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plots.py
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plots.py
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import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from loss_functions import KL, calculate_iou
from helper import get_dm
matplotlib.use('pdf')
def plot_histogram_and_image(hist_pred, hist_true, img, tile_name, out_dir=None, volume_weighted=False):
"""
plotting the predicted histogram on the top of original histogram, next to the image of original tile
"""
img = img.astype(np.uint8)
index = np.arange(len(hist_pred)) + 0.5
KL_div = KL(hist_true, hist_pred)
iou = calculate_iou(hist_true, hist_pred)
dm_true = get_dm(hist_true, volume_weighted=volume_weighted)
dm_pred = get_dm(hist_pred, volume_weighted=volume_weighted)
# Create Figure and Axes instances
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(14, 6.5))
# add title of the whole figure
fig.suptitle('Comparison of Distribution\n%s' % (tile_name), fontsize=18)
ax1.bar(index, hist_true, width=1.0, label='true histogram')
ax1.bar(index, hist_pred, width=1.0, alpha=0.5, label='predicted histogram')
ax1.legend(fontsize=14)
# axis labels
ax1.set_xlabel('Grain diameter [cm]', fontsize=16)
if volume_weighted:
ax1.set_ylabel('Relative volume', fontsize=16)
else:
ax1.set_ylabel('Relative frequency', fontsize=16)
# x ticks labels
group_labels = np.array([0.00, 0.01, 0.02, 0.03, 0.04, 0.06, 0.08, 0.10, 0.12, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.50, 0.60, 0.80, 1.0, 1.2, 1.5, 2.0]) * 100
group_labels = np.array(group_labels, dtype=np.int)
ax1.set_xticks(np.arange(len(group_labels)))
ax1.set_xticklabels(group_labels, rotation='vertical')
ax1.text( # position text relative to Axes
0.98, 0.82, 'KL: %.2f' % (KL_div),
ha='right', va='top',
transform=ax1.transAxes,
fontsize=16
)
ax1.text( # position text relative to Axes
0.98, 0.76, 'IoU: %.2f' % (iou),
ha='right', va='top',
transform=ax1.transAxes,
fontsize=16
)
ax1.text( # position text relative to Axes
0.98, 0.70, 'dm true: %.2f cm' % (dm_true),
ha='right', va='top',
transform=ax1.transAxes,
fontsize=16
)
ax1.text( # position text relative to Axes
0.98, 0.64, 'dm pred: %.2f cm' % (dm_pred),
ha='right', va='top',
transform=ax1.transAxes,
fontsize=16
)
ax2.set_xticks(())
ax2.set_yticks(())
ax2.imshow(img)
fig.tight_layout()
fig.subplots_adjust(top=0.88)
if out_dir is not None:
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
plt.savefig(os.path.join(out_dir, '{}.png'.format(tile_name)), bbox_inches='tight')
plt.close(fig) # close the figure