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import cv2
import numpy as np
import streamlit as st
from PIL import Image
def Match(image_file1, image_file2):
t = st.sidebar.selectbox("Good or Great", ["Good", "Great"])
rate = st.sidebar.slider("特徴量抽出の厳しさ", 0.0, 1.0, 0.8)
if (t == "Good"):
Good_Match(image_file1, image_file2, rate)
elif (t == "Great"):
Great_Match(image_file1, image_file2, rate)
def Good_Match(image_file1, image_file2,rate = 0.8):
image1 = Image.open(image_file1)
image2 = Image.open(image_file2)
image1_cv = cv2.cvtColor(np.array(image1), cv2.COLOR_RGB2BGR)
image2_cv = cv2.cvtColor(np.array(image2), cv2.COLOR_RGB2BGR)
gray1 = cv2.cvtColor(image1_cv, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(image2_cv, cv2.COLOR_BGR2GRAY)
sift = cv2.SIFT_create()
keypoints1, descriptors1 = sift.detectAndCompute(gray1, None)
keypoints2, descriptors2 = sift.detectAndCompute(gray2, None)
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(descriptors1, descriptors2, k=2)
good_matches = []
for m, n in matches:
if m.distance < rate * n.distance:
good_matches.append(m)
img_matches = cv2.drawMatches(image1_cv, keypoints1, image2_cv, keypoints2, good_matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
st.image(cv2.cvtColor(img_matches, cv2.COLOR_BGR2RGB), caption='Good Matches', use_column_width=True)
def Great_Match(image_file1, image_file2, rate=0.8):
image1 = Image.open(image_file1)
image2 = Image.open(image_file2)
# NumPy配列に変換
image1_cv = cv2.cvtColor(np.array(image1), cv2.COLOR_RGB2BGR)
image2_cv = cv2.cvtColor(np.array(image2), cv2.COLOR_RGB2BGR)
gray1 = cv2.cvtColor(image1_cv, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(image2_cv, cv2.COLOR_BGR2GRAY)
sift = cv2.SIFT_create()
keypoints1, descriptors1 = sift.detectAndCompute(gray1, None)
keypoints2, descriptors2 = sift.detectAndCompute(gray2, None)
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(descriptors1, descriptors2, k=2)
good_matches = []
for m, n in matches:
if m.distance < rate * n.distance:
good_matches.append(m)
if len(good_matches) > 4:
src_pts = np.float32([keypoints1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)
# RANSACを用いてホモグラフィを計算し、外れ値を除去
H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
matches_mask = mask.ravel().tolist()
# 射影変換を実行
height, width, channels = image2_cv.shape
transformed_img = cv2.warpPerspective(image1_cv, H, (width, height))
else:
matches_mask = None
# cv2.drawMatchesを使用してマッチング結果を描画
img_matches = cv2.drawMatches(image1_cv, keypoints1, image2_cv, keypoints2, good_matches, None,
matchesMask=matches_mask, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
# 結果を表示
st.image(cv2.cvtColor(img_matches, cv2.COLOR_BGR2RGB), caption='Great Matches', use_column_width=True)
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