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General Video Detection Pipeline Tutorial

1. Introduction to the General Video Detection Pipeline

Video detection is a technology used to identify and locate specific objects or events within video content. It is widely used in fields such as security monitoring, traffic management, and behavior analysis. This technology can capture and analyze dynamic changes in video in real-time, such as human activities, vehicle movements, and abnormal events. By leveraging deep learning models, especially convolutional neural networks (CNNs), video detection efficiently extracts spatial and temporal features from videos, enabling precise recognition and localization. Video detection not only enhances the intelligence of monitoring systems but also provides critical support for improving safety and operational efficiency. As technology advances, video detection will play a key role in even more scenarios.

👉Details of Model List

Model	Model Download Link	Frame-mAP (@ IoU 0.5)	Model Storage Size (M)	Description
YOWO	Inference Model/训练模型	80.94	462.891M	YOWO is a single-stage network with two branches. One branch extracts spatial features of key frames (i.e., the current frame) through a 2D-CNN, while the other branch captures spatiotemporal features of a clip composed of previous frames using a 3D-CNN. To accurately aggregate these features, YOWO employs a channel fusion and attention mechanism to maximize the utilization of inter-channel dependencies. Finally, the fused features are used for frame-level detection.

Note: The above accuracy metrics refer to Frame-mAP (@ IoU 0.5) Accuracy on the UCF101-24 test set. All model GPU inference times are based on NVIDIA Tesla T4 machines, with precision type FP32. CPU inference speeds are based on Intel® Xeon® Gold 5117 CPU @ 2.00GHz, with 8 threads and precision type FP32.

2. Quick Start

PaddleX supports experiencing the effects of pipelines locally using the command line or Python.

Before using the general Video Detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the PaddleX local installation tutorial.

2.1 Command Line Experience

A single command is all you need to quickly experience the Video Detection pipeline, Use the test file, and replace --input with the local path to perform prediction.

paddlex --pipeline video_detection --input HorseRiding.avi --device gpu:0

Parameter Explanation:

--pipeline: The name of the pipeline, here it is the Video Detection pipeline.
--input: The local path or URL of the input video to be processed.
--device: The GPU index to use (e.g., gpu:0 for the first GPU, gpu:1,2 for the second and third GPUs). You can also choose to use CPU (--device cpu).

When executing the above command, the default Video Detection pipeline configuration file is loaded. If you need to customize the configuration file, you can execute the following command to obtain it:

👉Click to expand

paddlex --get_pipeline_config video_detection

After execution, the Video Detection pipeline configuration file will be saved in the current path. If you wish to customize the save location, you can execute the following command (assuming the custom save location is ./my_path):

paddlex --get_pipeline_config video_detection --save_path ./my_path

After obtaining the pipeline configuration file, replace --pipeline with the configuration file's save path to make the configuration file take effect. For example, if the configuration file's save path is ./video_detection.yaml, simply execute:

paddlex --pipeline ./video_detection.yaml --input HorseRiding.avi  --device gpu:0

Here, parameters such as --model and --device do not need to be specified, as they will use the parameters in the configuration file. If you still specify parameters, the specified parameters will take precedence.

After running, the result will be:

{'input_path': 'HorseRiding.avi', 'result': [[[[110, 40, 170, 171], 0.8385784886274905, 'HorseRiding']], [[[112, 31, 168, 167], 0.8587647461352432, 'HorseRiding']], [[[106, 28, 164, 165], 0.8579590929730969, 'HorseRiding']], [[[106, 24, 165, 171], 0.8743957465404151, 'HorseRiding']], [[[107, 22, 165, 172], 0.8488322619908999, 'HorseRiding']], [[[112, 22, 173, 171], 0.8446755521458691, 'HorseRiding']], [[[115, 23, 177, 176], 0.8454028365262367, 'HorseRiding']], [[[117, 22, 178, 179], 0.8484261880748285, 'HorseRiding']], [[[117, 22, 181, 181], 0.8319480115446183, 'HorseRiding']], [[[117, 39, 182, 183], 0.820551099084625, 'HorseRiding']], [[[117, 41, 183, 185], 0.8202395831914338, 'HorseRiding']], [[[121, 47, 185, 190], 0.8261058921745246, 'HorseRiding']], [[[123, 46, 188, 196], 0.8307278306829033, 'HorseRiding']], [[[125, 44, 189, 197], 0.8259781361122833, 'HorseRiding']], [[[128, 47, 191, 195], 0.8227593229866699, 'HorseRiding']], [[[127, 44, 192, 193], 0.8205373129456817, 'HorseRiding']], [[[129, 39, 192, 185], 0.8223318812628619, 'HorseRiding']], [[[127, 31, 196, 179], 0.8501208612019866, 'HorseRiding']], [[[128, 22, 193, 171], 0.8315708410681566, 'HorseRiding']], [[[130, 22, 192, 169], 0.8318588228062005, 'HorseRiding']], [[[132, 18, 193, 170], 0.8310494469100611, 'HorseRiding']], [[[132, 18, 194, 172], 0.8302132445350239, 'HorseRiding']], [[[133, 18, 194, 176], 0.8339063714162727, 'HorseRiding']], [[[134, 26, 200, 183], 0.8365876380675275, 'HorseRiding']], [[[133, 16, 198, 182], 0.8395230321418268, 'HorseRiding']], [[[133, 17, 199, 184], 0.8198139782724922, 'HorseRiding']], [[[140, 28, 204, 189], 0.8344166596681291, 'HorseRiding']], [[[139, 27, 204, 187], 0.8412694521771158, 'HorseRiding']], [[[139, 28, 204, 185], 0.8500098862888805, 'HorseRiding']], [[[135, 19, 199, 179], 0.8506627974981384, 'HorseRiding']], [[[132, 15, 201, 178], 0.8495054272547193, 'HorseRiding']], [[[136, 14, 199, 173], 0.8451630721500223, 'HorseRiding']], [[[136, 12, 200, 167], 0.8366456814214907, 'HorseRiding']], [[[133, 8, 200, 168], 0.8457252233401213, 'HorseRiding']], [[[131, 7, 197, 162], 0.8400586356358062, 'HorseRiding']], [[[131, 8, 195, 163], 0.8320492682901985, 'HorseRiding']], [[[129, 4, 194, 159], 0.8298043752822792, 'HorseRiding']], [[[127, 5, 194, 162], 0.8348390851948722, 'HorseRiding']], [[[125, 7, 190, 164], 0.8299688814865505, 'HorseRiding']], [[[125, 6, 191, 164], 0.8303107088154711, 'HorseRiding']], [[[123, 8, 190, 168], 0.8348342187965798, 'HorseRiding']], [[[125, 14, 189, 170], 0.8356523950497134, 'HorseRiding']], [[[127, 18, 191, 171], 0.8392671764931521, 'HorseRiding']], [[[127, 30, 193, 178], 0.8441704160826191, 'HorseRiding']], [[[128, 18, 190, 181], 0.8438125326146775, 'HorseRiding']], [[[128, 12, 189, 186], 0.8390128962093542, 'HorseRiding']], [[[129, 15, 190, 185], 0.8471056476788448, 'HorseRiding']], [[[129, 16, 191, 184], 0.8536121834731034, 'HorseRiding']], [[[129, 16, 192, 185], 0.8488154629800881, 'HorseRiding']], [[[128, 15, 194, 184], 0.8417711698421471, 'HorseRiding']], [[[129, 13, 195, 187], 0.8412510238991473, 'HorseRiding']], [[[129, 14, 191, 187], 0.8404350980083457, 'HorseRiding']], [[[129, 13, 190, 189], 0.8382891279858882, 'HorseRiding']], [[[129, 11, 187, 191], 0.8318282305903217, 'HorseRiding']], [[[128, 8, 188, 195], 0.8043430817880264, 'HorseRiding']], [[[131, 25, 193, 199], 0.826184954516826, 'HorseRiding']], [[[124, 35, 191, 203], 0.8270462809459467, 'HorseRiding']], [[[121, 38, 191, 206], 0.8350931715324705, 'HorseRiding']], [[[124, 41, 195, 212], 0.8331239341053625, 'HorseRiding']], [[[128, 42, 194, 211], 0.8343046153103657, 'HorseRiding']], [[[131, 40, 192, 203], 0.8309784496027532, 'HorseRiding']], [[[130, 32, 195, 202], 0.8316640083647542, 'HorseRiding']], [[[135, 30, 196, 197], 0.8272172409555161, 'HorseRiding']], [[[131, 16, 197, 186], 0.8388410406147955, 'HorseRiding']], [[[134, 15, 202, 184], 0.8485738297037244, 'HorseRiding']], [[[136, 15, 209, 182], 0.8529430205135213, 'HorseRiding']], [[[134, 13, 218, 182], 0.8601191479922718, 'HorseRiding']], [[[144, 10, 213, 183], 0.8591963099263467, 'HorseRiding']], [[[151, 12, 219, 184], 0.8617965108346937, 'HorseRiding']], [[[151, 10, 220, 186], 0.8631923599955371, 'HorseRiding']], [[[145, 10, 216, 186], 0.8800860885204287, 'HorseRiding']], [[[144, 10, 216, 186], 0.8858840451538228, 'HorseRiding']], [[[146, 11, 214, 190], 0.8773644144886106, 'HorseRiding']], [[[145, 24, 214, 193], 0.8605544385867248, 'HorseRiding']], [[[146, 23, 214, 193], 0.8727294882672254, 'HorseRiding']], [[[148, 22, 212, 198], 0.8713131467067079, 'HorseRiding']], [[[146, 29, 213, 197], 0.8579099324651196, 'HorseRiding']], [[[154, 29, 217, 199], 0.8547794072847914, 'HorseRiding']], [[[151, 26, 217, 203], 0.8641733722316758, 'HorseRiding']], [[[146, 24, 212, 199], 0.8613466257602624, 'HorseRiding']], [[[142, 25, 210, 194], 0.8492670944810214, 'HorseRiding']], [[[134, 24, 204, 192], 0.8428117300203049, 'HorseRiding']], [[[136, 25, 204, 189], 0.8486779356971397, 'HorseRiding']], [[[127, 21, 199, 179], 0.8513896296400709, 'HorseRiding']], [[[125, 10, 192, 192], 0.8510201771386576, 'HorseRiding']], [[[124, 8, 191, 192], 0.8493999019508465, 'HorseRiding']], [[[121, 8, 192, 193], 0.8487097098892171, 'HorseRiding']], [[[119, 6, 187, 193], 0.847543279648022, 'HorseRiding']], [[[118, 12, 190, 190], 0.8503535936620565, 'HorseRiding']], [[[122, 22, 189, 194], 0.8427901493276977, 'HorseRiding']], [[[118, 24, 188, 195], 0.8418835400352087, 'HorseRiding']], [[[120, 25, 188, 205], 0.847192725785284, 'HorseRiding']], [[[122, 25, 189, 207], 0.8444105420674433, 'HorseRiding']], [[[120, 23, 189, 208], 0.8470784016639392, 'HorseRiding']], [[[121, 23, 188, 205], 0.843428111269418, 'HorseRiding']], [[[117, 23, 186, 206], 0.8420809714166708, 'HorseRiding']], [[[119, 5, 199, 197], 0.8288265053231356, 'HorseRiding']], [[[121, 8, 192, 195], 0.8197548738023599, 'HorseRiding']]]}

The visualized video not saved by default. You can customize the save path through --save_path, and then all results will be saved in the specified path.

2.2 Python Script Integration

A few lines of code can complete the quick inference of the pipeline. Taking the general Video Detection pipeline as an example:

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="video_detection")

output = pipeline.predict("HorseRiding.avi")
for res in output:
    res.print() # Print the structured output of the prediction
    res.save_to_video("./output/")  # Save the result visualization video
    res.save_to_json("./output/") # Save the structured output of the prediction

The results obtained are the same as those obtained through the command line method.

In the above Python script, the following steps are executed:

(1) Instantiate the create_pipeline to create a pipeline object: The specific parameter descriptions are as follows:

Parameter	Description	Type	Default
`pipeline`	The name of the pipeline or the path to the pipeline configuration file. If it is the name of the pipeline, it must be a pipeline supported by PaddleX.	`str`	None
`device`	The device for pipeline model inference. Supports: "gpu", "cpu".	`str`	"gpu"
`use_hpip`	Whether to enable high-performance inference, which is only available when the pipeline supports it.	`bool`	`False`

(2) Call the predict method of the Video Detection pipeline object for inference prediction: The predict method parameter is x, which is used to input data to be predicted, supporting multiple input methods, as shown in the following examples:

Parameter Type	Description
Python Var	Supports directly passing Python variables, such as numpy.ndarray representing video data.
`str`	Supports passing the path of the file to be predicted, such as the local path of an video file: `/root/data/video.mp4。`.
`str`	Supports passing the URL of the file to be predicted, such as the network URL of an video file: Example.
`str`	Supports passing a local directory, which should contain files to be predicted, such as the local path: `/root/data/`.
`dict`	Supports passing a dictionary type, where the key needs to correspond to the specific task, such as "video" for the Video Detection task, and the value of the dictionary supports the above data types, e.g., `{"video": "/root/data1"}`.
`list`	Supports passing a list, where the list elements need to be the above data types, such as `[numpy.ndarray, numpy.ndarray]`, `["/root/data/video1.mp4", "/root/data/video2.mp4"]`, `["/root/data1", "/root/data2"]`, `[{"video": "/root/data1"}, {"video": "/root/data2/video.mp4"}]`.

3）Obtain prediction results by calling the predict method: The predict method is a generator, so prediction results need to be obtained through iteration. The predict method predicts data in batches, so the prediction results are in the form of a list.

（4）Process the prediction results: The prediction result for each sample is of dict type and supports printing or saving to files, with the supported file types depending on the specific pipeline. For example:

Method	Description	Method Parameters
print	Prints results to the terminal	`- format_json`: bool, whether to format the output content with json indentation, default is True; `- indent`: int, json formatting setting, only valid when format_json is True, default is 4; `- ensure_ascii`: bool, json formatting setting, only valid when format_json is True, default is False;
save_to_json	Saves results as a json file	`- save_path`: str, the path to save the file, when it's a directory, the saved file name is consistent with the input file type; `- indent`: int, json formatting setting, default is 4; `- ensure_ascii`: bool, json formatting setting, default is False;
save_to_video	Saves results as an video file	`- save_path`: str, the path to save the file, when it's a directory, the saved file name is consistent with the input file type;

If you have a configuration file, you can customize the configurations of the video anomaly detection pipeline by simply modifying the pipeline parameter in the create_pipeline method to the path of the pipeline configuration file.

For example, if your configuration file is saved at ./my_path/video_detection.yaml, you only need to execute:

from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="./my_path/video_detection.yaml")
output = pipeline.predict("HorseRiding.avi ")
for res in output:
    res.print()  # Print the structured output of prediction
    res.save_to_video("./output/")  # Save the visualization video of the result
    res.save_to_json("./output/")  # Save the structured output of prediction

3. Development Integration/Deployment

If the pipeline meets your requirements for inference speed and accuracy, you can proceed directly with development integration/deployment.

If you need to apply the pipeline directly in your Python project, refer to the example code in 2.2 Python Script Integration.

Additionally, PaddleX provides three other deployment methods, detailed as follows:

🚀 High-Performance Inference: In actual production environments, many applications have stringent standards for the performance metrics of deployment strategies (especially response speed) to ensure efficient system operation and smooth user experience. To this end, PaddleX provides high-performance inference plugins aimed at deeply optimizing model inference and pre/post-processing for significant end-to-end speedups. For detailed high-performance inference procedures, refer to the PaddleX High-Performance Inference Guide.

☁️ Serving: Serving is a common deployment strategy in real-world production environments. By encapsulating inference functions into services, clients can access these services via network requests to obtain inference results. PaddleX supports various solutions for serving pipelines. For detailed pipeline serving procedures, please refer to the PaddleX Pipeline Serving Guide.

Below are the API reference and multi-language service invocation examples for the basic serving solution:

API Reference

For primary operations provided by the service:

The HTTP request method is POST.
The request body and the response body are both JSON data (JSON objects).
When the request is processed successfully, the response status code is 200, and the response body properties are as follows:

Name	Type	Description
`logId`	`string`	UUID for the request.
`errorCode`	`integer`	Error code. Fixed as `0`.
`errorMsg`	`string`	Error description. Fixed as `"Success"`.
`result`	`object`	Operation result.

When the request is not processed successfully, the response body properties are as follows:

Name	Type	Description
`logId`	`string`	UUID for the request.
`errorCode`	`integer`	Error code. Same as the response status code.
`errorMsg`	`string`	Error description.

Primary operations provided by the service are as follows:

infer

Classify videos.

POST /video-classification

The request body properties are as follows:

Name	Type	Description	Required
`video`	`string`	The URL of an video file accessible by the server or the Base64 encoded result of the video file content.	Yes
`inferenceParams`	`object`	Inference parameters.	No

The properties of inferenceParams are as follows:

Name	Type	Description	Required
`score_threshold`	`integer`	Only the box score greater than `score_threshold` will be retained in the results.	No

When the request is processed successfully, the result of the response body has the following properties:

Name	Type	Description
`categories`	`array`	video category information.
`video`	`string`	The Video Detection result video. The video is in JPEG format and encoded using Base64.

Each element in categories is an object with the following properties:

Name	Type	Description
`id`	`integer`	Category ID.
`name`	`string`	Category name.
`score`	`number`	Category score.

An example of result is as follows:

{
"categories": [
{
"id": 5,
"name": "Rabbit",
"score": 0.93
}
],
"video": "xxxxxx"
}

Multi-Language Service Invocation Examples

Python

import base64
import requests

API_URL = "http://localhost:8080/video-classification"
video_path = "./demo.mp4"
output_video_path = "./out.mp4"

with open(video_path, "rb") as file:
    video_bytes = file.read()
    video_data = base64.b64encode(video_bytes).decode("ascii")

payload = {"video": video_data}

response = requests.post(API_URL, json=payload)

assert response.status_code == 200
result = response.json()["result"]
with open(output_video_path, "wb") as file:
    file.write(base64.b64decode(result["video"]))
print(f"Output video saved at {output_video_path}")
print("\nCategories:")
print(result["categories"])

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 videoPath = "./demo.mp4";
    const std::string outputvideoPath = "./out.mp4";

    httplib::Headers headers = {
        {"Content-Type", "application/json"}
    };

    std::ifstream file(videoPath, 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 encodedVideo = base64::to_base64(bufferStr);

    nlohmann::json jsonObj;
    jsonObj["video"] = encodedVideo;
    std::string body = jsonObj.dump();

    auto response = client.Post("/video-classification", headers, body, "application/json");
    if (response && response->status == 200) {
        nlohmann::json jsonResponse = nlohmann::json::parse(response->body);
        auto result = jsonResponse["result"];

        encodedVideo = result["video"];
        std::string decodedString = base64::from_base64(encodedVideo);
        std::vector<unsigned char> decodedImage(decodedString.begin(), decodedString.end());
        std::ofstream outputImage(outPutvideoPath, std::ios::binary | std::ios::out);
        if (outputImage.is_open()) {
            outputImage.write(reinterpret_cast<char*>(decodedImage.data()), decodedImage.size());
            outputImage.close();
            std::cout << "Output video saved at " << outPutvideoPath << std::endl;
        } else {
            std::cerr << "Unable to open file for writing: " << outPutvideoPath << std::endl;
        }

        auto categories = result["categories"];
        std::cout << "\nCategories:" << std::endl;
        for (const auto& category : categories) {
            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/video-classification";
        String videoPath = "./demo.mp4";
        String outputvideoPath = "./out.mp4";

        File file = new File(videoPath);
        byte[] fileContent = java.nio.file.Files.readAllBytes(file.toPath());
        String videoData = Base64.getEncoder().encodeToString(fileContent);

        ObjectMapper objectMapper = new ObjectMapper();
        ObjectNode params = objectMapper.createObjectNode();
        params.put("video", videoData);

        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();

        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("video").asText();
                JsonNode categories = result.get("categories");

                byte[] videoBytes = Base64.getDecoder().decode(base64Image);
                try (FileOutputStream fos = new FileOutputStream(outputvideoPath)) {
                    fos.write(videoBytes);
                }
                System.out.println("Output video saved at " + outputvideoPath);
                System.out.println("\nCategories: " + categories.toString());
            } else {
                System.err.println("Request failed with code: " + response.code());
            }
        }
    }
}

package main

import (
    "bytes"
    "encoding/base64"
    "encoding/json"
    "fmt"
    "io/ioutil"
    "net/http"
)

func main() {
    API_URL := "http://localhost:8080/video-classification"
    videoPath := "./demo.mp4"
    outputvideoPath := "./out.mp4"

    videoBytes, err := ioutil.ReadFile(videoPath)
    if err != nil {
        fmt.Println("Error reading video file:", err)
        return
    }
    videoData := base64.StdEncoding.EncodeToString(videoBytes)

    payload := map[string]string{"video": videoData}
    payloadBytes, err := json.Marshal(payload)
    if err != nil {
        fmt.Println("Error marshaling payload:", err)
        return
    }

    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()

    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:"video"`
            Categories []map[string]interface{} `json:"categories"`
        } `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 video data:", err)
        return
    }
    err = ioutil.WriteFile(outputvideoPath, outputImageData, 0644)
    if err != nil {
        fmt.Println("Error writing video to file:", err)
        return
    }
    fmt.Printf("Image saved at %s.mp4\n", outputvideoPath)
    fmt.Println("\nCategories:")
    for _, category := range respData.Result.Categories {
        fmt.Println(category)
    }
}

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/video-classification";
    static readonly string videoPath = "./demo.mp4";
    static readonly string outputvideoPath = "./out.mp4";

    static async Task Main(string[] args)
    {
        var httpClient = new HttpClient();

        byte[] videoBytes = File.ReadAllBytes(videoPath);
        string video_data = Convert.ToBase64String(videoBytes);

        var payload = new JObject{ { "video", video_data } };
        var content = new StringContent(payload.ToString(), Encoding.UTF8, "application/json");

        HttpResponseMessage response = await httpClient.PostAsync(API_URL, content);
        response.EnsureSuccessStatusCode();

        string responseBody = await response.Content.ReadAsStringAsync();
        JObject jsonResponse = JObject.Parse(responseBody);

        string base64Image = jsonResponse["result"]["video"].ToString();
        byte[] outputImageBytes = Convert.FromBase64String(base64Image);

        File.WriteAllBytes(outputvideoPath, outputImageBytes);
        Console.WriteLine($"Output video saved at {outputvideoPath}");
        Console.WriteLine("\nCategories:");
        Console.WriteLine(jsonResponse["result"]["categories"].ToString());
    }
}

Node.js

const axios = require('axios');
const fs = require('fs');

const API_URL = 'http://localhost:8080/video-classification'
const videoPath = './demo.mp4'
const outputvideoPath = "./out.mp4";

let config = {
   method: 'POST',
   maxBodyLength: Infinity,
   url: API_URL,
   data: JSON.stringify({
    'video': encodeImageToBase64(videoPath)
  })
};

function encodeImageToBase64(filePath) {
  const bitmap = fs.readFileSync(filePath);
  return Buffer.from(bitmap).toString('base64');
}

axios.request(config)
.then((response) => {
    const result = response.data["result"];
    const videoBuffer = Buffer.from(result["video"], 'base64');
    fs.writeFile(outputvideoPath, videoBuffer, (err) => {
      if (err) throw err;
      console.log(`Output video saved at ${outputvideoPath}`);
    });
    console.log("\nCategories:");
    console.log(result["categories"]);
})
.catch((error) => {
  console.log(error);
});

PHP

<?php

$API_URL = "http://localhost:8080/video-classification";
$video_path = "./demo.mp4";
$output_video_path = "./out.mp4";

$video_data = base64_encode(file_get_contents($video_path));
$payload = array("video" => $video_data);

$ch = curl_init($API_URL);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$response = curl_exec($ch);
curl_close($ch);

$result = json_decode($response, true)["result"];
file_put_contents($output_video_path, base64_decode($result["video"]));
echo "Output video saved at " . $output_video_path . "\n";
echo "\nCategories:\n";
print_r($result["categories"]);
?>

📱 Edge Deployment: Edge deployment is a method that places computing and data processing functions on user devices themselves, allowing devices to process data directly without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed edge deployment procedures, refer to the PaddleX Edge Deployment Guide. You can choose the appropriate deployment method for your model pipeline based on your needs and proceed with subsequent AI application integration.

4. Custom Development

If the default model weights provided by the general Video Detection pipeline do not meet your requirements for accuracy or speed in your specific scenario, you can try to further fine-tune the existing model using data from your specific domain or application scenario to improve the recognition performance of the general Video Detection pipeline in your scenario.

4.1 Model Fine-tuning

Since the general Video Detection pipeline includes an Video Detection module, if the performance of the pipeline does not meet expectations, you need to refer to the Customization section in the Video Detection Module Development Tutorial and use your private dataset to fine-tune the Video Detection model.

4.2 Model Application

After you have completed fine-tuning training using your private dataset, you will obtain local model weight files.

If you need to use the fine-tuned model weights, simply modify the pipeline configuration file by replacing the local path of the fine-tuned model weights to the corresponding location in the pipeline configuration file:

......
Pipeline:
  model: PPTSMv2_LCNet_k400_8frames_uniform  # Can be modified to the local path of the fine-tuned model
  device: "gpu"
  batch_size: 1
......

Then, refer to the command line method or Python script method in the local experience section to load the modified pipeline configuration file.

5. Multi-hardware Support

PaddleX supports various mainstream hardware devices such as NVIDIA GPUs, Kunlun XPU, Ascend NPU, and Cambricon MLU. Simply modify the --device parameter to seamlessly switch between different hardware.

For example, if you use an NVIDIA GPU for inference in the Video Detection pipeline, the Python command is:

paddlex --pipeline video_detection --input HorseRiding.avi  --device gpu:0
``````
At this point, if you wish to switch the hardware to Ascend NPU, simply modify the `--device` in the Python command to `npu:0`:

bash paddlex --pipeline video_detection --input HorseRiding.avi --device npu:0 ``` If you want to use the General Video Detection Pipeline on more types of hardware, please refer to the PaddleX Multi-Device Usage Guide.

video_detection.en.md 36 KB History Raw