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# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Model inference module for Depth Anything 3 Gradio app.
This module handles all model-related operations including inference,
data processing, and result preparation.
"""
import gc
import glob
import os
from typing import Any, Dict, Optional, Tuple
import numpy as np
import torch
from depth_anything_3.api import DepthAnything3
from depth_anything_3.utils.export.glb import export_to_glb
from depth_anything_3.utils.export.gs import export_to_gs_video
class ModelInference:
"""
Handles model inference and data processing for Depth Anything 3.
"""
def __init__(self):
"""Initialize the model inference handler."""
self.model = None
def initialize_model(self, device: str = "cuda") -> None:
"""
Initialize the DepthAnything3 model.
Args:
device: Device to load the model on
"""
if self.model is None:
# Get model directory from environment variable or use default
model_dir = os.environ.get(
"DA3_MODEL_DIR", "/dev/shm/da3_models/DA3HF-VITG-METRIC_VITL"
)
self.model = DepthAnything3.from_pretrained(model_dir)
self.model = self.model.to(device)
else:
self.model = self.model.to(device)
self.model.eval()
def run_inference(
self,
target_dir: str,
filter_black_bg: bool = False,
filter_white_bg: bool = False,
process_res_method: str = "upper_bound_resize",
show_camera: bool = True,
selected_first_frame: Optional[str] = None,
save_percentage: float = 30.0,
num_max_points: int = 1_000_000,
infer_gs: bool = False,
gs_trj_mode: str = "extend",
gs_video_quality: str = "high",
) -> Tuple[Any, Dict[int, Dict[str, Any]]]:
"""
Run DepthAnything3 model inference on images.
Args:
target_dir: Directory containing images
apply_mask: Whether to apply mask for ambiguous depth classes
mask_edges: Whether to mask edges
filter_black_bg: Whether to filter black background
filter_white_bg: Whether to filter white background
process_res_method: Method for resizing input images
show_camera: Whether to show camera in 3D view
selected_first_frame: Selected first frame filename
save_percentage: Percentage of points to save (0-100)
infer_gs: Whether to infer 3D Gaussian Splatting
Returns:
Tuple of (prediction, processed_data)
"""
print(f"Processing images from {target_dir}")
# Device check
device = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(device)
# Initialize model if needed
self.initialize_model(device)
# Get image paths
print("Loading images...")
image_folder_path = os.path.join(target_dir, "images")
all_image_paths = sorted(glob.glob(os.path.join(image_folder_path, "*")))
# Filter for image files
image_extensions = [".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif"]
all_image_paths = [
path
for path in all_image_paths
if any(path.lower().endswith(ext) for ext in image_extensions)
]
print(f"Found {len(all_image_paths)} images")
print(f"All image paths: {all_image_paths}")
# Apply first frame selection logic
if selected_first_frame:
# Find the image with matching filename
selected_path = None
for path in all_image_paths:
if os.path.basename(path) == selected_first_frame:
selected_path = path
break
if selected_path:
# Move selected frame to the front
image_paths = [selected_path] + [
path for path in all_image_paths if path != selected_path
]
print(f"User selected first frame: {selected_first_frame} -> {selected_path}")
print(f"Reordered image paths: {image_paths}")
else:
# Use default order if no match found
image_paths = all_image_paths
print(
f"Selected frame '{selected_first_frame}' not found in image paths. "
"Using default order."
)
first_frame_display = image_paths[0] if image_paths else "No images"
print(f"Using default order (first frame): {first_frame_display}")
else:
# Use default order (sorted)
image_paths = all_image_paths
first_frame_display = image_paths[0] if image_paths else "No images"
print(f"Using default order (first frame): {first_frame_display}")
if len(image_paths) == 0:
raise ValueError("No images found. Check your upload.")
# Map UI options to actual method names
method_mapping = {"high_res": "lower_bound_resize", "low_res": "upper_bound_resize"}
actual_method = method_mapping.get(process_res_method, "upper_bound_crop")
# Run model inference
print(f"Running inference with method: {actual_method}")
with torch.no_grad():
prediction = self.model.inference(
image_paths, export_dir=None, process_res_method=actual_method, infer_gs=infer_gs
)
# num_max_points: int = 1_000_000,
export_to_glb(
prediction,
filter_black_bg=filter_black_bg,
filter_white_bg=filter_white_bg,
export_dir=target_dir,
show_cameras=show_camera,
conf_thresh_percentile=save_percentage,
num_max_points=int(num_max_points),
)
# export to gs video if needed
if infer_gs:
mode_mapping = {"extend": "extend", "smooth": "interpolate_smooth"}
print(f"GS mode: {gs_trj_mode}; Backend mode: {mode_mapping[gs_trj_mode]}")
export_to_gs_video(
prediction,
export_dir=target_dir,
chunk_size=4,
trj_mode=mode_mapping.get(gs_trj_mode, "extend"),
enable_tqdm=True,
vis_depth="hcat",
video_quality=gs_video_quality,
)
# Save predictions.npz for caching metric depth data
self._save_predictions_cache(target_dir, prediction)
# Process results
processed_data = self._process_results(target_dir, prediction, image_paths)
# Clean up
torch.cuda.empty_cache()
return prediction, processed_data
def _save_predictions_cache(self, target_dir: str, prediction: Any) -> None:
"""
Save predictions data to predictions.npz for caching.
Args:
target_dir: Directory to save the cache
prediction: Model prediction object
"""
try:
output_file = os.path.join(target_dir, "predictions.npz")
# Build save dict with prediction data
save_dict = {}
# Save processed images if available
if prediction.processed_images is not None:
save_dict["images"] = prediction.processed_images
# Save depth data
if prediction.depth is not None:
save_dict["depths"] = np.round(prediction.depth, 6)
# Save confidence if available
if prediction.conf is not None:
save_dict["conf"] = np.round(prediction.conf, 2)
# Save camera parameters
if prediction.extrinsics is not None:
save_dict["extrinsics"] = prediction.extrinsics
if prediction.intrinsics is not None:
save_dict["intrinsics"] = prediction.intrinsics
# Save to file
np.savez_compressed(output_file, **save_dict)
print(f"Saved predictions cache to: {output_file}")
except Exception as e:
print(f"Warning: Failed to save predictions cache: {e}")
def _process_results(
self, target_dir: str, prediction: Any, image_paths: list
) -> Dict[int, Dict[str, Any]]:
"""
Process model results into structured data.
Args:
target_dir: Directory containing results
prediction: Model prediction object
image_paths: List of input image paths
Returns:
Dictionary containing processed data for each view
"""
processed_data = {}
# Read generated depth visualization files
depth_vis_dir = os.path.join(target_dir, "depth_vis")
if os.path.exists(depth_vis_dir):
depth_files = sorted(glob.glob(os.path.join(depth_vis_dir, "*.jpg")))
for i, depth_file in enumerate(depth_files):
# Use processed images directly from API
processed_image = None
if prediction.processed_images is not None and i < len(
prediction.processed_images
):
processed_image = prediction.processed_images[i]
processed_data[i] = {
"depth_image": depth_file,
"image": processed_image,
"original_image_path": image_paths[i] if i < len(image_paths) else None,
"depth": prediction.depth[i] if i < len(prediction.depth) else None,
"intrinsics": (
prediction.intrinsics[i]
if prediction.intrinsics is not None and i < len(prediction.intrinsics)
else None
),
"mask": None, # No mask information available
}
return processed_data
def cleanup(self) -> None:
"""Clean up GPU memory."""
if torch.cuda.is_available():
torch.cuda.empty_cache()
gc.collect()
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