# Single image
result = pipe("image.jpg")

# Batch
results = pipe(["img1.jpg", "img2.jpg"], batch_size=2)

# CLI — batch predict
depth-estimate predict "images/*.jpg" --model depth-anything-v2-vitb --output-dir results/

from depth_estimation import pipeline

pipe = pipeline("depth-estimation", model="depth-anything-v2-vitb")

# Stream a video file — yields DepthOutput per frame
for result in pipe.stream("video.mp4", temporal_smoothing=0.5):
    depth = result.depth                  # (H, W) float32
    colored = result.colored_depth        # (H, W, 3) uint8
    print(result.metadata["frame_index"])

# Webcam stream
for result in pipe.stream(0):            # device index
    ...

# Frame glob (sorted alphabetically)
for result in pipe.stream("frames/*.png"):
    ...

# Write output video to disk
pipe.process_video(
    "input.mp4",
    "output_depth.mp4",
    colormap="inferno",
    side_by_side=True,       # RGB | depth composite
    temporal_smoothing=0.5,
)

# CLI — video prediction
depth-estimate predict video.mp4 --model depth-anything-v2-vitb --output depth_video.mp4

See docs/video.md.

from depth_estimation.viz import (
    show_depth, compare_depths, overlay_depth,
    create_anaglyph, animate_3d, plot_error_map,
)

# Display a depth result
show_depth(result, colormap="Spectral_r", title="Depth Anything V2")

# Side-by-side comparison of multiple models
compare_depths([result_v2, result_pro], labels=["DA V2", "DepthPro"], save="compare.png")

# Blend depth over RGB image
overlay = overlay_depth(image, result.depth, alpha=0.5, colormap="inferno")

# Red-cyan anaglyph stereo image
anaglyph = create_anaglyph(image, result.depth, baseline=0.065)

# Rotating 3D surface animation
animate_3d(image, result.depth, "rotation.gif", frames=60)

# Per-pixel error heatmap (requires ground truth)
plot_error_map(pred_depth, gt_depth, metric="abs_rel", save="errors.png")

See docs/viz.md.

from depth_estimation.evaluation import evaluate, compare, Evaluator

# Single model on NYU Depth V2
results = evaluate("depth-anything-v2-vitb", "nyu_depth_v2", split="test")

# Compare multiple models — prints table with best values marked (*)
compare(["depth-anything-v2-vits", "depth-anything-v2-vitb"], dataset="nyu_depth_v2")

# Accumulate metrics over your own dataloader
ev = Evaluator()
for pred, gt, mask in dataloader:
    ev.update(pred, gt, mask)
final = ev.compute()    # abs_rel, sq_rel, rmse, rmse_log, delta1/2/3

See docs/evaluation.md.

from depth_estimation import DepthTrainer, DepthTrainingArguments, load_dataset
from depth_estimation.models.depth_anything_v2 import DepthAnythingV2Model
from depth_estimation.data.transforms import get_train_transforms, get_val_transforms

model    = DepthAnythingV2Model.from_pretrained("depth-anything-v2-vits", for_training=True)
train_ds = load_dataset("nyu_depth_v2", split="train", transform=get_train_transforms(518))
val_ds   = load_dataset("nyu_depth_v2", split="test",  transform=get_val_transforms(518))

args = DepthTrainingArguments(output_dir="./checkpoints", num_epochs=25, batch_size=8,
                               freeze_backbone_epochs=5, mixed_precision=True)
DepthTrainer(model=model, args=args, train_dataset=train_ds, eval_dataset=val_ds).train()

Any torch.utils.data.Dataset returning pixel_values / depth_map / valid_mask works directly — no subclassing needed. See docs/training.md.

from depth_estimation import load_dataset

ds = load_dataset("nyu_depth_v2",  split="test")                                    # auto-downloads ~2.8 GB
ds = load_dataset("diode",         split="val", scene_type="indoors")               # auto-downloads ~2.6 GB
ds = load_dataset("kitti_eigen",   split="test", root="/data/kitti")               # local path
ds = load_dataset("folder",        image_dir="rgb/", depth_dir="depth/")           # any folder

See docs/data.md.

from depth_estimation import AutoDepthModel, export_onnx

model = AutoDepthModel.from_pretrained("depth-anything-v2-vitb")
export_onnx(model, "depth_anything_v2_vitb.onnx", input_size=518, verify=True)

# CLI
depth-estimate export --model depth-anything-v2-vitb --output model.onnx --verify

Verified working for depth-anything-v1/v2/v3, depth-pro, and moge, with model-specific limitations documented in the compatibility table. zoedepth and marigold-dc are not exportable because both wrap opaque external pipelines; export_onnx() fails early with a clear error. Export requires pip install "depth-estimation[export]"; using verify=True additionally requires onnxruntime or onnxruntime-gpu. See docs/export.md for the full supported-models table and GPU setup.

from depth_estimation import AutoDepthModel, prune_model, compute_sparsity

model = AutoDepthModel.from_pretrained("depth-anything-v2-vitb")
prune_model(model, amount=0.3)          # zero out the smallest-magnitude 30% of weights
print(compute_sparsity(model)["overall"])  # ~0.3

model.export_onnx("pruned.onnx", verify=True)  # pruned models export like any other

Pure PyTorch (torch.nn.utils.prune) — no special hardware or SDK. See docs/pruning.md for prune-aware fine-tuning and what pruning does/doesn't buy you (it zeros weights, it doesn't shrink tensors).

For a complete prune → fine-tune → export → quantize workflow, see examples/optimize.py.

from depth_estimation import AutoDepthModel, quantize_onnx

model = AutoDepthModel.from_pretrained("depth-anything-v2-vitb")
model.quantize(dtype="float16")   # in-place GPU precision cast

model.export_onnx("model.onnx")
quantize_onnx("model.onnx", "model_uint8.onnx")  # default weight_type="uint8", verify=True

verify defaults to True — quantization accuracy is model-dependent, not reliably safe: testing across the 28 registered variants found uint8 produces badly wrong output for several real pretrained checkpoints, not just an edge case. See docs/quantization.md — including which formats are actually verified working (int16/uint16 are not, despite being accepted by the underlying APIs, and int8 needs onnxruntime>=1.26.0 for models with Conv2d layers).