API参考
对于服务提供的主要操作：
- HTTP请求方法为POST。
- 请求体和响应体均为JSON数据（JSON对象）。
- 当请求处理成功时，响应状态码为
200，响应体的属性如下：
| 名称 |
类型 |
含义 |
logId |
string |
请求的UUID。 |
errorCode |
integer |
错误码。固定为0。 |
errorMsg |
string |
错误说明。固定为"Success"。 |
result |
object |
操作结果。 |
| 名称 |
类型 |
含义 |
logId |
string |
请求的UUID。 |
errorCode |
integer |
错误码。与响应状态码相同。 |
errorMsg |
string |
错误说明。 |
服务提供的主要操作如下：
对图像进行目标检测。
POST /object-detection
| 名称 |
类型 |
含义 |
是否必填 |
image |
string |
服务器可访问的图像文件的URL或图像文件内容的Base64编码结果。 |
是 |
- 请求处理成功时，响应体的
result具有如下属性：
| 名称 |
类型 |
含义 |
detectedObjects |
array |
目标的位置、类别等信息。 |
image |
string |
目标检测结果图。图像为JPEG格式，使用Base64编码。 |
detectedObjects中的每个元素为一个object，具有如下属性：
| 名称 |
类型 |
含义 |
bbox |
array |
目标位置。数组中元素依次为边界框左上角x坐标、左上角y坐标、右下角x坐标以及右下角y坐标。 |
categoryId |
integer |
目标类别ID。 |
score |
number |
目标得分。 |
result示例如下：
{
"detectedObjects": [
{
"bbox": [
404.4967956542969,
90.15770721435547,
506.2465515136719,
285.4187316894531
],
"categoryId": 0,
"score": 0.7418514490127563
},
{
"bbox": [
155.33145141601562,
81.10954284667969,
199.71136474609375,
167.4235382080078
],
"categoryId": 1,
"score": 0.7328268885612488
}
],
"image": "xxxxxx"
}
多语言调用服务示例
Python
import base64
import requests
API_URL = "http://localhost:8080/object-detection" # 服务URL
image_path = "./demo.jpg"
output_image_path = "./out.jpg"
# 对本地图像进行Base64编码
with open(image_path, "rb") as file:
image_bytes = file.read()
image_data = base64.b64encode(image_bytes).decode("ascii")
payload = {"image": image_data} # Base64编码的文件内容或者图像URL
# 调用API
response = requests.post(API_URL, json=payload)
# 处理接口返回数据
assert response.status_code == 200
result = response.json()["result"]
with open(output_image_path, "wb") as file:
file.write(base64.b64decode(result["image"]))
print(f"Output image saved at {output_image_path}")
print("\nDetected objects:")
print(result["detectedObjects"])
C++
#include <iostream>
#include "cpp-httplib/httplib.h" // https://github.com/Huiyicc/cpp-httplib
#include "nlohmann/json.hpp" // https://github.com/nlohmann/json
#include "base64.hpp" // https://github.com/tobiaslocker/base64
int main() {
httplib::Client client("localhost:8080");
const std::string imagePath = "./demo.jpg";
const std::string outputImagePath = "./out.jpg";
httplib::Headers headers = {
{"Content-Type", "application/json"}
};
// 对本地图像进行Base64编码
std::ifstream file(imagePath, std::ios::binary | std::ios::ate);
std::streamsize size = file.tellg();
file.seekg(0, std::ios::beg);
std::vector<char> buffer(size);
if (!file.read(buffer.data(), size)) {
std::cerr << "Error reading file." << std::endl;
return 1;
}
std::string bufferStr(reinterpret_cast<const char*>(buffer.data()), buffer.size());
std::string encodedImage = base64::to_base64(bufferStr);
nlohmann::json jsonObj;
jsonObj["image"] = encodedImage;
std::string body = jsonObj.dump();
// 调用API
auto response = client.Post("/object-detection", headers, body, "application/json");
// 处理接口返回数据
if (response && response->status == 200) {
nlohmann::json jsonResponse = nlohmann::json::parse(response->body);
auto result = jsonResponse["result"];
encodedImage = result["image"];
std::string decodedString = base64::from_base64(encodedImage);
std::vector<unsigned char> decodedImage(decodedString.begin(), decodedString.end());
std::ofstream outputImage(outPutImagePath, std::ios::binary | std::ios::out);
if (outputImage.is_open()) {
outputImage.write(reinterpret_cast<char*>(decodedImage.data()), decodedImage.size());
outputImage.close();
std::cout << "Output image saved at " << outPutImagePath << std::endl;
} else {
std::cerr << "Unable to open file for writing: " << outPutImagePath << std::endl;
}
auto detectedObjects = result["detectedObjects"];
std::cout << "\nDetected objects:" << std::endl;
for (const auto& obj : detectedObjects) {
std::cout << obj << std::endl;
}
} else {
std::cout << "Failed to send HTTP request." << std::endl;
return 1;
}
return 0;
}
Java
import okhttp3.*;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.node.ObjectNode;
import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.util.Base64;
public class Main {
public static void main(String[] args) throws IOException {
String API_URL = "http://localhost:8080/object-detection"; // 服务URL
String imagePath = "./demo.jpg"; // 本地图像
String outputImagePath = "./out.jpg"; // 输出图像
// 对本地图像进行Base64编码
File file = new File(imagePath);
byte[] fileContent = java.nio.file.Files.readAllBytes(file.toPath());
String imageData = Base64.getEncoder().encodeToString(fileContent);
ObjectMapper objectMapper = new ObjectMapper();
ObjectNode params = objectMapper.createObjectNode();
params.put("image", imageData); // Base64编码的文件内容或者图像URL
// 创建 OkHttpClient 实例
OkHttpClient client = new OkHttpClient();
MediaType JSON = MediaType.Companion.get("application/json; charset=utf-8");
RequestBody body = RequestBody.Companion.create(params.toString(), JSON);
Request request = new Request.Builder()
.url(API_URL)
.post(body)
.build();
// 调用API并处理接口返回数据
try (Response response = client.newCall(request).execute()) {
if (response.isSuccessful()) {
String responseBody = response.body().string();
JsonNode resultNode = objectMapper.readTree(responseBody);
JsonNode result = resultNode.get("result");
String base64Image = result.get("image").asText();
JsonNode detectedObjects = result.get("detectedObjects");
byte[] imageBytes = Base64.getDecoder().decode(base64Image);
try (FileOutputStream fos = new FileOutputStream(outputImagePath)) {
fos.write(imageBytes);
}
System.out.println("Output image saved at " + outputImagePath);
System.out.println("\nDetected objects: " + detectedObjects.toString());
} else {
System.err.println("Request failed with code: " + response.code());
}
}
}
}
Go
package main
import (
"bytes"
"encoding/base64"
"encoding/json"
"fmt"
"io/ioutil"
"net/http"
)
func main() {
API_URL := "http://localhost:8080/object-detection"
imagePath := "./demo.jpg"
outputImagePath := "./out.jpg"
// 对本地图像进行Base64编码
imageBytes, err := ioutil.ReadFile(imagePath)
if err != nil {
fmt.Println("Error reading image file:", err)
return
}
imageData := base64.StdEncoding.EncodeToString(imageBytes)
payload := map[string]string{"image": imageData} // Base64编码的文件内容或者图像URL
payloadBytes, err := json.Marshal(payload)
if err != nil {
fmt.Println("Error marshaling payload:", err)
return
}
// 调用API
client := &http.Client{}
req, err := http.NewRequest("POST", API_URL, bytes.NewBuffer(payloadBytes))
if err != nil {
fmt.Println("Error creating request:", err)
return
}
res, err := client.Do(req)
if err != nil {
fmt.Println("Error sending request:", err)
return
}
defer res.Body.Close()
// 处理接口返回数据
body, err := ioutil.ReadAll(res.Body)
if err != nil {
fmt.Println("Error reading response body:", err)
return
}
type Response struct {
Result struct {
Image string `json:"image"`
DetectedObjects []map[string]interface{} `json:"detectedObjects"`
} `json:"result"`
}
var respData Response
err = json.Unmarshal([]byte(string(body)), &respData)
if err != nil {
fmt.Println("Error unmarshaling response body:", err)
return
}
outputImageData, err := base64.StdEncoding.DecodeString(respData.Result.Image)
if err != nil {
fmt.Println("Error decoding base64 image data:", err)
return
}
err = ioutil.WriteFile(outputImagePath, outputImageData, 0644)
if err != nil {
fmt.Println("Error writing image to file:", err)
return
}
fmt.Printf("Image saved at %s.jpg\n", outputImagePath)
fmt.Println("\nDetected objects:")
for _, obj := range respData.Result.DetectedObjects {
fmt.Println(obj)
}
}
C#
using System;
using System.IO;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Text;
using System.Threading.Tasks;
using Newtonsoft.Json.Linq;
class Program
{
static readonly string API_URL = "http://localhost:8080/object-detection";
static readonly string imagePath = "./demo.jpg";
static readonly string outputImagePath = "./out.jpg";
static async Task Main(string[] args)
{
var httpClient = new HttpClient();
// 对本地图像进行Base64编码
byte[] imageBytes = File.ReadAllBytes(imagePath);
string image_data = Convert.ToBase64String(imageBytes);
var payload = new JObject{ { "image", image_data } }; // Base64编码的文件内容或者图像URL
var content = new StringContent(payload.ToString(), Encoding.UTF8, "application/json");
// 调用API
HttpResponseMessage response = await httpClient.PostAsync(API_URL, content);
response.EnsureSuccessStatusCode();
// 处理接口返回数据
string responseBody = await response.Content.ReadAsStringAsync();
JObject jsonResponse = JObject.Parse(responseBody);
string base64Image = jsonResponse["result"]["image"].ToString();
byte[] outputImageBytes = Convert.FromBase64String(base64Image);
File.WriteAllBytes(outputImagePath, outputImageBytes);
Console.WriteLine($"Output image saved at {outputImagePath}");
Console.WriteLine("\nDetected objects:");
Console.WriteLine(jsonResponse["result"]["detectedObjects"].ToString());
}
}
Node.js
const axios = require('axios');
const fs = require('fs');
const API_URL = 'http://localhost:8080/object-detection'
const imagePath = './demo.jpg'
const outputImagePath = "./out.jpg";
let config = {
method: 'POST',
maxBodyLength: Infinity,
url: API_URL,
data: JSON.stringify({
'image': encodeImageToBase64(imagePath) // Base64编码的文件内容或者图像URL
})
};
// 对本地图像进行Base64编码
function encodeImageToBase64(filePath) {
const bitmap = fs.readFileSync(filePath);
return Buffer.from(bitmap).toString('base64');
}
// 调用API
axios.request(config)
.then((response) => {
// 处理接口返回数据
const result = response.data["result"];
const imageBuffer = Buffer.from(result["image"], 'base64');
fs.writeFile(outputImagePath, imageBuffer, (err) => {
if (err) throw err;
console.log(`Output image saved at ${outputImagePath}`);
});
console.log("\nDetected objects:");
console.log(result["detectedObjects"]);
})
.catch((error) => {
console.log(error);
});
PHP
<?php
$API_URL = "http://localhost:8080/object-detection"; // 服务URL
$image_path = "./demo.jpg";
$output_image_path = "./out.jpg";
// 对本地图像进行Base64编码
$image_data = base64_encode(file_get_contents($image_path));
$payload = array("image" => $image_data); // Base64编码的文件内容或者图像URL
// 调用API
$ch = curl_init($API_URL);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
curl_setopt($ch, CURLOPT_HTTPHEADER, array('Content-Type: application/json'));
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$response = curl_exec($ch);
curl_close($ch);
// 处理接口返回数据
$result = json_decode($response, true)["result"];
file_put_contents($output_image_path, base64_decode($result["image"]));
echo "Output image saved at " . $output_image_path . "\n";
echo "\nDetected objects:\n";
print_r($result["detectedObjects"]);
?>
通用目标检测产线中包含了目标检测模块，如您更考虑模型精度，请选择精度较高的模型，如您更考虑模型推理速度，请选择推理速度较快的模型，如您更考虑模型存储大小，请选择存储大小较小的模型。
如果您对产线运行的效果满意，可以直接对产线进行集成部署，如果不满意，您也可以利用私有数据对产线中的模型进行在线微调。
### 2.2 本地体验
在本地使用通用目标检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
#### 2.2.1 命令行方式体验
一行命令即可快速体验目标检测产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png)，并将 `--input` 替换为本地路径，进行预测
```bash
paddlex --pipeline object_detection \
--input general_object_detection_002.png \
--threshold 0.5 \
--save_path ./output/ \
--device gpu:0
```
相关参数和运行结果说明可以参考[2.2.2 Python脚本方式集成](#222-python脚本方式集成)中的参数说明和结果解释。
可视化结果保存至`save_path`，如下所示：
#### 2.2.2 Python脚本方式集成
通过上述命令行方式可快速体验查看效果，在项目中往往需要代码集成，您可以通过如下几行代码完成产线的快速推理：
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="object_detection")
output = pipeline.predict("general_object_detection_002.png", threshold=0.5)
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_json("./output/")
```
在上述 Python 脚本中，执行了如下几个步骤：
（1）调用 `create_pipeline` 实例化产线对象：具体参数说明如下：
