Welcome to the Image Quality Assessment, An ML, DL based project! This repository hosts a cutting-edge tool for evaluating the aesthetic quality of images (By Providing "Quality of an image" score of USER INPUT image) using advanced AI, ML, and deep learning techniques. Whether you are a photographer, designer, or an enthusiast of visual aesthetics, this empowers you to assess, select and enhance the quality of your images effortlessly.

🚀 Features

• 🔍 Multimodal Analysis: Leverages both visual and textual data for comprehensive image quality assessment.
• 🎨 Customizable Prompts: Allows users to define personalized criteria for evaluating image aesthetics.
• 🤖 Advanced AI Models: Utilizes CLIP IQA (Image Quality Assessment), CLIP:Classification, and BLIP:Classification for precise and context-aware image evaluations.

📦 Installation

1. Clone the Repository:

   git clone https://github.com/yourusername/Image_Quality_Assessment.git
   cd Image_Quality_Assessment
   

2. Install Dependencies::

   pip install torch torchvision pillow lavis pandas
   

🛠️ Usage

You can upload your images to the repository and use the provided functions to evaluate their aesthetic scores. Follow these steps:

1. Step 1: Load and Process Images: Place your images in the images folder.

2. Step 2: Customize Prompts: Modify the cls_names list to use your own descriptions for good(Postives) and bad(Negatives) photos.

3. Step 3: Run the Evaluation Script: Modify the img_files list in the evaluation script to include the paths to your uploaded images.

   img_files = ['images/your_image1.jpg', 'images/your_image2.jpg']

🎨 Examples

Evaluating a Single Image

 from PIL import Image
 
 img = Image.open("path/to/your/image.jpg")
 aesthetics_score = score_image(img)
 print(f"Aesthetics Score: {aesthetics_score}")

Batch Evaluation

img_files = ["path/to/your/image1.jpg", "path/to/your/image2.jpg"]
scores = []
for img_file in img_files:
   img = Image.open(img_file)
   aesthetics_score = score_image(img)
   scores.append({'img': img_file.split('/')[-1], 'score': aesthetics_score})
   print(f"{img_file.split('/')[-1]} - Aesthetics Score: {aesthetics_score}")

import pandas as pd
pd.DataFrame(scores).describe()

🤝 Contributing

We welcome contributions to enhance this project! Feel free to submit issues or pull requests on GitHub.