Python cv2.CV_16SC2 Examples
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code examples of cv2.CV_16SC2().
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Example #1
Source File: test-mvs.py From dfc2019 with MIT License | 6 votes |
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 # get NITF metadata that we embedded in the GeoTIFF header
Example #2
Source File: get-undistortion-maps.py From fsoco with Apache License 2.0 | 5 votes |
def main(): basler_undistorted = pickle.load(open("basler_pickle.p", "rb")) #objects = [] #with (open("./basler_pickle.p", "rb")) as openfile: # while True: # try: # basler_undistorted.append(pickle.load(openfile)) # except EOFError: # break image_width = 1280 image_height = 1024 #print(basler_undistorted) cam_mtx = basler_undistorted["mtx"] cam_dist = basler_undistorted["dist"] rvecs = basler_undistorted["rvecs"] tvecs = basler_undistorted["tvecs"] #imageSize = image_height * image_width imageSize = ( image_height, image_width ) #getOptimal...Mtx(cameraMatrix, distCoeffs, imageSize, alpha[, newImgSize[, centerPrincipalPoint]]) -> retval, validPixROI # Doesn't take Rect of validPixROI, contrary to the cpp method new_cam_mtx, valid_roi = cv.getOptimalNewCameraMatrix(cam_mtx, cam_dist, imageSize, 1, imageSize, 1) # getOptimalNewCameraMatrix() possibly not working like in cpp #map1, map2 = cv.initUndistortRectifyMap(cam_mtx, cam_dist, np.eye(3), new_cam_mtx, imageSize, cv.CV_16SC2); map1, map2 = cv.initUndistortRectifyMap(cam_mtx, cam_dist, np.eye(3), cam_mtx, imageSize, cv.CV_16SC2); # map1 and map2 can be used together with cv.remap() for efficient real-time undistortion # Only need to be calculated once. maps = { "map1": map1, "map2": map2 } pickle.dump( maps, open("maps.p", "wb"))
Example #3
Source File: test-mvs.py From dfc2019 with MIT License | 5 votes |
def rectify_images_rgb(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) # 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) rimg1 = cv2.remap(img1, map1x, map1y, interpolation=cv.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0)) rimg2 = cv2.remap(img2, map2x, map2y, interpolation=cv.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0)) return rimg1, rimg2, rms, max_error # rectify a floating point image pair based on the Fundamental matrix # use this for XYZ images
Example #4
Source File: main.py From fisheye with Apache License 2.0 | 5 votes |
def undistort(img_path,K,D,DIM,scale=0.6,imshow=False): img = cv2.imread(img_path) dim1 = img.shape[:2][::-1] #dim1 is the dimension of input image to un-distort assert dim1[0]/dim1[1] == DIM[0]/DIM[1], "Image to undistort needs to have same aspect ratio as the ones used in calibration" if dim1[0]!=DIM[0]: img = cv2.resize(img,DIM,interpolation=cv2.INTER_AREA) Knew = K.copy() if scale:#change fov Knew[(0,1), (0,1)] = scale * Knew[(0,1), (0,1)] map1, map2 = cv2.fisheye.initUndistortRectifyMap(K, D, np.eye(3), Knew, DIM, cv2.CV_16SC2) undistorted_img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT) if imshow: cv2.imshow("undistorted", undistorted_img) return undistorted_img
Example #5
Source File: utils.py From PartiallyReversibleUnet with BSD 3-Clause "New" or "Revised" License | 5 votes |
def dense_image_warp(im, dx, dy, interp=cv2.INTER_LINEAR): map_x, map_y = deformation_to_transformation(dx, dy) do_optimization = (interp == cv2.INTER_LINEAR) # The following command converts the maps to compact fixed point representation # this leads to a ~20% increase in speed but could lead to accuracy losses # Can be uncommented if do_optimization: map_x, map_y = cv2.convertMaps(map_x, map_y, dstmap1type=cv2.CV_16SC2) remapped = cv2.remap(im, map_x, map_y, interpolation=interp, borderMode=cv2.BORDER_REFLECT) #borderValue=float(np.min(im))) if im.ndim > remapped.ndim: remapped = np.expand_dims(remapped, im.ndim) return remapped
Example #6
Source File: utils.py From PHiSeg-code with Apache License 2.0 | 5 votes |
def dense_image_warp(im, dx, dy, interp=cv2.INTER_LINEAR, do_optimisation=True): map_x, map_y = deformation_to_transformation(dx, dy) # The following command converts the maps to compact fixed point representation # this leads to a ~20% increase in speed but could lead to accuracy losses # Can be uncommented if do_optimisation: map_x, map_y = cv2.convertMaps(map_x, map_y, dstmap1type=cv2.CV_16SC2) return cv2.remap(im, map_x, map_y, interpolation=interp, borderMode=cv2.BORDER_REFLECT) #borderValue=float(np.min(im)))
Example #7
Source File: test-mvs.py From dfc2019 with MIT License | 4 votes |
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 # rectify an image pair based on the Fundamental matrix