Python Logger.getInstance方法代码示例

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    # parse cmd arguments
    parser = getParser()
    parser.parse_args()
    args = getArguments(parser)
    
    # prepare logger
    logger = Logger.getInstance()
    if args.debug: logger.setLevel(logging.DEBUG)
    elif args.verbose: logger.setLevel(logging.INFO)
    
    # load input image using nibabel
    logger.info('Loading image {}...'.format(args.input))
    image_labels_data, _ = load(args.image)    
    
    # load mask image
    logger.info('Loading mask {}...'.format(args.mask))
    image_mask_data, image_mask_data_header = load(args.mask)
    
    # check if output image exists
    if not args.force:
        if os.path.exists(args.output):
            logger.warning('The output image {} already exists. Skipping this image.'.format(args.output))
    
    # create a mask from the label image
    logger.info('Reducing the label image...')
    image_reduced_data = fit_labels_to_mask(image_labels_data, image_mask_data)
    
    # save resulting mask
    logger.info('Saving resulting mask as {} in the same format as input mask, only with data-type int8...'.format(args.output))
    image_reduced_data = image_reduced_data.astype(numpy.bool, copy=False) # bool sadly not recognized
    save(image_reduced_data, args.output, image_mask_data_header, args.force)
    
    logger.info('Successfully terminated.')

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    args = getArguments(getParser())

    # prepare logger
    logger = Logger.getInstance()
    if args.debug:
        logger.setLevel(logging.DEBUG)
    elif args.verbose:
        logger.setLevel(logging.INFO)

    # check if output image exists
    if not args.force:
        if os.path.exists(args.output):
            logger.warning("The output image {} already exists. Exiting.".format(args.output))
            exit(-1)

    # load input image
    input_data, input_header = load(args.input)

    logger.debug("Old number of regions={}.".format(len(scipy.unique(input_data))))

    # cut and relabel along the required dimension
    logger.info("Cutting and relabeling...")
    dimensions = range(input_data.ndim)
    del dimensions[args.dimension]
    __split_along(input_data, dimensions)

    logger.debug("New number of regions={}.".format(len(scipy.unique(input_data))))

    # save result contour volume
    save(input_data, args.output, input_header, args.force)

    logger.info("Successfully terminated.")

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    args = getArguments(getParser())

    # prepare logger
    logger = Logger.getInstance()
    if args.debug: logger.setLevel(logging.DEBUG)
    elif args.verbose: logger.setLevel(logging.INFO)
    
    # load input image
    data_input, header_input = load(args.input)
    
    # transform to uin8
    data_input = data_input.astype(scipy.uint8)
                                      
    # reduce to 3D, if larger dimensionality
    if data_input.ndim > 3:
        for _ in range(data_input.ndim - 3): data_input = data_input[...,0]
        
    # iter over slices (2D) until first with content is detected
    for plane in data_input:
        if scipy.any(plane):
            # set pixel spacing
            spacing = list(header.get_pixel_spacing(header_input))
            spacing = spacing[1:3]
            __update_header_from_array_nibabel(header_input, plane)
            header.set_pixel_spacing(header_input, spacing)
            # save image
            save(plane, args.output, header_input, args.force)
            break
    
    logger.info("Successfully terminated.")    

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    args = getArguments(getParser())

    # prepare logger
    logger = Logger.getInstance()
    if args.debug: logger.setLevel(logging.DEBUG)
    elif args.verbose: logger.setLevel(logging.INFO)

    # load input images
    input_data, input_header = load(args.input)
    original_data, _ = load(args.original)
    
    logger.debug('Old shape={}.'.format(input_data.shape))
    
    # compute position
    logger.info('Computing positon and pad volume...')
    position = __parse_contour_list(args.contours, input_data)
    
    # pad volume
    output_data = scipy.zeros(original_data.shape, input_data.dtype)
    output_data[position] = input_data
    
    
    logger.debug('New shape={}.'.format(input_data.shape))
    
    # save result contour volume
    save(output_data, args.output, input_header, args.force)

    logger.info("Successfully terminated.")

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    # parse cmd arguments
    parser = getParser()
    parser.parse_args()
    args = getArguments(parser)
    
    # prepare logger
    logger = Logger.getInstance()
    if args.debug: logger.setLevel(logging.DEBUG)
    elif args.verbose: logger.setLevel(logging.INFO)
    
    # check if output image exists (will also be performed before saving, but as the smoothing might be very time intensity, a initial check can save frustration)
    if not args.force:
        if os.path.exists(args.output):
            raise parser.error('The output image {} already exists.'.format(args.output))
    
    # loading image
    data_input, header_input = load(args.input)
    
    # apply the watershed
    logger.info('Applying anisotropic diffusion with settings: niter={} / kappa={} / gamma={}...'.format(args.iterations, args.kappa, args.gamma))
    data_output = anisotropic_diffusion(data_input, args.iterations, args.kappa, args.gamma, get_pixel_spacing(header_input))

    # save file
    save(data_output, args.output, header_input, args.force)
    
    logger.info('Successfully terminated.')

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    args = getArguments(getParser())

    # prepare logger
    logger = Logger.getInstance()
    if args.debug: logger.setLevel(logging.DEBUG)
    elif args.verbose: logger.setLevel(logging.INFO)  
    
    # constants
    colours = {'i': 10, 'o': 11}
    
    # load volumes
    marker_data, _ = load(args.marker)
    contour_data, _ = load(args.contour)
    
    # perform check
    contour_data = contour_data == colours[args.type]
    marker_data_fg = marker_data == 1
    marker_data_bg = marker_data == 2
    if scipy.logical_and(contour_data, marker_data_fg).any():
        logger.warning('Intersection between {} and {} (type {}) in foreground.'.format(args.marker, args.contour, args.type))
    elif scipy.logical_and(contour_data, marker_data_bg).any():
        logger.warning('Intersection between {} and {} (type {}) in background.'.format(args.marker, args.contour, args.type))
    else:
        print "No intersection."

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    # parse cmd arguments
    parser = getParser()
    parser.parse_args()
    args = getArguments(parser)
    
    # prepare logger
    logger = Logger.getInstance()
    if args.debug: logger.setLevel(logging.DEBUG)
    elif args.verbose: logger.setLevel(logging.INFO)
        
    # check if output image exists (will also be performed before saving, but as the gradient might be time intensity, a initial check can save frustration)
    if not args.force:
        if os.path.exists(args.output):
            raise ArgumentError('The output image {} already exists.'.format(args.output))        
        
    # loading image
    data_input, header_input = load(args.input)
    
    logger.debug('Input array: dtype={}, shape={}'.format(data_input.dtype, data_input.shape))
    
    # execute the gradient map filter
    logger.info('Applying gradient map filter...')
    data_output = filter.gradient_magnitude(data_input, header.get_pixel_spacing(header_input))
        
    logger.debug('Resulting array: dtype={}, shape={}'.format(data_output.dtype, data_output.shape))
    
    # save image
    save(data_output, args.output, header_input, args.force)
    
    logger.info('Successfully terminated.')

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    args = getArguments(getParser())

    # prepare logger
    logger = Logger.getInstance()
    if args.debug:
        logger.setLevel(logging.DEBUG)
    elif args.verbose:
        logger.setLevel(logging.INFO)

    # collect slice-wise results and compute average
    results_i, results_o = parseImageResults(args.score)
    results_i = splitResults(results_i)
    results_o = splitResults(results_o)

    # print results
    if args.csv:
        print "Inner contours"
        csvPrintResults(results_i)
        print "Outer contours"
        csvPrintResults(results_o)
    else:
        print "########## Inner contours ##########"
        prettyPrintResults(results_i)
        print "####################################"
        print

        print "########## Outer contours ##########"
        prettyPrintResults(results_o)
        print "####################################"

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    # parse cmd arguments
    parser = getParser()
    parser.parse_args()
    args = getArguments(parser)
    
    # prepare logger
    logger = Logger.getInstance()
    if args.debug: logger.setLevel(logging.DEBUG)
    elif args.verbose: logger.setLevel(logging.INFO)

    # check if output image already exists
    if not args.force:
        if os.path.exists(args.output):
            logger.warning('The output image {} already exists. Exiting.'.format(args.output))
            exit(-1)

    # load input image
    image_smoothed_data, image_header = load(args.input)
        
    # apply additional hole closing step
    logger.info('Closing holes...')
    def fun_holes(arr):
        return scipy.ndimage.morphology.binary_fill_holes(arr)
    xd_iterator(image_smoothed_data, (1, 2), fun_holes)
        
    # perform opening resp. closing
    # in 3D case: size 1 = 6-connectedness, 2 = 12-connectedness, 3 = 18-connectedness, etc.
    if 'erosion' == args.type:
        logger.info('Applying erosion...')
        def fun(arr):
            if 0 == args.iterations: return arr
            footprint = scipy.ndimage.morphology.generate_binary_structure(arr.ndim, args.size)
            return scipy.ndimage.morphology.binary_erosion(arr, footprint, iterations=args.iterations)
    elif 'dilation' == args.type:
        logger.info('Applying dilation...')
        def fun(arr):
            if 0 == args.iterations: return arr
            footprint = scipy.ndimage.morphology.generate_binary_structure(arr.ndim, args.size)
            return scipy.ndimage.morphology.binary_dilation(arr, footprint, iterations=args.iterations)
    elif 'opening' == args.type:
        logger.info('Applying opening...')
        def fun(arr):
            if 0 == args.iterations: return arr
            footprint = scipy.ndimage.morphology.generate_binary_structure(arr.ndim, args.size)
            return scipy.ndimage.morphology.binary_opening(arr, footprint, iterations=args.iterations)
    else: # closing
        logger.info('Applying closing...')
        def fun(arr):
            if 0 == args.iterations: return arr
            footprint = scipy.ndimage.morphology.generate_binary_structure(arr.ndim, args.size)
            return scipy.ndimage.morphology.binary_closing(arr, footprint, iterations=args.iterations)

    # iterate over slices and apply selected operation
    xd_iterator(image_smoothed_data, (1, 2), fun)

    # save resulting mas
    save(image_smoothed_data, args.output, image_header, args.force)
            
    logger.info('Successfully terminated.')

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    # parse cmd arguments
    parser = getParser()
    parser.parse_args()
    args = getArguments(parser)
    
    # prepare logger
    logger = Logger.getInstance()
    if args.debug: logger.setLevel(logging.DEBUG)
    elif args.verbose: logger.setLevel(logging.INFO)
    
    # laod input image
    data_input, header_input = load(args.input)
    
#    # check if output image exists
#    if not args.force:
#        if os.path.exists(image_gradient_name):
#            logger.warning('The output image {} already exists. Skipping this step.'.format(image_gradient_name))
#            continue        
        
    # prepare result image
    data_output = scipy.zeros(data_input.shape, dtype=scipy.float32)
        
    # apply the gradient magnitude filter
    logger.info('Computing the gradient magnitude with Prewitt operator...')
    generic_gradient_magnitude(data_input, prewitt, output=data_output) # alternative to prewitt is sobel
        
    # save resulting mask
    save(data_output, args.output, header_input, args.force)
    
    logger.info('Successfully terminated.')

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def zoom(image, factor, dimension, hdr = False, order = 3):
    """
    Zooms the provided image by the supplied factor in the supplied dimension.
    The factor is an integer determining how many slices should be put between each
    existing pair.
    If an image header (hdr) is supplied, its voxel spacing gets updated.
    Returns the image and the updated header or false.
    """
    # check if supplied dimension is valid
    if dimension >= image.ndim:
        raise argparse.ArgumentError('The supplied zoom-dimension {} exceeds the image dimensionality of 0 to {}.'.format(dimension, image.ndim - 1))
    
    # get logger
    logger = Logger.getInstance()

    logger.debug('Old shape = {}.'.format(image.shape))

    # perform the zoom
    zoom = [1] * image.ndim
    zoom[dimension] = (image.shape[dimension] + (image.shape[dimension] - 1) * factor) / float(image.shape[dimension])
    logger.debug('Reshaping with = {}.'.format(zoom))
    image = interpolation.zoom(image, zoom, order=order)
        
    logger.debug('New shape = {}.'.format(image.shape))
    
    if hdr:
        new_spacing = list(header.get_pixel_spacing(hdr))
        new_spacing[dimension] = new_spacing[dimension] / float(factor + 1)
        logger.debug('Setting pixel spacing from {} to {}....'.format(header.get_pixel_spacing(hdr), new_spacing))
        header.set_pixel_spacing(hdr, tuple(new_spacing))
    
    return image, hdr

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    # parse cmd arguments
    parser = getParser()
    parser.parse_args()
    args = getArguments(parser)

    # prepare logger
    logger = Logger.getInstance()
    if args.debug:
        logger.setLevel(logging.DEBUG)
    elif args.verbose:
        logger.setLevel(logging.INFO)

    # check if output image exists (will also be performed before saving, but as the watershed might be very time intensity, a initial check can save frustration)
    if not args.force:
        if os.path.exists(args.output):
            raise ArgumentError("The output image {} already exists.".format(args.output))

    # loading image
    data_input, header_input = load(args.input)

    # apply the watershed
    logger.info("Watershedding with settings: thr={} / level={}...".format(args.threshold, args.level))
    data_output = watershed(data_input, get_pixel_spacing(header_input), args.threshold, args.level)

    # save file
    save(data_output, args.output, header_input, args.force)

    logger.info("Successfully terminated.")

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
 def __init__(self, image_labels, image_original):
     """
     Computes a number of statistics for the labels of a label image.
     These include beside others:
     1. a histogram of the sizes of the regions
     2. a histogram of the sphericity (not roundness) of the regions
     3. a histogram of how the intensity distribution in each region differs
     from a Gaussian distribution
     
     @param image_lables: The label image for which the statistics should be
                          computed as a numpy array
     @param image_original: The original image for which the label image was created.
     """
     if image_labels.shape != image_original.shape:
         raise ValueError('The input images must be of the same shape.')
     
     if not 3 == len(image_labels.shape):
         raise ValueError('Currently this class is only working with 3D images.')
     
     # prepare logger
     self._logger = Logger.getInstance()
     
     self._image_labels = image_labels
     self._image_original = image_original
     
     self._compute()

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    # parse cmd arguments
    parser = getParser()
    parser.parse_args()
    args = getArguments(parser)
    
    # prepare logger
    logger = Logger.getInstance()
    if args.debug: logger.setLevel(logging.DEBUG)
    elif args.verbose: logger.setLevel(logging.INFO)
        
    # write header line
    print('image;labels\n')
    
    # iterate over input images
    for image in args.images:
        
        # get and prepare image data
        logger.info('Processing image {}...'.format(image))
        image_data, _ = load(image)
        
        # count number of labels and flag a warning if they reach the ushort border
        count = len(numpy.unique(image_data)) 
        
        # count number of labels and write
        print('{};{}\n'.format(image.split('/')[-1], count))
        
        sys.stdout.flush()
            
    logger.info('Successfully terminated.')

# 需要导入模块: from medpy.core import Logger [as 别名]
# 或者: from medpy.core.Logger import getInstance [as 别名]
def main():
    args = getArguments(getParser())

    # prepare logger
    logger = Logger.getInstance()
    if args.debug: logger.setLevel(logging.DEBUG)
    elif args.verbose: logger.setLevel(logging.INFO)

    # load input image
    input_data, input_header = load(args.input)
    
    logger.debug('Old shape={}.'.format(input_data.shape))
    
    # compute cut
    logger.info('Computing cut and cropping volume...')
    cut = __parse_contour_list(args.contours, input_data)
    # crop volume
    input_data = input_data[cut]
    
    logger.debug('New shape={}.'.format(input_data.shape))
    
    # save result contour volume
    save(input_data, args.output, input_header, args.force)

    logger.info("Successfully terminated.")