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使用深度摄像头可以在捕获图像时估计图像与摄像头之间的距离。但是对于普通摄像头，可以使用属于立体视觉（stereo vision）几何学的极几何（Epipolar Geometry）从同一物体的两张不同图像上提取三维信息。

如果运行失败（）。

下面是一个例子：

# 从视差图得到图像深度import cv2import numpy as npfrom matplotlib import pyplot as pltdef to_uint8(data):    # maximum pixel    latch = np.zeros_like(data)    latch[:] = 255    # minimum pixel    zeros = np.zeros_like(data)    # unrolled to illustrate steps    d = np.maximum(zeros, data)    d = np.minimum(latch, d)    # cast to uint8    return np.asarray(d, dtype="uint8")def draw_lines(img1, img2, lines, pts1, pts2):    ''' img1 - image on which we draw the epilines for the points in img2        lines - corresponding epilines '''    print    img1.shape    r, c, ch = img1.shape    clr1 = cv2.pyrDown(cv2.imread('stacked1.png', 0))    clr2 = cv2.pyrDown(cv2.imread('stacked2.png', 0))    for r, pt1, pt2 in zip(lines, pts1, pts2):        color = tuple(np.random.randint(0, 255, 3).tolist())        x0, y0 = map(int, [0, -r[2] / r[1]])        x1, y1 = map(int, [c, -(r[2] + r[0] * c) / r[1]])        clr1 = cv2.line(clr1, (x0, y0), (x1, y1), color, 1)        clr1 = cv2.circle(clr1, tuple(pt1), 5, color, -1)        clr2 = cv2.circle(clr2, tuple(pt2), 5, color, -1)        return clr1, clr2img1 = to_uint8(cv2.pyrDown(cv2.imread('stacked1.png', cv2.COLOR_BGR2GRAY)))  # queryimage # left imageimg2 = to_uint8(cv2.pyrDown(cv2.imread('stacked2.png', cv2.COLOR_BGR2GRAY)))  # trainimage # right imagesift = cv2.xfeatures2d.SIFT_create()# find the keypoints and descriptors with SIFTkp1, des1 = sift.detectAndCompute(img1, None)kp2, des2 = sift.detectAndCompute(img2, None)# FLANN parametersFLANN_INDEX_KDTREE = 0index_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 = []# ratio test as per Lowe's paperfor i, (m, n) in enumerate(matches):    if m.distance < 0.8 * n.distance:        good.append(m)        pts2.append(kp2[m.trainIdx].pt)        pts1.append(kp1[m.queryIdx].pt)pts1 = np.int32(pts1)pts2 = np.int32(pts2)F, mask = cv2.findFundamentalMat(pts1, pts2, cv2.FM_LMEDS)# We select only inlier pointspts1 = pts1[mask.ravel() == 1]pts2 = pts2[mask.ravel() == 1]# Find epilines corresponding to points in right image (second image) and# drawing its lines on left imagelines1 = cv2.computeCorrespondEpilines(pts2.reshape(-1, 1, 2), 2, F)lines1 = lines1.reshape(-1, 3)img5, img6 = draw_lines(img1, img2, lines1, pts1, pts2)# Find epilines corresponding to points in left image (first image) and# drawing its lines on right imagelines2 = cv2.computeCorrespondEpilines(pts1.reshape(-1, 1, 2), 1, F)lines2 = lines2.reshape(-1, 3)img3, img4 = draw_lines(img2, img1, lines2, pts2, pts1)plt.subplot(121), plt.imshow(img5)plt.subplot(122), plt.imshow(img3)plt.show()

• stacked1.png

• stacked2.png

• 运行结果

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