Datasets:
Formats:
imagefolder
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
1K - 10K
License:
import os, glob | |
import cv2 | |
import numpy as np | |
from tqdm import tqdm | |
# adjust these paths | |
color_dir = "path/to/your/color/images" | |
depth_dir = "path/to/your/depth/images" | |
out_dir = "path/to/your/rgbd/images" | |
os.makedirs(out_dir, exist_ok=True) | |
print("Processing color and depth images...") | |
for color_path in tqdm(glob.glob(os.path.join(color_dir, "*.png"))): | |
base = os.path.basename(color_path) | |
depth_path = os.path.join(depth_dir, base) | |
if not os.path.exists(depth_path): | |
continue | |
rgb = cv2.imread(color_path, cv2.IMREAD_UNCHANGED) # H×W×3 | |
depth = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED) # H×W (raw depth) | |
depth = cv2.normalize(depth, None, 0, 255, cv2.NORM_MINMAX) | |
depth = depth.astype(np.uint8) | |
# merge to H×W×4 (B,G,R,Depth) | |
rgba = cv2.merge([ rgb[:,:,0], rgb[:,:,1], rgb[:,:,2], depth ]) | |
cv2.imwrite(os.path.join(out_dir, base), rgba) | |
print("RGBD data preparation completed.") | |