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import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import argparse
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--image', type=str, help='input image')
parser.add_argument('--output', type=str, help='output file')
parser.add_argument('--mask', type=str, default='', help='ground truth mask (optional)')
args = parser.parse_args()
image_path = args.image
output_path = args.output
mask_path = args.mask
result = np.load(output_path)
cols = 3
if mask_path != '':
cols += 1
mask = Image.open(mask_path)
else:
mask = None
if 'np++' in result:
cols += 1
noisepr = result['np++']
else:
noisepr = None
fig, axs = plt.subplots(1, cols)
fig.suptitle('score: %.3f' % result['score'])
for ax in axs:
ax.axis('off')
index = 0
ax = axs[index]
ax.imshow(Image.open(image_path)), ax.set_title('Image')
if mask is not None:
index += 1
ax = axs[index]
ax.imshow(mask, cmap='gray'), ax.set_title('Ground Truth')
ax.set_yticks(list()), ax.set_xticks(list()), ax.axis('on')
if noisepr is not None:
index += 1
ax = axs[index]
# for a better visualization of the noiseprint++, we remove the border and do a down-sampling (useful if the image is too big)
ax.imshow(noisepr[16:-16:5, 16:-16:5], cmap='gray'), ax.set_title('Noiseprint++')
index += 1
ax = axs[index]
ax.imshow(result['map'], cmap='RdBu_r', clim=[0,1]), ax.set_title('Localization map')
index += 1
ax = axs[index]
ax.imshow(result['conf'], cmap='gray', clim=[0,1]), ax.set_title('Confidence map')
ax.set_yticks(list()), ax.set_xticks(list()), ax.axis('on')
plt.show()
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