oladokedamilola/image-processing-api

🎯 Image Processing Server

A high-performance computer vision server built with FastAPI and OpenCV, specializing in real-time image and video analysis. This production-ready solution provides advanced image processing capabilities for surveillance, monitoring, and multimedia analysis applications.

   

🚀 Live Demo & API Endpoint

🎯 Core Image Processing Capabilities

Detection & Recognition

• —Multi-class Object Detection: Identify 80+ object categories using YOLOv8 Nano
• —Human Detection: Specialized people detection using HOG descriptors and Haar Cascades
• —Vehicle Recognition: Car, truck, motorcycle, and bus detection
• —Motion Analysis: Frame differencing for movement detection and activity monitoring
• —Bounding Box Intelligence: Precise object localization with confidence scoring

Image Analysis Features

• —Real-time Processing: Sub-3-second response times for image analysis
• —Confidence Thresholding: Adjustable sensitivity (0.1-0.9) for detection accuracy
• —Size-based Filtering: Configurable minimum object dimensions for filtering
• —Multi-model Processing: Ensemble approach combining YOLOv8 with traditional CV algorithms

🔧 Processing Functionality

Image Processing Pipeline

Input → Validation → Decoding → Preprocessing → Detection → Analysis → Response
    ↓           ↓           ↓           ↓           ↓           ↓           ↓
File Check → Format Verify → OpenCV Load → Optimization → Model Inference → JSON Format → API Return

Supported Operations

• —Single Image Analysis: Immediate processing with detailed detection results
• —Batch Image Processing: Multiple image analysis in optimized sequences
• —Video Frame Extraction: Intelligent frame sampling for video analysis
• —Format Conversion: Automatic normalization across different image formats
• —Metadata Extraction: Image dimensions, properties, and quality assessment

📊 Detection & Output

Detection Results Structure

{
  "detections": [
    {
      "label": "person",
      "confidence": 0.92,
      "bbox": [120, 85, 310, 480],
      "dimensions": {"width": 190, "height": 395},
      "position": {"center_x": 215, "center_y": 282.5}
    },
    {
      "label": "car",
      "confidence": 0.87,
      "bbox": [450, 200, 620, 320],
      "dimensions": {"width": 170, "height": 120},
      "position": {"center_x": 535, "center_y": 260}
    }
  ],
  "image_analysis": {
    "resolution": "1920x1080",
    "color_profile": "RGB",
    "detection_summary": {
      "total_objects": 7,
      "people_count": 3,
      "vehicles_count": 2,
      "other_objects": 2
    },
    "processing_metrics": {
      "inference_time": 1.23,
      "total_processing_time": 2.45,
      "frames_per_second": 40.8
    }
  }
}

Advanced Analysis Features

• —Density Estimation: Object count per region/quadrant
• —Activity Heatmaps: Movement concentration visualization
• —Object Tracking: Basic trajectory analysis across video frames
• —Scene Understanding: Dominant object and activity classification

🖼️ Supported Image Formats & Specifications

Input Formats

Processing Specifications

• —Resolution Handling: Automatic scaling for optimal processing
• —Aspect Ratio Preservation: Maintains original image proportions
• —Color Normalization: Standardized color processing pipeline
• —Noise Reduction: Pre-processing filters for improved detection

⚡ Performance & Optimization

Speed & Efficiency

Quality & Accuracy

• —Detection Rate: >85% accuracy for person detection
• —Precision Control: Adjustable confidence thresholds (0.1-0.9)
• —False Positive Reduction: Size-based filtering and multi-model validation
• —Lighting Adaptation: Robust performance across varied lighting conditions

🔌 API Endpoints

Primary Processing Endpoints

Job Management

Advanced Features

System Endpoints

🔑 Authentication

All processing endpoints require an API key in the header:

curl -X POST "https://oladokedamilola-image-processing-api.hf.space/api/v1/process/image" \
  -H "X-API-Key: your-api-key-here" \
  -F "file=@image.jpg"

📘 Quick Start Examples

Python

import requests

url = "https://oladokedamilola-image-processing-api.hf.space/api/v1/process/image"
headers = {"X-API-Key": "your-api-key-here"}
files = {"file": open("image.jpg", "rb")}

response = requests.post(url, headers=headers, files=files)
results = response.json()

print(f"Detected {results['detection_count']} objects")
for detection in results['detections']:
    print(f"  {detection['label']}: {detection['confidence']:.2f}")

JavaScript

const formData = new FormData();
formData.append('file', imageFile);

fetch('https://oladokedamilola-image-processing-api.hf.space/api/v1/process/image', {
  method: 'POST',
  headers: { 'X-API-Key': 'your-api-key-here' },
  body: formData
})
.then(response => response.json())
.then(data => console.log(data));

🛠️ Technical Stack

🚀 Deployment Status

status: "Production Ready"
platform: "Hugging Face Spaces"
hardware: "CPU (2 vCPU, 16GB RAM)"
uptime: "24/7 with auto-sleep after 48h idle"
region: "Global CDN"

📋 Environment Variables

📄 License

MIT License © 2026 Oladoke Damilola

🤝 Support

• —Issues: GitHub Issues
• —Documentation: /docs endpoint
• —Status: `/health`

Status: Production Ready | Image Processing Focus: Core Functionality Optimized For: Real-time analysis, High-volume processing, Accurate detection Deployed on: Hugging Face Spaces 🚀

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