Techsolut SDK Documentation

Easily integrate Techsolut's capabilities into your applications with our official SDKs available for multiple programming languages.

Version 2.5.0 Last updated: April 15, 2025

Overview

Techsolut SDKs provide a straightforward way to access our computer vision API with syntax that's native to your preferred programming language. Our SDKs support object detection, image classification, OCR, and other advanced features.

Easy Integration

Simple installation with standard package managers like pip, npm, and composer.

Complete API

Access all Techsolut API features with comprehensive documentation.

Robust Error Handling

Detailed error messages and specific exceptions for easy debugging.

Installation

Python
JavaScript
PHP
Java
pip 
pip install techsolut-sdk
1
Install the package

Use pip to install the Techsolut SDK

2
Import the SDK
import techsolut as ts
3
Configure your API key
client = ts.Client("your_api_key_here")
npm 
npm install @techsolut/sdk
1
Install the package

Use npm or yarn to install the Techsolut SDK

2
Import the SDK
// CommonJS
const Techsolut = require('@techsolut/sdk');

// ES Modules
import Techsolut from '@techsolut/sdk';
3
Configure your API key
const client = new Techsolut.Client("your_api_key_here");
composer 
composer require techsolut/sdk
1
Install the package

Use Composer to install the Techsolut SDK

2
Import the SDK
require_once 'vendor/autoload.php';

use Techsolut\SDK\Client;
3
Configure your API key
$client = new Client("your_api_key_here");
maven 
<dependency>
    <groupId>fr.techsolut</groupId>
    <artifactId>techsolut-sdk</artifactId>
    <version>2.5.0</version>
</dependency>
1
Install the package

Add the Maven or Gradle dependency to your project

2
Import the SDK
import fr.techsolut.sdk.Client;
import fr.techsolut.sdk.models.*;
3
Configure your API key
Client client = new Client("your_api_key_here");

Usage Examples

Object Detection

Python
JavaScript
PHP
Java
Python 
import techsolut as ts

# Initialize the client
client = ts.Client("your_api_key_here")

# Detect objects in an image
result = client.detect_objects(
    image_url="https://example.com/image.jpg",
    confidence=0.5,
    model="yolov8x"  # Optional, defaults to the latest model
)

# Process the results
for detection in result.detections:
    print(f"Found {detection.class_name} with {detection.confidence:.2f} confidence")
    box = detection.box
    print(f"Position: ({box.x1}, {box.y1}) to ({box.x2}, {box.y2})")
JavaScript 
// Import the SDK
import Techsolut from '@techsolut/sdk';

// Initialize the client
const client = new Techsolut.Client("your_api_key_here");

// Detect objects in an image
async function detectObjects() {
  try {
    const result = await client.detectObjects({
      imageUrl: "https://example.com/image.jpg",
      confidence: 0.5,
      model: "yolov8x"  // Optional, defaults to the latest model
    });
    
    // Process the results
    result.detections.forEach(detection => {
      console.log(`Found ${detection.className} with ${detection.confidence.toFixed(2)} confidence`);
      const box = detection.box;
      console.log(`Position: (${box.x1}, ${box.y1}) to (${box.x2}, ${box.y2})`);
    });
  } catch (error) {
    console.error("Detection failed:", error.message);
  }
}

detectObjects();
PHP 
// Import the SDK
require_once 'vendor/autoload.php';

use Techsolut\SDK\Client;

// Initialize the client
$client = new Client("your_api_key_here");

// Detect objects in an image
try {
    $result = $client->detectObjects([
        'image_url' => 'https://example.com/image.jpg',
        'confidence' => 0.5,
        'model' => 'yolov8x'  // Optional, defaults to the latest model
    ]);
    
    // Process the results
    foreach ($result->getDetections() as $detection) {
        echo "Found " . $detection->getClassName() . " with " . 
             round($detection->getConfidence(), 2) . " confidence\n";
        
        $box = $detection->getBox();
        echo "Position: (" . $box->getX1() . ", " . $box->getY1() . ") to (" . 
             $box->getX2() . ", " . $box->getY2() . ")\n";
    }
} catch (Exception $e) {
    echo "Detection failed: " . $e->getMessage();
}
Java 
// Import the SDK
import fr.techsolut.sdk.Client;
import fr.techsolut.sdk.models.DetectionResult;
import fr.techsolut.sdk.models.Detection;
import fr.techsolut.sdk.models.Box;
import fr.techsolut.sdk.exceptions.TechsolutException;

import java.util.Map;
import java.util.HashMap;

public class ObjectDetectionExample {
    public static void main(String[] args) {
        // Initialize the client
        Client client = new Client("your_api_key_here");
        
        // Prepare request parameters
        Map params = new HashMap<>();
        params.put("imageUrl", "https://example.com/image.jpg");
        params.put("confidence", 0.5);
        params.put("model", "yolov8x");  // Optional, defaults to the latest model
        
        try {
            // Detect objects in an image
            DetectionResult result = client.detectObjects(params);
            
            // Process the results
            for (Detection detection : result.getDetections()) {
                System.out.printf("Found %s with %.2f confidence%n", 
                                  detection.getClassName(), detection.getConfidence());
                
                Box box = detection.getBox();
                System.out.printf("Position: (%.1f, %.1f) to (%.1f, %.1f)%n", 
                                  box.getX1(), box.getY1(), box.getX2(), box.getY2());
            }
        } catch (TechsolutException e) {
            System.err.println("Detection failed: " + e.getMessage());
        }
    }
}

Image Classification

Python
JavaScript
Python 
import techsolut as ts

# Initialize the client
client = ts.Client("your_api_key_here")

# Classify an image
result = client.classify_image(
    image_url="https://example.com/image.jpg",
    top_k=5  # Return top 5 classes
)

# Process the results
for classification in result.classifications:
    print(f"{classification.label}: {classification.confidence:.2f}")
JavaScript 
// Import the SDK
import Techsolut from '@techsolut/sdk';

// Initialize the client
const client = new Techsolut.Client("your_api_key_here");

// Classify an image
async function classifyImage() {
  try {
    const result = await client.classifyImage({
      imageUrl: "https://example.com/image.jpg",
      topK: 5  // Return top 5 classes
    });
    
    // Process the results
    result.classifications.forEach(classification => {
      console.log(`${classification.label}: ${classification.confidence.toFixed(2)}`);
    });
  } catch (error) {
    console.error("Classification failed:", error.message);
  }
}

classifyImage();

API Reference

detect_objects() Core

Detects objects in an image and returns their bounding boxes, classes, and confidence scores.

Parameters
Name Type Description
image_url string URL of the image to analyze Required if image_data is not provided
image_data string Base64-encoded image data Required if image_url is not provided
confidence float Minimum confidence threshold (0-1) Default: 0.25
model string Model to use for detection Default: "yolov8x"
Returns
{
  "success": true,
  "detections": [
    {
      "box": {
        "x1": 23.5,
        "y1": 74.2,
        "x2": 483.1,
        "y2": 352.7
      },
      "class": "person",
      "confidence": 0.92
    },
    ...
  ],
  "processing_time": 0.234
}
classify_image() Core

Classifies the content of an image by assigning labels and confidence scores.

Parameters
Name Type Description
image_url string URL of the image to analyze Required if image_data is not provided
image_data string Base64-encoded image data Required if image_url is not provided
top_k int Number of top categories to return Default: 5
model string Model to use for classification Default: "resnet101"

Support

If you encounter any issues or have questions about our SDKs, please check out the resources below:

Full Documentation

Browse our detailed documentation
Code Examples

Explore practical examples
Technical Support

Contact our expert team
Contribute

Our SDKs are open source! You can view the source code, report issues, and contribute on GitHub at github.com/techsolut/sdk.

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