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未验证 提交 b918067c 编写于 作者: Y yiicy 提交者： GitHub
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add yolov3 demo, test=develop (#2731)

上级 8fef7532
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Showing with 433 addition and 67 deletion
lite/CMakeLists.txt
浏览文件 @ b918067c
......@@ -220,8 +220,10 @@ if (LITE_WITH_LIGHT_WEIGHT_FRAMEWORK AND LITE_WITH_ARM)
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_full/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_full/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_light" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_light/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_light/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/ssd_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/ssd_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/ssd_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/yolov3_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/yolov3_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/yolov3_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_classify" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_classify/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_classify/Makefile"
)
......@@ -235,8 +237,10 @@ if (LITE_WITH_LIGHT_WEIGHT_FRAMEWORK AND LITE_WITH_ARM)
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/README.md" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_light" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_light/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_light/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/ssd_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/ssd_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/ssd_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/yolov3_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/yolov3_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/yolov3_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_classify" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_classify/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_classify/Makefile"
)
......
lite/demo/cxx/README.md
浏览文件 @ b918067c
# C++ Demo
1. 使用`lite/tools/Dockerfile.mobile`生成docker镜像
2. 运行并进入docker镜像环境，执行`wget http://paddle-inference-dist.bj.bcebos.com/lite_release/v2.1.0/inference_lite_lib.android.armv8.tar.gz `下载所需demo环境。(armv7 demo可使用命令`wget http://paddle-inference-dist.bj.bcebos.com/lite_release/v2.1.0/inference_lite_lib.android.armv7.tar.gz` 进行下载)。
3. 解压下载文件`tar zxvf inference_lite_lib.android.armv8.tar.gz `
4. 执行以下命令准备模拟器环境
```shell
# armv8
adb kill-server
adb devices | grep emulator | cut -f1 | while read line; do adb -s $line emu kill; done
echo n | avdmanager create avd -f -n paddle-armv8 -k "system-images;android-24;google_apis;arm64-v8a"
echo -ne '\n' | ${ANDROID_HOME}/emulator/emulator -avd paddle-armv8 -noaudio -no-window -gpu off -port 5554 &
sleep 1m
```
```shell
# armv7
adb kill-server
adb devices | grep emulator | cut -f1 | while read line; do adb -s $line emu kill; done
echo n | avdmanager create avd -f -n paddle-armv7 -k "system-images;android-24;google_apis;armeabi-v7a"
echo -ne '\n' | ${ANDROID_HOME}/emulator/emulator -avd paddle-armv7 -noaudio -no-window -gpu off -port 5554 &
sleep 1m
```
5. 准备模型、编译并运行完整api的demo
1. 环境准备
- 保证Android NDK在/opt目录下
- 一台armv7或armv8架构的安卓手机
2. 编译并运行全量api的demo(注：当编译模式为tiny_pubish时将不存在该demo)
```shell
cd inference_lite_lib.android.armv8/demo/cxx/mobile_full
wget http://paddle-inference-dist.bj.bcebos.com/mobilenet_v1.tar.gz
tar zxvf mobilenet_v1.tar.gz
make
adb -s emulator-5554 push mobilenet_v1 /data/local/tmp/
adb -s emulator-5554 push mobilenetv1_full_api /data/local/tmp/
adb -s emulator-5554 push ../../../cxx/lib/libpaddle_full_api_shared.so /data/local/tmp/
adb -s emulator-5554 shell chmod +x /data/local/tmp/mobilenetv1_full_api
adb -s emulator-5554 shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
adb push mobilenet_v1 /data/local/tmp/
adb push mobilenetv1_full_api /data/local/tmp/
adb push ../../../cxx/lib/libpaddle_full_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/mobilenetv1_full_api
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
/data/local/tmp/mobilenetv1_full_api --model_dir=/data/local/tmp/mobilenet_v1 --optimized_model_dir=/data/local/tmp/mobilenet_v1.opt"
```
运行成功将在控制台输出预测结果的前10个类别的预测概率
6. 编译并运行轻量级api的demo
3. 编译并运行轻量级api的demo
```shell
cd ../mobile_light
make
adb -s emulator-5554 push mobilenetv1_light_api /data/local/tmp/
adb -s emulator-5554 push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb -s emulator-5554 shell chmod +x /data/local/tmp/mobilenetv1_light_api
adb -s emulator-5554 shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
adb push mobilenetv1_light_api /data/local/tmp/
adb push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/mobilenetv1_light_api
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
/data/local/tmp/mobilenetv1_light_api /data/local/tmp/mobilenet_v1.opt"
```
运行成功将在控制台输出预测结果的前10个类别的预测概率
7. 编译并运行目标检测的demo
4. 编译并运行ssd目标检测的demo
```shell
cd ../mobile_detection
cd ../ssd_detection
wget https://paddle-inference-dist.bj.bcebos.com/mobilenetv1-ssd.tar.gz
tar zxvf mobilenetv1-ssd.tar.gz
make
adb -s emulator-5554 push mobile_detection /data/local/tmp/
adb -s emulator-5554 push test.jpg /data/local/tmp/
adb -s emulator-5554 push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb -s emulator-5554 shell chmod +x /data/local/tmp/mobile_detection
adb -s emulator-5554 shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
/data/local/tmp/mobile_detection /data/local/tmp/mobilenetv1-ssd /data/local/tmp/test.jpg"
adb -s emulator-5554 pull /data/local/tmp/test_detection_result.jpg ./
adb push ssd_detection /data/local/tmp/
adb push test.jpg /data/local/tmp/
adb push mobilenetv1-ssd /data/local/tmp
adb push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/ssd_detection
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
/data/local/tmp/ssd_detection /data/local/tmp/mobilenetv1-ssd /data/local/tmp/test.jpg"
adb pull /data/local/tmp/test_ssd_detection_result.jpg ./
```
运行成功将在ssd_detection目录下看到生成的目标检测结果图像: test_ssd_detection_result.jpg
5. 编译并运行yolov3目标检测的demo
```shell
cd ../yolov3_detection
wget https://paddle-inference-dist.bj.bcebos.com/mobilenetv1-yolov3.tar.gz
tar zxvf mobilenetv1-yolov3.tar.gz
make
adb push yolov3_detection /data/local/tmp/
adb push test.jpg /data/local/tmp/
adb push mobilenetv1-yolov3 /data/local/tmp
adb push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/yolov3_detection
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
/data/local/tmp/yolov3_detection /data/local/tmp/mobilenetv1-yolov3 /data/local/tmp/test.jpg"
adb pull /data/local/tmp/test_yolov3_detection_result.jpg ./
```
运行成功将在mobile_detection目录下看到生成的目标检测结果图像: test_detection_result.jpg
运行成功将在yolov3_detection目录下看到生成的目标检测结果图像: test_yolov3_detection_result.jpg
8. 编译并运行物体分类的demo
6. 编译并运行物体分类的demo
```shell
cd ../mobile_classify
wget http://paddle-inference-dist.bj.bcebos.com/mobilenet_v1.tar.gz
tar zxvf mobilenet_v1.tar.gz
make
adb -s emulator-5554 push mobile_classify /data/local/tmp/
adb -s emulator-5554 push test.jpg /data/local/tmp/
adb -s emulator-5554 push labels.txt /data/local/tmp/
adb -s emulator-5554 push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb -s emulator-5554 shell chmod +x /data/local/tmp/mobile_classify
adb -s emulator-5554 shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
adb push mobile_classify /data/local/tmp/
adb push test.jpg /data/local/tmp/
adb push labels.txt /data/local/tmp/
adb push ../../../cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/
adb shell chmod +x /data/local/tmp/mobile_classify
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
/data/local/tmp/mobile_classify /data/local/tmp/mobilenet_v1 /data/local/tmp/test.jpg /data/local/tmp/labels.txt"
```
运行成功将在控制台输出预测结果的前5个类别的预测概率
- 如若想看前10个类别的预测概率，在运行命令输入topk的值即可
eg:
```shell
adb -s emulator-5554 shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
/data/local/tmp/mobile_classify /data/local/tmp/mobilenet_v1 /data/local/tmp/test.jpg /data/local/tmp/labels.txt 10"
```
- 如若想看其他模型的分类结果， 在运行命令输入model_dir 及其model的输入大小即可
eg:
```shell
adb -s emulator-5554 shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
adb shell "export LD_LIBRARY_PATH=/data/local/tmp/:$LD_LIBRARY_PATH &&
/data/local/tmp/mobile_classify /data/local/tmp/mobilenet_v2 /data/local/tmp/test.jpg /data/local/tmp/labels.txt 10 224 224"
```
lite/demo/cxx/makefiles/mobile_detection/Makefile.android.armv7 → lite/demo/cxx/makefiles/ssd_detection/Makefile.android.armv7
浏览文件 @ b918067c
......@@ -40,11 +40,11 @@ CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SY
#CXX_LIBS = $(LITE_ROOT)/cxx/lib/libpaddle_api_light_bundled.a $(SYSTEM_LIBS)
mobile_detection: fetch_opencv mobile_detection.o
$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) mobile_detection.o -o mobile_detection $(CXX_LIBS) $(LDFLAGS)
ssd_detection: fetch_opencv ssd_detection.o
$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) ssd_detection.o -o ssd_detection $(CXX_LIBS) $(LDFLAGS)
mobile_detection.o: mobile_detection.cc
$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o mobile_detection.o -c mobile_detection.cc
ssd_detection.o: ssd_detection.cc
$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o ssd_detection.o -c ssd_detection.cc
fetch_opencv:
@ test -d ${THIRD_PARTY_DIR} || mkdir ${THIRD_PARTY_DIR}
......@@ -57,5 +57,5 @@ fetch_opencv:
.PHONY: clean
clean:
rm -f mobile_detection.o
rm -f mobile_detection
rm -f ssd_detection.o
rm -f ssd_detection
lite/demo/cxx/makefiles/mobile_detection/Makefile.android.armv8 → lite/demo/cxx/makefiles/ssd_detection/Makefile.android.armv8
浏览文件 @ b918067c
......@@ -40,11 +40,11 @@ CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SY
#CXX_LIBS = $(LITE_ROOT)/cxx/lib/libpaddle_api_light_bundled.a $(SYSTEM_LIBS)
mobile_detection: fetch_opencv mobile_detection.o
$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) mobile_detection.o -o mobile_detection $(CXX_LIBS) $(LDFLAGS)
ssd_detection: fetch_opencv ssd_detection.o
$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) ssd_detection.o -o ssd_detection $(CXX_LIBS) $(LDFLAGS)
mobile_detection.o: mobile_detection.cc
$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o mobile_detection.o -c mobile_detection.cc
ssd_detection.o: ssd_detection.cc
$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o ssd_detection.o -c ssd_detection.cc
fetch_opencv:
@ test -d ${THIRD_PARTY_DIR} || mkdir ${THIRD_PARTY_DIR}
......@@ -57,5 +57,5 @@ fetch_opencv:
.PHONY: clean
clean:
rm -f mobile_detection.o
rm -f mobile_detection
rm -f ssd_detection.o
rm -f ssd_detection
lite/demo/cxx/makefiles/yolov3_detection/Makefile.android.armv7 0 → 100644
浏览文件 @ b918067c
ARM_ABI = arm7
export ARM_ABI
include ../Makefile.def
LITE_ROOT=../../../
THIRD_PARTY_DIR=${LITE_ROOT}/third_party
OPENCV_VERSION=opencv4.1.0
OPENCV_LIBS = ../../../third_party/${OPENCV_VERSION}/armeabi-v7a/libs/libopencv_imgcodecs.a \
../../../third_party/${OPENCV_VERSION}/armeabi-v7a/libs/libopencv_imgproc.a \
../../../third_party/${OPENCV_VERSION}/armeabi-v7a/libs/libopencv_core.a \
../../../third_party/${OPENCV_VERSION}/armeabi-v7a/3rdparty/libs/libtegra_hal.a \
../../../third_party/${OPENCV_VERSION}/armeabi-v7a/3rdparty/libs/liblibjpeg-turbo.a \
../../../third_party/${OPENCV_VERSION}/armeabi-v7a/3rdparty/libs/liblibwebp.a \
../../../third_party/${OPENCV_VERSION}/armeabi-v7a/3rdparty/libs/liblibpng.a \
../../../third_party/${OPENCV_VERSION}/armeabi-v7a/3rdparty/libs/liblibjasper.a \
../../../third_party/${OPENCV_VERSION}/armeabi-v7a/3rdparty/libs/liblibtiff.a \
../../../third_party/${OPENCV_VERSION}/armeabi-v7a/3rdparty/libs/libIlmImf.a \
../../../third_party/${OPENCV_VERSION}/armeabi-v7a/3rdparty/libs/libtbb.a \
../../../third_party/${OPENCV_VERSION}/armeabi-v7a/3rdparty/libs/libcpufeatures.a
OPENCV_INCLUDE = -I../../../third_party/${OPENCV_VERSION}/armeabi-v7a/include
CXX_INCLUDES = $(INCLUDES) ${OPENCV_INCLUDE} -I$(LITE_ROOT)/cxx/include
CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SYSTEM_LIBS)
###############################################################
# 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`
#CXX_LIBS = $(LITE_ROOT)/cxx/lib/libpaddle_api_light_bundled.a $(SYSTEM_LIBS)
yolov3_detection: fetch_opencv yolov3_detection.o
$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) yolov3_detection.o -o yolov3_detection $(CXX_LIBS) $(LDFLAGS)
yolov3_detection.o: yolov3_detection.cc
$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o yolov3_detection.o -c yolov3_detection.cc
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 -zxvf ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz -C ${THIRD_PARTY_DIR}
.PHONY: clean
clean:
rm -f yolov3_detection.o
rm -f yolov3_detection
lite/demo/cxx/makefiles/yolov3_detection/Makefile.android.armv8 0 → 100644
浏览文件 @ b918067c
ARM_ABI = arm8
export ARM_ABI
include ../Makefile.def
LITE_ROOT=../../../
THIRD_PARTY_DIR=${LITE_ROOT}/third_party
OPENCV_VERSION=opencv4.1.0
OPENCV_LIBS = ../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgcodecs.a \
../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgproc.a \
../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_core.a \
../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtegra_hal.a \
../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjpeg-turbo.a \
../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibwebp.a \
../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibpng.a \
../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjasper.a \
../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibtiff.a \
../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libIlmImf.a \
../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtbb.a \
../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libcpufeatures.a
OPENCV_INCLUDE = -I../../../third_party/${OPENCV_VERSION}/arm64-v8a/include
CXX_INCLUDES = $(INCLUDES) ${OPENCV_INCLUDE} -I$(LITE_ROOT)/cxx/include
CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SYSTEM_LIBS)
###############################################################
# 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`
#CXX_LIBS = $(LITE_ROOT)/cxx/lib/libpaddle_api_light_bundled.a $(SYSTEM_LIBS)
yolov3_detection: fetch_opencv yolov3_detection.o
$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) yolov3_detection.o -o yolov3_detection $(CXX_LIBS) $(LDFLAGS)
yolov3_detection.o: yolov3_detection.cc
$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o yolov3_detection.o -c yolov3_detection.cc
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 -zxvf ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz -C ${THIRD_PARTY_DIR}
.PHONY: clean
clean:
rm -f yolov3_detection.o
rm -f yolov3_detection
lite/demo/cxx/mobile_detection/mobile_detection.cc → lite/demo/cxx/ssd_detection/ssd_detection.cc
浏览文件 @ b918067c
......@@ -194,7 +194,7 @@ void RunModel(std::string model_dir, std::string img_path) {
}
auto rec_out = detect_object(outptr, static_cast<int>(cnt / 6), 0.6f, img);
std::string result_name =
img_path.substr(0, img_path.find(".")) + "_detection_result.jpg";
img_path.substr(0, img_path.find(".")) + "_ssd_detection_result.jpg";
cv::imwrite(result_name, img);
}
......
lite/demo/cxx/yolov3_detection/yolov3_detection.cc 0 → 100644
浏览文件 @ b918067c
// Copyright (c) 2019 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 <iostream>
#include <vector>
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "paddle_api.h" // NOLINT
using namespace paddle::lite_api; // NOLINT
struct Object {
cv::Rect rec;
int class_id;
float prob;
};
int64_t ShapeProduction(const shape_t& shape) {
int64_t res = 1;
for (auto i : shape) res *= i;
return res;
}
const char* class_names[] = {"person", "bicycle", "car",
"motorcycle", "airplane", "bus",
"train", "truck", "boat",
"traffic light", "fire hydrant", "stop sign",
"parking meter", "bench", "bird",
"cat", "dog", "horse",
"sheep", "cow", "elephant",
"bear", "zebra", "giraffe",
"backpack", "umbrella", "handbag",
"tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball",
"kite", "baseball bat", "baseball glove",
"skateboard", "surfboard", "tennis racket",
"bottle", "wine glass", "cup",
"fork", "knife", "spoon",
"bowl", "banana", "apple",
"sandwich", "orange", "broccoli",
"carrot", "hot dog", "pizza",
"donut", "cake", "chair",
"couch", "potted plant", "bed",
"dining table", "toilet", "tv",
"laptop", "mouse", "remote",
"keyboard", "cell phone", "microwave",
"oven", "toaster", "sink",
"refrigerator", "book", "clock",
"vase", "scissors", "teddy bear",
"hair drier", "toothbrush"};
// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
void neon_mean_scale(const float* din,
float* dout,
int size,
const std::vector<float> mean,
const std::vector<float> scale) {
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(1.f / scale[0]);
float32x4_t vscale1 = vdupq_n_f32(1.f / scale[1]);
float32x4_t vscale2 = vdupq_n_f32(1.f / scale[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]) * scale[0];
*(dout_c0++) = (*(din++) - mean[1]) * scale[1];
*(dout_c0++) = (*(din++) - mean[2]) * scale[2];
}
}
void pre_process(const cv::Mat& img, int width, int height, float* data) {
cv::Mat rgb_img;
cv::cvtColor(img, rgb_img, cv::COLOR_BGR2RGB);
cv::resize(
rgb_img, rgb_img, cv::Size(width, height), 0.f, 0.f, cv::INTER_CUBIC);
cv::Mat imgf;
rgb_img.convertTo(imgf, CV_32FC3, 1 / 255.f);
std::vector<float> mean = {0.485f, 0.456f, 0.406f};
std::vector<float> scale = {0.229f, 0.224f, 0.225f};
const float* dimg = reinterpret_cast<const float*>(imgf.data);
neon_mean_scale(dimg, data, width * height, mean, scale);
}
std::vector<Object> detect_object(const float* data,
int count,
float thresh,
cv::Mat& image) { // NOLINT
if (data == nullptr) {
std::cerr << "[ERROR] data can not be nullptr\n";
exit(1);
}
std::vector<Object> rect_out;
for (int iw = 0; iw < count; iw++) {
int oriw = image.cols;
int orih = image.rows;
if (data[1] > thresh) {
Object obj;
int x = static_cast<int>(data[2]);
int y = static_cast<int>(data[3]);
int w = static_cast<int>(data[4] - data[2] + 1);
int h = static_cast<int>(data[5] - data[3] + 1);
cv::Rect rec_clip =
cv::Rect(x, y, w, h) & cv::Rect(0, 0, image.cols, image.rows);
obj.class_id = static_cast<int>(data[0]);
obj.prob = data[1];
obj.rec = rec_clip;
if (w > 0 && h > 0 && obj.prob <= 1) {
rect_out.push_back(obj);
cv::rectangle(image, rec_clip, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
std::string str_prob = std::to_string(obj.prob);
std::string text = std::string(class_names[obj.class_id]) + ": " +
str_prob.substr(0, str_prob.find(".") + 4);
int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL;
double font_scale = 1.f;
int thickness = 1;
cv::Size text_size =
cv::getTextSize(text, font_face, font_scale, thickness, nullptr);
float new_font_scale = w * 0.5 * font_scale / text_size.width;
text_size = cv::getTextSize(
text, font_face, new_font_scale, thickness, nullptr);
cv::Point origin;
origin.x = x + 3;
origin.y = y + text_size.height + 3;
cv::putText(image,
text,
origin,
font_face,
new_font_scale,
cv::Scalar(0, 255, 255),
thickness,
cv::LINE_AA);
std::cout << "detection, image size: " << image.cols << ", "
<< image.rows
<< ", detect object: " << class_names[obj.class_id]
<< ", score: " << obj.prob << ", location: x=" << x
<< ", y=" << y << ", width=" << w << ", height=" << h
<< std::endl;
}
}
data += 6;
}
return rect_out;
}
void RunModel(std::string model_dir, std::string img_path) {
// 1. Set MobileConfig
MobileConfig config;
config.set_model_dir(model_dir);
// 2. Create PaddlePredictor by MobileConfig
std::shared_ptr<PaddlePredictor> predictor =
CreatePaddlePredictor<MobileConfig>(config);
const int in_width = 608;
const int in_height = 608;
// 3. Prepare input data from image
// input 0
std::unique_ptr<Tensor> input_tensor0(std::move(predictor->GetInput(0)));
input_tensor0->Resize({1, 3, in_height, in_width});
auto* data0 = input_tensor0->mutable_data<float>();
cv::Mat img = imread(img_path, cv::IMREAD_COLOR);
pre_process(img, in_width, in_height, data0);
// input1
std::unique_ptr<Tensor> input_tensor1(std::move(predictor->GetInput(1)));
input_tensor1->Resize({1, 2});
auto* data1 = input_tensor1->mutable_data<int>();
data1[0] = img.rows;
data1[1] = img.cols;
// 4. Run predictor
predictor->Run();
// 5. Get output and post process
std::unique_ptr<const Tensor> output_tensor(
std::move(predictor->GetOutput(0)));
auto* outptr = output_tensor->data<float>();
auto shape_out = output_tensor->shape();
int64_t cnt = 1;
for (auto& i : shape_out) {
cnt *= i;
}
auto rec_out = detect_object(outptr, static_cast<int>(cnt / 6), 0.5f, img);
std::string result_name =
img_path.substr(0, img_path.find(".")) + "_yolov3_detection_result.jpg";
cv::imwrite(result_name, img);
}
int main(int argc, char** argv) {
if (argc < 3) {
std::cerr << "[ERROR] usage: " << argv[0] << " model_dir image_path\n";
exit(1);
}
std::string model_dir = argv[1];
std::string img_path = argv[2];
RunModel(model_dir, img_path);
return 0;
}
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