Official implementation of Adabins: Depth Estimation using adaptive bins
- You can download the pretrained models "AdaBins_nyu.pt" and "AdaBins_kitti.pt" from here
- You can download the predicted depths in 16-bit format for NYU-Depth-v2 official test set and KITTI Eigen split test set here
Move the downloaded weights to a directory of your choice (we will use "./pretrained/" here). You can then use the pretrained models like so:
from models import UnetAdaptiveBins
import model_io
from PIL import Image
MIN_DEPTH = 1e-3
MAX_DEPTH_NYU = 10
MAX_DEPTH_KITTI = 80
N_BINS = 256
# NYU
model = UnetAdaptiveBins.build(n_bins=N_BINS, min_val=MIN_DEPTH, max_val=MAX_DEPTH_NYU)
pretrained_path = "./pretrained/AdaBins_nyu.pt"
model, _, _ = model_io.load_checkpoint(pretrained_path, model)
bin_edges, predicted_depth = model(example_rgb_batch)
# KITTI
model = UnetAdaptiveBins.build(n_bins=N_BINS, min_val=MIN_DEPTH, max_val=MAX_DEPTH_KITTI)
pretrained_path = "./pretrained/AdaBins_kitti.pt"
model, _, _ = model_io.load_checkpoint(pretrained_path, model)
bin_edges, predicted_depth = model(example_rgb_batch)Note that the model returns bin-edges (instead of bin-centers).
Recommended way: InferenceHelper class in infer.py provides an easy interface for inference and handles various types of inputs (with any prepocessing required). It uses Test-Time-Augmentation (H-Flips) and also calculates bin-centers for you:
from infer import InferenceHelper
infer_helper = InferenceHelper(dataset='nyu')
# predict depth of a batched rgb tensor
example_rgb_batch = ...
bin_centers, predicted_depth = infer_helper.predict(example_rgb_batch)
# predict depth of a single pillow image
img = Image.open("test_imgs/classroom__rgb_00283.jpg") # any rgb pillow image
bin_centers, predicted_depth = infer_helper.predict_pil(img)
# predict depths of images stored in a directory and store the predictions in 16-bit format in a given separate dir
infer_helper.predict_dir("/path/to/input/dir/containing_only_images/", "path/to/output/dir/")- Add instructions for Evaluation and Training.
- Add Colab demo
- Add UI demo
- Remove unnecessary dependencies