删除冗余算法

image_fusion/Image_Registration_test.py

@@ -14,48 +14,6 @@ yolo_model = YOLO("yolov8n.pt")  # 可替换为yolov8s/m/l等
 yolo_model.to('cuda')  # 启用GPU加速
 
 
-def sift_registration(img1, img2):
-    img1gray = cv2.normalize(img1, dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX).astype(np.uint8)
-    img2gray = img2
-    
-    sift = cv2.SIFT_create()
-    # find the keypoints and descriptors with SIFT
-    kp1, des1 = sift.detectAndCompute(img1gray, None)
-    kp2, des2 = sift.detectAndCompute(img2gray, None)
-    # FLANN parameters
-    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(des1, des2, k=2)
-    
-    good = []
-    pts1 = []
-    pts2 = []
-    
-    for i, (m, n) in enumerate(matches):
-        if m.distance < 0.75 * n.distance:
-            good.append(m)
-            pts2.append(kp2[m.trainIdx].pt)
-            pts1.append(kp1[m.queryIdx].pt)
-    
-    MIN_MATCH_COUNT = 4
-    if len(good) > MIN_MATCH_COUNT:
-        src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
-        dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
-        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
-    else:
-        print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))
-        M = np.array([[1, 0, 0],
-                      [0, 1, 0],
-                      [0, 0, 1]], dtype=np.float64)
-    if M is None:
-        M = np.array([[1, 0, 0],
-                      [0, 1, 0],
-                      [0, 0, 1]], dtype=np.float64)
-    return 1, M[0], len(pts2)
-
-
 # 裁剪线性RGB对比度拉伸：（去掉2%百分位以下的数，去掉98%百分位以上的数，上下百分位数一般相同，并设置输出上下限）
 def truncated_linear_stretch(image, truncated_value=2, maxout=255, min_out=0):
     """