🧠 DeepVision Studio — AI-Powered Image Processing Web App

DeepVision Studio is a full-featured Flask web application that enables advanced image processing using deep learning. Built with scalability in mind, the app allows users to enhance, transform, and blend images through a clean web interface, using state-of-the-art computer vision models.

This project is being developed incrementally over multiple days — a new AI/ML feature is added each day, making it ideal for learning, showcasing, and real-world applications.

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🚀 Features

• 📷 Image Upload & Preview
  Upload and preview images in the browser with automatic handling.

• 🔍 Super-Resolution Enhancement
  Uses OpenCV's DNN Super Resolution module with the ESPCN model to upscale and sharpen low-res images.

• 🔧 Modular Design
  Clean codebase ready to integrate features like:

  • Image blending (Laplacian Pyramid, Seamless Cloning)
  • Noise reduction
  • Style transfer
  • Object detection
  • Segmentation

• 💡 Deep Learning Ready
  Uses pre-trained models for high-quality results without requiring GPU setup.

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📁 Tech Stack

• Backend: Python, Flask
• Image Processing: OpenCV (cv2.dnn_superres)
• Frontend: HTML, CSS (Jinja2 templating)
• Model: ESPCN (Efficient Sub-Pixel Convolutional Neural Network)
• Deployment Ready: Lightweight & extensible for local or cloud deployment

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🛠️ Getting Started

1. Clone the Repository

git clone https://github.com/FairozAhmadSheikh/DeepVision_Studio
cd DeepVision_Studio

2.Install Dependencies

pip install -r requirements.txt

3. Run the App

python app.py

Then open your browser and visit: http://localhost:5000

💡 Want to contribute or suggest a feature?

Open an issue or submit a pull request!

📌 Project Timeline (Development Log)

| Day | Feature | Status |

| --- | --------------------------------------- | ----------- | | 1 | Basic Image Upload & Display | ✅ Done | | 2 | Super-Resolution Enhancement (ESPCN) | ✅ Done | | 3 | Image Blending using OpenCV | ✅ Done | | 4 | Style Transfer with VGG (Neural Style) | ✅ Done | | 5 | Image Denoising (fastNlMeansDenoising) | ✅ Done | | 6 | Cartoonize Image using Bilateral Filter | ✅ Done | | 7 | Semantic Segmentation (DeepLabV3) | ✅ Done | | 8 | Super-Resolution (SRGAN or Upscaling) | 🔜 Upcoming | | 9 | Background Removal (U^2-Net / Deeplab) | 🔜 Upcoming | | 10 | Object Detection (YOLOv5 or SSD) | 🔜 Upcoming | | 11 | Image Colorization (Grayscale to Color) | 🔜 Upcoming | | 12 | Face Detection & Blur | 🔜 Upcoming | | 13 | Image Caption Generator (CNN + RNN) | 🔜 Upcoming | | 14 | Text Detection (EAST or OCR) | 🔜 Upcoming | | 15 | Depth Estimation | 🔜 Upcoming | | 16 | Real-Time Style Transfer (Webcam-based) | 🔜 Upcoming | | 17 | Face Swapping | 🔜 Upcoming | | 18 | AnimeGAN Filter | 🔜 Upcoming | | 19 | Full PDF Export of All Results | 🔜 Upcoming | | 20 | Final UI Polishing & Dockerization | 🔜 Upcoming |

📜 License

This project is licensed under the MIT License. Feel free to fork, use, or contribute to it.