148 lines
5.1 KiB
Python
148 lines
5.1 KiB
Python
# -*- coding: utf-8 -*-
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# @Time :
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# @Author :
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import cv2
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import numpy as np
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sift = cv2.SIFT_create()
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def compuerSift2GetPts(img1, img2):
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# sift 查找关键点，关键点 And 描述
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kp1, des1 = sift.detectAndCompute(img1, None)
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kp2, des2 = sift.detectAndCompute(img2, None)
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matcher = cv2.BFMatcher()
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raw_matches = matcher.knnMatch(des1, des2, k=2)
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good_matches = []
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ratio = 0.75
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for m1, m2 in raw_matches:
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# 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
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if m1.distance < ratio * m2.distance:
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good_matches.append([m1])
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matches = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good_matches, None, flags=2)
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ptsA = np.float32([kp1[m[0].queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
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ptsB = np.float32([kp2[m[0].trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)
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ransacReprojThreshold = 4
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# 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
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# print(len(ptsA), len(ptsB))
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if len(ptsA) == 0: return ptsA, ptsB, 0
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H, status = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, ransacReprojThreshold)
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cv2.imshow("matcher", matches)
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cv2.waitKey(100)
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return ptsA, ptsB, 1
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def findBestDistanceAndPts(ptsA, ptsB):
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x_dct = {}
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y_dct = {}
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best_x, best_y = int(ptsA[0][0][0] - ptsB[0][0][0]), int(ptsA[0][0][1] - ptsB[0][0][1])
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x_cnt, y_cnt = 0, 0
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for i in range(len(ptsA)):
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# print(ptsA[i], ' ', ptsB[i])
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x_dis = int(ptsA[i][0][0] - ptsB[i][0][0])
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y_dis = int(ptsA[i][0][1] - ptsB[i][0][1])
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# print(x_dis)
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if x_dis in x_dct:
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x_dct.update({x_dis: int(x_dct.get(x_dis) + 1)})
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if x_dct.get(x_dis) > x_cnt:
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best_x = x_dis
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x_cnt = x_dct.get(x_dis)
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# print(x_dct.get(x_dis))
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else:
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x_dct.update({x_dis: 1})
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# print(x_dct.get(x_dis))
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# print(y_dis)
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if y_dis in y_dct:
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y_dct.update({y_dis: int(y_dct.get(y_dis) + 1)})
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if y_dct.get(y_dis) > y_cnt:
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best_y = y_dis
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y_cnt = y_dct.get(y_dis)
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# print(y_dct.get(y_dis))
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else:
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y_dct.update({y_dis: 1})
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# print(y_dct.get(y_dis))
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print(best_x, best_y)
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pt = []
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ptb = []
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for i in range(len(ptsA)):
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x_dis = int(ptsA[i][0][0] - ptsB[i][0][0])
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y_dis = int(ptsA[i][0][1] - ptsB[i][0][1])
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if abs(best_x - x_dis) <= 0:
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pt.append([ptsA[i][0][0], ptsA[i][0][1]])
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# print(pt)
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return pt, best_x, best_y
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def minDistanceHasXy(ptsA, ptsB):
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dct = {}
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cnt = 0
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best = 's'
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for i in range(len(ptsA)):
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disx = int(ptsA[i][0][0] - ptsB[i][0][0] + 0.5)
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disy = int(ptsA[i][0][1] - ptsB[i][0][1] + 0.5)
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s = str(disx) + ',' + str(disy)
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# print(s)
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if s in dct:
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dct.updata({s: int(dct.get(s) + 1)})
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if dct.get(s) >= cnt:
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cnt = dct.get(s)
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best = s
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print(s)
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else:
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dct.update({s: int(1)})
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for i, j in dct.items():
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print(i, j)
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print(best)
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def detectImg(img1, img2, pta, best_x, best_y):
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# print(pta)
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min_x = int(min(x[0] for x in pta))
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max_x = int(max(x[0] for x in pta))
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min_y = int(min(x[1] for x in pta))
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max_y = int(max(x[1] for x in pta))
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# print(min_x, max_x)
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# print(min_x - best_x, max_x - best_x)
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# print(min_y, max_y)
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# print(min_y - best_y, max_y - best_y)
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newimg1 = img1[min_y: max_y, min_x: max_x]
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newimg2 = img2[min_y - best_y: max_y - best_y, min_x - best_x: max_x - best_x]
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# cv2.imshow("newimg1", newimg1)
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# cv2.imshow("newimg2", newimg2)
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# cv2.waitKey(0)
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return newimg1, newimg2
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if __name__ == '__main__':
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j = 0
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for i in range(20, 4771, 1):
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print(i)
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path1 = './data/907dat/gray/camera1-' + str(i) + '.png'
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path2 = './data/907dat/color/camera0-' + str(i) + '.png'
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img1 = cv2.imread(path1)
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img2 = cv2.imread(path2)
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if (img1 is None or img2 is None): continue
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PtsA, PtsB, f = compuerSift2GetPts(img1, img2)
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if (f == 0): continue
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pt, best_x, best_y = findBestDistanceAndPts(PtsA, PtsB)
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newimg1, newimg2 = detectImg(img1, img2, pt, best_x, best_y)
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if newimg1.shape[0] < 10 or newimg1.shape[1] < 10: continue
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print(newimg1.shape, newimg2.shape)
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# newimg1 = cv2.resize(newimg1, (320, 240))
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# newimg2 = cv2.resize(newimg2, (320, 240))
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wirtePath1 = './result/dat_result_2/gray/camera1-' + str(j) + '.png'
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wirtePath2 = './result/dat_result_2/color/camera0-' + str(j) + '.png'
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if newimg1.shape[0] > 255 and newimg1.shape[1] > 255 and newimg1.shape == newimg2.shape:
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# cv2.imwrite(wirtePath1, newimg1)
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# cv2.imwrite(wirtePath2, newimg2)
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j += 1
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cv2.imshow("newimg1", newimg1)
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cv2.imshow("newimg2", newimg2)
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cv2.waitKey()
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print(j)
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pass
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