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import cv2
import matplotlib.pyplot as plt
import gradio as gr
import os
def find_descriptors(img1, img2):
if img1 is None or img2 is None:
return None, None, None, None, None, None
img1 = cv2.cvtColor(img1, cv2.IMREAD_GRAYSCALE)
img2 = cv2.cvtColor(img2, cv2.IMREAD_GRAYSCALE)
sift = cv2.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
good = []
for m, n in matches:
if m.distance < 0.7 * n.distance:
good.append([m])
img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
plt.imshow(img3), plt.show()
score = (100 * len(good) / min(len(kp1), len(kp2)))
if score >= 10:
result = "Totally YES"
elif score >= 3:
result = "YES"
elif score >= 1:
result = "MAYBE"
else:
result = "NO"
descriptors = len(good)
keypoints1 = len(kp1)
keypoints2 = len(kp2)
return img3, descriptors, keypoints1, keypoints2, result, score
sift_result = gr.Interface(
fn=find_descriptors,
inputs=["image", "image"],
outputs=["image", gr.Textbox(label="Descriptors"),
gr.Textbox(label="First image keypoints"),
gr.Textbox(label="Second image keypoints"),
gr.Textbox(label="Are these places equals?"),
gr.Textbox(label="Score")],
examples=[[
os.path.join(os.path.dirname(__file__), "images/1.jpg"),
os.path.join(os.path.dirname(__file__), "images/2.jpg"),
], [os.path.join(os.path.dirname(__file__), "images/3.png"),
os.path.join(os.path.dirname(__file__), "images/4.png")],
[os.path.join(os.path.dirname(__file__), "images/5.jpg"),
os.path.join(os.path.dirname(__file__), "images/6.jpg")],
[os.path.join(os.path.dirname(__file__), "images/7.jpg"),
os.path.join(os.path.dirname(__file__), "images/8.jpg")]],
title="SIFT Image Compare",
batch=False,
description="A model that uses SIFT method for image compare.",
live=True)
sift_result.launch()
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