File size: 9,638 Bytes
ea3c0ad | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 | # 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 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.memory import cleanup_cuda_memory
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,
save_percentage: float = 30.0,
num_max_points: int = 1_000_000,
infer_gs: bool = False,
ref_view_strategy: str = "saddle_balanced",
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
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
save_percentage: Percentage of points to save (0-100)
num_max_points: Maximum number of points in point cloud
infer_gs: Whether to infer 3D Gaussian Splatting
ref_view_strategy: Reference view selection strategy
gs_trj_mode: Trajectory mode for 3DGS
gs_video_quality: Video quality for 3DGS
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}")
# Use sorted image order (reference view will be selected automatically)
image_paths = all_image_paths
print(f"Reference view selection strategy: {ref_view_strategy}")
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,
ref_view_strategy=ref_view_strategy,
)
# 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 using centralized memory utilities for consistency with backend
cleanup_cuda_memory()
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
# cleanup() removed: call cleanup_cuda_memory() directly where needed.
|