def from_narrow_png(self, file_name, step_size=0.1):
""" import from png file
Source file is a full color PNG, sufficiently narrow that
density is uniform along its short dimension. The image density along
its long dimension is reflective of a dose distribution.
Parameters
----------
file_name : str
step-size : float, optional
Returns
-------
Profile
Raises
------
ValueError
if aspect ratio <= 5, i.e. not narrow
AssertionError
if step_size <= 12.7 over dpi, i.e. small
"""
image_file = PIL.Image.open(file_name)
assert image_file.mode == "RGB"
dpi_horiz, dpi_vert = image_file.info["dpi"]
image_array = mpimg.imread(file_name)
# DIMENSIONS TO AVG ACROSS DIFFERENT FOR HORIZ VS VERT IMG
if image_array.shape[0] > 5 * image_array.shape[1]: # VERT
image_vector = np.average(image_array, axis=(1, 2))
pixel_size_in_cm = 2.54 / dpi_vert
elif image_array.shape[1] > 5 * image_array.shape[0]: # HORIZ
image_vector = np.average(image_array, axis=(0, 2))
pixel_size_in_cm = 2.54 / dpi_horiz
else:
raise ValueError("The PNG file is not a narrow strip.")
assert step_size > 5 * pixel_size_in_cm, "step size too small"
if image_vector.shape[0] % 2 == 0:
image_vector = image_vector[:-1] # SO ZERO DISTANCE IS MID-PIXEL
length_in_cm = image_vector.shape[0] * pixel_size_in_cm
full_resolution_distances = np.arange(-length_in_cm / 2,
length_in_cm / 2,
pixel_size_in_cm)
# TO MOVE FROM FILM RESOLUTION TO DESIRED PROFILE RESOLUTION
num_pixels_to_avg_over = int(step_size / pixel_size_in_cm)
sample_indices = np.arange(
num_pixels_to_avg_over / 2,
len(full_resolution_distances),
num_pixels_to_avg_over,
).astype(int)
downsampled_distances = list(full_resolution_distances[sample_indices])
downsampled_density = []
for idx in sample_indices: # AVERAGE OVER THE SAMPLING WINDOW
avg_density = np.average(
image_vector[int(idx - num_pixels_to_avg_over /
2):int(idx + num_pixels_to_avg_over / 2)])
downsampled_density.append(avg_density)
zipped_profile = list(zip(downsampled_distances, downsampled_density))
return Profile().from_tuples(zipped_profile)
def read_narrow_png(file_name, step_size=0.1):
""" Extract a an relative-density profilee from a narrow png file.
Source file is a full color PNG that is sufficiently narrow that
density uniform along its short dimension. The image density along
its long dimension is reflective of a dose distribution. Requires
Python PIL.
Parameters
----------
file_name : str
step-size : float, optional
Distance output increment in cm, defaults to 1 mm
Returns
-------
array_like
Image profile as a list of (distance, density) tuples, where density
is an average color intensity value as represented in the source file.
Raises
------
ValueError
If image is not narrow, i.e. aspect ratio <= 5
AssertionError
If step_size is too small, i.e. step_size <= 12.7 / dpi
"""
image_file = Image.open(file_name)
assert image_file.mode == "RGB"
dpi_horiz, dpi_vert = image_file.info["dpi"]
image_array = mpimg.imread(file_name)
# DIMENSIONS TO AVG ACROSS DIFFERENT FOR HORIZ VS VERT IMG
if image_array.shape[0] > 5 * image_array.shape[1]: # VERT
image_vector = np.average(image_array, axis=(1, 2))
pixel_size_in_cm = 2.54 / dpi_vert
elif image_array.shape[1] > 5 * image_array.shape[0]: # HORIZ
image_vector = np.average(image_array, axis=(0, 2))
pixel_size_in_cm = 2.54 / dpi_horiz
else:
raise ValueError("The PNG file is not a narrow strip.")
assert step_size > 5 * pixel_size_in_cm, "step size too small"
if image_vector.shape[0] % 2 == 0:
image_vector = image_vector[:-1] # SO ZERO DISTANCE IS MID-PIXEL
length_in_cm = image_vector.shape[0] * pixel_size_in_cm
full_resolution_distances = np.arange(-length_in_cm / 2, length_in_cm / 2,
pixel_size_in_cm)
# TO MOVE FROM FILM RESOLUTION TO DESIRED PROFILE RESOLUTION
num_pixels_to_avg_over = int(step_size / pixel_size_in_cm)
sample_indices = np.arange(
num_pixels_to_avg_over / 2,
len(full_resolution_distances),
num_pixels_to_avg_over,
).astype(int)
downsampled_distances = list(full_resolution_distances[sample_indices])
downsampled_density = []
for idx in sample_indices: # AVERAGE OVER THE SAMPLING WINDOW
avg_density = np.average(
image_vector[int(idx - num_pixels_to_avg_over /
2):int(idx + num_pixels_to_avg_over / 2)])
downsampled_density.append(avg_density)
zipped_profile = list(zip(downsampled_distances, downsampled_density))
return zipped_profile
def normalise_profile(
distance,
relative_dose,
pdd_distance=None,
pdd_relative_dose=None,
scan_depth=None,
normalisation_position="cra",
scale_to_pdd=False,
smoothed_normalisation=False,
):
"""Normalise a profile given a defined normalisation position and
normalisation scaling
"""
# If scaling is to PDD interpolate along the PDD to find the scaling,
# otherwise set scaling to 100.
if scale_to_pdd:
# If insufficient information has been supplies raise a meaningful
# error
if pdd_distance is None or pdd_relative_dose is None or scan_depth is None:
raise Exception(
"Scaling to PDD requires pdd_distance, pdd_relative_dose, "
"and scan_depth to be defined.")
pdd_interpolation = interp1d(pdd_distance, pdd_relative_dose)
scaling = pdd_interpolation(scan_depth)
else:
scaling = 100
# Linear interpolation function
if smoothed_normalisation:
filtered = savgol_filter(relative_dose, 21, 2)
interpolation = interp1d(distance, filtered)
else:
interpolation = interp1d(distance, relative_dose)
try:
# Check if user wrote a number for normalisation position
float_position = float(normalisation_position)
except ValueError:
# If text was written the conversion to float will fail
float_position = None
# If position was given by the user as a number then define the
# normalisation to that position
if float_position is not None:
normalisation = scaling / interpolation(float_position)
# Otherwise if the user gave 'cra' (case independent) normalise at 0
elif normalisation_position.lower() == "cra":
normalisation = scaling / interpolation(0)
# Otherwise if the user gave 'cm' (case independent) normalise to the
# centre of mass
elif normalisation_position.lower() == "cm":
threshold = 0.5 * np.max(relative_dose)
weights = relative_dose.copy()
weights[weights < threshold] = 0
centre_of_mass = np.average(distance, weights=weights)
normalisation = scaling / interpolation(centre_of_mass)
# Otherwise if the user gave 'max' (case independent) normalise to the
# point of dose maximum
elif normalisation_position.lower() == "max":
normalisation = scaling / np.max(relative_dose)
else:
raise TypeError("Expected either a float for `normalisation_position` "
"or one of 'cra', 'cm', or 'max'")
return relative_dose * normalisation