def transform(diff_image, map_patch):
os.environ['PYOPENCL_COMPILER_OUTPUT'] = '1'
h, w = diff_image.shape[::-1]
pad = np.zeros(map_patch.shape)
pad[:diff_image.shape[0], :diff_image.shape[1]] = diff_image
diff_image = pad
sift_ocl1 = sift.SiftPlan(template=diff_image, devicetype="GPU")
sift_ocl2 = sift.SiftPlan(template=map_patch, devicetype="GPU")
kp1 = sift_ocl1.keypoints(diff_image)
kp2 = sift_ocl2.keypoints(map_patch)
sift_m = sift.MatchPlan(devicetype="GPU")
matches = sift_m.match(kp1, kp2)
sa = sift.LinearAlign(diff_image, devicetype="GPU")
transformed_img = sa.align(map_patch)
transformed_img = transformed_img[:w, :h]
theta_rad = abs(matches.angle[0][0]) + abs(matches.angle[0][1])
theta = (theta_rad * 180) / math.pi
return transformed_img, theta
def __createSiftData(self, image, second_image):
"""Generate key points and aligned images from 2 images.
If no keypoints matches, unaligned data are anyway returns.
:rtype: Tuple(numpy.ndarray,numpy.ndarray)
"""
devicetype = "GPU"
# Compute base image
sift_ocl = sift.SiftPlan(template=image, devicetype=devicetype)
keypoints = sift_ocl(image)
# Check image compatibility
second_keypoints = sift_ocl(second_image)
mp = sift.MatchPlan()
match = mp(keypoints, second_keypoints)
_logger.info("Number of Keypoints within image 1: %i" % keypoints.size)
_logger.info(" within image 2: %i" %
second_keypoints.size)
self.__matching_keypoints = (match[:].x[:, 0], match[:].y[:, 0],
match[:].scale[:, 0])
matching_keypoints = match.shape[0]
_logger.info("Matching keypoints: %i" % matching_keypoints)
if matching_keypoints == 0:
return image, second_image
# TODO: Problem here is we have to compute 2 time sift
# The first time to extract matching keypoints, second time
# to extract the aligned image.
# Normalize the second image
sa = sift.LinearAlign(image, devicetype=devicetype)
data1 = image
# TODO: Create a sift issue: if data1 is RGB and data2 intensity
# it returns None, while extracting manually keypoints (above) works
result = sa.align(second_image, return_all=True)
data2 = result["result"]
self.__transformation = self.__toAffineTransformation(result)
return data1, data2