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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
def image_similarity(target_image, image):
target_image = target_image.astype(np.uint8)
image = image.astype(np.uint8)
scores = compute_similarity(target_image, [image])
result = (str(image.tolist()), f"Score: {scores[0]:.4f}")
return [result]
# 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")
],
outputs="text",
title="Image Similarity Calculator",
description="Upload a target image and another image. Get the similarity score."
)
# Launch the interface
iface.launch()
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