"""This module contains image processing routines that improve
images prior to PIV processing."""
import numpy as np
from scipy.ndimage import median_filter, gaussian_filter, binary_fill_holes,\
map_coordinates
from skimage import img_as_float, exposure, img_as_ubyte
from skimage import filters
from skimage.measure import find_contours, approximate_polygon, points_in_poly
from skimage.transform import rescale
import matplotlib.pyplot as plt
from openpiv.tools import imread
__licence_ = """
Copyright (C) 2011 www.openpiv.net
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
[docs]def dynamic_masking(image, method="edges", filter_size=7, threshold=0.005):
"""Dynamically masks out the objects in the PIV images
Parameters
----------
image: image
a two dimensional array of uint16, uint8 or similar type
method: string
'edges' or 'intensity':
'edges' method is used for relatively dark and sharp objects,
with visible edges, on
dark backgrounds, i.e. low contrast
'intensity' method is useful for smooth bright objects or dark objects
or vice versa,
i.e. images with high contrast between the object and the background
filter_size: integer
a scalar that defines the size of the Gaussian filter
threshold: float
a value of the threshold to segment the background from the object
default value: None, replaced by sckimage.filter.threshold_otsu value
Returns
-------
image : array of the same datatype as the incoming image with the
object masked out
as a completely black region(s) of zeros (integers or floats).
Example
--------
frame_a = openpiv.tools.imread( 'Camera1-001.tif' )
imshow(frame_a) # original
frame_a = dynamic_masking(frame_a,method='edges',filter_size=7,
threshold=0.005)
imshow(frame_a) # masked
"""
imcopy = np.copy(image)
# stretch the histogram
image = exposure.rescale_intensity(img_as_float(image), in_range=(0, 1))
# blur the image, low-pass
blurback = img_as_ubyte(gaussian_filter(image, filter_size))
if method == "edges":
# identify edges
edges = filters.sobel(blurback)
blur_edges = gaussian_filter(edges, 21)
# create the boolean mask
mask = blur_edges > threshold
mask = img_as_ubyte(binary_fill_holes(mask))
imcopy -= blurback
imcopy[mask] = 0
elif method == "intensity":
background = gaussian_filter(median_filter(image, filter_size),
filter_size)
mask = background > filters.threshold_otsu(background)
imcopy[mask] = 0
else:
raise ValueError(f"method {method} is not implemented")
return imcopy, mask
[docs]def mask_coordinates(image_mask, tolerance=1.5, min_length=10, plot=False):
""" Creates set of coordinates of polygons from the image mask
Inputs:
mask : binary image of a mask.
[tolerance] : float - tolerance for approximate_polygons, default = 1.5
[min_length] : int - minimum length of the polygon, filters out
the small polygons like noisy regions, default = 10
Outputs:
mask_coord : list of mask coordinates in pixels
Example:
# if masks of image A and B are slightly different:
image_mask = np.logical_and(image_mask_a, image_mask_b)
mask_coords = mask_coordinates(image_mask)
"""
mask_coords = []
if plot:
plt.figure()
plt.imshow(image_mask)
for contour in find_contours(image_mask, 0):
coords = approximate_polygon(contour, tolerance=tolerance)
if len(coords) > min_length:
if plot:
plt.plot(coords[:, 1], coords[:, 0], '-r', linewidth=3)
mask_coords = coords.copy()
return mask_coords
[docs]def prepare_mask_from_polygon(x, y, mask_coords):
""" Converts mask coordinates of the image mask
to the grid of 1/0 on the x,y grid
Inputs:
x,y : grid of x,y points
mask_coords : array of coordinates in pixels of the image_mask
Outputs:
grid of points of the mask, of the shape of x
"""
xymask = points_in_poly(np.c_[y.flatten(), x.flatten()], mask_coords)
return xymask.reshape(x.shape)
[docs]def prepare_mask_on_grid(
x: np.ndarray,
y: np.ndarray,
image_mask: np.ndarray,
)->np.array:
"""_summary_
Args:
x (np.ndarray): x coordinates of vectors in pixels
y (np.ndarray): y coordinates of vectors in pixels
image_mask (np.ndarray): image of the mask, 1 or True is to be masked
Returns:
np.ndarray: boolean array of the size of x,y with 1 where the values are masked
"""
return map_coordinates(image_mask, [y,x]).astype(bool)
[docs]def normalize_array(array, axis = None):
"""
Min/max normalization to [0,1].
Parameters
----------
array: np.ndarray
array to normalize
axis: int, tuple
axis to find values for normalization
Returns
-------
array: np.ndarray
normalized array
"""
array = array.astype(np.float32)
if axis is None:
return((array - np.nanmin(array)) / (np.nanmax(array) - np.nanmin(array)))
else:
return((array - np.nanmin(array, axis = axis)) /
(np.nanmax(array, axis = axis) - np.nanmin(array, axis = axis)))
[docs]def standardize_array(array, axis = None):
"""
Standardize an array.
Parameters
----------
array: np.ndarray
array to normalize
axis: int, tuple
axis to find values for standardization
Returns
-------
array: np.ndarray
normalized array
"""
array = array.astype(np.float32)
if axis is None:
return((array - np.nanmean(array) / np.nanstd(array)))
else:
return((array - np.nanmean(array, axis = axis) / np.nanstd(array, axis = axis)))
[docs]def instensity_cap(img, std_mult = 2):
"""
Simple intensity capping.
Parameters
----------
img: image
a two dimensional array of float32 or float64,
but can be uint16, uint8 or similar type
std_mult: int
how strong the intensity capping is. Lower values
yields a lower threshold
Returns
-------
img: image
a filtered two dimensional array of the input image
"""
upper_limit = np.mean(img) + std_mult * img.std()
img[img > upper_limit] = upper_limit
return img
[docs]def intensity_clip(img, min_val = 0, max_val = None, flag = 'clip'):
"""
Simple intensity clipping
Parameters
----------
img: image
a two dimensional array of float32 or float64,
but can be uint16, uint8 or similar type
min_val: int or float
min allowed pixel intensity
max_val: int or float
min allowed pixel intensity
flag: str
one of two methods to set invalid pixels intensities
Returns
-------
img: image
a filtered two dimensional array of the input image
"""
if flag not in ['clip', 'cap']:
raise ValueError(f'Flag not supported {flag}')
if flag == 'clip':
flag_min, flag_max = 0 , 0
elif flag == 'cap':
flag_min, flag_max = min_val, max_val
img[img < min_val] = flag_min
if max_val is not None:
img[img > max_val] = flag_max
return img
[docs]def high_pass(img, sigma = 5, clip = False):
"""
Simple high pass filter
Parameters
----------
img: image
a two dimensional array of float32 or float64,
but can be uint16, uint8 or similar type
sigma: float
sigma value of the gaussian filter
Returns
-------
img: image
a filtered two dimensional array of the input image
"""
low_pass = gaussian_filter(img, sigma = sigma)
img -= low_pass
if clip:
img[img < 0] = 0
return img
[docs]def local_variance_normalization(img, sigma_1 = 2, sigma_2 = 1, clip = True):
"""
Local variance normalization by two gaussian filters.
This method is used by common commercial softwares
Parameters
----------
img: image
a two dimensional array of float32 or float64,
but can be uint16, uint8 or similar type
sigma_1: float
sigma value of the first gaussian low pass filter
sigma_2: float
sigma value of the second gaussian low pass filter
clip: bool
set negative pixels to zero
Returns
-------
img: image
a filtered two dimensional array of the input image
"""
_high_pass = img - gaussian_filter(img, sigma_1)
img_blur = gaussian_filter(_high_pass * _high_pass, sigma = sigma_2)
den = np.sqrt(img_blur)
img = np.divide( # stops image from being all black
_high_pass, den,
out = np.zeros_like(img),
where = (den != 0.0)
)
if clip:
img[img < 0] = 0
img = (img - img.min()) / (img.max() - img.min())
return img
[docs]def contrast_stretch(img, lower_limit = 2, upper_limit = 98):
"""
Simple percentile-based contrast stretching
Parameters
----------
img: image
a two dimensional array of float32 or float64,
but can be uint16, uint8 or similar type
lower_limit: int
lower percentile limit
upper_limit: int
upper percentile limit
Returns
-------
img: image
a filtered two dimensional array of the input image
"""
if lower_limit < 0:
lower_limit = 0
if upper_limit > 100:
upper_limit = 100
lower = np.percentile(img, lower_limit)
upper = np.percentile(img, upper_limit)
img = exposure.rescale_intensity(img, in_range = (lower, upper))
return img
[docs]def threshold_binarize(img, threshold, max_val = 255):
"""
Simple binarizing threshold
Parameters
----------
img: image
a two dimensional array of float32 or float64,
but can be uint16, uint8 or similar type
threshold: int or float
boundary where pixels set lower than the threshold are set to zero
and values higher than the threshold are set to the maximum user selected value
max_val: int or float
maximum pixel value of the image
Returns
-------
img: image
a filtered two dimensional array of the input image
"""
img[img < threshold] = 0
img[img > threshold] = max_val
return img
[docs]def gen_min_background(img_list, resize = 255):
"""
Generate a background by averaging the minimum intensity
of all images in an image list.
Apply by subtracting generated background image.
Parameters
----------
img_list: list
list of image directories
resize: int or float
disabled by default, normalize array and set value to user
selected max pixel intensity
Returns
-------
img: image
a mean of all images
"""
background = imread(img_list[0])
if resize is not None:
background = normalize_array(background) * resize
for img in img_list:
if img == img_list: # the original image is already included, so skip it in the for loop
pass
else:
img = imread(img)
if resize is not None:
img = normalize_array(img) * resize
background = np.min(np.array([background, img]), axis = 0)
return(background)
[docs]def gen_lowpass_background(img_list, sigma = 3, resize = None):
"""
Generate a background by averaging a low pass of all images in an image list.
Apply by subtracting generated background image.
Parameters
----------
img_list: list
list of image directories
sigma: float
sigma of the gaussian filter
resize: int or float
disabled by default, normalize array and set value to user
selected max pixel intensity
Returns
-------
img: image
a mean of all low-passed images
"""
for img_file in img_list:
if resize is not None:
img = normalize_array(imread(img_file)) * resize
else:
img = imread(img_file)
img = gaussian_filter(img, sigma = sigma)
if img_file == img_list[0]:
background = img
else:
background += img
return (background / len(img_list))
[docs]def offset_image(img, offset_x, offset_y, pad = 'zero'):
"""
Offset an image by padding.
Parameters
----------
img: image
a two dimensional array of float32 or float64,
but can be uint16, uint8 or similar type
offset_x: int
offset an image by integer values. Positive values shifts
the image to the right and negative values shift to the left
offset_y: int
offset an image by integer values. Positive values shifts
the image to the top and negative values shift to the bottom
pad: str
pad the shift with zeros or a reflection of the shift
Returns
-------
img: image
a transformed two dimensional array of the input image
"""
if pad not in [
'zero', 'reflect'
]:
raise ValueError(f'pad method not supported: {pad}')
end_y, end_x = img.shape
start_x = 0; start_y = 0
if offset_x > 0:
offset_x1 = offset_x
offset_x2 = 0
else:
offset_x1 = 0
offset_x2 = offset_x * -1
start_x = offset_x2
end_x += offset_x2
if offset_y > 0:
offset_y1 = offset_y
offset_y2 = 0
else:
offset_y1 = 0
offset_y2 = offset_y * -1
start_y = offset_y2
end_y += offset_y2
if pad == 'zero':
pad = 'constant'
img = np.pad(
img,
((offset_y1, offset_y2),
(offset_x1, offset_x2)),
mode = pad
)
return img[start_y:end_y, start_x:end_x]
[docs]def stretch_image(img,
x_axis = 0,
y_axis = 0,
):
"""
Stretch an image by interplation.
Parameters
----------
img: image
a two dimensional array of float32 or float64,
but can be uint16, uint8 or similar type
x_axis: float
stretch the x-axis of an image where 0 == no stretching
y_axis: float
stretch the y-axis of an image where 0 == no stretching
Returns
-------
img: image
a transformed two dimensional array of the input image
"""
y_axis += 1 # set so zero = no stretch
x_axis += 1
x_axis = max(x_axis, 1)
y_axis = max(y_axis, 1)
return rescale(img, (y_axis, x_axis))