec_model_path + config_json["Global"]["rec_label_path"] = args.rec_label_path + config_json["Global"]["labe_list"] = config_yaml["Global"]["labe_list"] + config_json["Global"]["rec_nms_thresold"] = config_yaml["Global"][ + "rec_nms_thresold"] + config_json["Global"]["max_det_results"] = config_yaml["Global"][ + "max_det_results"] + config_json["Global"]["det_fpn_stride"] = args.fpn_stride + config_json["Global"]["det_arch"] = args.arch + config_json["Global"]["return_k"] = config_yaml["IndexProcess"]["return_k"] + + # config_json["DetPreProcess"] = config_yaml["DetPreProcess"] + config_json["DetPreProcess"] = {} + config_json["DetPreProcess"]["transform_ops"] = [] + for x in config_yaml["DetPreProcess"]["transform_ops"]: + k = list(x.keys())[0] + y = x[k] + y['type'] = k + config_json["DetPreProcess"]["transform_ops"].append(y) + + config_json["DetPostProcess"] = { + "keep_top_k": args.keep_top_k, + "name": args.nms_name, + "nms_threshold": args.nms_threshold, + "nms_top_k": args.nms_top_k, + "score_threshold": args.score_threshold + } + # config_json["RecPreProcess"] = config_yaml["RecPreProcess"] + config_json["RecPreProcess"] = {} + config_json["RecPreProcess"]["transform_ops"] = [] + for x in config_yaml["RecPreProcess"]["transform_ops"]: + k = list(x.keys())[0] + y = x[k] + if y is not None: + y["type"] = k + config_json["RecPreProcess"]["transform_ops"].append(y) + + with open('shitu_config.json', 'w') as fd: + json.dump(config_json, fd, indent=4) + + +if __name__ == '__main__': + main() diff --git a/deploy/lite_shitu/include/config_parser.h b/deploy/lite_shitu/include/config_parser.h new file mode 100644 index 00000000..1228d198 --- /dev/null +++ b/deploy/lite_shitu/include/config_parser.h @@ -0,0 +1,92 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once +#include +#include +#include +#include +#include + +#include "json/json.h" + +#ifdef _WIN32 +#define OS_PATH_SEP "\\" +#else +#define OS_PATH_SEP "/" +#endif + +namespace PPShiTu { + +void load_jsonf(std::string jsonfile, Json::Value& jsondata); + +// Inference model configuration parser +class ConfigPaser { + public: + ConfigPaser() {} + + ~ConfigPaser() {} + + bool load_config(const Json::Value& config) { + + // Get model arch : YOLO, SSD, RetinaNet, RCNN, Face + if (config["Global"].isMember("det_arch")) { + arch_ = config["Global"]["det_arch"].as(); + } else { + std::cerr << "Please set model arch," + << "support value : YOLO, SSD, RetinaNet, RCNN, Face." + << std::endl; + return false; + } + + // Get Preprocess for preprocessing + if (config.isMember("DetPreProcess")) { + preprocess_info_ = config["DetPreProcess"]["transform_ops"]; + } else { + std::cerr << "Please set Preprocess." << std::endl; + return false; + } + // Get label_list for visualization + if (config["Global"].isMember("label_list")) { + label_list_.clear(); + for (auto item : config["label_list"]) { + label_list_.emplace_back(item.as()); + } + } else { + std::cerr << "Please set label_list." << std::endl; + return false; + } + + // Get NMS for postprocess + if (config.isMember("DetPostProcess")) { + nms_info_ = config["DetPostProcess"]; + } + // Get fpn_stride in PicoDet + if (config["Global"].isMember("det_fpn_stride")) { + fpn_stride_.clear(); + for (auto item : config["Global"]["det_fpn_stride"]) { + fpn_stride_.emplace_back(item.as()); + } + } + + return true; + } + std::string arch_; + Json::Value preprocess_info_; + Json::Value nms_info_; + std::vector label_list_; + std::vector fpn_stride_; +}; + +} // namespace PPShiTu diff --git a/deploy/lite_shitu/include/object_detector.h b/deploy/lite_shitu/include/object_detector.h new file mode 100644 index 00000000..6e4fc4ef --- /dev/null +++ b/deploy/lite_shitu/include/object_detector.h @@ -0,0 +1,102 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include +#include +#include + +#include +#include +#include +#include "json/json.h" + +#include "paddle_api.h" // NOLINT + +#include "include/config_parser.h" +#include "include/preprocess_op.h" +#include "include/utils.h" +#include "include/picodet_postprocess.h" + +using namespace paddle::lite_api; // NOLINT + +namespace PPShiTu { + +// Generate visualization colormap for each class +std::vector GenerateColorMap(int num_class); + +// Visualiztion Detection Result +cv::Mat VisualizeResult(const cv::Mat& img, + const std::vector& results, + const std::vector& lables, + const std::vector& colormap, + const bool is_rbox); + +class ObjectDetector { + public: + explicit ObjectDetector(const Json::Value& config, + const std::string& model_dir, + int cpu_threads = 1, + const int batch_size = 1) { + config_.load_config(config); + printf("config created\n"); + preprocessor_.Init(config_.preprocess_info_); + printf("before object detector\n"); + if(config["Global"]["det_model_path"].as().empty()){ + std::cout << "Please set [det_model_path] in config file" << std::endl; + return -1; + } + LoadModel(config["Global"]["det_model_path"].as(), cpu_threads); + printf("create object detector\n"); } + + // Load Paddle inference model + void LoadModel(std::string model_file, int num_theads); + + // Run predictor + void Predict(const std::vector& imgs, + const double threshold = 0.5, + const int warmup = 0, + const int repeats = 1, + std::vector* result = nullptr, + std::vector* bbox_num = nullptr, + std::vector* times = nullptr); + + // Get Model Label list + const std::vector& GetLabelList() const { + return config_.label_list_; + } + + private: + // Preprocess image and copy data to input buffer + void Preprocess(const cv::Mat& image_mat); + // Postprocess result + void Postprocess(const std::vector mats, + std::vector* result, + std::vector bbox_num, + bool is_rbox); + + std::shared_ptr predictor_; + Preprocessor preprocessor_; + ImageBlob inputs_; + std::vector output_data_; + std::vector out_bbox_num_data_; + float threshold_; + ConfigPaser config_; + +}; + +} // namespace PPShiTu diff --git a/deploy/lite_shitu/include/picodet_postprocess.h b/deploy/lite_shitu/include/picodet_postprocess.h new file mode 100644 index 00000000..758795b5 --- /dev/null +++ b/deploy/lite_shitu/include/picodet_postprocess.h @@ -0,0 +1,38 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "include/utils.h" + +namespace PPShiTu { + +void PicoDetPostProcess(std::vector* results, + std::vector outs, + std::vector fpn_stride, + std::vector im_shape, + std::vector scale_factor, + float score_threshold = 0.3, + float nms_threshold = 0.5, + int num_class = 80, + int reg_max = 7); + +} // namespace PPShiTu diff --git a/deploy/lite_shitu/include/preprocess_op.h b/deploy/lite_shitu/include/preprocess_op.h new file mode 100644 index 00000000..f7050fa8 --- /dev/null +++ b/deploy/lite_shitu/include/preprocess_op.h @@ -0,0 +1,188 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include "json/json.h" + +namespace PPShiTu { + +// Object for storing all preprocessed data +class ImageBlob { + public: + // image width and height + std::vector im_shape_; + // Buffer for image data after preprocessing + std::vector im_data_; + // in net data shape(after pad) + std::vector in_net_shape_; + // Evaluation image width and height + // std::vector eval_im_size_f_; + // Scale factor for image size to origin image size + std::vector scale_factor_; +}; + +// Abstraction of preprocessing opration class +class PreprocessOp { + public: + virtual void Init(const Json::Value& item) = 0; + virtual void Run(cv::Mat* im, ImageBlob* data) = 0; +}; + +class InitInfo : public PreprocessOp { + public: + virtual void Init(const Json::Value& item) {} + virtual void Run(cv::Mat* im, ImageBlob* data); +}; + +class NormalizeImage : public PreprocessOp { + public: + virtual void Init(const Json::Value& item) { + mean_.clear(); + scale_.clear(); + for (auto tmp : item["mean"]) { + mean_.emplace_back(tmp.as()); + } + for (auto tmp : item["std"]) { + scale_.emplace_back(tmp.as()); + } + is_scale_ = item["is_scale"].as(); + } + + virtual void Run(cv::Mat* im, ImageBlob* data); + + private: + // CHW or HWC + std::vector mean_; + std::vector scale_; + bool is_scale_; +}; + +class Permute : public PreprocessOp { + public: + virtual void Init(const Json::Value& item) {} + virtual void Run(cv::Mat* im, ImageBlob* data); +}; + +class Resize : public PreprocessOp { + public: + virtual void Init(const Json::Value& item) { + interp_ = item["interp"].as(); + // max_size_ = item["target_size"].as(); + keep_ratio_ = item["keep_ratio"].as(); + target_size_.clear(); + for (auto tmp : item["target_size"]) { + target_size_.emplace_back(tmp.as()); + } + } + + // Compute best resize scale for x-dimension, y-dimension + std::pair GenerateScale(const cv::Mat& im); + + virtual void Run(cv::Mat* im, ImageBlob* data); + + private: + int interp_; + bool keep_ratio_; + std::vector target_size_; + std::vector in_net_shape_; +}; + +// Models with FPN need input shape % stride == 0 +class PadStride : public PreprocessOp { + public: + virtual void Init(const Json::Value& item) { + stride_ = item["stride"].as(); + } + + virtual void Run(cv::Mat* im, ImageBlob* data); + + private: + int stride_; +}; + +class TopDownEvalAffine : public PreprocessOp { + public: + virtual void Init(const Json::Value& item) { + trainsize_.clear(); + for (auto tmp : item["trainsize"]) { + trainsize_.emplace_back(tmp.as()); + } + } + + virtual void Run(cv::Mat* im, ImageBlob* data); + + private: + int interp_ = 1; + std::vector trainsize_; +}; + +void CropImg(cv::Mat& img, + cv::Mat& crop_img, + std::vector& area, + std::vector& center, + std::vector& scale, + float expandratio = 0.15); + +class Preprocessor { + public: + void Init(const Json::Value& config_node) { + // initialize image info at first + ops_["InitInfo"] = std::make_shared(); + for (const auto& item : config_node) { + auto op_name = item["type"].as(); + + ops_[op_name] = CreateOp(op_name); + ops_[op_name]->Init(item); + } + } + + std::shared_ptr CreateOp(const std::string& name) { + if (name == "DetResize") { + return std::make_shared(); + } else if (name == "DetPermute") { + return std::make_shared(); + } else if (name == "DetNormalizeImage") { + return std::make_shared(); + } else if (name == "DetPadStride") { + // use PadStride instead of PadBatch + return std::make_shared(); + } else if (name == "TopDownEvalAffine") { + return std::make_shared(); + } + std::cerr << "can not find function of OP: " << name + << " and return: nullptr" << std::endl; + return nullptr; + } + + void Run(cv::Mat* im, ImageBlob* data); + + public: + static const std::vector RUN_ORDER; + + private: + std::unordered_map> ops_; +}; + +} // namespace PPShiTu diff --git a/deploy/lite_shitu/include/recognition.h b/deploy/lite_shitu/include/recognition.h new file mode 100644 index 00000000..da945f7e --- /dev/null +++ b/deploy/lite_shitu/include/recognition.h @@ -0,0 +1,110 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle_api.h" // NOLINT +#include "json/json.h" +#include +#include +#include +#include +#include +#include +#include +#include + +using namespace paddle::lite_api; // NOLINT +using namespace std; + +namespace PPShiTu { + +struct RESULT { + std::string class_name; + int class_id; + float score; +}; + +class Recognition { + +public: + void Recognition(const Json::Value &config_file) { + MobileConfig config; + if (config_file["Global"]["rec_model_path"].as().empty()) { + std::cout << "Please set [rec_model_path] in config file" << std::endl; + return -1; + } + config.set_model_from_file( + config_file["Global"]["rec_model_path"].as()); + predictor = CreatePaddlePredictor(config); + + if (config_file["Global"]["rec_label_path"].as().empty()) { + std::cout << "Please set [rec_label_path] in config file" << std::endl; + return -1; + } + LoadLabel(config_file["Global"]["rec_label_path"].as()); + SetPreProcessParam(config_file["RecPreProcess"]["transform_ops"]); + if (!config_file["Global"]["return_k"].as().empty()) + topk = config_file["Global"]["return_k"].as(); + } + + void LoadLabel(std::string path) { + std::ifstream file; + std::vector label_list; + file.open(path); + while (file) { + std::string line; + std::getline(file, line); + std::string::size_type pos = line.find(" "); + if (pos != std::string::npos) { + line = line.substr(pos); + } + label_list.push_back(line); + } + file.clear(); + file.close(); + } + + void SetPreProcessParam(const Json::Value &config_file) { + for (const auto &item : config_file) { + auto op_name = item["type"].as(); + if (op_name == "ResizeImage") { + size = item["size"].as(); + } else if (op_name == "NormalizeImage") { + mean.clear(); + std.clear(); + for (auto tmp : item["mean"]) { + mean.emplace_back(tmp.as()); + } + for (auto tmp : item["std"]) { + std.emplace_back(1 / tmp.as()); + } + scale = item["scale"].as(); + } + } + + std::vector RunRecModel(const cv::Mat &img, double &const_time); + std::vector PostProcess(const float *output_data, int output_size, + cv::Mat &output_image); + cv::Mat ResizeImage(const cv::Mat &img); + void NeonMeanScale(const float *din, float *dout, int size); + + private: + std::shared_ptr predictor; + std::vector label_list; + std::vector mean = {0.485f, 0.456f, 0.406f}; + std::vector std = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f}; + double scale = 0.00392157; + float size = 224; + int topk = 5; + } +} diff --git a/deploy/lite_shitu/include/utils.h b/deploy/lite_shitu/include/utils.h new file mode 100644 index 00000000..cbda311e --- /dev/null +++ b/deploy/lite_shitu/include/utils.h @@ -0,0 +1,39 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +namespace PPShiTu { + +// Object Detection Result +struct ObjectResult { + // Rectangle coordinates of detected object: left, right, top, down + std::vector rect; + // Class id of detected object + int class_id; + // Confidence of detected object + float confidence; +}; + +void nms(std::vector &input_boxes, float nms_threshold); + +} // namespace PPShiTu diff --git a/deploy/lite_shitu/src/config_parser.cc b/deploy/lite_shitu/src/config_parser.cc new file mode 100644 index 00000000..d98b2f90 --- /dev/null +++ b/deploy/lite_shitu/src/config_parser.cc @@ -0,0 +1,32 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/config_parser.h" + +namespace PPShiTu { + +void load_jsonf(std::string jsonfile, Json::Value &jsondata) { + std::ifstream ifs; + ifs.open(jsonfile); + + Json::CharReaderBuilder builder; + builder["collectComments"] = true; + JSONCPP_STRING errs; + if (!parseFromStream(builder, ifs, &jsondata, &errs)) { + std::cout << errs << std::endl; + return; + } +} + +} // namespace PPShiTu diff --git a/deploy/lite_shitu/src/main.cc b/deploy/lite_shitu/src/main.cc new file mode 100644 index 00000000..3b8dee69 --- /dev/null +++ b/deploy/lite_shitu/src/main.cc @@ -0,0 +1,263 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "include/config_parser.h" +#include "include/object_detector.h" +#include "include/preprocess_op.h" +#include "include/recognition.h" +#include "json/json.h" + +Json::Value RT_Config; + +static std::string DirName(const std::string &filepath) { + auto pos = filepath.rfind(OS_PATH_SEP); + if (pos == std::string::npos) { + return ""; + } + return filepath.substr(0, pos); +} + +static bool PathExists(const std::string &path) { + struct stat buffer; + return (stat(path.c_str(), &buffer) == 0); +} + +static void MkDir(const std::string &path) { + if (PathExists(path)) + return; + int ret = 0; + ret = mkdir(path.c_str(), 0755); + if (ret != 0) { + std::string path_error(path); + path_error += " mkdir failed!"; + throw std::runtime_error(path_error); + } +} + +static void MkDirs(const std::string &path) { + if (path.empty()) + return; + if (PathExists(path)) + return; + + MkDirs(DirName(path)); + MkDir(path); +} + +void DetPredictImage(const std::vector &batch_imgs, + std::vector &im_result, + const int batch_size_det, const int max_det_num, + const bool run_benchmark, PPShiTu::ObjectDetector *det) { + std::vector det_t = {0, 0, 0}; + int steps = ceil(float(batch_imgs.size()) / batch_size_det); + for (int idx = 0; idx < steps; idx++) { + std::vector 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 result; + std::vector bbox_num; + std::vector det_times; + + bool is_rbox = false; + if (run_benchmark) { + det->Predict(batch_imgs, 50, 50, &result, &bbox_num, &det_times); + } else { + det->Predict(batch_imgs, 0, 1, &result, &bbox_num, &det_times); + } + + int item_start_idx = 0; + for (int i = 0; i < left_image_cnt; i++) { + cv::Mat im = batch_imgs[i]; + // std::vector im_result; + int detect_num = 0; + for (int j = 0; j < min(bbox_num[i], max_det_num); j++) { + PPShiTu::ObjectResult item = result[item_start_idx + j]; + if (item.class_id == -1) { + continue; + } + 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]; + } + + det_t[0] += det_times[0]; + det_t[1] += det_times[1]; + det_t[2] += det_times[2]; + } +} + +void PrintResult(const std::string &image_path, + std::vector &det_result, + std::vector> &rec_results) { + printf("%s:\n", img_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[t].rect[3], rec_results[i].score, + rec_results[i].class_name.c_str()); + } +} + +int main(int argc, char **argv) { + std::cout << "Usage: " << argv[0] + << " [config_path](option) [image_dir](option)\n"; + if (argc < 2) { + std::cout << "Usage: ./main det_runtime_config.json" << std::endl; + return -1; + } + std::string config_path = argv[1]; + std::string img_path = ""; + + if (argc >= 3) { + img_path = argv[2]; + } + // Parsing command-line + PPShiTu::load_jsonf(config_path, RT_Config); + if (RT_Config["Global"]["det_inference_model_dir"] + .as() + .empty()) { + std::cout << "Please set [det_inference_model_dir] in " << config_path + << std::endl; + return -1; + } + if (RT_Config["Global"]["infer_imgs"].as().empty() && + img_path.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()) { + 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"] = ""; + } + // Load model and create a object detector + PPShiTu::ObjectDetector det( + RT_Config, + RT_Config["Global"]["det_inference_model_dir"].as(), + RT_Config["Global"]["cpu_num_threads"].as(), + RT_Config["Global"]["batch_size"].as()); + // create rec model + PPShiTu::Recognition rec(RT_Config); + // Do inference on input image + + std::vector det_result; + std::vector batch_imgs; + std::vector> rec_results; + double rec_time; + if (!RT_Config["Global"]["infer_imgs"].as().empty() || + !RT_Config["Global"]["infer_imgs_dir"].as().empty()) { + std::vector all_img_paths; + std::vector cv_all_img_paths; + if (!RT_Config["Global"]["infer_imgs"].as().empty()) { + all_img_paths.push_back( + RT_Config["Global"]["infer_imgs"].as()); + if (RT_Config["Global"]["batch_size"].as() > 1) { + std::cout << "batch_size_det should be 1, when set `image_file`." + << std::endl; + return -1; + } + } else { + cv::glob(RT_Config["Global"]["infer_imgs_dir"].as(), + cv_all_img_paths); + for (const auto &img_path : cv_all_img_paths) { + all_img_paths.push_back(img_path); + } + } + for (int i = 0; i < all_img_paths.size(); ++i) { + std::string img_path = img_files_list[idx]; + cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR); + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " << img_path + << "\n"; + exit(-1); + } + cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB); + batch_imgs.push_back(srcimg); + DetPredictImage( + batch_imgs, det_result, RT_Config["Global"]["batch_size"].as(), + RT_Config["Global"]["max_det_results"].as(), false, &det); + + // 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}; + det_result.push_back(result_whole_img); + + // get rec result + for (int j = 0; j < det_result.size(); ++j) { + int w = det_result[j].rect[2] - det_result[j].rect[0]; + int h = det_result[j].rect[3] - det_result[j].rect[1]; + cv::Rect rect(det_result[j].rect[0], det_result[j].rect[1], w, h); + cv::Mat crop_img = srcimg(rect); + std::vector result = + rec.RunRecModel(crop_img, rec_time); + rec_results.push_back(result); + } + PrintResult(img_path, det_result, rec_results); + + batch_imgs.clear(); + det_result.clear(); + rec_results.clear(); + } + } + return 0; +} diff --git a/deploy/lite_shitu/src/object_detector.cc b/deploy/lite_shitu/src/object_detector.cc new file mode 100644 index 00000000..59ccba3a --- /dev/null +++ b/deploy/lite_shitu/src/object_detector.cc @@ -0,0 +1,328 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +#include +// for setprecision +#include +#include +#include "include/object_detector.h" + +namespace PPShiTu { + +// Load Model and create model predictor +void ObjectDetector::LoadModel(std::string model_file, int num_theads) { + MobileConfig config; + config.set_threads(num_theads); + config.set_model_from_file(model_file); + config.set_power_mode(LITE_POWER_HIGH); + + predictor_ = CreatePaddlePredictor(config); +} + +// Visualiztion MaskDetector results +cv::Mat VisualizeResult(const cv::Mat& img, + const std::vector& results, + const std::vector& lables, + const std::vector& colormap, + const bool is_rbox = false) { + cv::Mat vis_img = img.clone(); + for (int i = 0; i < results.size(); ++i) { + // Configure color and text size + std::ostringstream oss; + oss << std::setiosflags(std::ios::fixed) << std::setprecision(4); + oss << lables[results[i].class_id] << " "; + oss << results[i].confidence; + std::string text = oss.str(); + int c1 = colormap[3 * results[i].class_id + 0]; + int c2 = colormap[3 * results[i].class_id + 1]; + int c3 = colormap[3 * results[i].class_id + 2]; + cv::Scalar roi_color = cv::Scalar(c1, c2, c3); + int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL; + double font_scale = 0.5f; + float thickness = 0.5; + cv::Size text_size = + cv::getTextSize(text, font_face, font_scale, thickness, nullptr); + cv::Point origin; + + if (is_rbox) { + // Draw object, text, and background + for (int k = 0; k < 4; k++) { + cv::Point pt1 = cv::Point(results[i].rect[(k * 2) % 8], + results[i].rect[(k * 2 + 1) % 8]); + cv::Point pt2 = cv::Point(results[i].rect[(k * 2 + 2) % 8], + results[i].rect[(k * 2 + 3) % 8]); + cv::line(vis_img, pt1, pt2, roi_color, 2); + } + } else { + int w = results[i].rect[2] - results[i].rect[0]; + int h = results[i].rect[3] - results[i].rect[1]; + cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[1], w, h); + // Draw roi object, text, and background + cv::rectangle(vis_img, roi, roi_color, 2); + } + + origin.x = results[i].rect[0]; + origin.y = results[i].rect[1]; + + // Configure text background + cv::Rect text_back = cv::Rect(results[i].rect[0], + results[i].rect[1] - text_size.height, + text_size.width, + text_size.height); + // Draw text, and background + cv::rectangle(vis_img, text_back, roi_color, -1); + cv::putText(vis_img, + text, + origin, + font_face, + font_scale, + cv::Scalar(255, 255, 255), + thickness); + } + return vis_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); + preprocessor_.Run(&im, &inputs_); +} + +void ObjectDetector::Postprocess(const std::vector mats, + std::vector* result, + std::vector bbox_num, + bool is_rbox = false) { + result->clear(); + int start_idx = 0; + for (int im_id = 0; im_id < mats.size(); im_id++) { + cv::Mat raw_mat = mats[im_id]; + int rh = 1; + int rw = 1; + if (config_.arch_ == "Face") { + rh = raw_mat.rows; + rw = raw_mat.cols; + } + for (int j = start_idx; j < start_idx + bbox_num[im_id]; j++) { + if (is_rbox) { + // Class id + int class_id = static_cast(round(output_data_[0 + j * 10])); + // Confidence score + float score = output_data_[1 + j * 10]; + int x1 = (output_data_[2 + j * 10] * rw); + int y1 = (output_data_[3 + j * 10] * rh); + int x2 = (output_data_[4 + j * 10] * rw); + int y2 = (output_data_[5 + j * 10] * rh); + int x3 = (output_data_[6 + j * 10] * rw); + int y3 = (output_data_[7 + j * 10] * rh); + int x4 = (output_data_[8 + j * 10] * rw); + int y4 = (output_data_[9 + j * 10] * rh); + + PPShiTu::ObjectResult result_item; + result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4}; + result_item.class_id = class_id; + result_item.confidence = score; + result->push_back(result_item); + } else { + // Class id + int class_id = static_cast(round(output_data_[0 + j * 6])); + // Confidence score + float score = output_data_[1 + j * 6]; + int xmin = (output_data_[2 + j * 6] * rw); + int ymin = (output_data_[3 + j * 6] * rh); + int xmax = (output_data_[4 + j * 6] * rw); + int ymax = (output_data_[5 + j * 6] * rh); + int wd = xmax - xmin; + int hd = ymax - ymin; + + PPShiTu::ObjectResult result_item; + result_item.rect = {xmin, ymin, xmax, ymax}; + result_item.class_id = class_id; + result_item.confidence = score; + result->push_back(result_item); + } + } + start_idx += bbox_num[im_id]; + } +} + +void ObjectDetector::Predict(const std::vector& imgs, + const int warmup, + const int repeats, + std::vector* result, + std::vector* bbox_num, + std::vector* times) { + auto preprocess_start = std::chrono::steady_clock::now(); + int batch_size = imgs.size(); + + // in_data_batch + std::vector in_data_all; + std::vector im_shape_all(batch_size * 2); + std::vector scale_factor_all(batch_size * 2); + // Preprocess image + for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) { + cv::Mat im = imgs.at(bs_idx); + Preprocess(im); + im_shape_all[bs_idx * 2] = inputs_.im_shape_[0]; + im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1]; + + scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0]; + scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1]; + + // TODO: reduce cost time + in_data_all.insert( + in_data_all.end(), inputs_.im_data_.begin(), inputs_.im_data_.end()); + } + auto preprocess_end = std::chrono::steady_clock::now(); + std::vector output_data_list_; + // Prepare input tensor + + auto input_names = predictor_->GetInputNames(); + for (const auto& tensor_name : input_names) { + auto in_tensor = predictor_->GetInputByName(tensor_name); + if (tensor_name == "image") { + int rh = inputs_.in_net_shape_[0]; + int rw = inputs_.in_net_shape_[1]; + in_tensor->Resize({batch_size, 3, rh, rw}); + auto* inptr = in_tensor->mutable_data(); + std::copy_n(in_data_all.data(), in_data_all.size(), inptr); + } else if (tensor_name == "im_shape") { + in_tensor->Resize({batch_size, 2}); + auto* inptr = in_tensor->mutable_data(); + std::copy_n(im_shape_all.data(), im_shape_all.size(), inptr); + } else if (tensor_name == "scale_factor") { + in_tensor->Resize({batch_size, 2}); + auto* inptr = in_tensor->mutable_data(); + std::copy_n(scale_factor_all.data(), scale_factor_all.size(), inptr); + } + } + + // Run predictor + // warmup + for (int i = 0; i < warmup; i++) { + predictor_->Run(); + // Get output tensor + auto output_names = predictor_->GetOutputNames(); + if (config_.arch_ == "PicoDet") { + for (int j = 0; j < output_names.size(); j++) { + auto output_tensor = predictor_->GetTensor(output_names[j]); + const float* outptr = output_tensor->data(); + std::vector output_shape = output_tensor->shape(); + output_data_list_.push_back(outptr); + } + } else { + auto out_tensor = predictor_->GetTensor(output_names[0]); + auto out_bbox_num = predictor_->GetTensor(output_names[1]); + } + } + + bool is_rbox = false; + auto inference_start = std::chrono::steady_clock::now(); + for (int i = 0; i < repeats; i++) { + predictor_->Run(); + } + auto inference_end = std::chrono::steady_clock::now(); + auto postprocess_start = std::chrono::steady_clock::now(); + // Get output tensor + output_data_list_.clear(); + int num_class = 80; + int reg_max = 7; + auto output_names = predictor_->GetOutputNames(); + // TODO: Unified model output. + if (config_.arch_ == "PicoDet") { + for (int i = 0; i < output_names.size(); i++) { + auto output_tensor = predictor_->GetTensor(output_names[i]); + const float* outptr = output_tensor->data(); + std::vector output_shape = output_tensor->shape(); + if (i == 0) { + num_class = output_shape[2]; + } + if (i == config_.fpn_stride_.size()) { + reg_max = output_shape[2] / 4 - 1; + } + output_data_list_.push_back(outptr); + } + } else { + auto output_tensor = predictor_->GetTensor(output_names[0]); + auto output_shape = output_tensor->shape(); + auto out_bbox_num = predictor_->GetTensor(output_names[1]); + auto out_bbox_num_shape = out_bbox_num->shape(); + // Calculate output length + int output_size = 1; + for (int j = 0; j < output_shape.size(); ++j) { + output_size *= output_shape[j]; + } + is_rbox = output_shape[output_shape.size() - 1] % 10 == 0; + + if (output_size < 6) { + std::cerr << "[WARNING] No object detected." << std::endl; + } + output_data_.resize(output_size); + std::copy_n( + output_tensor->mutable_data(), output_size, output_data_.data()); + + int out_bbox_num_size = 1; + for (int j = 0; j < out_bbox_num_shape.size(); ++j) { + out_bbox_num_size *= out_bbox_num_shape[j]; + } + out_bbox_num_data_.resize(out_bbox_num_size); + std::copy_n(out_bbox_num->mutable_data(), + out_bbox_num_size, + out_bbox_num_data_.data()); + } + // Postprocessing result + result->clear(); + if (config_.arch_ == "PicoDet") { + PPShiTu::PicoDetPostProcess( + result, output_data_list_, config_.fpn_stride_, + inputs_.im_shape_, inputs_.scale_factor_, + config_.nms_info_["score_threshold"].as(), + config_.nms_info_["nms_threshold"].as(), num_class, reg_max); + bbox_num->push_back(result->size()); + } else { + Postprocess(imgs, result, out_bbox_num_data_, is_rbox); + bbox_num->clear(); + for (int k = 0; k < out_bbox_num_data_.size(); k++) { + int tmp = out_bbox_num_data_[k]; + bbox_num->push_back(tmp); + } + } + auto postprocess_end = std::chrono::steady_clock::now(); + + std::chrono::duration preprocess_diff = + preprocess_end - preprocess_start; + times->push_back(double(preprocess_diff.count() * 1000)); + std::chrono::duration inference_diff = inference_end - inference_start; + times->push_back(double(inference_diff.count() / repeats * 1000)); + std::chrono::duration postprocess_diff = + postprocess_end - postprocess_start; + times->push_back(double(postprocess_diff.count() * 1000)); +} + +std::vector GenerateColorMap(int num_class) { + auto colormap = std::vector(3 * num_class, 0); + for (int i = 0; i < num_class; ++i) { + int j = 0; + int lab = i; + while (lab) { + colormap[i * 3] |= (((lab >> 0) & 1) << (7 - j)); + colormap[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j)); + colormap[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j)); + ++j; + lab >>= 3; + } + } + return colormap; +} + +} // namespace PPShiTu diff --git a/deploy/lite_shitu/src/picodet_postprocess.cc b/deploy/lite_shitu/src/picodet_postprocess.cc new file mode 100644 index 00000000..a77e0ee3 --- /dev/null +++ b/deploy/lite_shitu/src/picodet_postprocess.cc @@ -0,0 +1,128 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// The code is based on: +// https://github.com/RangiLyu/nanodet/blob/main/demo_mnn/nanodet_mnn.cpp + +#include "include/picodet_postprocess.h" + +namespace PPShiTu { + +float fast_exp(float x) { + union { + uint32_t i; + float f; + } v{}; + v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f); + return v.f; +} + +template +int activation_function_softmax(const _Tp *src, _Tp *dst, int length) { + const _Tp alpha = *std::max_element(src, src + length); + _Tp denominator{0}; + + for (int i = 0; i < length; ++i) { + dst[i] = fast_exp(src[i] - alpha); + denominator += dst[i]; + } + + for (int i = 0; i < length; ++i) { + dst[i] /= denominator; + } + + return 0; +} + +// PicoDet decode +PPShiTu::ObjectResult +disPred2Bbox(const float *&dfl_det, int label, float score, int x, int y, + int stride, std::vector im_shape, int reg_max) { + float ct_x = (x + 0.5) * stride; + float ct_y = (y + 0.5) * stride; + std::vector dis_pred; + dis_pred.resize(4); + for (int i = 0; i < 4; i++) { + float dis = 0; + float *dis_after_sm = new float[reg_max + 1]; + activation_function_softmax(dfl_det + i * (reg_max + 1), dis_after_sm, + reg_max + 1); + for (int j = 0; j < reg_max + 1; j++) { + dis += j * dis_after_sm[j]; + } + dis *= stride; + dis_pred[i] = dis; + delete[] dis_after_sm; + } + int xmin = (int)(std::max)(ct_x - dis_pred[0], .0f); + int ymin = (int)(std::max)(ct_y - dis_pred[1], .0f); + int xmax = (int)(std::min)(ct_x + dis_pred[2], (float)im_shape[0]); + int ymax = (int)(std::min)(ct_y + dis_pred[3], (float)im_shape[1]); + + PPShiTu::ObjectResult result_item; + result_item.rect = {xmin, ymin, xmax, ymax}; + result_item.class_id = label; + result_item.confidence = score; + + return result_item; +} + +void PicoDetPostProcess(std::vector *results, + std::vector outs, + std::vector fpn_stride, + std::vector im_shape, + std::vector scale_factor, float score_threshold, + float nms_threshold, int num_class, int reg_max) { + std::vector> bbox_results; + bbox_results.resize(num_class); + int in_h = im_shape[0], in_w = im_shape[1]; + for (int i = 0; i < fpn_stride.size(); ++i) { + int feature_h = ceil((float)in_h / fpn_stride[i]); + int feature_w = ceil((float)in_w / fpn_stride[i]); + for (int idx = 0; idx < feature_h * feature_w; idx++) { + const float *scores = outs[i] + (idx * num_class); + + int row = idx / feature_w; + int col = idx % feature_w; + float score = 0; + int cur_label = 0; + for (int label = 0; label < num_class; label++) { + if (scores[label] > score) { + score = scores[label]; + cur_label = label; + } + } + if (score > score_threshold) { + const float *bbox_pred = + outs[i + fpn_stride.size()] + (idx * 4 * (reg_max + 1)); + bbox_results[cur_label].push_back( + disPred2Bbox(bbox_pred, cur_label, score, col, row, fpn_stride[i], + im_shape, reg_max)); + } + } + } + for (int i = 0; i < (int)bbox_results.size(); i++) { + PPShiTu::nms(bbox_results[i], nms_threshold); + + for (auto box : bbox_results[i]) { + box.rect[0] = box.rect[0] / scale_factor[1]; + box.rect[2] = box.rect[2] / scale_factor[1]; + box.rect[1] = box.rect[1] / scale_factor[0]; + box.rect[3] = box.rect[3] / scale_factor[0]; + results->push_back(box); + } + } +} + +} // namespace PPShiTu diff --git a/deploy/lite_shitu/src/preprocess_op.cc b/deploy/lite_shitu/src/preprocess_op.cc new file mode 100644 index 00000000..90a54a1c --- /dev/null +++ b/deploy/lite_shitu/src/preprocess_op.cc @@ -0,0 +1,185 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include +#include +#include + +#include "include/preprocess_op.h" + +namespace PPShiTu { + +void InitInfo::Run(cv::Mat* im, ImageBlob* data) { + data->im_shape_ = {static_cast(im->rows), + static_cast(im->cols)}; + data->scale_factor_ = {1., 1.}; + data->in_net_shape_ = {static_cast(im->rows), + static_cast(im->cols)}; +} + +void NormalizeImage::Run(cv::Mat* im, ImageBlob* data) { + double e = 1.0; + if (is_scale_) { + e *= 1./255.0; + } + (*im).convertTo(*im, CV_32FC3, e); + for (int h = 0; h < im->rows; h++) { + for (int w = 0; w < im->cols; w++) { + im->at(h, w)[0] = + (im->at(h, w)[0] - mean_[0]) / scale_[0]; + im->at(h, w)[1] = + (im->at(h, w)[1] - mean_[1]) / scale_[1]; + im->at(h, w)[2] = + (im->at(h, w)[2] - mean_[2]) / scale_[2]; + } + } +} + +void Permute::Run(cv::Mat* im, ImageBlob* data) { + (*im).convertTo(*im, CV_32FC3); + int rh = im->rows; + int rw = im->cols; + int rc = im->channels(); + (data->im_data_).resize(rc * rh * rw); + float* base = (data->im_data_).data(); + for (int i = 0; i < rc; ++i) { + cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i); + } +} + +void Resize::Run(cv::Mat* im, ImageBlob* data) { + auto resize_scale = GenerateScale(*im); + data->im_shape_ = {static_cast(im->cols * resize_scale.first), + static_cast(im->rows * resize_scale.second)}; + data->in_net_shape_ = {static_cast(im->cols * resize_scale.first), + static_cast(im->rows * resize_scale.second)}; + cv::resize( + *im, *im, cv::Size(), resize_scale.first, resize_scale.second, interp_); + data->im_shape_ = { + static_cast(im->rows), static_cast(im->cols), + }; + data->scale_factor_ = { + resize_scale.second, resize_scale.first, + }; +} + +std::pair Resize::GenerateScale(const cv::Mat& im) { + std::pair resize_scale; + int origin_w = im.cols; + int origin_h = im.rows; + + if (keep_ratio_) { + int im_size_max = std::max(origin_w, origin_h); + int im_size_min = std::min(origin_w, origin_h); + int target_size_max = + *std::max_element(target_size_.begin(), target_size_.end()); + int target_size_min = + *std::min_element(target_size_.begin(), target_size_.end()); + float scale_min = + static_cast(target_size_min) / static_cast(im_size_min); + float scale_max = + static_cast(target_size_max) / static_cast(im_size_max); + float scale_ratio = std::min(scale_min, scale_max); + resize_scale = {scale_ratio, scale_ratio}; + } else { + resize_scale.first = + static_cast(target_size_[1]) / static_cast(origin_w); + resize_scale.second = + static_cast(target_size_[0]) / static_cast(origin_h); + } + return resize_scale; +} + +void PadStride::Run(cv::Mat* im, ImageBlob* data) { + if (stride_ <= 0) { + return; + } + int rc = im->channels(); + int rh = im->rows; + int rw = im->cols; + int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_; + int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_; + cv::copyMakeBorder( + *im, *im, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT, cv::Scalar(0)); + data->in_net_shape_ = { + static_cast(im->rows), static_cast(im->cols), + }; +} + +void TopDownEvalAffine::Run(cv::Mat* im, ImageBlob* data) { + cv::resize(*im, *im, cv::Size(trainsize_[0], trainsize_[1]), 0, 0, interp_); + // todo: Simd::ResizeBilinear(); + data->in_net_shape_ = { + static_cast(trainsize_[1]), static_cast(trainsize_[0]), + }; +} + +// Preprocessor op running order +const std::vector Preprocessor::RUN_ORDER = {"InitInfo", + "TopDownEvalAffine", + "Resize", + "NormalizeImage", + "PadStride", + "Permute"}; + +void Preprocessor::Run(cv::Mat* im, ImageBlob* data) { + for (const auto& name : RUN_ORDER) { + if (ops_.find(name) != ops_.end()) { + ops_[name]->Run(im, data); + } + } +} + +void CropImg(cv::Mat& img, + cv::Mat& crop_img, + std::vector& area, + std::vector& center, + std::vector& scale, + float expandratio) { + int crop_x1 = std::max(0, area[0]); + int crop_y1 = std::max(0, area[1]); + int crop_x2 = std::min(img.cols - 1, area[2]); + int crop_y2 = std::min(img.rows - 1, area[3]); + + int center_x = (crop_x1 + crop_x2) / 2.; + int center_y = (crop_y1 + crop_y2) / 2.; + int half_h = (crop_y2 - crop_y1) / 2.; + int half_w = (crop_x2 - crop_x1) / 2.; + + if (half_h * 3 > half_w * 4) { + half_w = static_cast(half_h * 0.75); + } else { + half_h = static_cast(half_w * 4 / 3); + } + + crop_x1 = + std::max(0, center_x - static_cast(half_w * (1 + expandratio))); + crop_y1 = + std::max(0, center_y - static_cast(half_h * (1 + expandratio))); + crop_x2 = std::min(img.cols - 1, + static_cast(center_x + half_w * (1 + expandratio))); + crop_y2 = std::min(img.rows - 1, + static_cast(center_y + half_h * (1 + expandratio))); + crop_img = + img(cv::Range(crop_y1, crop_y2 + 1), cv::Range(crop_x1, crop_x2 + 1)); + + center.clear(); + center.emplace_back((crop_x1 + crop_x2) / 2); + center.emplace_back((crop_y1 + crop_y2) / 2); + scale.clear(); + scale.emplace_back((crop_x2 - crop_x1)); + scale.emplace_back((crop_y2 - crop_y1)); +} + +} // namespace PPShiTu diff --git a/deploy/lite_shitu/src/recognition.cc b/deploy/lite_shitu/src/recognition.cc new file mode 100644 index 00000000..1411fa0f --- /dev/null +++ b/deploy/lite_shitu/src/recognition.cc @@ -0,0 +1,143 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/recognition.h" + +namespace PPShiTu { +std::vector Recognition::RunRecModel(const cv::Mat &img, + double &const_time) { + + // Read img + cv::Mat resize_image = ResizeImage(img); + + cv::Mat img_fp; + resize_image.convertTo(img_fp, CV_32FC3, scale); + + // Prepare input data from image + std::unique_ptr input_tensor(std::move(predictor->GetInput(0))); + input_tensor->Resize({1, 3, img_fp.rows, img_fp.cols}); + auto *data0 = input_tensor->mutable_data(); + + const float *dimg = reinterpret_cast(img_fp.data); + NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols); + + auto start = std::chrono::system_clock::now(); + // Run predictor + predictor->Run(); + + // Get output and post process + std::unique_ptr output_tensor( + std::move(predictor->GetOutput(0))); + auto *output_data = output_tensor->data(); + auto end = std::chrono::system_clock::now(); + auto duration = + std::chrono::duration_cast(end - start); + cost_time = double(duration.count()) * + std::chrono::microseconds::period::num / + std::chrono::microseconds::period::den; + + int output_size = 1; + for (auto dim : output_tensor->shape()) { + output_size *= dim; + } + + cv::Mat output_image; + auto results = PostProcess(output_data, output_size, output_image); + return results; +} + +void Recognition::NeonMeanScale(const float *din, float *dout, int size) { + + if (mean.size() != 3 || scale.size() != 3) { + std::cerr << "[ERROR] mean or scale size must equal to 3\n"; + exit(1); + } + float32x4_t vmean0 = vdupq_n_f32(mean[0]); + float32x4_t vmean1 = vdupq_n_f32(mean[1]); + float32x4_t vmean2 = vdupq_n_f32(mean[2]); + float32x4_t vscale0 = vdupq_n_f32(std[0]); + float32x4_t vscale1 = vdupq_n_f32(std[1]); + float32x4_t vscale2 = vdupq_n_f32(std[2]); + + float *dout_c0 = dout; + float *dout_c1 = dout + size; + float *dout_c2 = dout + size * 2; + + int i = 0; + for (; i < size - 3; i += 4) { + float32x4x3_t vin3 = vld3q_f32(din); + float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0); + float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1); + float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2); + float32x4_t vs0 = vmulq_f32(vsub0, vscale0); + float32x4_t vs1 = vmulq_f32(vsub1, vscale1); + float32x4_t vs2 = vmulq_f32(vsub2, vscale2); + vst1q_f32(dout_c0, vs0); + vst1q_f32(dout_c1, vs1); + vst1q_f32(dout_c2, vs2); + + din += 12; + dout_c0 += 4; + dout_c1 += 4; + dout_c2 += 4; + } + for (; i < size; i++) { + *(dout_c0++) = (*(din++) - mean[0]) * std[0]; + *(dout_c1++) = (*(din++) - mean[1]) * std[1]; + *(dout_c2++) = (*(din++) - mean[2]) * std[2]; + } +} + +cv::Mat Recognition::ResizeImage(const cv::Mat &img) { + cv::Mat resize_img; + cv::resize(img, resize_img, cv::Size(size, size)); + return resize_img; +} +std::vector Recognition::PostProcess(const float *output_data, + int output_size, + cv::Mat &output_image) { + + int max_indices[topk]; + double max_scores[topk]; + for (int i = 0; i < topk; i++) { + max_indices[i] = 0; + max_scores[i] = 0; + } + for (int i = 0; i < output_size; i++) { + float score = output_data[i]; + int index = i; + for (int j = 0; j < topk; j++) { + if (score > max_scores[j]) { + index += max_indices[j]; + max_indices[j] = index - max_indices[j]; + index -= max_indices[j]; + score += max_scores[j]; + max_scores[j] = score - max_scores[j]; + score -= max_scores[j]; + } + } + } + + std::vector results(topk); + for (int i = 0; i < results.size(); i++) { + results[i].class_name = "Unknown"; + if (max_indices[i] >= 0 && max_indices[i] < label_list.size()) { + results[i].class_name = label_list[max_indices[i]]; + } + results[i].score = max_scores[i]; + results[i].class_id = max_indices[i]; + } + return results; +} +} diff --git a/deploy/lite_shitu/src/utils.cc b/deploy/lite_shitu/src/utils.cc new file mode 100644 index 00000000..88b2c563 --- /dev/null +++ b/deploy/lite_shitu/src/utils.cc @@ -0,0 +1,49 @@ +// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/utils.h" + +namespace PPShiTu { + +void nms(std::vector &input_boxes, float nms_threshold) { + std::sort(input_boxes.begin(), + input_boxes.end(), + [](ObjectResult a, ObjectResult b) { return a.confidence > b.confidence; }); + std::vector 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); + } + for (int i = 0; i < int(input_boxes.size()); ++i) { + for (int j = i + 1; j < int(input_boxes.size());) { + float xx1 = (std::max)(input_boxes[i].rect[0], input_boxes[j].rect[0]); + float yy1 = (std::max)(input_boxes[i].rect[1], input_boxes[j].rect[1]); + float xx2 = (std::min)(input_boxes[i].rect[2], input_boxes[j].rect[2]); + float yy2 = (std::min)(input_boxes[i].rect[3], input_boxes[j].rect[3]); + float w = (std::max)(float(0), xx2 - xx1 + 1); + float h = (std::max)(float(0), yy2 - yy1 + 1); + 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++; + } + } + } +} + +} // namespace PPShiTu -- GitLab