New command: sai cli convert

python/cli/convert/convert.py

@@ -0,0 +1,9 @@
+"""Convert data to Spectacular AI format"""
+
+from .tum import define_subparser as define_subparser_tum
+
+def define_subparser(subparsers):
+    sub = subparsers.add_parser('convert', help=__doc__.strip())
+    format_subparsers = sub.add_subparsers(title='format', dest='format', required=True)
+    define_subparser_tum(format_subparsers)
+    return sub

python/cli/convert/tum.py

@@ -0,0 +1,318 @@
+#!/usr/bin/env python3
+"""
+Convert data from TUM "Euroc / DSO / ASL" benchmark format to Spectacular AI
+format. See <https://vision.in.tum.de/data/datasets/visual-inertial-dataset>
+for details about the data format.
+"""
+
+import argparse
+import csv
+import json
+import os
+from pathlib import Path
+import subprocess
+import yaml
+from contextlib import contextmanager
+import shutil
+import tempfile
+import tarfile
+import zipfile
+import numpy as np
+
+def define_args(parser):
+    parser.add_argument('input', help='Path to the input data in TUM format (.tar, .zip or directory)')
+    parser.add_argument('output', help='Path to the output directory')
+    parser.add_argument('--fps', type=int, default=20, help='Frames per second (metadata only)')
+    parser.add_argument('--crf', type=int, default=15, help='FFmpeg video compression quality (0=lossless)')
+    parser.add_argument('--mono', action='store_true', help='Monocular mode')
+
+def convertVideo(files, output, fps, crf):
+    # Use `-crf 0` for lossless compression.
+    subprocess.check_call(["ffmpeg",
+        "-y",
+        "-r", str(fps),
+        "-f", "image2",
+        "-pattern_type", "glob", "-i", files,
+        "-c:v", "libx264",
+        "-preset", "veryfast",
+        "-crf", str(crf),
+        "-vf", "format=yuv420p",
+        "-an",
+        "-hide_banner",
+        "-loglevel", "error",
+        output])
+
+@contextmanager
+def maybe_extract_tar_or_zip(path):
+    temp_dir = None
+    try:
+        if not os.path.exists(path):
+            raise FileNotFoundError(f"The path '{path}' does not exist.")
+
+        if path.lower().endswith('.tar') and os.path.isfile(path):
+            temp_dir = tempfile.mkdtemp(prefix="extracted_tar_")
+            try:
+                with tarfile.open(path, 'r') as tar:
+                    tar.extractall(path=temp_dir)
+            except tarfile.TarError as e:
+                shutil.rmtree(temp_dir)
+                raise tarfile.TarError(f"Failed to extract tar file '{path}': {e}")
+
+            subdirs = [d for d in os.listdir(temp_dir) if os.path.isdir(os.path.join(temp_dir, d))]
+            if len(subdirs) != 1:
+                raise ValueError(f"The directory '{path}' does not contain exactly one subfolder.")
+
+            yield os.path.join(temp_dir, subdirs[0])
+
+        elif path.lower().endswith('.zip') and os.path.isfile(path):
+            temp_dir = tempfile.mkdtemp(prefix="extracted_zip_")
+            try:
+                with zipfile.ZipFile(path, 'r') as zip_ref:
+                    zip_ref.extractall(path=temp_dir)
+            except zipfile.BadZipFile as e:
+                shutil.rmtree(temp_dir)
+                raise zipfile.BadZipFile(f"Failed to extract zip file '{path}': {e}")
+            yield temp_dir
+
+        elif os.path.isdir(path):
+            yield path
+        else:
+            raise ValueError(f"The path '{path}' is neither a .tar file nor a directory.")
+    finally:
+        if temp_dir and os.path.isdir(temp_dir):
+            try:
+                shutil.rmtree(temp_dir)
+            except Exception as e:
+                print(f"Warning: Failed to delete temporary directory '{temp_dir}': {e}")
+
+def get_calibration(input_dir, stereo):
+    calibration = { "cameras": [] }
+
+    def convert_distortion(model, coeffs):
+        if coeffs is None:
+            return ('pinhole', None)
+        if model == "radial-tangential":
+            c1,c2,c3,c4 = coeffs
+            return ('brown-conrady', [c1, c2, c3, c4, 0, 0, 0, 0])
+        elif model == "equidistant":
+            return ('kannala-brandt4', coeffs)
+        else:
+            raise ValueError("Unknown distortion model: " + model)
+
+    def convert_camera_model(yaml_data):
+        intrinsics = yaml_data["intrinsics"]
+        out = {
+            "focalLengthX": intrinsics[0],
+            "focalLengthY": intrinsics[1],
+            "principalPointX": intrinsics[2],
+            "principalPointY": intrinsics[3],
+            "imageWidth": yaml_data["resolution"][0],
+            "imageHeight": yaml_data["resolution"][1],
+        }
+
+        model, coeffs = convert_distortion(
+            yaml_data.get("distortion_model"),
+            yaml_data.get("distortion_coeffs", yaml_data.get("distortion_coefficients")))
+
+        if 'T_cam_imu' in yaml_data:
+            out['imuToCamera'] = yaml_data['T_cam_imu']
+        else:
+            if 'T_imu_cam' in yaml_data:
+                cam_to_imu = yaml_data['T_imu_cam']
+            elif 'T_BS' in yaml_data:
+                cam_to_imu = np.array(yaml_data['T_BS']['data']).reshape((4, 4))
+            else:
+                raise ValueError("No IMU to cam transformation found")
+            out['imuToCamera'] = np.linalg.inv(cam_to_imu).tolist()
+
+        out['model'] = model
+        out['distortionCoefficients'] = coeffs
+        return out
+
+    if stereo:
+        cams = [0, 1]
+    else:
+        cams = [0]
+
+    dsoPath = os.path.join(input_dir, 'dso', 'camchain.yaml')
+    if os.path.exists(dsoPath):
+        with open(dsoPath) as yamlFile:
+            data = yaml.load(yamlFile, Loader=yaml.FullLoader)
+            for i in cams:
+                cam = "cam{}".format(i)
+                d = data[cam]
+                calibration["cameras"].append(convert_camera_model(d))
+
+    elif os.path.exists(os.path.join(input_dir, 'mav0', 'cam0', 'sensor.yaml')):
+        for cam in cams:
+            with open(os.path.join(input_dir, 'mav0', 'cam%d' % cam, 'sensor.yaml')) as f:
+                p = yaml.load(f, Loader=yaml.FullLoader)
+                calibration["cameras"].append(convert_camera_model(p))
+    else:
+        print('Warning: no TUM calibration files found')
+        return None
+
+    return calibration
+
+def convert_with_existing_folders(rawPath, outPath, fps, crf, stereo):
+    calibration = get_calibration(rawPath, stereo)
+    if calibration is not None:
+        with open(os.path.join(outPath, "calibration.json"), "w") as f:
+            f.write(json.dumps(calibration, indent=2))
+
+    # The two stereo folder image files seem to be perfectly matched, unlike in the EuRoC data.
+    NS_TO_SECONDS = 1000 * 1000 * 1000 # Timestamps are in nanoseconds
+
+    # Use images that are present for both cameras.
+    # Rename bad files so that they do not match glob `*.png` given for ffmpeg.
+    timestamps = []
+    timestamps0 = []
+    timestamps1 = []
+    dir0 = os.path.join(rawPath, 'mav0', 'cam0', 'data')
+    dir1 = os.path.join(rawPath, 'mav0', 'cam1', 'data')
+    n_bad_frames = 0
+    for filename in os.listdir(dir0):
+        timestamps0.append(filename)
+    if stereo:
+        for filename in os.listdir(dir1):
+            timestamps1.append(filename)
+    for t in timestamps0:
+        if stereo and t not in timestamps1:
+            n_bad_frames += 1
+        else:
+            timestamps.append(int(os.path.splitext(t)[0]))
+
+    temp_dir = None
+    if n_bad_frames > 0:
+        print('Warning: {} frame(s) are missing in one of the stereo cameras, creating temp dir'.format(n_bad_frames))
+        assert(stereo)
+        temp_dir = tempfile.mkdtemp(prefix="fixed_frames_")
+        for cam in ["cam0", "cam1"]:
+            tmp_cam_dir = os.path.join(temp_dir, cam, 'data')
+            os.makedirs(tmp_cam_dir)
+            for t in timestamps:
+                src = os.path.join(rawPath, 'mav0', cam, 'data', '{}.png'.format(t))
+                dst = os.path.join(tmp_cam_dir, '{}.png'.format(t))
+                shutil.copyfile(src, dst)
+
+    # shift timestamps to around zero to avoid floating point accuracy issues.
+    timestamps = sorted(timestamps)
+    t0 = timestamps[0]
+
+    output = []
+    number = 0
+    for timestamp in timestamps:
+        t = (timestamp - t0) / NS_TO_SECONDS
+        x = {
+            "number": number,
+            "time": t,
+            "frames": [
+                {"cameraInd": 0, "time": t},
+            ],
+        }
+        if stereo:
+            x['frames'].append({"cameraInd": 1, "time": t})
+        output.append(x)
+        number += 1
+
+    with open(os.path.join(rawPath, 'mav0', 'imu0', 'data.csv')) as csvfile:
+        # timestamp [ns],w_RS_S_x [rad s^-1],w_RS_S_y [rad s^-1],w_RS_S_z [rad s^-1],
+        # a_RS_S_x [m s^-2],a_RS_S_y [m s^-2],a_RS_S_z [m s^-2]
+        csvreader = csv.reader(csvfile, delimiter=',')
+        next(csvreader) # Skip header
+        for row in csvreader:
+            timestamp = (int(row[0]) - t0) / NS_TO_SECONDS
+            output.append({
+                "sensor": {
+                    "type": "gyroscope",
+                    "values": [float(row[1]), float(row[2]), float(row[3])]
+                },
+                "time": timestamp
+            })
+            output.append({
+                "sensor": {
+                    "type": "accelerometer",
+                    "values": [float(row[4]), float(row[5]), float(row[6])]
+                },
+                "time": timestamp
+            })
+
+    gtPath = None
+
+    mocapPath = os.path.join(rawPath, 'mav0', 'mocap0', 'data.csv')
+    gtStatePath = os.path.join(rawPath, 'mav0', 'state_groundtruth_estimate0', 'data.csv')
+    if os.path.exists(mocapPath):
+        gtPath = mocapPath
+    elif os.path.exists(gtStatePath):
+        gtPath = gtStatePath
+
+    if gtPath is not None:
+        with open(gtPath) as csvfile:
+            # timestamp [ns], p_RS_R_x [m], p_RS_R_y [m], p_RS_R_z [m],
+            # q_RS_w [], q_RS_x [], q_RS_y [], q_RS_z []
+            csvreader = csv.reader(csvfile, delimiter=',')
+            next(csvreader) # Skip header
+            for row in csvreader:
+                timestamp = (int(row[0]) - t0) / NS_TO_SECONDS
+                output.append({
+                    "groundTruth": {
+                        "position": {
+                            "x": float(row[1]), "y": float(row[2]), "z": float(row[3])
+                        },
+                        "orientation": {
+                            "w": float(row[4]), "x": float(row[5]), "y": float(row[6]), "z": float(row[7])
+                        }
+                    },
+                    "time": timestamp
+                })
+
+    output = sorted(output, key=lambda row: row["time"]) # Sort by time
+    with open(os.path.join(outPath, 'data.jsonl'), "w") as f:
+        for obj in output:
+            f.write(json.dumps(obj, separators=(',', ':')))
+            f.write("\n")
+
+    if not stereo:
+        # would be nicer if this was not needed
+        with open(os.path.join(outPath, 'vio_config.yaml'), 'w') as f:
+            f.write('useStereo: false')
+
+    if temp_dir is None:
+        video_dir = os.path.join(rawPath, 'mav0')
+    else:
+        video_dir = temp_dir
+    try:
+        # convert videos last. This is the slowest step
+        convertVideo(os.path.join(video_dir, 'cam0', 'data', '*.png'), os.path.join(outPath, 'data.mp4'), fps, crf)
+        if stereo:
+            convertVideo(os.path.join(video_dir, 'cam1', 'data', '*.png'), os.path.join(outPath, 'data2.mp4'), fps, crf)
+    finally:
+        if temp_dir:
+            shutil.rmtree(temp_dir)
+
+
+def convert(inputPath, outputPath, **kwargs):
+    Path(outputPath).mkdir(parents=True, exist_ok=True)
+    with maybe_extract_tar_or_zip(inputPath) as path:
+        convert_with_existing_folders(path, outputPath, **kwargs)
+
+def convert_cli(args):
+    convert(args.input, args.output, fps=args.fps, crf=args.crf, stereo=not args.mono)
+
+def define_subparser(subparsers):
+    sub = subparsers.add_parser('tum',
+                description="Convert data from TUM format to Spectacular AI format",
+                epilog=__doc__,
+                formatter_class=argparse.RawDescriptionHelpFormatter)
+    sub.set_defaults(func=convert_cli)
+    return define_args(sub)
+
+if __name__ == '__main__':
+    def parse_args():
+        import argparse
+        parser = argparse.ArgumentParser(description=__doc__.strip())
+        define_args(parser)
+        return parser.parse_args()
+
+    args = parse_args()
+    convert_cli(args)

python/cli/sai_cli.py

@@ -2,6 +2,7 @@
 
 from .process.process import define_subparser as process_define_subparser
 from .record.record import define_subparser as record_define_subparser
+from .convert.convert import define_subparser as convert_define_subparser
 from .smooth import define_subparser as smooth_define_subparser
 
 def parse_args():
@@ -10,6 +11,7 @@ def parse_args():
     process_define_subparser(subparsers)
     record_define_subparser(subparsers)
     smooth_define_subparser(subparsers)
+    convert_define_subparser(subparsers)
     return parser.parse_args()
 
 def main():