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Browse files- app.py +71 -0
- requirements.txt +4 -0
app.py
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from cProfile import label
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import gradio as gr
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import cv2
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import matplotlib.pyplot as plt
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from scipy import ndimage
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from scipy.ndimage.filters import convolve
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import numpy as np
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def sift(img1, img2):
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sift = cv2.SIFT_create()
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keypoints_1, descriptors_1 = sift.detectAndCompute(img1,None)
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keypoints_2, descriptors_2 = sift.detectAndCompute(img2,None)
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bf = cv2.BFMatcher(cv2.NORM_L1, crossCheck=True)
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matches = bf.match(descriptors_1,descriptors_2)
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matches = sorted(matches, key = lambda x:x.distance)
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img3 = cv2.drawMatches(img1, keypoints_1, img2, keypoints_2, matches[:50], img2, flags=2)
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return img3
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def orb(img1, img2):
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orb = cv2.ORB_create()
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keypoints_1, descriptors_1 = orb.detectAndCompute(img1,None)
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keypoints_2, descriptors_2 = orb.detectAndCompute(img2,None)
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bf = cv2.BFMatcher(cv2.NORM_L1, crossCheck=True)
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matches = bf.match(descriptors_1,descriptors_2)
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matches = sorted(matches, key = lambda x:x.distance)
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img3 = cv2.drawMatches(img1, keypoints_1, img2, keypoints_2, matches[:50], img2, flags=2)
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return img3
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def match(img1, img2):
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img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
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img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
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sift_res = sift(img1, img2)
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orb_res = orb(img1, img2)
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return [sift_res, orb_res]
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interface = gr.Interface(
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title = "SIFT and ORB Image Matching ๐ผ ๐ ๐ผ",
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description = "<h3>Scale Invariant Feature Transform (SIFT) & Oriented FAST and Rotated BRIEF (ORB) </h3> <br> <b>Select training and query images ๐ผ</b>",
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article='~ Ivanrs',
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allow_flagging = "never",
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fn = match,
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inputs = [
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gr.Image(label = "Train Image", shape = [300, 200]),
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gr.Image(label = "Query Image", shape = [300, 200]),
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],
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outputs = [
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gr.Image(label = "SIFT Output"),
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gr.Image(label = "ORB Output"),
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],
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examples = [
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["images/img1.jpg", "images/img2.jpg"],
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["images/img3.jpg", "images/img4.jpg"],
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["images/img5.jpg", "images/img6.png"],
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["images/img7.jpeg", "images/img8.jpeg"]
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]
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)
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interface.launch(share = False)
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requirements.txt
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opencv-python
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matplotlib
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scipy
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numpy
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