app.py

import numpy as np
from skimage.metrics import structural_similarity as ssim
import gradio as gr
import cv2

# Function to calculate SSIM between two images
def calculate_similarity(img1, img2):
    if len(img1.shape) == 2:
        img1 = cv2.cvtColor(img1, cv2.COLOR_GRAY2RGB)
    if len(img2.shape) == 2:
        img2 = cv2.cvtColor(img2, cv2.COLOR_GRAY2RGB)
    return ssim(img1, img2, win_size=3)


# Function to compute similarity scores for all images
def compute_similarity(target_image, image_list):
    scores = []
    target_image_resized = cv2.resize(target_image, (target_image.shape[1], target_image.shape[0]))
    for image in image_list:
        image_resized = cv2.resize(image, (target_image.shape[1], target_image.shape[0]))
        similarity_score = calculate_similarity(target_image_resized, image_resized)
        scores.append(similarity_score)
    return scores


# Function to handle the Gradio interface
def image_similarity(target_image, image_list):
    target_image = target_image.astype(np.uint8)
    image_list = [image.astype(np.uint8) for image in image_list]
    scores = compute_similarity(target_image, image_list)
    results = list(zip(image_list, scores))
    results.sort(key=lambda x: x[1], reverse=True)  # Sort by similarity score
    formatted_results = [(str(image.tolist()), f"{score:.4f}") for image, score in results]
    return formatted_results

# Prepare Gradio interface
iface = gr.Interface(
    fn=image_similarity,
    inputs=[
        gr.inputs.Image(type="numpy", label="Target Image"),
        gr.inputs.Image(type="numpy", label="Image List")
    ],
    outputs="text",
    title="Image Similarity Calculator",
    description="Upload an image and a list of images. Get similarity scores."
)

# Launch the interface
iface.launch()