Prompt-Driven Image Analysis: Integrating Gen-AI for Segmentation, Object Transformation, and Cognitive Interpretation

Author: Kaleem Ahmad

Repo: PromptDrivenImageAnalysis

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Overview

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.

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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.

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Technology Stack

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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.

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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.

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Usage Instructions

Gradio Interface

1. Upload Image:
   • Load an image into the platform.
2. Segment Objects:
   • Specify objects to be segmented using natural language descriptions.
3. Apply Transformations:
   • Use commands to apply masks, replace objects, or alter styles.
4. Cognitive Analysis:
   • Input prompts to receive detailed semantic interpretations of the image.

Main Code

1. Setup Environment:
   • Install necessary libraries: jupyter, tensorflow, opencv, gradio, etc.
   • Use a GPU-accelerated environment (e.g., T4 GPU).
2. Run the Notebook:
   • Open main_code.ipynb in Jupyter Notebook or JupyterLab.
   • Execute code cells sequentially to initialize models and perform tasks.
3. Interactive Features:
   • Utilize widgets for dynamic parameter adjustments.

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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.

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Project Structure

Files and Notebooks

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.

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References

1. GroundingDINO Paper
2. Segment Anything by Meta
3. Stable Diffusion by StabilityAI
4. LLaVA

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License

Creative Commons Attribution-ShareAlike (CC BY-SA).

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