Commit 4ec81dbf authored by Clément Pinard's avatar Clément Pinard
Browse files

Add ground truth scripts

parent d9de4dee
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from path import Path
from imageio import imread, imwrite
import numpy as np
from colmap import read_model as rm
from skimage.transform import rescale
from skimage.measure import block_reduce
parser = ArgumentParser(description='create a vizualisation from ground truth created',
parser.add_argument('--img_root', metavar='DIR', type=Path)
parser.add_argument('--depth_dir', metavar='DIR', type=Path)
parser.add_argument('--input_model', metavar='DIR', type=Path)
parser.add_argument('--output_dir', metavar='DIR', default=None, type=Path)
parser.add_argument('--downscale', type=int, default=1)
def save_intrinsics(cam, output_dir):
assert('PINHOLE' in cam.model)
if 'SIMPLE' in cam.model:
fx, cx, cy = cam.params
fy = fx
fx, fy, cx, cy = cam.params
intrinsics = np.array([[fx, 0, cx],
[0, fy, cy],
[0, 0, 1]])
np.savetxt(output_dir/'intrinsics.txt', intrinsics)
def save_depth_maps(cam, depth_maps, output_dir, downscale=1):
h, w = cam.width, cam.height
for depth_path in depth_maps:
depth = np.fromfile(depth_path, np.float32).reshape(h, w)
downscale_depth = block_reduce(depth, (downscale, downscale), np.min)
depth_name = depth_path.namebase + '.npy' / depth_name, downscale_depth)
def save_imgs(img_root, images, depth_maps, output_dir, downscale=1):
for _, img in images.items():
img_path = img_root/
image = rescale(imread(img_path), 1/downscale, multichannel=True)*255
imwrite(output_dir/img_path.basename(), image.astype(np.uint8))
def save_positions(images, output_dir):
starting_pos = None
positions = []
for _, img in images.items():
current_pos = to_transform_matrix(img.qvec, img.tvec)
if starting_pos is None:
starting_pos = current_pos
relative_position = np.linalg.inv(starting_pos) @ current_pos
positions = np.stack(positions)
np.savetxt(output_dir/'poses.txt', positions.reshape((len(images), -1)))
def to_transform_matrix(q, t):
cam_R = rm.qvec2rotmat(q).T
cam_t = (- cam_R @ t).reshape(3, 1)
transform = np.vstack((np.hstack([cam_R, cam_t]), [0, 0, 0, 1]))
return transform
def main():
args = parser.parse_args()
cameras, images, _ = rm.read_model(args.input_model, '.txt')
assert(len(cameras) == 1)
cam = cameras[list(cameras.keys())[0]]
save_intrinsics(cam, args.output_dir)
depth_maps = []
for key, i in images.items():
fname = Path(
depth_path = args.depth_dir / fname
if depth_path.isfile():
print("Image {} was not registered".format(fname))
images[key] = None
save_depth_maps(cam, depth_maps, args.output_dir, args.downscale)
save_imgs(args.img_root, images, depth_maps, args.output_dir, args.downscale)
save_positions(images, args.output_dir)
if __name__ == '__main__':
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from path import Path
parser = ArgumentParser(description='Create vizualisation for specified video',
parser.add_argument('--gt', metavar='PATH',
help='path to folder with raw groundtruth', type=Path)
parser.add_argument('--images', metavar='PATH',
help='path to folder with images', type=Path)
parser.add_argument('--range', metavar='R', default=10, type=int,
parser.add_argument('--output', metavar='DIR', default=None, type=Path)
def main():
args = parser.parse_args()
depth_paths = sorted(
image_paths = sorted(args.images.files())
if len(depth_paths) != len(image_paths):
print("{} depth groundtruth maps for {} images".format(len(depth_paths), len(image_paths)))
if __name__ == '__main__':
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