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Running on Zero
Running on Zero
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4b35c4e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import torch
import cv2
import argparse
import os
import numpy as np
from tqdm import tqdm
from utils.projection import intrins_to_intrins_inv, get_cam_coords
from utils.visualize import normal_to_rgb
from utils.d2n.plane_svd import Depth2normal as d2n_svd
def count_data(root_dir, scenes):
count = 0
# Walk through all directories and files in the root directory
for scene in scenes:
for dirpath, dirnames, filenames in os.walk(root_dir):
# Filter files that end with the given suffix
for filename in filenames:
if filename.startswith("depth") and f'Scene{scene}' in dirpath:
count += 1
return count
def args_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--data_path", required=True)
parser.add_argument("--depth_min", type=float, default=1e-3)
parser.add_argument("--depth_max", type=float, default=80.0)
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument("--scenes", type=str, nargs="+",
default=['01', '02', '06', '18', '20'])
return parser.parse_args()
if __name__ == "__main__":
args = args_parser()
cuda_avail = torch.cuda.is_available()
device = torch.device("cuda" if cuda_avail else "cpu")
data_path = args.data_path
depth_min = args.depth_min
depth_max = args.depth_max
batch_size = args.batch_size
# scenes = ['01', '02', '06', '18', '20']
scenes = args.scenes
print(f"Processing scenes: {scenes}. ")
num_data = count_data(data_path, scenes)
print(f'Total number of data: {num_data}. ')
conditions = [
'15-deg-left', '15-deg-right', '30-deg-left', '30-deg-right', 'clone',
'fog', 'morning', 'overcast', 'rain', 'sunset'
]
cameras = ['0', '1']
# SceneX/Y/frames/depth/Camera_Z/depth_%05d.png
# SceneX/Y/intrinsic.txt
pbar = tqdm(total=num_data)
for scene in scenes:
for cond in conditions:
intrinsic_file = os.path.join(data_path,f'Scene{scene}/{cond}/intrinsic.txt')
with open(intrinsic_file, 'r') as file:
lines = file.readlines()
frames_x_cameras = len(lines)-1
intrinsics = np.zeros((frames_x_cameras//len(cameras), len(cameras),4))
for line in lines[1:]:
line = line.strip().split(" ")
frame_id = int(line[0])
camera_id = int(line[1])
k_00 = float(line[2])
k_11 = float(line[3])
k_02 = float(line[4])
k_12 = float(line[5])
intrinsics[frame_id][camera_id] = np.array([k_00, k_11, k_02, k_12])
for cam in cameras:
depth_dir = os.path.join(data_path, f'Scene{scene}/{cond}/frames/depth/Camera_{cam}')
depth_files = os.listdir(depth_dir)
num_batch = len(depth_files) // batch_size
batches = [batch_size]*num_batch
res = len(depth_files) % batch_size
if res > 0:
num_batch += 1
batches += [res]
for idx_b, batch in enumerate(batches):
depth_batch = []
intrins_inv_batch = []
depth_path_batch = []
for i in range(batch):
depth_file_idx = idx_b * batch_size + i
depth_file = depth_files[depth_file_idx]
depth_path = os.path.join(depth_dir, depth_file)
depth_path_batch.append(depth_path)
depth = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
depth = depth / 100 # cm -> m
depth = depth[:, :, None] # (H, W, 1)
depth = torch.from_numpy(depth).permute(2, 0, 1).unsqueeze(0) # (1, 1, H, W)
depth = depth.to(device)
depth_batch.append(depth)
frame_id = int(depth_file.split(".")[0].split("_")[-1])
k_00 = intrinsics[frame_id][int(cam)][0]
k_11 = intrinsics[frame_id][int(cam)][1]
k_02 = intrinsics[frame_id][int(cam)][2]
k_12 = intrinsics[frame_id][int(cam)][3]
intrins = np.array([
[k_00, 0, k_02],
[0, k_11, k_12],
[0, 0, 1 ]
])
intrins_inv = intrins_to_intrins_inv(intrins)
intrins_inv = torch.from_numpy(intrins_inv).unsqueeze(0).to(device)
intrins_inv_batch.append(intrins_inv)
depth_batch = torch.cat(depth_batch, dim=0)
intrins_inv_batch = torch.cat(intrins_inv_batch, dim=0)
points = get_cam_coords(intrins_inv_batch, depth_batch)
with torch.no_grad():
D2N = d2n_svd(d_min=depth_min, d_max=depth_max, k=5, d=1, gamma=0.05, min_nghbr=4)
normal, valid_mask = D2N(points)
normal_rgb = normal_to_rgb(normal, valid_mask)
for i in range(batch):
norm_rgb = normal_rgb[i]
norm_rgb = Image.fromarray(norm_rgb, "RGB")
norm_rgb_save_path = depth_path_batch[i].replace("depth",'normal')
os.makedirs(os.path.dirname(norm_rgb_save_path), exist_ok=True)
norm_rgb.save(norm_rgb_save_path)
pbar.update(batch)
pbar.close() |