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import os
import json
import shutil
import numpy as np
import cv2 as cv
import imageio
from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor, as_completed
import open3d as o3d
import scipy.ndimage
import pickle
# Set environment variable to limit OpenBLAS threads
os.environ["OPENBLAS_NUM_THREADS"] = "1"
DEPTH_SCALE_FACTOR = 5000
# Point cloud from depth
def pointcloudify_depth(depth, intrinsics, dist_coeff, undistort=True):
shape = depth.shape[::-1]
if undistort:
undist_intrinsics, _ = cv.getOptimalNewCameraMatrix(
intrinsics, dist_coeff, shape, 1, shape
)
inv_undist_intrinsics = np.linalg.inv(undist_intrinsics)
map_x, map_y = cv.initUndistortRectifyMap(
intrinsics, dist_coeff, None, undist_intrinsics, shape, cv.CV_32FC1
)
undist_depth = cv.remap(depth, map_x, map_y, cv.INTER_NEAREST)
else:
inv_undist_intrinsics = np.linalg.inv(intrinsics)
undist_depth = depth
# Generate x,y grid for H x W image
grid_x, grid_y = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]))
grid = np.stack((grid_x, grid_y, np.ones_like(grid_x)), axis=-1)
# Reshape and compute local grid
grid_flat = grid.reshape(-1, 3).T
local_grid = inv_undist_intrinsics @ grid_flat
# Multiply by depth
local_grid = local_grid.T * undist_depth.reshape(-1, 1)
return local_grid.astype(np.float32)
def project_pcd_to_depth(pcd, undist_intrinsics, img_size, config):
h, w = img_size
points = np.asarray(pcd.points)
d = points[:, 2]
normalized_points = points / points[:, 2][:, np.newaxis]
proj_pcd = np.round((undist_intrinsics @ normalized_points.T).T).astype(np.int64)
proj_mask = (
(proj_pcd[:, 0] >= 0)
& (proj_pcd[:, 0] < w)
& (proj_pcd[:, 1] >= 0)
& (proj_pcd[:, 1] < h)
)
proj_pcd = proj_pcd[proj_mask]
d = d[proj_mask]
pcd_image = np.zeros((config["res_h"], config["res_w"]), dtype=np.float32)
pcd_image[proj_pcd[:, 1], proj_pcd[:, 0]] = d
return pcd_image
def smooth_depth(depth):
MAX_DEPTH_VAL = 1e5
KERNEL_SIZE = 11
depth = depth.copy()
depth[depth == 0] = MAX_DEPTH_VAL
smoothed_depth = scipy.ndimage.minimum_filter(depth, KERNEL_SIZE)
smoothed_depth[smoothed_depth == MAX_DEPTH_VAL] = 0
return smoothed_depth
def align_rgb_depth(rgb, depth, roi, config, rgb_cnf, config_dict, T):
# Undistort rgb image
undist_rgb = cv.undistort(
rgb,
rgb_cnf["intrinsics"],
rgb_cnf["dist_coeff"],
None,
rgb_cnf["undist_intrinsics"],
)
# Create point cloud from depth
pcd = o3d.geometry.PointCloud()
points = pointcloudify_depth(
depth, config_dict["depth"]["dist_mtx"], config_dict["depth"]["dist_coef"]
)
pcd.points = o3d.utility.Vector3dVector(points)
# Align point cloud with depth reference frame
pcd.transform(T)
# Project aligned point cloud to rgb
aligned_depth = project_pcd_to_depth(
pcd, rgb_cnf["undist_intrinsics"], rgb.shape[:2], config
)
smoothed_aligned_depth = smooth_depth(aligned_depth)
x, y, w, h = roi
depth_res = smoothed_aligned_depth[y : y + h, x : x + w]
rgb_res = undist_rgb[y : y + h, x : x + w]
return rgb_res, depth_res, rgb_cnf["undist_intrinsics"]
def process_pair(args):
(
pair,
smartphone_folder,
azure_depth_folder,
final_folder,
config,
rgb_cnf,
config_dict,
T,
) = args
try:
rgb_image = cv.imread(os.path.join(smartphone_folder, f"{pair[0]}.png"))
depth_array = np.load(
os.path.join(azure_depth_folder, f"{pair[1]}.npy"), allow_pickle=True
)
rgb_image_aligned, depth_array_aligned, intrinsics = align_rgb_depth(
rgb_image,
depth_array,
(0, 0, config["res_w"], config["res_h"]),
config,
rgb_cnf,
config_dict,
T,
)
# Save rgb as 8-bit png
cv.imwrite(
os.path.join(final_folder, "rgb", f"{pair[0]}.png"), rgb_image_aligned
)
# # Save depth as 16-bit unsigned int with scale factor
# depth_array_aligned = (depth_array_aligned *
# DEPTH_SCALE_FACTOR).astype(np.uint16)
# imageio.imwrite(os.path.join(final_folder, 'depth', f"{pair[1]}.png"), depth_array_aligned)
np.save(
os.path.join(final_folder, "depth", f"{pair[0]}.npy"), depth_array_aligned
)
np.savez(
os.path.join(final_folder, "cam", f"{pair[0]}.npz"), intrinsics=intrinsics
)
except Exception as e:
return f"Error processing pair {pair}: {e}"
return None
def main():
DATA_DIR_ = "data_smartportraits/SmartPortraits" # REPLACE WITH YOUR OWN DATA PATH!
DATA_DIR = DATA_DIR_.rstrip("/")
print(f"{DATA_DIR_} {DATA_DIR}/")
# Folder where the data in TUM format will be put
curr_dir = os.path.dirname(os.path.abspath(__file__))
with open(os.path.join(curr_dir, "config.json")) as conf_f:
config = json.load(conf_f)
# Pre-load shared data
with open(os.path.join(curr_dir, config["depth_conf"]), "rb") as config_f:
config_dict = pickle.load(config_f)
rgb_cnf = np.load(
os.path.join(curr_dir, config["rgb_intristics"]), allow_pickle=True
).item()
T = np.load(os.path.join(curr_dir, config["transform_intristics"]))
final_root = "processed_smartportraits1" # REPLACE WITH YOUR OWN DATA PATH!
seqs = []
for scene in os.listdir(DATA_DIR):
scene_path = os.path.join(DATA_DIR, scene)
if not os.path.isdir(scene_path):
continue
for s in os.listdir(scene_path):
s_path = os.path.join(scene_path, s)
if not os.path.isdir(s_path):
continue
for date in os.listdir(s_path):
date_path = os.path.join(s_path, date)
if os.path.isdir(date_path):
seqs.append((scene, s, date))
for seq in tqdm(seqs):
scene, s, date = seq
dataset_path = os.path.join(DATA_DIR, scene, s, date)
final_folder = os.path.join(final_root, "_".join([scene, s, date]))
azure_depth_folder = os.path.join(dataset_path, "_azure_depth_image_raw")
smartphone_folder = os.path.join(dataset_path, "smartphone_video_frames")
depth_files = [
file for file in os.listdir(azure_depth_folder) if file.endswith(".npy")
]
depth_ts = np.array([int(file.split(".")[0]) for file in depth_files])
depth_ts.sort()
rgb_files = [
file for file in os.listdir(smartphone_folder) if file.endswith(".png")
]
rgb_ts = np.array([int(file.split(".")[0]) for file in rgb_files])
rgb_ts.sort()
print(
f"Depth timestamps from {depth_ts[0]} to {depth_ts[-1]} (cnt {len(depth_ts)})"
)
print(f"RGB timestamps from {rgb_ts[0]} to {rgb_ts[-1]} (cnt {len(rgb_ts)})")
# Build correspondences between depth and rgb by nearest neighbour algorithm
rgbd_pairs = []
for depth_t in depth_ts:
idx = np.argmin(np.abs(rgb_ts - depth_t))
closest_rgb_t = rgb_ts[idx]
rgbd_pairs.append((closest_rgb_t, depth_t))
# Prepare folder infrastructure
if os.path.exists(final_folder):
shutil.rmtree(final_folder)
os.makedirs(os.path.join(final_folder, "depth"), exist_ok=True)
os.makedirs(os.path.join(final_folder, "rgb"), exist_ok=True)
os.makedirs(os.path.join(final_folder, "cam"), exist_ok=True)
# Prepare arguments for processing
tasks = [
(
pair,
smartphone_folder,
azure_depth_folder,
final_folder,
config,
rgb_cnf,
config_dict,
T,
)
for pair in rgbd_pairs
]
num_workers = os.cpu_count()
with ProcessPoolExecutor(max_workers=num_workers) as executor:
futures = {executor.submit(process_pair, task): task[0] for task in tasks}
for future in tqdm(
as_completed(futures),
total=len(futures),
desc=f"Processing {scene}_{s}_{date}",
):
error = future.result()
if error:
print(error)
if __name__ == "__main__":
main()
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