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README.md

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----
-title: App
-emoji: 💻
-colorFrom: blue
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.37.1
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+from PIL import Image
+from io import BytesIO
+
+from match import Match, Great_Match
+from diff import diff
+
+def main():
+    st.title("画像差分検知")
+    TYPE = st.sidebar.selectbox("選択", ["差分抽出", "特徴量抽出"])
+    image_file1 = st.sidebar.file_uploader("1枚目の画像をアップロードしてください", type=["jpg", "jpeg", "png", "PNG"])
+    image_file2 = st.sidebar.file_uploader("2枚目の画像をアップロードしてください", type=["jpg", "jpeg", "png", "PNG"])
+    if image_file1 and image_file2:
+        if TYPE == "差分抽出":
+            diff(image_file1, image_file2)
+        elif TYPE == "特徴量抽出":
+            Match(image_file1, image_file2)
+    else:
+        st.info("画像をアップロードしてください")
+
+if __name__ == "__main__":
+    main()
+

diff.py

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+import streamlit as st
+from PIL import Image
+import cv2
+import numpy as np
+
+def diff(image_file1, image_file2):
+    TYPE = st.sidebar.selectbox("可視化手法の選択", ["矩形", "点群"])
+    if (TYPE == "矩形"):
+        rectangle_diff(image_file1, image_file2)
+    elif (TYPE == "点群"):
+        point_diff(image_file1, image_file2)
+
+def point_diff(image_file1, image_file2):
+    image1 = Image.open(image_file1)
+    image2 = Image.open(image_file2)
+    
+    col1, col2  = st.columns(2)
+    diff_Thresholds = st.sidebar.slider("差分の閾値処理", 10, 255, 50)
+    with col1:
+        st.image(image1, caption='1枚目の画像', use_column_width=True)
+
+    with col2:
+        st.image(image2, caption='2枚目の画像', use_column_width=True)	
+
+    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 < 0.8 * 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)
+
+        H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
+
+        height, width, channels = image2_cv.shape
+        transformed_img = cv2.warpPerspective(image1_cv, H, (width, height))
+
+        transformed_gray = cv2.cvtColor(transformed_img, cv2.COLOR_BGR2GRAY)
+
+        img_diff = cv2.absdiff(transformed_gray, gray2)
+
+        _, img_th = cv2.threshold(img_diff, diff_Thresholds, 255, cv2.THRESH_BINARY)
+
+        kernel = np.ones((3, 3), np.uint8)
+        img_th = cv2.morphologyEx(img_th, cv2.MORPH_OPEN, kernel, iterations=2)
+        img_th = cv2.dilate(img_th, kernel, iterations=1)
+
+        points = np.column_stack(np.where(img_th > 0.00000001))
+
+        for point in points:
+            cv2.circle(transformed_img, (point[1], point[0]), 2, (0, 0, 255), -1)
+
+        st.image(cv2.cvtColor(transformed_img, cv2.COLOR_BGR2RGB), caption='変換後の画像と差異', use_column_width=True)
+
+def merge(rectangles, image_area, dist_threshold=10, diff_rate=0.5): #矩形の計算
+    merged_rec = []
+    
+    used = [False] * len(rectangles)
+    
+    for i, rect1 in enumerate(rectangles):
+        if used[i]:
+            continue
+        
+        x1, y1, w1, h1 = rect1
+        area1 = w1 * h1
+        merged = False
+
+        for j, rect2 in enumerate(rectangles):
+            if i == j or used[j]:
+                continue
+            
+            x2, y2, w2, h2 = rect2
+            area2 = w2 * h2
+
+            center1 = np.array([x1 + w1 / 2, y1 + h1 / 2])
+            center2 = np.array([x2 + w2 / 2, y2 + h2 / 2])
+            distance = np.linalg.norm(center1 - center2)
+
+            if distance < dist_threshold and abs(area1 - area2) < diff_rate * max(area1, area2):
+                new_x = min(x1, x2)
+                new_y = min(y1, y2)
+                new_w = max(x1 + w1, x2 + w2) - new_x
+                new_h = max(y1 + h1, y2 + h2) - new_y
+                merged_rec.append((new_x, new_y, new_w, new_h))
+                used[i] = used[j] = True
+                merged = True
+                break
+        
+        if not merged:
+            merged_rec.append(rect1)
+    
+    filter_rect = []
+    for rect in merged_rec:
+        x, y, w, h = rect
+        area = w * h
+        ok = True
+
+        if area >= (1/3) * image_area:
+            ok = False
+
+        for other_rect in merged_rec:
+            if rect == other_rect:
+                continue
+            ox, oy, ow, oh = other_rect
+            other_area = ow * oh
+            if area < other_area and abs(area - other_area) > diff_rate * max(area, other_area):
+                ok = False
+                break
+        
+        if ok:
+            filter_rect.append(rect)
+
+    return filter_rect
+
+def rectangle_diff(image_file1, image_file2):
+    image1 = Image.open(image_file1)
+    image2 = Image.open(image_file2)
+    
+    col1, col2  = st.columns(2)
+    diff_Thresholds = st.sidebar.slider("差分の閾値処理", 10, 255, 50)
+    distance_threshold = st.sidebar.slider("矩形の結合距離", 1, 50, 10)
+    size_difference_ratio = st.sidebar.slider("サイズ差異の割合", 0.0, 1.0, 0.5)
+    
+    with col1:
+        st.image(image1, caption='1枚目の画像', use_column_width=True)
+
+    with col2:
+        st.image(image2, caption='2枚目の画像', use_column_width=True)	
+
+    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 < 0.8 * 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)
+
+        H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
+
+        height, width, channels = image2_cv.shape
+        transformed_img = cv2.warpPerspective(image1_cv, H, (width, height))
+
+        transformed_gray = cv2.cvtColor(transformed_img, cv2.COLOR_BGR2GRAY)
+
+        img_diff = cv2.absdiff(transformed_gray, gray2)
+
+        _, img_th = cv2.threshold(img_diff, diff_Thresholds, 255, cv2.THRESH_BINARY)
+
+        kernel = np.ones((3, 3), np.uint8)
+        img_th = cv2.morphologyEx(img_th, cv2.MORPH_OPEN, kernel, iterations=2)
+        img_th = cv2.dilate(img_th, kernel, iterations=1)
+
+        contours, _ = cv2.findContours(img_th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        rectangles = [cv2.boundingRect(contour) for contour in contours]
+
+        image_area = height * width
+        filtered_rectangles = merge(rectangles, image_area, distance_threshold, size_difference_ratio)
+
+        for x, y, w, h in filtered_rectangles:
+            cv2.rectangle(transformed_img, (x, y), (x + w, y + h), (0, 0, 255), 2)
+
+        st.image(cv2.cvtColor(transformed_img, cv2.COLOR_BGR2RGB), caption='変換後の画像と差異', use_column_width=True)

match.py

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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)

requirements.txt

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+streamlit
+Pillow
+opencv-python
+numpy
+matplotlib