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Add Nerfcapture (app output) as an export format #104

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Add Nerfcapture (app output) as an export format #104

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63 changes: 48 additions & 15 deletions python/mapping/replay_to_nerf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("input", help="Path to folder with session to process")
parser.add_argument("output", help="Output folder")
parser.add_argument('--format', choices=['taichi', 'nerfstudio'], default='nerfstudio', help='Output format')
parser.add_argument('--format', choices=['taichi', 'nerfstudio', 'nerfcapture'], default='nerfstudio', help='Output format')
parser.add_argument("--cell_size", help="Point cloud decimation cell size", type=float, default=0.025)
parser.add_argument("--distance_quantile", help="Max point distance filter quantile (0 = disabled)", type=float, default=0.99)
parser.add_argument("--key_frame_distance", help="Minimum distance between keyframes (meters)", type=float, default=0.05)
Expand All @@ -31,6 +31,7 @@
args = parser.parse_args()

useMono = None
useAlignedDepth = False

def interpolate_missing_properties(df_source, df_query, k_nearest=3):
xyz = list('xyz')
Expand Down Expand Up @@ -220,6 +221,7 @@ def onMappingOutput(output):
global intrinsics
global visualizer
global useMono
global useAlignedDepth

if visualizer is not None:
visualizer.onMappingOutput(output)
Expand Down Expand Up @@ -253,7 +255,10 @@ def onMappingOutput(output):
cv2.imwrite(fileName, bgrImage)

if frameSet.depthFrame.image is not None and not useMono:
alignedDepth = frameSet.getAlignedDepthFrame(undistortedFrame)
if useAlignedDepth:
alignedDepth = frameSet.getAlignedDepthFrame(undistortedFrame)
else:
alignedDepth = frameSet.depthFrame
depthData = alignedDepth.image.toArray()
depthFrameName = f"{args.output}/tmp/depth_{frameId:05}.png"
cv2.imwrite(depthFrameName, depthData)
Expand Down Expand Up @@ -350,25 +355,46 @@ def onMappingOutput(output):

with open(f"{args.output}/val.json", "w") as outFile:
json.dump(validationFrames, outFile, indent=2, sort_keys=True)
elif args.format == 'nerfstudio':
elif args.format == 'nerfstudio' or args.format == 'nerfcapture':
allFrames = trainingFrames + validationFrames
framesConverted = convert_json_taichi_to_nerfstudio(allFrames)

if args.format == 'nerfcapture':
# Quick fix, just rename files and dirs
for dir in ['rgb', 'depth']:
os.makedirs(f"{args.output}/{dir}", exist_ok=True)
for frame in framesConverted['frames']:
old_rgb_fn = f"{args.output}/{frame['file_path']}"
old_depth_fn = f"{args.output}/{frame['depth_file_path']}"
new_rgb = frame['file_path'].replace('./images/', 'rgb/')
new_depth = new_rgb.replace('rgb', 'depth')
assert('.png' in new_rgb)
frame['file_path'] = new_rgb
frame['depth_file_path'] = new_depth
os.rename(old_rgb_fn, f"{args.output}/{new_rgb}")
os.rename(old_depth_fn, f"{args.output}/{new_depth}")
os.rmdir(f"{args.output}/images")

with open(f"{args.output}/transforms.json", "w") as outFile:
json.dump(convert_json_taichi_to_nerfstudio(allFrames), outFile, indent=2, sort_keys=True)
json.dump(framesConverted, outFile, indent=2, sort_keys=True)

if args.format == 'nerfstudio':
# colmap text point format
fake_colmap = f"{args.output}/colmap/sparse/0"
os.makedirs(fake_colmap, exist_ok=True)

# colmap text point format
fake_colmap = f"{args.output}/colmap/sparse/0"
os.makedirs(fake_colmap, exist_ok=True)
c_points, c_images, c_cameras = convert_json_taichi_to_colmap(allFrames, merged_df, nerfstudio_fake_obs=True)

c_points, c_images, c_cameras = convert_json_taichi_to_colmap(allFrames, merged_df, nerfstudio_fake_obs=True)
def write_colmap_csv(data, fn):
with open(fn, 'wt') as f:
for row in data:
f.write(' '.join([str(c) for c in row])+'\n')

write_colmap_csv(c_points, f"{fake_colmap}/points3D.txt")
write_colmap_csv(c_images, f"{fake_colmap}/images.txt")
write_colmap_csv(c_cameras, f"{fake_colmap}/cameras.txt")

def write_colmap_csv(data, fn):
with open(fn, 'wt') as f:
for row in data:
f.write(' '.join([str(c) for c in row])+'\n')

write_colmap_csv(c_points, f"{fake_colmap}/points3D.txt")
write_colmap_csv(c_images, f"{fake_colmap}/images.txt")
write_colmap_csv(c_cameras, f"{fake_colmap}/cameras.txt")
except Exception as e:
print(f"Something went wrong: {e}")

Expand Down Expand Up @@ -412,6 +438,7 @@ def detect_device_preset(input_dir):
def main():
global visualizer
global useMono
global useAlignedDepth

# Clear output dir
shutil.rmtree(f"{args.output}/images", ignore_errors=True)
Expand Down Expand Up @@ -458,18 +485,24 @@ def main():
parameter_sets.append(device_preset)

if device_preset == 'k4a':
useAlignedDepth = True
if prefer_icp:
parameter_sets.extend(['icp'])
if not args.fast: parameter_sets.append('offline-icp')
elif device_preset == 'realsense':
useAlignedDepth = True
if prefer_icp:
parameter_sets.extend(['icp', 'realsense-icp'])
if not args.fast: parameter_sets.append('offline-icp')
config['stereoPointCloudStride'] = 15
elif device_preset == 'oak-d':
useAlignedDepth = True
config['stereoPointCloudMinDepth'] = 0.5
config['stereoPointCloudStride'] = 30

if args.format == 'nerfcapture':
config['keyframeCandidateInterval'] = 2

if args.preview3d:
from visualization.visualizer import Visualizer
visualizer = Visualizer()
Expand Down