def rectify_images_float(img1, x1, img2, x2, K, d, F, shearing=False):
imsize = (img1.shape[1], img1.shape[0])
H1, H2, rms, max_error = epipolar.rectify_uncalibrated(x1, x2, F, imsize)
if shearing:
S = epipolar.rectify_shearing(H1, H2, imsize)
H1 = S.dot(H1)
rH = la.inv(K).dot(H1).dot(K)
lH = la.inv(K).dot(H2).dot(K)
map1x, map1y = cv2.initUndistortRectifyMap(K, d, rH, K, imsize,
cv.CV_16SC2)
map2x, map2y = cv2.initUndistortRectifyMap(K, d, lH, K, imsize,
cv.CV_16SC2)
rimg1 = cv2.remap(img1,
map1x,
map1y,
interpolation=cv.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0, 0, 0, 0))
rimg2 = cv2.remap(img2,
map2x,
map2y,
interpolation=cv.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0, 0, 0, 0))
return rimg1, rimg2
def rectify_images(img1, x1, img2, x2, K, d, F, shearing=False):
imsize = (img1.shape[1], img1.shape[0])
H1, H2 = epipolar.rectify_uncalibrated(x1, x2, F, imsize)
#x1_1d = np.empty((2*x1.shape[1],), dtype=float)
#x1_1d[0::2] = x1[0,:]
#x1_1d[1::2] = x1[1,:]
#x2_1d = np.empty((2*x2.shape[1],), dtype=float)
#x2_1d[0::2] = x2[0,:]
#x2_1d[1::2] = x2[1,:]
#success, cvH1, cvH2 = cv2.stereoRectifyUncalibrated(x1_1d, x2_1d, F, imsize)
if shearing:
S = epipolar.rectify_shearing(H1, H2, imsize)
H1 = S.dot(H1)
rH = la.inv(K).dot(H1).dot(K)
lH = la.inv(K).dot(H2).dot(K)
# TODO: lRect or rRect for img1/img2 ??
map1x, map1y = cv2.initUndistortRectifyMap(K, d, rH, K, imsize,
cv.CV_16SC2)
map2x, map2y = cv2.initUndistortRectifyMap(K, d, lH, K, imsize,
cv.CV_16SC2)
# Convert the images to RGBA (add an axis with 4 values)
img1 = np.tile(img1[:, :, np.newaxis], [1, 1, 4])
img1[:, :, 3] = 255
img2 = np.tile(img2[:, :, np.newaxis], [1, 1, 4])
img2[:, :, 3] = 255
rimg1 = cv2.remap(img1,
map1x,
map1y,
interpolation=cv.CV_INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0, 0, 0, 0))
rimg2 = cv2.remap(img2,
map2x,
map2y,
interpolation=cv.CV_INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0, 0, 0, 0))
# Put a red background on the invalid values
# TODO: Return a mask for valid/invalid values
# TODO: There is aliasing hapenning on the images border. We should
# invalidate a margin around the border so we're sure we have only valid
# pixels
rimg1[rimg1[:, :, 3] == 0, :] = (255, 0, 0, 255)
rimg2[rimg2[:, :, 3] == 0, :] = (255, 0, 0, 255)
return rimg1, rimg2
def rectify_images(img1, x1, img2, x2, K, d, F, shearing=False):
imsize = (img1.shape[1], img1.shape[0])
H1, H2 = epipolar.rectify_uncalibrated(x1, x2, F, imsize)
#x1_1d = np.empty((2*x1.shape[1],), dtype=float)
#x1_1d[0::2] = x1[0,:]
#x1_1d[1::2] = x1[1,:]
#x2_1d = np.empty((2*x2.shape[1],), dtype=float)
#x2_1d[0::2] = x2[0,:]
#x2_1d[1::2] = x2[1,:]
#success, cvH1, cvH2 = cv2.stereoRectifyUncalibrated(x1_1d, x2_1d, F, imsize)
if shearing:
S = epipolar.rectify_shearing(H1, H2, imsize)
H1 = S.dot(H1)
rH = la.inv(K).dot(H1).dot(K)
lH = la.inv(K).dot(H2).dot(K)
# TODO: lRect or rRect for img1/img2 ??
map1x, map1y = cv2.initUndistortRectifyMap(K, d, rH, K, imsize,
cv.CV_16SC2)
map2x, map2y = cv2.initUndistortRectifyMap(K, d, lH, K, imsize,
cv.CV_16SC2)
# Convert the images to RGBA (add an axis with 4 values)
img1 = np.tile(img1[:,:,np.newaxis], [1,1,4])
img1[:,:,3] = 255
img2 = np.tile(img2[:,:,np.newaxis], [1,1,4])
img2[:,:,3] = 255
rimg1 = cv2.remap(img1, map1x, map1y,
interpolation=cv.CV_INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0,0,0,0))
rimg2 = cv2.remap(img2, map2x, map2y,
interpolation=cv.CV_INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0,0,0,0))
# Put a red background on the invalid values
# TODO: Return a mask for valid/invalid values
# TODO: There is aliasing hapenning on the images border. We should
# invalidate a margin around the border so we're sure we have only valid
# pixels
rimg1[rimg1[:,:,3] == 0,:] = (255,0,0,255)
rimg2[rimg2[:,:,3] == 0,:] = (255,0,0,255)
return rimg1, rimg2
def rectify_images(img1, x1, img2, x2, K, d, F, shearing=False):
imsize = (img1.shape[1], img1.shape[0])
H1, H2, rms, max_error = epipolar.rectify_uncalibrated(x1, x2, F, imsize)
if shearing:
S = epipolar.rectify_shearing(H1, H2, imsize)
H1 = S.dot(H1)
rH = la.inv(K).dot(H1).dot(K)
lH = la.inv(K).dot(H2).dot(K)
# check for y parallax
max_yparallax = get_y_parallax(x1, x2, rH, lH, imsize)
# TODO: lRect or rRect for img1/img2 ??
map1x, map1y = cv2.initUndistortRectifyMap(K, d, rH, K, imsize,
cv.CV_16SC2)
map2x, map2y = cv2.initUndistortRectifyMap(K, d, lH, K, imsize,
cv.CV_16SC2)
# Convert the images to RGBA (add an axis with 4 values)
img1 = np.tile(img1[:, :, np.newaxis], [1, 1, 4])
img1[:, :, 3] = 255
img2 = np.tile(img2[:, :, np.newaxis], [1, 1, 4])
img2[:, :, 3] = 255
rimg1 = cv2.remap(img1,
map1x,
map1y,
interpolation=cv.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0, 0, 0, 0))
rimg2 = cv2.remap(img2,
map2x,
map2y,
interpolation=cv.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0, 0, 0, 0))
# Put a red background on the invalid values
# TODO: Return a mask for valid/invalid values
# TODO: There is aliasing happening on the images border. We should
# invalidate a margin around the border so we're sure we have only valid
# pixels
rimg1[rimg1[:, :, 3] == 0, :] = (255, 0, 0, 255)
rimg2[rimg2[:, :, 3] == 0, :] = (255, 0, 0, 255)
return rimg1, rimg2, rms, max_error, lH, rH, max_yparallax
def rectify_images(img1, x1, img2, x2, K, d, F, shearing=False):
imsize = (img1.shape[1], img1.shape[0])
H1, H2 = epipolar.rectify_uncalibrated(x1, x2, F, imsize)
if shearing:
S = epipolar.rectify_shearing(H1, H2, imsize)
H1 = S.dot(H1)
print('The homographies are: \n', H1, '\nR\n', H2)
rH = la.inv(K).dot(H1).dot(K)
lH = la.inv(K).dot(H2).dot(K)
print('The homographies are: \n', lH, '\nR\n', rH)
map1x, map1y = cv.initUndistortRectifyMap(K, d, rH, K, imsize, cv.CV_16SC2)
map2x, map2y = cv.initUndistortRectifyMap(K, d, lH, K, imsize, cv.CV_16SC2)
# Convert the images to RGBA (add an axis with 4 values)
img1 = np.tile(img1[:, :, np.newaxis], [1, 1, 4])
img1[:, :, 3] = 255
img2 = np.tile(img2[:, :, np.newaxis], [1, 1, 4])
img2[:, :, 3] = 255
rimg1 = cv.remap(img1,
map1x,
map1y,
interpolation=cv.CV_INTER_LINEAR,
borderMode=cv.BORDER_CONSTANT,
borderValue=(0, 0, 0, 0))
rimg2 = cv.remap(img2,
map2x,
map2y,
interpolation=cv.CV_INTER_LINEAR,
borderMode=cv.BORDER_CONSTANT,
borderValue=(0, 0, 0, 0))
# Put a red background on the invalid values
# TODO: Return a mask for valid/invalid values
# TODO: There is aliasing hapenning on the images border. We should
# invalidate a margin around the border so we're sure we have only valid
# pixels
rimg1[rimg1[:, :, 3] == 0, :] = (255, 0, 0, 255)
rimg2[rimg2[:, :, 3] == 0, :] = (255, 0, 0, 255)
return rimg1, rimg2
def get_epipolar_rectification_matrices(img1, x1, img2, x2, K, d, F):
imsize = (img1.shape[1], img1.shape[0])
H1, H2, rms, max_error = epipolar.rectify_uncalibrated(x1, x2, F, imsize)
rH = la.inv(K).dot(H1).dot(K)
lH = la.inv(K).dot(H2).dot(K)
return rH, lH