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import argparse
import os
from batchgenerators.utilities.file_and_folder_operations import join, isdir, maybe_mkdir_p
from skimage import io
import matplotlib.pyplot as plt
import matplotlib.image as pltimage
import cv2
def main(predictions, labels, sar_images):
prediction_files = os.listdir(predictions)
output_folder = join(predictions, 'eval')
maybe_mkdir_p(output_folder)
for file in prediction_files:
if file.endswith('.png'):
file = file[:-len('.png')]
prediction_path = join(predictions, file + '.png')
label_path = join(labels, file + '_front.png')
image_path = join(sar_images, file + '.png')
prediction = io.imread(prediction_path, as_gray=True)
label = io.imread(label_path)
image = io.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
image_rgb[label > 0] = [0, 255, 0]
image_rgb[prediction > 0] = [255, 0, 0]
output_path = join(output_folder, file+'.png')
pltimage.imsave(output_path, image_rgb)
print(file)
return
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-p", '--predictions', help="Folder with png files of prediction")
parser.add_argument("-l", '--labels', help="Folder with png files of labels")
parser.add_argument("-i", '--sar_images', help="Folder with sar_images of glacier")
args = parser.parse_args()
predictions = args.predictions
labels = args.labels
sar_images = args.sar_images
main(predictions, labels, sar_images) |