Plot images overlay

overlay.py

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")