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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
# 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 = []
for image, score in zip(image_list, scores):
formatted_image = format_image(image)
result = f"Image: {formatted_image}\nScore: {score:.4f}\n"
results.append(result)
return "".join(results)
# Function to format the image representation as a string
def format_image(image):
formatted_image = ""
for row in image:
formatted_row = ", ".join([f"[{', '.join(map(str, pixel))}]" for pixel in row])
formatted_image += f"[{formatted_row}],\n"
return formatted_image[:-2]
# 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", multiple=True)
],
outputs="text",
title="Image Similarity Calculator",
description="Upload a target image and a list of images. Get similarity scores."
)
# Launch the interface
iface.launch()
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