| Parameter |
Description |
Type |
Options |
Default |
input |
Data to be predicted, supports multiple input types, required |
Python Var|str|list |
- Python Var: Image data represented by
numpy.ndarray
- str: Local path of an image file or PDF file, such as
/root/data/img.jpg; URL link, such as a network URL of an image file or PDF file: Example; Local directory, which should contain images to be predicted, such as /root/data/ (currently does not support prediction of directories containing PDF files, PDF files need to be specified to specific file paths)
- List: Elements of the list must be of the above types, such as
[numpy.ndarray, numpy.ndarray], ["/root/data/img1.jpg", "/root/data/img2.jpg"], ["/root/data1", "/root/data2"]
|
None |
threshold |
Filtering threshold for low-confidence object |
None|float|dict[int, float] |
- None: If set to
None, the default initialization parameter 0.5 will be used, i.e., 0.5 will be used as the low-score object filtering threshold for all categories
- float: Any floating-point number greater than 0 and less than 1
- dict[int, float]: The key represents the category ID, and the value represents the threshold corresponding to the category, indicating different low-score filtering thresholds for different categories, such as
{0:0.5, 1:0.35} means using 0.5 and 0.35 as low-score filtering thresholds for category 0 and category 1 respectively
|
None |
(3) Process the prediction results. The prediction result for each sample is of type `dict` and supports operations such as printing, saving as an image, and saving as a `json` file:
| Method |
Description |
Parameter |
Type |
Parameter Description |
Default Value |
print() |
Print the result to the terminal |
format_json |
bool |
Whether to format the output content using JSON indentation |
True |
indent |
int |
Specify the indentation level to beautify the output JSON data, making it more readable. Effective only when format_json is True |
4 |
ensure_ascii |
bool |
Control whether non-ASCII characters are escaped to Unicode. When set to True, all non-ASCII characters will be escaped; False retains the original characters. Effective only when format_json is True |
False |
save_to_json() |
Save the result as a json file |
save_path |
str |
Path to save the file. If it is a directory, the saved file name will be consistent with the input file type |
None |
indent |
int |
Specify the indentation level to beautify the output JSON data, making it more readable. Effective only when format_json is True |
4 |
ensure_ascii |
bool |
Control whether non-ASCII characters are escaped to Unicode. When set to True, all non-ASCII characters will be escaped; False retains the original characters. Effective only when format_json is True |
False |
save_to_img() |
Save the result as an image file |
save_path |
str |
Path to save the file. Supports directory or file path |
None |
- Calling the `print()` method will print the result to the terminal. The content printed to the terminal is explained as follows:
- `input_path`: `(str)` Input path of the image to be predicted
- `page_index`: `(Union[int, None])` If the input is a PDF file, it indicates the current page of the PDF; otherwise, it is `None`
- `boxes`: `(list)` Detection box information, each element is a dictionary containing the following fields
- `cls_id`: `(int)` Class ID
- `label`: `(str)` Class name
- `score`: `(float)` Confidence
- `coordinates`: `(list)` Detection box coordinates, in the format `[xmin, ymin, xmax, ymax]`
- Calling the `save_to_json()` method will save the above content to the specified `save_path`. If specified as a directory, the saved path will be `save_path/{your_img_basename}_res.json`. If specified as a file, it will be saved directly to that file. Since json files do not support saving numpy arrays, `numpy.array` types will be converted to lists.
- Calling the `save_to_img()` method will save the visualization result to the specified `save_path`. If specified as a directory, the saved path will be `save_path/{your_img_basename}_res.{your_img_extension}`. If specified as a file, it will be saved directly to that file.
* In addition, you can also obtain the visualized image and prediction results through attributes, as follows:
API Reference
For the main operations provided by the service:
- The HTTP request method is POST.
- Both the request body and response body are JSON data (JSON objects).
- When the request is processed successfully, the response status code is
200, and the properties of the response body are as follows:
| Name |
Type |
Meaning |
logId |
string |
The UUID of the request. |
errorCode |
integer |
Error code. Fixed as 0. |
errorMsg |
string |
Error message. Fixed as "Success". |
result |
object |
The result of the operation. |
- When the request is not processed successfully, the properties of the response body are as follows:
| Name |
Type |
Meaning |
logId |
string |
The UUID of the request. |
errorCode |
integer |
Error code. Same as the response status code. |
errorMsg |
string |
Error message. |
The main operations provided by the service are as follows:
Perform object detection on an image.
POST /small-object-detection
- The properties of the request body are as follows:
| Name |
Type |
Meaning |
Required |
image |
string |
The URL of an image file accessible by the server or the Base64-encoded content of an image file. |
Yes |
threshold |
number | object | null |
Please refer to the description of the threshold parameter of the pipeline object's predict method. |
No |
- When the request is processed successfully, the
result in the response body has the following properties:
| Name |
Type |
Meaning |
detectedObjects |
array |
Information about the location, category, and other details of detected objects. |
image |
string| null |
The result image of object detection. The image is in JPEG format and is Base64-encoded. |
Each element in detectedObjects is an object with the following properties:
| Name |
Type |
Meaning |
bbox |
array |
The location of the detected object. The elements of the array are the x-coordinate of the top-left corner, the y-coordinate of the top-left corner, the x-coordinate of the bottom-right corner, and the y-coordinate of the bottom-right corner. |
categoryId |
integer |
The category ID of the detected object. |
categoryName |
string |
The name of the target category. |
score |
number |
The score of the detected object. |
result example is as follows:
{
"detectedObjects": [
{
"bbox": [
404.4967956542969,
90.15770721435547,
506.2465515136719,
285.4187316894531
],
"categoryId": 0,
"categoryName": "person",
"score": 0.7418514490127563
},
{
"bbox": [
155.33145141601562,
81.10954284667969,
199.71136474609375,
167.4235382080078
],
"categoryId": 1,
"categoryName": "bottle",
"score": 0.7328268885612488
}
],
"image": "xxxxxx"
}
Multi-language Service Call Examples
Python
import base64
import requests
API_URL = "http://localhost:8080/small-object-detection" # Service URL
image_path = "./demo.jpg"
output_image_path = "./out.jpg"
# Encode the local image using Base64
with open(image_path, "rb") as file:
image_bytes = file.read()
image_data = base64.b64encode(image_bytes).decode("ascii")
payload = {"image": image_data} # Base64-encoded file content or image URL
# Call the API
response = requests.post(API_URL, json=payload)
# Process the API response data
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
#include "cpp-httplib/httplib.h" // [GitHub - Huiyicc/cpp-httplib: A C++ header-only HTTP/HTTPS server and client library](https://github.com/Huiyicc/cpp-httplib)
#include "nlohmann/json.hpp" // [GitHub - nlohmann/json: JSON for Modern C++](https://github.com/nlohmann/json)
#include "base64.hpp" // [GitHub - tobiaslocker/base64: A modern C++ base64 encoder / decoder](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"}
};
// Encode the local image using Base64
std::ifstream file(imagePath, std::ios::binary | std::ios::ate);
std::streamsize size = file.tellg();
file.seekg(0, std::ios::beg);
std::vector buffer(size);
if (!file.read(buffer.data(), size)) {
std::cerr << "Error reading file." << std::endl;
return 1;
}
std::string bufferStr(reinterpret_cast(buffer.data()), buffer.size());
std::string encodedImage = base64::to_base64(bufferStr);
nlohmann::json jsonObj;
jsonObj["image"] = encodedImage;
std::string body = jsonObj.dump();
// Call the API
auto response = client.Post("/small-object-detection", headers, body, "application/json");
// Process the API response data
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 decodedImage(decodedString.begin(), decodedString.end());
std::ofstream outputImage(outputImagePath, std::ios::binary | std::ios::out);
if (outputImage.is_open()) {
outputImage.write(reinterpret_cast(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& category : detectedObjects) {
std::cout << category << 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/small-object-detection"; // Service URL
String imagePath = "./demo.jpg"; // Local image
String outputImagePath = "./out.jpg"; // Output image
// Encode the local image using Base64
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-encoded file content or image URL
// Create OkHttpClient instance
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();
// Call the API and process the response data
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/small-object-detection"
imagePath := "./demo.jpg"
outputImagePath := "./out.jpg"
// Encode the local image to Base64
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-encoded file content or image URL
payloadBytes, err := json.Marshal(payload)
if err != nil {
fmt.Println("Error marshaling payload:", err)
return
}
// Call the 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()
// Process the response data
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 _, category := range respData.Result.DetectedObjects {
fmt.Println(category)
}
}
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/small-object-detection";
static readonly string imagePath = "./demo.jpg";
static readonly string outputImagePath = "./out.jpg";
static async Task Main(string[] args)
{
var httpClient = new HttpClient();
// Encode the local image in Base64
byte[] imageBytes = File.ReadAllBytes(imagePath);
string image_data = Convert.ToBase64String(imageBytes);
var payload = new JObject{ { "image", image_data } }; // Base64-encoded file content or image URL
var content = new StringContent(payload.ToString(), Encoding.UTF8, "application/json");
// Call the API
HttpResponseMessage response = await httpClient.PostAsync(API_URL, content);
response.EnsureSuccessStatusCode();
// Process the returned data
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/small-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-encoded file content or image URL
})
};
// Encode the local image using Base64
function encodeImageToBase64(filePath) {
const bitmap = fs.readFileSync(filePath);
return Buffer.from(bitmap).toString('base64');
}
// Call the API
axios.request(config)
.then((response) => {
// Process the API response data
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/small-object-detection"; // Service URL
$image_path = "./demo.jpg";
$output_image_path = "./out.jpg";
// Encode the local image with Base64
$image_data = base64_encode(file_get_contents($image_path));
$payload = array("image" => $image_data); // Base64 encoded file content or image URL
// Call the 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);
// Process the returned data from the interface
$result = json_decode($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"]);
?>
The small object detection pipeline includes a small object detection module. If you prioritize model accuracy, choose a model with higher accuracy. If you prioritize inference speed, select a model with faster inference. If you prioritize model storage size, opt for a model with a smaller storage size.
If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can fine-tune the models in the pipeline using your private data. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
### 2.2 Local Experience
Before using the small object detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
#### 2.2.1 Command Line Experience
* You can quickly experience the small object detection pipeline effect with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg), and replace `--input` with the local path for prediction.
```bash
paddlex --pipeline small_object_detection \
--input small_object_detection.jpg \
--threshold 0.5 \
--save_path ./output \
--device gpu:0
```
The relevant parameter descriptions can be referred to in the parameter explanations in [2.2.2 Python Script Integration](#222-python-script-integration). Supports specifying multiple devices simultaneously for parallel inference. For details, please refer to the documentation on pipeline parallel inference.
After running, the result will be printed to the terminal as follows:
```bash
{'res': {'input_path': 'small_object_detection.jpg', 'page_index': None, 'boxes': [{'cls_id': 0, 'label': 'pedestrian', 'score': 0.8182944655418396, 'coordinate': [203.60147, 701.3809, 224.2007, 743.8429]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.8150849342346191, 'coordinate': [185.01398, 710.8665, 201.76335, 744.9308]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.7748839259147644, 'coordinate': [295.1978, 500.2161, 309.33438, 532.0253]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.7688254714012146, 'coordinate': [851.5233, 436.13293, 863.2146, 466.8981]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.689735472202301, 'coordinate': [802.1584, 460.10693, 815.6586, 488.85086]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.6697502136230469, 'coordinate': [479.947, 309.43323, 489.1534, 332.5485]}, ...]}}
```
The explanation of the result parameters can be referred to in [2.2.2 Python Script Integration](#222-python脚本方式集成).
The visualization results are saved under `save_path`, and the visualization result of small object detection is as follows:
#### 2.1.2 Python Script Integration
* The above command line is for quickly experiencing and viewing the effect. Generally, in a project, it is often necessary to integrate through code. You can complete the quick inference of the pipeline with just a few lines of code. The inference code is as follows:
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="small_object_detection")
output = pipeline.predict(input="small_object_detection.jpg", threshold=0.5)
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/")
```
In the above Python script, the following steps are performed:
(1) Instantiate the small object detection pipeline object through `create_pipeline()`, with specific parameter descriptions as follows:
