Spaces:
Sleeping
Sleeping
File size: 6,428 Bytes
463afdd |
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 |
import onnxruntime as ort
import numpy as np
from pathlib import Path
from typing import Optional, Tuple
import cv2
class DepthAnythingV2:
"""
Depth Anything V2 model wrapper for ONNX inference
Supports both small (25M) and large (1.3B) models
"""
def __init__(
self,
model_path: str,
use_gpu: bool = True,
use_tensorrt: bool = False
):
"""
Initialize Depth Anything V2 model
Args:
model_path: Path to ONNX model file
use_gpu: Whether to use GPU acceleration
use_tensorrt: Whether to use TensorRT optimization
"""
self.model_path = Path(model_path)
if not self.model_path.exists():
raise FileNotFoundError(f"Model not found: {model_path}")
# Setup ONNX Runtime session
providers = self._get_providers(use_gpu, use_tensorrt)
session_options = ort.SessionOptions()
session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
self.session = ort.InferenceSession(
str(self.model_path),
sess_options=session_options,
providers=providers
)
# Get input/output names
self.input_name = self.session.get_inputs()[0].name
self.output_name = self.session.get_outputs()[0].name
# Get expected input shape
input_shape = self.session.get_inputs()[0].shape
# Handle dynamic dimensions (e.g., ['batch_size', 3, 'height', 'width'])
# Default to 518x518 for Depth-Anything V2
if isinstance(input_shape[2], str):
self.input_height = 518
self.input_width = 518
else:
self.input_height = input_shape[2]
self.input_width = input_shape[3]
print(f"✓ Loaded model: {self.model_path.name}")
print(f" Input shape: {input_shape}")
print(f" Providers: {providers}")
def _get_providers(self, use_gpu: bool, use_tensorrt: bool) -> list:
"""Get ONNX Runtime execution providers"""
providers = []
if use_tensorrt and use_gpu:
providers.append('TensorrtExecutionProvider')
if use_gpu:
providers.append('CUDAExecutionProvider')
providers.append('CPUExecutionProvider')
return providers
def preprocess(self, image: np.ndarray) -> Tuple[np.ndarray, Tuple[int, int]]:
"""
Preprocess image for model input
Args:
image: Input image (RGB, HxWx3)
Returns:
Tuple of (preprocessed_image, original_size)
"""
h, w = image.shape[:2]
original_size = (h, w)
# Resize to model input size
image = cv2.resize(
image,
(self.input_width, self.input_height),
interpolation=cv2.INTER_LINEAR
)
# Normalize
image = image.astype(np.float32) / 255.0
# ImageNet normalization
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
image = (image - mean) / std
# Transpose to NCHW format
image = image.transpose(2, 0, 1)
image = np.expand_dims(image, axis=0)
return image, original_size
def postprocess(
self,
depth: np.ndarray,
original_size: Tuple[int, int]
) -> np.ndarray:
"""
Postprocess depth map output
Args:
depth: Raw depth output from model
original_size: Original image size (h, w)
Returns:
Depth map resized to original size
"""
# Remove batch dimension
if len(depth.shape) == 4:
depth = depth[0]
# Remove channel dimension if present
if len(depth.shape) == 3:
depth = depth[0]
# Resize to original size
h, w = original_size
depth = cv2.resize(depth, (w, h), interpolation=cv2.INTER_LINEAR)
# Normalize to 0-1 range
depth = (depth - depth.min()) / (depth.max() - depth.min() + 1e-8)
return depth
def predict(
self,
image: np.ndarray,
resize_output: bool = True
) -> np.ndarray:
"""
Run depth estimation on image
Args:
image: Input image (RGB, HxWx3)
resize_output: Whether to resize output to original size
Returns:
Depth map (same size as input if resize_output=True)
"""
# Preprocess
input_tensor, original_size = self.preprocess(image)
# Run inference
outputs = self.session.run(
[self.output_name],
{self.input_name: input_tensor}
)
depth = outputs[0]
# Postprocess
if resize_output:
depth = self.postprocess(depth, original_size)
return depth
def __call__(self, image: np.ndarray) -> np.ndarray:
"""Convenience method for prediction"""
return self.predict(image)
class ModelManager:
"""
Manages multiple depth models and provides a unified interface
"""
def __init__(self):
self.models = {}
def load_model(
self,
name: str,
model_path: str,
use_gpu: bool = True,
use_tensorrt: bool = False
) -> DepthAnythingV2:
"""
Load a depth model
Args:
name: Model identifier (e.g., 'small', 'large')
model_path: Path to ONNX model
use_gpu: Whether to use GPU
use_tensorrt: Whether to use TensorRT
Returns:
Loaded model instance
"""
model = DepthAnythingV2(model_path, use_gpu, use_tensorrt)
self.models[name] = model
return model
def get_model(self, name: str) -> Optional[DepthAnythingV2]:
"""Get a loaded model by name"""
return self.models.get(name)
def predict(self, image: np.ndarray, model_name: str = 'small') -> np.ndarray:
"""
Run prediction using specified model
Args:
image: Input image
model_name: Name of model to use
Returns:
Depth map
"""
model = self.get_model(model_name)
if model is None:
raise ValueError(f"Model '{model_name}' not loaded")
return model.predict(image)
|