格式化代码

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):
     """
@@ -91,6 +49,10 @@ def Images_matching(img_base, img_target):
     """
     start = time.time()
     orb = cv2.ORB_create()
+    
+    # 对可见光图像进行对比度拉伸
+    # img_base = truncated_linear_stretch(img_base)
+    
     img_base = cv2.cvtColor(img_base, cv2.COLOR_BGR2GRAY)
     sift = cv2.SIFT_create()
     # 使用sift算子计算特征点和特征点周围的特征向量
@@ -98,7 +60,9 @@ def Images_matching(img_base, img_target):
     kp1, des1 = sift.detectAndCompute(img_base, None)  # 1136    1136, 64
     kp2, des2 = sift.detectAndCompute(img_target, None)
     en1 = time.time()
+    
     # print(en1 - st1, "特征提取")
+    
     # 进行KNN特征匹配
     # FLANN_INDEX_KDTREE = 0  # 建立FLANN匹配器的参数
     # indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)  # 配置索引，密度树的数量为5
@@ -110,6 +74,7 @@ def Images_matching(img_base, img_target):
     # search_params = dict(checks=50)
     # flann = cv2.FlannBasedMatcher(index_params, search_params)
     # matches = flann.knnMatch(des1, des2, k=2)
+    
     st2 = time.time()
     matcher = cv2.BFMatcher()
     matches = matcher.knnMatch(des1, des2, k=2)
@@ -185,7 +150,7 @@ def main(matchimg_vi, matchimg_in):
             return 0, None, 0
         else:
             matched_ni = cv2.warpPerspective(orimg_in, H, (w, h))
-            # matched_ni,left,right,top,bottom=removeBlackBorder(matched_ni)
+            matched_ni,left,right,top,bottom=removeBlackBorder(matched_ni)
             # fusion = fusions(orimg_vi[left:right, top:bottom], matched_ni)
             fusion = fusions(orimg_vi, matched_ni)