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import cv2
import numpy as np
import gradio as gr
def sift_ransac_matching(image, template):
# Convert to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray_template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
# Initialize SIFT detector
sift = cv2.SIFT_create()
# Find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(gray_image, None)
kp2, des2 = sift.detectAndCompute(gray_template, None)
# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
# Apply ratio test
good_matches = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
good_matches.append(m)
if len(good_matches) > 4:
# Extract location of good matches
src_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)
# Use RANSAC to find homography
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
if M is not None:
# Compute the match score
num_matches = np.sum(mask)
match_score = num_matches / len(good_matches) # Matching score as a ratio
# Set a threshold for match score
threshold = 0.8 # Adjust this threshold as needed
# Determine if the template is found based on the match score
if match_score >= threshold:
return "Template found"
else:
return "Template not found"
else:
return "Template not found"
else:
return "Not enough good matches are found. Template not found"
# Gradio interface
iface = gr.Interface(
fn=sift_ransac_matching,
inputs=[
gr.Image(type="numpy", label="Image"),
gr.Image(type="numpy", label="Template"),
],
outputs=gr.Text(label="Result"),
title="Advanced Template Matching",
description="Upload an image and a template to check if the template is present in the image using SIFT and RANSAC.",
)
iface.launch()
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