File size: 4,342 Bytes
e4c5b8d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | import numpy as np
import json
import os
import argparse
def convert_opencv_to_opengl(w2c_opencv):
"""
Convert extrinsics from OpenCV format to OpenGL format.
"""
R = w2c_opencv[:3, :3]
t = w2c_opencv[:3, 3].reshape(3, 1)
R_opengl = R.T
t_opengl = -R.T @ t
w2c_opengl = np.hstack((R_opengl, t_opengl))
w2c_opengl = np.vstack((w2c_opengl, np.array([0, 0, 0, 1])))
return w2c_opengl
def load_camera_parameters(cam_dir):
"""Load camera parameters from a directory."""
cam_extr_file = os.path.join(cam_dir, "camera_extrinsics.npy")
cam_intr_file = os.path.join(cam_dir, "camera_params.npy")
c2w = np.load(cam_extr_file)
r2c=np.load("/home/ubuntu/magicsim/gs-dynamics/data/r2c0.npy")
R_rc=r2c[:3,:3]
U,S,Vt=np.linalg.svd(R_rc)
R_rc=U @ Vt
t_rc=r2c[3,:3]
T_rc=np.eye(4,dtype=np.float32)
T_rc[:3,:3]=R_rc
T_rc[:3,3]= t_rc
T_cw=c2w
T_rw=T_cw @ T_rc
T_w2r=np.linalg.inv(T_rw)
w2c = T_w2r
w2c = np.linalg.inv(w2c)
w2c = convert_opencv_to_opengl(w2c)
fx, fy, cx, cy = np.load(cam_intr_file)
k = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
return k, w2c
def extract_image_data(cam_dir, foreground_dir, step=1, start_index=0, num_images=200):
"""Extract image file names and associated data from a specified index and limit the number of images."""
cam_id = os.path.basename(cam_dir)
file_list = os.listdir(foreground_dir)
image_list = [f for f in file_list if f.endswith(".png")]
image_list.sort(key=lambda n: int(n[:-4].split('_')[-1]) if n[:-4].split('_')[-1].isdigit() else 0)
# Ensure start_index is within the range of image_list
start_index = max(0, min(start_index, len(image_list) - 1))
# If num_images is specified and within range, adjust the end_index
if num_images is not None:
end_index = start_index + step * num_images
else:
end_index = len(image_list)
# Select every 'step'th image starting from 'start_index', up to 'end_index'
image_list = image_list[start_index:end_index:step]
return [os.path.join(cam_id, 'foreground', img) for img in image_list], cam_id
def main():
argparser = argparse.ArgumentParser()
argparser.add_argument("--data_path",default="/home/ubuntu/magicsim/gs-dynamics/data/episode_rope/episode_00")
args = argparser.parse_args()
data_path = args.data_path
data_list = os.listdir(data_path)
data_list=sorted(data_list)
cam_path = []
foreground_path = []
for item in data_list:
if item.startswith("camera"):
cam_path.append(os.path.join(data_path, item))
foreground_path.append(os.path.join(data_path, item, 'foreground'))
fn_list = []
per_cam_k_list = []
per_cam_w2c_list = []
per_cam_id_list = []
# Determine the minimum number of frames across all foreground directories
min_num_images = float('inf')
for foreground_dir in foreground_path:
file_list = os.listdir(foreground_dir)
file_list=sorted(file_list)
image_list = [f for f in file_list if f.endswith(".png")]
num_images = len(image_list)
if num_images < min_num_images:
min_num_images = num_images
# Process each camera directory
for cam_dir, foreground_dir in zip(cam_path, foreground_path):
k, w2c = load_camera_parameters(cam_dir)
# Extract image data
images, cam_id = extract_image_data(cam_dir, foreground_dir, num_images=383)
k_list = [k] * len(images)
w2c_list = [w2c] * len(images)
cam_id_list = [cam_id] * len(images)
# Append to the per-camera lists
per_cam_k_list.append(k_list)
per_cam_w2c_list.append(w2c_list)
fn_list.append(images)
per_cam_id_list.append(cam_id_list)
meta = {
'w': 480,
'h': 480,
'k': np.array(per_cam_k_list).transpose(1, 0, 2, 3).tolist(),
'w2c': np.array(per_cam_w2c_list).transpose(1, 0, 2, 3).tolist(),
'fn': np.array(fn_list).transpose(1, 0).tolist(),
'cam_id': np.array(per_cam_id_list).transpose(1, 0).tolist()
}
with open(os.path.join(data_path, 'train_meta.json'), 'w') as f:
json.dump(meta, f)
if __name__ == "__main__":
main()
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