if __name__ == '__main__':

    ### Parse command line arguments
    tiff_fn, boundaries_fn, out_fn = parse_command_line_args()
    if not out_fn:
        cell_label = re.findall(r'\d+', boundaries_fn)[0]
        fn = 'cell%s.avi' % cell_label
        out_fn = os.path.join('.', fn)
        print 'Output movie name unspecified, movie will be saved to %s.' % out_fn

    ### Load data
    print 'Loading %s...' % tiff_fn,
    sys.stdout.flush()
    frames = tiff_to_ndarray(tiff_fn)
    all_boundary_pts = np.load(boundaries_fn)
    print 'done.'
    sys.stdout.flush()

    ### compute masks from boundaries
    frame_sz = frames[0].shape
    masks = [grid_points_in_poly(frame_sz, pts) for pts in all_boundary_pts]

    ### compute window size (max mask size + padding)
    padding = 10
    bboxes = [regionprops(mask)[0].bbox for mask in masks]
    bbox_widths = [(bbox[2] - bbox[0]) for bbox in bboxes]
    bbox_heights = [(bbox[3] - bbox[1]) for bbox in bboxes]
    win_size = (max(bbox_widths) + 2 * padding,
                max(bbox_heights) + 2 * padding)
Example #2
0
    args = parser.parse_args()
    return args.tiff_movie, args.out, args.alpha, args.beta, args.spacing




if __name__ == '__main__':

    ### parse command line arguments
    tiff_fn, np_fn, alpha, beta, spacing = parse_command_line_args()


    ### load raw movie frames
    print 'Loading %s...' % tiff_fn,
    sys.stdout.flush()
    frames = tiff_to_ndarray(tiff_fn).astype(float)
    print 'done.'
    sys.stdout.flush()


    ### Compute the big mask (contains all cells, same for all frames)
    print 'Computing global mask...',
    sys.stdout.flush()
    mask_frame = frames[0,:,:]
    mask = create_mask(mask_frame)
    print 'done.'
    sys.stdout.flush()


    ### Segment first frame via ridge detection + watershed
    print 'Computing initial segmentation...',
if __name__ == '__main__':

    ### Parse command line arguments
    tiff_fn, boundaries_fn, out_fn = parse_command_line_args()
    if not out_fn:
        cell_label = re.findall(r'\d+', boundaries_fn)[0]
        fn = 'cell%s.avi' % cell_label
        out_fn = os.path.join('.', fn)
        print 'Output movie name unspecified, movie will be saved to %s.' % out_fn


    ### Load data
    print 'Loading %s...' % tiff_fn,
    sys.stdout.flush()
    frames = tiff_to_ndarray(tiff_fn)
    all_boundary_pts = np.load(boundaries_fn)
    print 'done.'
    sys.stdout.flush()


    ### compute masks from boundaries
    frame_sz = frames[0].shape
    masks = [grid_points_in_poly(frame_sz , pts) for pts in all_boundary_pts]


    ### compute window size (max mask size + padding)
    padding = 10
    bboxes = [regionprops(mask)[0].bbox for mask in masks]
    bbox_widths = [(bbox[2]-bbox[0]) for bbox in bboxes]
    bbox_heights = [(bbox[3]-bbox[1]) for bbox in bboxes]
Example #4
0
    args = parser.parse_args()
    return args.tiff_movie, args.out, args.alpha, args.beta, args.spacing




if __name__ == '__main__':

    ### parse command line arguments
    tiff_fn, np_fn, alpha, beta, spacing = parse_command_line_args()


    ### load raw movie frames
    print('Loading %s...' % tiff_fn, end='')
    sys.stdout.flush()
    frames = tiff_to_ndarray(tiff_fn).astype(float)
    print('done.')
    sys.stdout.flush()


    ### Compute the big mask (contains all cells, same for all frames)
    print('Computing global mask...', end='')
    sys.stdout.flush()
    mask_frame = frames[0]
    mask = create_mask(mask_frame)
    print('done.')
    sys.stdout.flush()


    ### Segment first frame via ridge detection + watershed
    print('Computing initial segmentation...', end='')