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August 15, 2017 13:38
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import pickle as pkl | |
import matplotlib.pyplot as plt | |
import matplotlib | |
import numpy as np | |
from sklearn import preprocessing | |
from mpl_toolkits.axes_grid1 import make_axes_locatable | |
from skimage import filters | |
discriminate = True | |
print("Reading Data") | |
images_orig = pkl.load(open('orig_imgs_n20_c1_h584_w565.pkl')) | |
images_gtruth = pkl.load(open('gtruth_imgs_n20_c1_h584_w565.pkl')) | |
images_pred = pkl.load(open('pred_imgs_n20_c1_h584_w565.pkl')) | |
min_max_scaler = preprocessing.MinMaxScaler() | |
print("Exporting images") | |
for i in range(images_orig.shape[0]): | |
print("> %3d of %3d" % (i,images_orig.shape[0])) | |
img_orig = images_orig[i][0] | |
img_gtruth = images_gtruth[i][0] | |
img_pred = images_pred[i][0] | |
# rescale to 0 and 1 | |
img_gtruth = min_max_scaler.fit_transform(img_gtruth) | |
img_pred = min_max_scaler.fit_transform(img_pred) | |
if discriminate: | |
img_orig = np.ma.masked_where(img_orig < np.std(img_orig), img_orig) | |
img_gtruth = np.ma.masked_where(img_gtruth < filters.threshold_otsu(img_gtruth), img_gtruth) | |
img_pred = np.ma.masked_where(img_pred < filters.threshold_otsu(img_pred), img_pred) | |
fig = plt.figure(frameon=False) | |
im1 = plt.imshow(img_orig, cmap=plt.cm.gray, interpolation='nearest') | |
im2 = plt.imshow(img_gtruth, cmap=plt.cm.plasma, alpha=.8, interpolation='none',clim=(0.0, 1.0)) | |
plt.colorbar() | |
fig.savefig('orig_gtruth_%d.png' % i, transparent=True) | |
plt.close(fig) | |
plt.clf() | |
fig = plt.figure(frameon=False) | |
im1 = plt.imshow(img_orig, cmap=plt.cm.gray, interpolation='nearest') | |
im2 = plt.imshow(img_pred, cmap=plt.cm.plasma, alpha=.8, interpolation='none', clim=(0.0, 1.0)) | |
plt.colorbar() | |
fig.savefig('orig_pred_%d.png' % i, transparent=True) | |
plt.close(fig) | |
plt.clf() | |
figs = [(img_orig,img_gtruth), (img_orig, img_pred)] | |
titles = ['Ground Truth','Prediction with U-Net'] | |
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12,8)) | |
for dat, ax, title in zip(figs, axes.flat, titles): | |
im1=ax.imshow(dat[0], cmap=plt.cm.gray, interpolation='nearest') | |
im2=ax.imshow(dat[1], cmap=plt.cm.plasma_r, alpha=.8, interpolation='none',clim=(0.0, 1.0)) | |
ax.set_title(title) | |
fig.colorbar(im2, ax=axes.ravel().tolist(), orientation='horizontal') | |
fig.savefig('comparison2_%d.png' % i, transparent=True) | |
plt.close(fig) | |
plt.clf() | |
print("Finised exporting images") |
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