[PaddlePaddle Hackathon4 No.186] Add PaddleDetection Models Deployment Go Examples

examples/application/go/ppyoloe/README.md

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+English | [简体中文](README_CN.md)
+# PaddleDetection Golang Deployment Example
+
+This directory provides examples that `infer.go` uses CGO to call FastDeploy C API and fast finish the deployment of PaddleDetection models, including PPYOLOE on CPU/GPU.
+
+Before deployment, two steps require confirmation
+
+- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
+- 2.  Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
+
+Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.4 above (x.x.x>1.0.4) or develop version (x.x.x=0.0.0) is required to support this model.
+
+### Use Golang and CGO to deploy PPYOLOE model
+
+Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above.
+```bash
+wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
+tar xvf fastdeploy-linux-x64-0.0.0.tgz
+```
+
+Copy FastDeploy C APIs from precompiled library to the current directory.
+```bash
+cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
+```
+
+Download the PPYOLOE model file and test images.
+```bash
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+tar xvf ppyoloe_crn_l_300e_coco.tgz
+```
+
+Configure the `cgo CFLAGS: -I` to FastDeploy C API directory path and  the `cgo LDFLAGS: -L` to FastDeploy dynamic library path. The FastDeploy dynamic library is located in the `/lib` directory.
+```bash
+cgo CFLAGS: -I./fastdeploy_capi
+cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
+```
+
+Use the following command to add Fastdeploy library path to the environment variable.
+```bash
+source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh 
+```
+
+Compile the Go file `infer.go`.
+```bash
+go build infer.go
+```
+
+After compiling, use the following command to obtain the predicted results.
+```bash
+# CPU inference
+./infer -model ./ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device 0
+# GPU inference
+./infer -model ./ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device 1
+```
+
+Then visualized inspection result is saved in the local image `vis_result.jpg`.

examples/application/go/ppyoloe/README_CN.md

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+[English](README.md) | 简体中文
+# PaddleDetection Golang 部署示例
+
+本目录下提供`infer.go`, 使用CGO调用FastDeploy C API快速完成PaddleDetection模型PPYOLOE在CPU/GPU上部署的示例
+
+在部署前，需确认以下两个步骤
+
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>1.0.4)或FastDeploy的Develop版本(x.x.x=0.0.0)
+### 使用Golang和CGO工具进行PPYOLOE模型推理部署
+
+在当前目录下，下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+```bash
+wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
+tar xvf fastdeploy-linux-x64-0.0.0.tgz
+```
+
+将FastDeploy C API文件拷贝至当前目录
+```bash
+cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
+```
+
+下载PPYOLOE模型文件和测试图片
+```bash
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+tar xvf ppyoloe_crn_l_300e_coco.tgz
+```
+
+配置`infer.go`中的`cgo CFLAGS: -I`参数配置为C API文件路径，`cgo LDFLAGS: -L`参数配置为FastDeploy的动态库路径，动态库位于预编译库的`/lib`目录中
+```bash
+cgo CFLAGS: -I./fastdeploy_capi
+cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
+```
+
+将FastDeploy的库路径添加到环境变量
+```bash
+source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh 
+```
+
+编译Go文件`infer.go`
+```bash
+go build infer.go
+```
+
+编译完成后，使用如下命令执行可得到预测结果
+```bash
+# CPU推理
+./infer -model ./ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device 0
+# GPU推理
+./infer -model ./ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device 1
+```
+
+可视化的检测结果图片保存在本地`vis_result.jpg`

examples/application/go/ppyoloe/infer.go

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+// Copyright (c) 2023 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.
+
+package main
+
+// #cgo CFLAGS:  -I./fastdeploy_capi
+// #cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
+// #include <fastdeploy_capi/vision.h>
+// #include <stdio.h>
+// #include <stdbool.h>
+// #include <stdlib.h>
+/*
+#include <stdio.h>
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+char* GetModelFilePath(char* model_dir, char* model_file, int max_size){
+    snprintf(model_file, max_size, "%s%c%s", model_dir, sep, "model.pdmodel");
+    return model_file;
+}
+
+char* GetParametersFilePath(char* model_dir, char* params_file, int max_size){
+    snprintf(params_file, max_size, "%s%c%s", model_dir, sep, "model.pdiparams");
+    return params_file;
+}
+
+char* GetConfigFilePath(char* model_dir, char* config_file, int max_size){
+    snprintf(config_file, max_size, "%s%c%s", model_dir, sep, "infer_cfg.yml");
+    return config_file;
+}
+*/
+import "C"
+import (
+	"flag"
+	"fmt"
+	"unsafe"
+)
+
+func FDBooleanToGo(b C.FD_C_Bool) bool {
+	var cFalse C.FD_C_Bool
+	if b != cFalse {
+		return true
+	}
+	return false
+}
+
+func CpuInfer(modelDir *C.char, imageFile *C.char) {
+
+	var modelFile = (*C.char)(C.malloc(C.size_t(100)))
+	var paramsFile = (*C.char)(C.malloc(C.size_t(100)))
+	var configFile = (*C.char)(C.malloc(C.size_t(100)))
+	var maxSize = 99
+
+	modelFile = C.GetModelFilePath(modelDir, modelFile, C.int(maxSize))
+	paramsFile = C.GetParametersFilePath(modelDir, paramsFile, C.int(maxSize))
+	configFile = C.GetConfigFilePath(modelDir, configFile, C.int(maxSize))
+
+	var option *C.FD_C_RuntimeOptionWrapper = C.FD_C_CreateRuntimeOptionWrapper()
+	C.FD_C_RuntimeOptionWrapperUseCpu(option)
+
+	var model *C.FD_C_PPYOLOEWrapper = C.FD_C_CreatePPYOLOEWrapper(
+		modelFile, paramsFile, configFile, option, C.FD_C_ModelFormat_PADDLE)
+
+	if !FDBooleanToGo(C.FD_C_PPYOLOEWrapperInitialized(model)) {
+		fmt.Printf("Failed to initialize.\n")
+		C.FD_C_DestroyRuntimeOptionWrapper(option)
+		C.FD_C_DestroyPPYOLOEWrapper(model)
+		return
+	}
+
+	var image C.FD_C_Mat = C.FD_C_Imread(imageFile)
+
+	var result *C.FD_C_DetectionResult = C.FD_C_CreateDetectionResult()
+
+	if !FDBooleanToGo(C.FD_C_PPYOLOEWrapperPredict(model, image, result)) {
+		fmt.Printf("Failed to predict.\n")
+		C.FD_C_DestroyRuntimeOptionWrapper(option)
+		C.FD_C_DestroyPPYOLOEWrapper(model)
+		C.FD_C_DestroyMat(image)
+		C.free(unsafe.Pointer(result))
+		return
+	}
+
+	var visImage C.FD_C_Mat = C.FD_C_VisDetection(image, result, 0.5, 1, 0.5)
+
+	C.FD_C_Imwrite(C.CString("vis_result.jpg"), visImage)
+	fmt.Printf("Visualized result saved in ./vis_result.jpg\n")
+
+	C.FD_C_DestroyRuntimeOptionWrapper(option)
+	C.FD_C_DestroyPPYOLOEWrapper(model)
+	C.FD_C_DestroyDetectionResult(result)
+	C.FD_C_DestroyMat(image)
+	C.FD_C_DestroyMat(visImage)
+}
+
+func GpuInfer(modelDir *C.char, imageFile *C.char) {
+
+	var modelFile = (*C.char)(C.malloc(C.size_t(100)))
+	var paramsFile = (*C.char)(C.malloc(C.size_t(100)))
+	var configFile = (*C.char)(C.malloc(C.size_t(100)))
+	var maxSize = 99
+
+	modelFile = C.GetModelFilePath(modelDir, modelFile, C.int(maxSize))
+	paramsFile = C.GetParametersFilePath(modelDir, paramsFile, C.int(maxSize))
+	configFile = C.GetConfigFilePath(modelDir, configFile, C.int(maxSize))
+
+	var option *C.FD_C_RuntimeOptionWrapper = C.FD_C_CreateRuntimeOptionWrapper()
+	C.FD_C_RuntimeOptionWrapperUseGpu(option, 0)
+
+	var model *C.FD_C_PPYOLOEWrapper = C.FD_C_CreatePPYOLOEWrapper(
+		modelFile, paramsFile, configFile, option, C.FD_C_ModelFormat_PADDLE)
+
+	if !FDBooleanToGo(C.FD_C_PPYOLOEWrapperInitialized(model)) {
+		fmt.Printf("Failed to initialize.\n")
+		C.FD_C_DestroyRuntimeOptionWrapper(option)
+		C.FD_C_DestroyPPYOLOEWrapper(model)
+		return
+	}
+
+	var image C.FD_C_Mat = C.FD_C_Imread(imageFile)
+
+	var result *C.FD_C_DetectionResult = C.FD_C_CreateDetectionResult()
+
+	if !FDBooleanToGo(C.FD_C_PPYOLOEWrapperPredict(model, image, result)) {
+		fmt.Printf("Failed to predict.\n")
+		C.FD_C_DestroyRuntimeOptionWrapper(option)
+		C.FD_C_DestroyPPYOLOEWrapper(model)
+		C.FD_C_DestroyMat(image)
+		C.free(unsafe.Pointer(result))
+		return
+	}
+
+	var visImage C.FD_C_Mat = C.FD_C_VisDetection(image, result, 0.5, 1, 0.5)
+
+	C.FD_C_Imwrite(C.CString("vis_result.jpg"), visImage)
+	fmt.Printf("Visualized result saved in ./vis_result.jpg\n")
+
+	C.FD_C_DestroyRuntimeOptionWrapper(option)
+	C.FD_C_DestroyPPYOLOEWrapper(model)
+	C.FD_C_DestroyDetectionResult(result)
+	C.FD_C_DestroyMat(image)
+	C.FD_C_DestroyMat(visImage)
+}
+
+var (
+	modelDir   string
+	imageFile  string
+	deviceType int
+)
+
+func init() {
+	flag.StringVar(&modelDir, "model", "", "paddle detection model to use")
+	flag.StringVar(&imageFile, "image", "", "image to predict")
+	flag.IntVar(&deviceType, "device", 0, "The data type of run_option is int, 0: run with cpu; 1: run with gpu")
+}
+
+func main() {
+	flag.Parse()
+
+	if modelDir != "" && imageFile != "" {
+		if deviceType == 0 {
+			CpuInfer(C.CString(modelDir), C.CString(imageFile))
+		} else if deviceType == 1 {
+			GpuInfer(C.CString(modelDir), C.CString(imageFile))
+		}
+	} else {
+		fmt.Printf("Usage: ./infer -model path/to/model_dir -image path/to/image -device run_option \n")
+		fmt.Printf("e.g ./infer -model ./ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device 0 \n")
+	}
+
+}

examples/application/go/yolov5/README.md

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+English | [简体中文](README_CN.md)
+# YOLOv5 Golang Deployment Example
+
+This directory provides examples that `infer.go` uses CGO to call FastDeploy C API and finish the deployment of YOLOv5 model on CPU/GPU.
+
+Before deployment, two steps require confirmation
+
+- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
+- 2.  Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
+
+Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.4 above (x.x.x>1.0.4) or develop version (x.x.x=0.0.0) is required to support this model.
+
+### Use Golang and CGO to deploy YOLOv5 model
+
+Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above.
+```bash
+wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
+tar xvf fastdeploy-linux-x64-0.0.0.tgz
+```
+
+Copy FastDeploy C APIs from precompiled library to the current directory.
+```bash
+cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
+```
+
+Download the YOLOv5 ONNX model file and test images
+```bash
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+```
+
+Configure the `cgo CFLAGS: -I` to FastDeploy C API directory path and  the `cgo LDFLAGS: -L` to FastDeploy dynamic library path. The FastDeploy dynamic library is located in the `/lib` directory.
+```bash
+cgo CFLAGS: -I./fastdeploy_capi
+cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
+```
+
+Use the following command to add Fastdeploy library path to the environment variable.
+```bash
+source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh 
+```
+
+Compile the Go file `infer.go`.
+```bash
+go build infer.go
+```
+
+After compiling, use the following command to obtain the predicted results.
+```bash
+# CPU inference
+./infer -model yolov5s.onnx -image 000000014439.jpg -device 0
+# GPU inference
+./infer -model yolov5s.onnx -image 000000014439.jpg -device 1
+```
+
+Then visualized inspection result is saved in the local image `vis_result.jpg`.