图像融合模块

image_fusion/Img_Registration.py

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+# -*- coding: utf-8 -*-
+# @Time :
+# @Author :
+import cv2
+import numpy as np
+
+sift = cv2.SIFT_create()
+
+
+def compuerSift2GetPts(img1, img2):
+    # sift 查找关键点，关键点 And 描述
+    kp1, des1 = sift.detectAndCompute(img1, None)
+    kp2, des2 = sift.detectAndCompute(img2, None)
+    
+    matcher = cv2.BFMatcher()
+    raw_matches = matcher.knnMatch(des1, des2, k=2)
+    good_matches = []
+    ratio = 0.75
+    for m1, m2 in raw_matches:
+        # 如果最接近和次接近的比值大于一个既定的值，那么我们保留这个最接近的值，认为它和其匹配的点为good_match
+        if m1.distance < ratio * m2.distance:
+            good_matches.append([m1])
+    matches = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good_matches, None, flags=2)
+    ptsA = np.float32([kp1[m[0].queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
+    ptsB = np.float32([kp2[m[0].trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)
+    
+    ransacReprojThreshold = 4
+    #  单应性矩阵可以将一张图通过旋转、变换等方式与另一张图对齐
+    # print(len(ptsA), len(ptsB))
+    if len(ptsA) == 0: return ptsA, ptsB, 0
+    H, status = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, ransacReprojThreshold)
+    cv2.imshow("matcher", matches)
+    cv2.waitKey(100)
+    
+    return ptsA, ptsB, 1
+
+
+def findBestDistanceAndPts(ptsA, ptsB):
+    x_dct = {}
+    y_dct = {}
+    best_x, best_y = int(ptsA[0][0][0] - ptsB[0][0][0]), int(ptsA[0][0][1] - ptsB[0][0][1])
+    x_cnt, y_cnt = 0, 0
+    for i in range(len(ptsA)):
+        # print(ptsA[i], '        ', ptsB[i])
+        x_dis = int(ptsA[i][0][0] - ptsB[i][0][0])
+        y_dis = int(ptsA[i][0][1] - ptsB[i][0][1])
+        # print(x_dis)
+        if x_dis in x_dct:
+            x_dct.update({x_dis: int(x_dct.get(x_dis) + 1)})
+            if x_dct.get(x_dis) > x_cnt:
+                best_x = x_dis
+                x_cnt = x_dct.get(x_dis)
+            # print(x_dct.get(x_dis))
+        else:
+            x_dct.update({x_dis: 1})
+            # print(x_dct.get(x_dis))
+        # print(y_dis)
+        if y_dis in y_dct:
+            y_dct.update({y_dis: int(y_dct.get(y_dis) + 1)})
+            if y_dct.get(y_dis) > y_cnt:
+                best_y = y_dis
+                y_cnt = y_dct.get(y_dis)
+            # print(y_dct.get(y_dis))
+        else:
+            y_dct.update({y_dis: 1})
+            # print(y_dct.get(y_dis))
+    print(best_x, best_y)
+    
+    pt = []
+    ptb = []
+    for i in range(len(ptsA)):
+        x_dis = int(ptsA[i][0][0] - ptsB[i][0][0])
+        y_dis = int(ptsA[i][0][1] - ptsB[i][0][1])
+        if abs(best_x - x_dis) <= 0:
+            pt.append([ptsA[i][0][0], ptsA[i][0][1]])
+    # print(pt)
+    return pt, best_x, best_y
+
+
+def minDistanceHasXy(ptsA, ptsB):
+    dct = {}
+    cnt = 0
+    best = 's'
+    for i in range(len(ptsA)):
+        disx = int(ptsA[i][0][0] - ptsB[i][0][0] + 0.5)
+        disy = int(ptsA[i][0][1] - ptsB[i][0][1] + 0.5)
+        s = str(disx) + ',' + str(disy)
+        # print(s)
+        if s in dct:
+            dct.updata({s: int(dct.get(s) + 1)})
+            if dct.get(s) >= cnt:
+                cnt = dct.get(s)
+                best = s
+                print(s)
+        else:
+            dct.update({s: int(1)})
+    for i, j in dct.items():
+        print(i, j)
+    print(best)
+
+
+def detectImg(img1, img2, pta, best_x, best_y):
+    # print(pta)
+    min_x = int(min(x[0] for x in pta))
+    max_x = int(max(x[0] for x in pta))
+    min_y = int(min(x[1] for x in pta))
+    max_y = int(max(x[1] for x in pta))
+    # print(min_x, max_x)
+    # print(min_x - best_x, max_x - best_x)
+    # print(min_y, max_y)
+    # print(min_y - best_y, max_y - best_y)
+    newimg1 = img1[min_y: max_y, min_x: max_x]
+    newimg2 = img2[min_y - best_y: max_y - best_y, min_x - best_x: max_x - best_x]
+    # cv2.imshow("newimg1", newimg1)
+    # cv2.imshow("newimg2", newimg2)
+    # cv2.waitKey(0)
+    return newimg1, newimg2
+
+
+if __name__ == '__main__':
+    j = 0
+    for i in range(20, 4771, 1):
+        print(i)
+        path1 = './data/907dat/gray/camera1-' + str(i) + '.png'
+        path2 = './data/907dat/color/camera0-' + str(i) + '.png'
+        img1 = cv2.imread(path1)
+        img2 = cv2.imread(path2)
+        if (img1 is None or img2 is None): continue
+        PtsA, PtsB, f = compuerSift2GetPts(img1, img2)
+        if (f == 0): continue
+        pt, best_x, best_y = findBestDistanceAndPts(PtsA, PtsB)
+        newimg1, newimg2 = detectImg(img1, img2, pt, best_x, best_y)
+        if newimg1.shape[0] < 10 or newimg1.shape[1] < 10: continue
+        print(newimg1.shape, newimg2.shape)
+        # newimg1 = cv2.resize(newimg1, (320, 240))
+        # newimg2 = cv2.resize(newimg2, (320, 240))
+        wirtePath1 = './result/dat_result_2/gray/camera1-' + str(j) + '.png'
+        wirtePath2 = './result/dat_result_2/color/camera0-' + str(j) + '.png'
+        if newimg1.shape[0] > 255 and newimg1.shape[1] > 255 and newimg1.shape == newimg2.shape:
+            # cv2.imwrite(wirtePath1, newimg1)
+            # cv2.imwrite(wirtePath2, newimg2)
+            j += 1
+            cv2.imshow("newimg1", newimg1)
+            cv2.imshow("newimg2", newimg2)
+            cv2.waitKey()
+    print(j)
+    pass