This project leverages state-of-the-art generative AI technologies to enable intuitive, natural language-driven image analysis. Users can perform tasks such as segmentation, object transformation, and cognitive interpretation of images through a prompt-driven interface. By combining advanced image processing models with NLP capabilities, the system makes complex tasks accessible and interactive.
Key Features
1. Natural Language Processing Interface
Allows users to input commands in plain language.
Simplifies the process of performing advanced image analysis tasks.
2. Advanced Image Segmentation and Transformation
Enables precise segmentation of images into distinct components using models like SAM (Segment Anything Model).
Supports transformations such as style alterations and object replacements.
3. Cognitive Image Analysis
Provides a deeper understanding of image context, content, and semantics using LLaVA.
Delivers detailed insights and interpretations based on user prompts.
Technology Stack
Component
Technology/Tool
Frontend
Gradio
Backend Frameworks
GANs, PyTorch, Transformers
Segmentation
SAM (Segment Anything Model) by Meta
Visual Grounding
GroundingDINO by IDEA-Research
Image Transformation
Stable Diffusion by StabilityAI
Cognitive Analysis
LLaVA
Model Integrations
1. GroundingDINO
Facilitates text-based object detection and annotation.
Accurately identifies and locates objects within images based on textual descriptions.
2. SAM (Segment Anything Model)
Used for precise image segmentation tasks.
Segments images into distinct regions as per user commands.
3. Stable Diffusion
Enables creative modifications and inpainting of images.
Applies artistic transformations seamlessly.
4. LLaVA
Combines vision and language to enhance cognitive analysis.
Understands and interprets image context for meaningful insights.
Core Components
1. Gradio Interface (gradio_demo.ipynb)
Interactive UI built with Gradio.
Features widgets for image upload, segmentation, and prompt input.
Includes elements like progress bars and HTML displays for real-time feedback.
2. Main Codebase (main_code.ipynb)
Core implementation for model initialization, image analysis, and transformation.
Leverages GPU acceleration for high-efficiency processing.
Usage Instructions
Gradio Interface
Upload Image:
Load an image into the platform.
Segment Objects:
Specify objects to be segmented using natural language descriptions.
Apply Transformations:
Use commands to apply masks, replace objects, or alter styles.
Cognitive Analysis:
Input prompts to receive detailed semantic interpretations of the image.
Main Code
Setup Environment:
Install necessary libraries: jupyter, tensorflow, opencv, gradio, etc.
Use a GPU-accelerated environment (e.g., T4 GPU).
Run the Notebook:
Open main_code.ipynb in Jupyter Notebook or JupyterLab.
Execute code cells sequentially to initialize models and perform tasks.
Interactive Features:
Utilize widgets for dynamic parameter adjustments.
Project Workflow
Step 1: Input
Upload an image and provide natural language prompts.
Step 2: Processing
Text-based commands are interpreted using GroundingDINO.
Segmentation and transformations are handled by SAM and Stable Diffusion.
Step 3: Output
Segmented images, transformed visuals, or cognitive analyses are presented to the user.
Project Structure
Files and Notebooks
File
Description
gradio_demo.ipynb
Contains the interactive Gradio-based interface.
main_code.ipynb
Core implementation of image analysis tasks.
Gradio Interface Components
HTMLModel: Displays HTML content in the interface.
FloatProgressModel: Visualizes task progress with a progress bar.
LayoutModel: Defines the layout and positioning of widgets.
