def main(args):
# timer
tic = time.time()
# load images
high_img = read(args.img, color=args.color)
low_img = read(args.low_img, color=args.color)
high_img, low_img = align_images(high_img, low_img)
# call function on each image channel
for d in range(high_img.shape[2]):
high_img[:, :, d] = combine(high_img[:, :, d],
low_img[:, :, d],
sigma=args.sigma,
size=args.kernel_size)
# show image in original format and rgb
show(high_img)
if args.save:
save(high_img,
args.low_img,
sigma=args.sigma,
img2=os.path.basename(args.img))
toc = time.time()
print("Time elapsed: " + str(toc - tic))
required=True,
help="path to input image that we'll assign to template")
ap.add_argument("-t",
"--template",
required=True,
help="path to input template image")
ap.add_argument("-d",
"--debug",
required=False,
help="enter True to run in debug mode")
args = vars(ap.parse_args())
print("[Info] loading images...")
image = cv2.imread(args["image"])
template = cv2.imread(args["template"])
print("[Info] aligning images...")
aligned = align_images.align_images(image, template, debug=args["debug"])
aligned = imutils.resize(aligned, width=700)
template = imutils.resize(template, width=700)
stacked = np.hstack([aligned, template])
overlay = template.copy()
output = aligned.copy()
cv2.addWeighted(overlay, 0.5, output, 0.5, 0, output)
cv2.imshow("Image Alignment: Stacked", stacked)
cv2.imshow("Image Alignment: Overlay", output)
cv2.waitKey(0)
#im2 = skio.imread('./originals/bobbycar.jpg')
#im1 = skio.imread('./originals/ferrari_gray.jpg')
#im2 = skio.imread('./originals/old_man.jpeg')
#im1 = skio.imread('./originals/matterhorn.jpg')
#im2 = skio.imread('./originals/wolf.jpg')
#im1 = skio.imread('./originals/1534638850129_gray.jpg')
#im2 = skio.imread('./originals/frown.JPG')
#im1 = skio.imread('./originals/smile_gray.JPG')
im2 = skio.imread('./originals/flo_finale.jpg')
im1 = skio.imread('./originals/me_boston.jpg')
im1 = sk.img_as_float(im1)
im2 = sk.img_as_float(im2)
# use this if you want to align the two images (e.g., by the eyes) and crop
# them to be of same size
im2, im1 = align_images(im2, im1)
# Choose the cutoff frequencies and compute the hybrid image (you supply
# this code)
arbitrary_value_1 = 7
arbitrary_value_2 = 10
cutoff_low = arbitrary_value_1
cutoff_high = arbitrary_value_2
im12 = hybrid_image(im1, im2, cutoff_low, cutoff_high)
# Crop resulting image (optional)
assert im12 is not None, "im12 is empty, implement hybrid_image!"
im12 = crop_image(im12)
im12 = sk.exposure.rescale_intensity(im12, in_range=(-1.0, 1.0))
def ResizeIMG(image):
#calculate the 50 percent of original dimensions
width = 700
(h, w) = image.shape[:2]
r = width / float(w)
dim = (width, int(h * r))
# resize image
image = cv2.resize(image, dim)
return image
resized_template = ResizeIMG(template_img)
if input_img is not None:
resized_image = ResizeIMG(input_img)
final_image = align_images(resized_image, resized_template, debug=True)
else:
final_image = resized_template
# REGION OF INTEREST (ROI) SELECTION
# initializing the list for storing the coordinates
coordinates = []
# Defining the event listener (callback function)
def shape_selection(event, x, y, flags, param):
# making coordinates global
global coordinates
def align_faces(self,
path_from=RAW_IMAGES_PATH,
path_to=ALIGNED_IMAGES_PATH):
align_images(path_from, path_to)
print("Images loaded...")
return cine_img, tagged_img, lm_coords
return None, None, None
if __name__ == '__main__':
hdf5_filepath = "C:\\Users\\arad572\\Documents\\Summer Research\\code\\prepare_data\\h5_files\\sa-oc-lvsc_new-filenames.hdf5"
my_hdf5_filepath = "C:\\Users\\arad572\\Documents\\Summer Research\\code\\prepare_data\\h5_files\\UK_Biobank_20cases.h5"
patient_name = "2B UK 9P 26 Bio"
dicom_filename = "1.3.12.2.1107.5.2.18.41754.2015060817574722496124508.dcm"
my_cine_img, my_tagged_img, lm_coords = get_images(my_hdf5_filepath,
dicom_filename)
mask, img_hdf5 = get_mask(hdf5_filepath, dicom_filename)
aligned_cine_img, aligned_mask = align_images(img_hdf5, my_cine_img, mask)
print(aligned_cine_img.dtype, img_hdf5.dtype)
plot_images(patient_name,
aligned_cine_img,
my_tagged_img,
img_hdf5,
mask,
aligned_mask,
lm_coords,
save_image=False)
#evaluate_on_hdf5('F://sa-oc-lvsc_new-filenames.hdf5')
print('Finished')
required=True,
help="path to input image that we'll align to template")
ap.add_argument("-t",
"--template",
required=True,
help="path to input template image")
args = vars(ap.parse_args())
# load the input image and template from disk
print("[INFO] loading images...")
image = cv2.imread(args["image"])
template = cv2.imread(args["template"])
# align the images
print("[INFO] aligning images...")
aligned = align_images(image, template, debug=True)
# resize both the aligned and template images so we can easily
# visualize them on our screen
aligned = imutils.resize(aligned, width=700)
template = imutils.resize(template, width=700)
# our first output visualization of the image alignment will be a
# side-by-side comparison of the output aligned image and the
# template
stacked = np.hstack([aligned, template])
# our second image alignment visualization will be *overlaying* the
# aligned image on the template, that way we can obtain an idea of
# how good our image alignment is
overlay = template.copy()