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83838f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import numpy as np
import cv2 as cv
import gradio as gr
import matplotlib.pyplot as plt
def match_features(img1, img2):
# Convert Gradio image inputs (PIL) to OpenCV format (numpy array)
img1 = np.array(img1.convert("L"))
img2 = np.array(img2.convert("L"))
# Initialize variables to store match counts
orb_matches_count = 0
sift_bf_matches_count = 0
sift_flann_matches_count = 0
# ORB feature matching
orb = cv.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(matches, key=lambda x: x.distance)
# Count good ORB matches (using top 10% of matches)
orb_matches_count = len(matches[:int(len(matches) * 0.1)])
img3_orb = cv.drawMatches(img1, kp1, img2, kp2, matches[:orb_matches_count], None,
flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
# SIFT with BFMatcher
sift = cv.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
bf = cv.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
good = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
good.append([m])
sift_bf_matches_count = len(good)
img3_sift_bf = cv.drawMatchesKnn(img1, kp1, img2, kp2, good, None,
flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
# SIFT with FLANN
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)
matchesMask = [[0, 0] for i in range(len(matches))]
good_matches = []
for i, (m, n) in enumerate(matches):
if m.distance < 0.7 * n.distance:
matchesMask[i] = [1, 0]
good_matches.append(m)
sift_flann_matches_count = len(good_matches)
draw_params = dict(matchColor=(0, 255, 0),
singlePointColor=(255, 0, 0),
matchesMask=matchesMask,
flags=cv.DrawMatchesFlags_DEFAULT)
img3_sift_flann = cv.drawMatchesKnn(img1, kp1, img2, kp2, matches, None, **draw_params)
# Convert images to RGB format for display in Gradio
img3_orb = cv.cvtColor(img3_orb, cv.COLOR_BGR2RGB)
img3_sift_bf = cv.cvtColor(img3_sift_bf, cv.COLOR_BGR2RGB)
img3_sift_flann = cv.cvtColor(img3_sift_flann, cv.COLOR_BGR2RGB)
# Add text with match counts to the images
font = cv.FONT_HERSHEY_SIMPLEX
font_scale = 1
font_color = (255, 255, 255) # White color
thickness = 2
# Add count text to each image
def add_count_text(img, count, method_name):
h, w = img.shape[:2]
text = f"{method_name} Matches: {count}"
# Get text size
(text_width, text_height), _ = cv.getTextSize(text, font, font_scale, thickness)
# Position text at bottom center
x = (w - text_width) // 2
y = h - 20 # 20 pixels from bottom
# Add black background for better visibility
cv.rectangle(img, (x-5, y-text_height-5), (x+text_width+5, y+5), (0,0,0), -1)
# Add text
cv.putText(img, text, (x, y), font, font_scale, font_color, thickness)
return img
img3_orb = add_count_text(img3_orb, orb_matches_count, "ORB")
img3_sift_bf = add_count_text(img3_sift_bf, sift_bf_matches_count, "SIFT-BF")
img3_sift_flann = add_count_text(img3_sift_flann, sift_flann_matches_count, "SIFT-FLANN")
return img3_orb, img3_sift_bf, img3_sift_flann
# Define example image pairs with proper descriptions
examples = [
["charminar_01.jpg", "charminar_02.jpg"], # Charminar example
["Taj_mahal_1.jpeg", "pexels-photo-28257145.webp"], # Taj Mahal example
["pexels-photo-18374134.jpeg", "free-photo-of-teenage-boy-balancing-on-railway-tracks.jpeg"], # Railway tracks example
["image_2A.jpg", "image_2B.jpg"] # Generic image pair example
]
# Gradio interface with examples
iface = gr.Interface(
fn=match_features,
inputs=[
gr.Image(type="pil", label="Image 1"),
gr.Image(type="pil", label="Image 2")
],
outputs=[
gr.Image(label="ORB Matches"),
gr.Image(label="SIFT (BFMatcher) Matches"),
gr.Image(label="SIFT (FLANN) Matches")
],
title="Image Feature Matching App",
description="""
Upload two images of the same subject taken from different angles to find best Feature Matches using below three different algorithms:
1. ORB (Oriented FAST and Rotated BRIEF)
2. SIFT (Scale-Invariant Feature Transform) with BFMatcher
3. SIFT with FLANN (Fast Library for Approximate Nearest Neighbors)
""",
examples=examples,
cache_examples=True # Cache examples for faster loading
)
iface.launch() |