def __init__(self, images_directory_path, resize_height=320, bnnRatio=0.8, contrastThreshold=0.04, ransacThreshold=10.0, min_samples=6, max_trials=1000):
self.bnnRatio = bnnRatio
self.contrastThreshold = contrastThreshold
self.ransacThreshold = ransacThreshold
self.min_samples = min_samples
self.max_trials = max_trials
self.resize_height = resize_height
image_paths = get_images_paths(images_directory_path)
self.images = [Image(image_path, index, resize_height=self.resize_height, contrastThreshold=self.contrastThreshold)
for index, image_path in enumerate(image_paths)]
image.original,
image.M,
(final_cols, final_rows),
borderMode=cv2.BORDER_TRANSPARENT)
mask = transformed_img[:, :, 3] / 255 == 1
numpy.copyto(
result, transformed_img[:, :, :3],
where=numpy.dstack((mask,) * 3))
numpy.copyto(global_mask, mask, where=mask)
result[~global_mask] = [255, 255, 255]
return result
if __name__ == '__main__':
images_dir = './data/Panorama/campus'
images_paths = natsorted(get_images_paths(images_dir))
# Read/Estimate focal length
f = k1 = k2 = None
if path.exists(path.join(images_dir, 'info.txt')):
with open(path.join(images_dir, 'info.txt')) as f:
for line in f:
key, val = line.split()
if key == 'f':
f = float(val)
elif key == 'k1':
k1 = float(val)
elif key == 'k2':
k2 = float(val)
else:
images = [cv2.imread(image_path) for image_path in images_paths]