rename alignment

Author: 孙啸寒

Diff for: registration/.idea/.gitignore renamed to alignment/.idea/.gitignore

@@ -1,60 +1,60 @@
-import numpy as np
-import cv2
-from matplotlib import pyplot as plt
-
-MIN_MATCH_COUNT = 10
-img1 = cv2.imread('assets/butterfly.png', 0)  # 原图像
-img2 = cv2.imread('assets/scan1.jpg', 0)  # 待搜索图像，下面称目标图像
-# 启动SIFT检测器
-sift = cv2.SIFT_create()
-# 使用SIFT查找关键点和描述符
-kp1, des1 = sift.detectAndCompute(img1, None)
-kp2, des2 = sift.detectAndCompute(img2, None)
-
-# 使用FLANN匹配器进行匹配
-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)  # 获得匹配结果
-
-# 按照Lowe的比率存储所有好的匹配。
-good = []
-for m, n in matches:
-    if m.distance < 0.7 * n.distance:
-        good.append(m)
-
-# 只有好的匹配点多于10个才查找目标，否则显示匹配不足
-if len(good) > MIN_MATCH_COUNT:
-    # 获取匹配点在原图像和目标图像中的的位置
-    # kp1：原图像的特征点
-    # m.queryIdx：匹配点在原图像特征点中的索引
-    # .pt：特征点的坐标
-    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)
-    # 获取变换矩阵，采用RANSAC算法
-    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
-    matchesMask = mask.ravel().tolist()
-    # 图像变换，将原图像变换为检测图像中匹配到的形状
-    # 获得原图像尺寸
-    h, w = img1.shape
-    # 使用得到的变换矩阵对原图像的四个角进行变换，获得在目标图像上对应的坐标。
-    pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]
-                     ).reshape(-1, 1, 2)
-    # 对角点进行变换
-    dst = cv2.perspectiveTransform(pts, M)
-    # 画出边框
-    img2 = cv2.polylines(img2, [np.int32(dst)], True, 255, 5, cv2.LINE_AA)
-else:
-    print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))
-    matchesMask = None
-
-# 画出匹配点
-draw_params = dict(matchColor=(0, 255, 0),  # draw matches in green color
-                   singlePointColor=None,
-                   matchesMask=matchesMask,  # draw only inliers
-                   flags=2)
-img3 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params)
-plt.imshow(img3), plt.title('Result'),
-plt.axis('off')
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+MIN_MATCH_COUNT = 10
+img1 = cv2.imread('assets/butterfly.png', 0)  # 原图像
+img2 = cv2.imread('assets/scan1.jpg', 0)  # 待搜索图像，下面称目标图像
+# 启动SIFT检测器
+sift = cv2.SIFT_create()
+# 使用SIFT查找关键点和描述符
+kp1, des1 = sift.detectAndCompute(img1, None)
+kp2, des2 = sift.detectAndCompute(img2, None)
+
+# 使用FLANN匹配器进行匹配
+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)  # 获得匹配结果
+
+# 按照Lowe的比率存储所有好的匹配。
+good = []
+for m, n in matches:
+    if m.distance < 0.7 * n.distance:
+        good.append(m)
+
+# 只有好的匹配点多于10个才查找目标，否则显示匹配不足
+if len(good) > MIN_MATCH_COUNT:
+    # 获取匹配点在原图像和目标图像中的的位置
+    # kp1：原图像的特征点
+    # m.queryIdx：匹配点在原图像特征点中的索引
+    # .pt：特征点的坐标
+    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)
+    # 获取变换矩阵，采用RANSAC算法
+    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
+    matchesMask = mask.ravel().tolist()
+    # 图像变换，将原图像变换为检测图像中匹配到的形状
+    # 获得原图像尺寸
+    h, w = img1.shape
+    # 使用得到的变换矩阵对原图像的四个角进行变换，获得在目标图像上对应的坐标。
+    pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]
+                     ).reshape(-1, 1, 2)
+    # 对角点进行变换
+    dst = cv2.perspectiveTransform(pts, M)
+    # 画出边框
+    img2 = cv2.polylines(img2, [np.int32(dst)], True, 255, 5, cv2.LINE_AA)
+else:
+    print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))
+    matchesMask = None
+
+# 画出匹配点
+draw_params = dict(matchColor=(0, 255, 0),  # draw matches in green color
+                   singlePointColor=None,
+                   matchesMask=matchesMask,  # draw only inliers
+                   flags=2)
+img3 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params)
+plt.imshow(img3), plt.title('Result'),
+plt.axis('off')
 plt.show()