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glob_funcs.py
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glob_funcs.py
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import numpy as np
import pydicom
import matplotlib.pyplot as plt
def normalize_data_ab(a, b, data):
# input (min_data, max_data) with range (max_data - min_data) is normalized to (a, b)
min_x = min(data.ravel())
max_x = max(data.ravel())
range_x = max_x - min_x
return((b-a)*((data-min_x)/range_x)+a)
def normalize_data_ab_cd(a, b, c, d, data):
# input data (min_data, max_data) with range (d-c) is normalized to (a, b)
min_x = c
max_x = d
range_x = max_x - min_x
return((b-a)*((data-min_x)/range_x)+a)
def add_rnl_white(rnl, b, mu, sigma):
""" sigma = std
var = sigma^2
"""
h, w = b.shape
randn = np.random.normal(loc = mu, scale = sigma, size = (h,w))
e = randn/np.linalg.norm(randn, ord = 2)
e = rnl*np.linalg.norm(b)*e;
return(b + e)
def pydicom_imread(path):
""" reads dicom image with filename path
and dtype be its original form
"""
input_image = pydicom.dcmread(path)
return(input_image.pixel_array.astype('float32'))
def plot2dlayers(arr, xlabel=None, ylabel=None, title=None, cmap=None, colorbar=True):
"""
'brg' is the best colormap for reb-green-blue image
'brg_r': in 'brg' colormap green color area will have
high values whereas in 'brg_r' blue area will have
the highest values
"""
if xlabel is None:
xlabel=''
if ylabel is None:
ylabel=''
if title is None:
title=''
if cmap is None:
cmap='Greys_r'
plt.imshow(arr, cmap=cmap)
cb = plt.colorbar()
if colorbar is False:
cb.remove()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.show()
def multi2dplots(nrows, ncols, fig_arr, axis, passed_fig_att=None):
"""
gf.multi2dplots(1, 2, lena_stack, axis=0, passed_fig_att={'colorbar': False, 'split_title': np.asanyarray(['a','b']),'out_path': 'last_lr.tif'})
where lena_stack is of size (2, 512, 512)
"""
default_att= {"suptitle": '',
"split_title": np.asanyarray(['']*(nrows*ncols)),
"supfontsize": 12,
"xaxis_vis" : False,
"yaxis_vis" : False,
"out_path" : '',
"figsize" : [8, 8],
"cmap" : 'Greys_r',
"plt_tight" : True,
"colorbar" : True
}
if passed_fig_att is None:
fig_att = default_att
else:
fig_att = default_att
for key, val in passed_fig_att.items():
fig_att[key]=val
f, axarr = plt.subplots(nrows, ncols, figsize = fig_att["figsize"])
img_ind = 0
f.suptitle(fig_att["suptitle"], fontsize = fig_att["supfontsize"])
for i in range(nrows):
for j in range(ncols):
if (axis==0):
each_img = fig_arr[img_ind, :, :]
if (axis==1):
each_img = fig_arr[:, img_ind, :]
if (axis==2):
each_img = fig_arr[:, :, img_ind]
if(nrows==1):
ax = axarr[j]
elif(ncols ==1):
ax =axarr[i]
else:
ax = axarr[i,j]
im = ax.imshow(each_img, cmap = fig_att["cmap"])
if fig_att["colorbar"] is True: f.colorbar(im, ax=ax)
ax.set_title(fig_att["split_title"][img_ind])
ax.get_xaxis().set_visible(fig_att["xaxis_vis"])
ax.get_yaxis().set_visible(fig_att["yaxis_vis"])
img_ind = img_ind + 1
if fig_att["plt_tight"] is True: plt.tight_layout()
if (len(fig_att["out_path"])==0):
plt.show()
else:
plt.savefig(fig_att["out_path"])
def raw_imread(path, shape=(256, 256), dtype='int16'):
input_image = np.fromfile(path, dtype=dtype).astype('float32')
input_image = input_image.reshape(shape)
return(input_image)
def dict_plot_of_2d_arr(rows, cols, arr_2d, cmap='Greys_r', save_plot=False, disp_plot=False, output_path=''):
# rows, cols indicate number of subplots along rows & columns
# rows*cols = len(arr_2d)
plt.figure(figsize=(14, 10))
for i, comp in enumerate(arr_2d):
plt.subplot(rows, cols, i + 1)
plt.imshow(comp, cmap=cmap, interpolation="nearest")
plt.xticks(())
plt.yticks(())
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
if save_plot: plt.savefig(output_path)
if disp_plot: plt.show()