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# utils/depth_estimation.py
import torch
import numpy as np
from PIL import Image
import open3d as o3d
from transformers import DPTImageProcessor, DPTForDepthEstimation
from pathlib import Path
import logging
logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR)
from utils.image_utils import (
resize_image_with_aspect_ratio
)
# Load models once during module import
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large", ignore_mismatched_sizes=True)
def estimate_depth(image):
# Ensure image is in RGB mode
if image.mode != "RGB":
image = image.convert("RGB")
# Resize the image for the model
image_resized = image.resize(
(image.width, image.height),
Image.Resampling.LANCZOS
)
# Prepare image for the model
encoding = image_processor(image_resized, return_tensors="pt")
# Forward pass
with torch.no_grad():
outputs = depth_model(**encoding)
predicted_depth = outputs.predicted_depth
# Interpolate to original size
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=(image.height, image.width),
mode="bicubic",
align_corners=False,
).squeeze()
# Convert to depth image
output = prediction.cpu().numpy()
depth_min = output.min()
depth_max = output.max()
max_val = (2**8) - 1
# Normalize and convert to 8-bit image
depth_image = max_val * (output - depth_min) / (depth_max - depth_min)
depth_image = depth_image.astype("uint8")
depth_pil = Image.fromarray(depth_image)
return depth_pil, output
def create_3d_model(rgb_image, depth_array, voxel_size_factor=0.01):
depth_o3d = o3d.geometry.Image(depth_array.astype(np.float32))
rgb_o3d = o3d.geometry.Image(np.array(rgb_image))
rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
rgb_o3d,
depth_o3d,
convert_rgb_to_intensity=False
)
# Create a point cloud from the RGBD image
camera_intrinsic = o3d.camera.PinholeCameraIntrinsic(
rgb_image.width,
rgb_image.height,
fx=1.0,
fy=1.0,
cx=rgb_image.width / 2.0,
cy=rgb_image.height / 2.0,
)
pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
rgbd_image,
camera_intrinsic
)
# Voxel downsample
voxel_size = max(pcd.get_max_bound() - pcd.get_min_bound()) * voxel_size_factor
voxel_grid = o3d.geometry.VoxelGrid.create_from_point_cloud(pcd, voxel_size=voxel_size)
# Save the 3D model to a temporary file
temp_dir = Path.cwd() / "temp_models"
temp_dir.mkdir(exist_ok=True)
model_path = temp_dir / "model.ply"
o3d.io.write_voxel_grid(str(model_path), voxel_grid)
return str(model_path)
def generate_depth_and_3d(input_image_path, voxel_size_factor):
image = Image.open(input_image_path).convert("RGB")
resized_image = resize_image_with_aspect_ratio(image, 2688, 1680)
depth_image, depth_array = estimate_depth(resized_image)
model_path = create_3d_model(resized_image, depth_array, voxel_size_factor=voxel_size_factor)
return depth_image, model_path
def generate_depth_button_click(depth_image_source, voxel_size_factor, input_image, output_image, overlay_image, bordered_image_output):
if depth_image_source == "Input Image":
image_path = input_image
elif depth_image_source == "Output Image":
image_path = output_image
elif depth_image_source == "Image with Margins":
image_path = bordered_image_output
else:
image_path = overlay_image
return generate_depth_and_3d(image_path, voxel_size_factor)
def create_3d_obj(rgb_image, raw_depth, image_path, depth=10, z_scale=200):
"""
Creates a 3D object from RGB and depth images.
Args:
rgb_image (np.ndarray): The RGB image as a NumPy array.
raw_depth (np.ndarray): The raw depth data.
image_path (Path): The path to the original image.
depth (int, optional): Depth parameter for Poisson reconstruction. Defaults to 10.
z_scale (float, optional): Scaling factor for the Z-axis. Defaults to 200.
Returns:
str: The file path to the saved GLTF model.
"""
# Normalize the depth image
depth_image = ((raw_depth - raw_depth.min()) / (raw_depth.max() - raw_depth.min()) * 255).astype("uint8")
depth_o3d = o3d.geometry.Image(depth_image)
image_o3d = o3d.geometry.Image(rgb_image)
# Create RGBD image
rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
image_o3d, depth_o3d, convert_rgb_to_intensity=False
)
height, width = depth_image.shape
# Define camera intrinsics
camera_intrinsic = o3d.camera.PinholeCameraIntrinsic(
width,
height,
fx=z_scale,
fy=z_scale,
cx=width / 2.0,
cy=height / 2.0,
)
# Generate point cloud from RGBD image
pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image, camera_intrinsic)
# Scale the Z dimension
points = np.asarray(pcd.points)
depth_scaled = ((raw_depth - raw_depth.min()) / (raw_depth.max() - raw_depth.min())) * (z_scale*100)
z_values = depth_scaled.flatten()[:len(points)]
points[:, 2] *= z_values
pcd.points = o3d.utility.Vector3dVector(points)
# Estimate and orient normals
pcd.estimate_normals(
search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=60)
)
pcd.orient_normals_towards_camera_location(camera_location=np.array([0.0, 0.0, 1.5 ]))
# Apply transformations
pcd.transform([[1, 0, 0, 0],
[0, -1, 0, 0],
[0, 0, -1, 0],
[0, 0, 0, 1]])
pcd.transform([[-1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]])
# Perform Poisson surface reconstruction
print(f"Running Poisson surface reconstruction with depth {depth}")
mesh_raw, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
pcd, depth=depth, width=0, scale=1.1, linear_fit=True
)
print(f"Raw mesh vertices: {len(mesh_raw.vertices)}, triangles: {len(mesh_raw.triangles)}")
# Simplify the mesh using vertex clustering
voxel_size = max(mesh_raw.get_max_bound() - mesh_raw.get_min_bound()) / (max(width, height) * 0.8)
mesh = mesh_raw.simplify_vertex_clustering(
voxel_size=voxel_size,
contraction=o3d.geometry.SimplificationContraction.Average,
)
print(f"Simplified mesh vertices: {len(mesh.vertices)}, triangles: {len(mesh.triangles)}")
# Crop the mesh to the bounding box of the point cloud
bbox = pcd.get_axis_aligned_bounding_box()
mesh_crop = mesh.crop(bbox)
# Save the mesh as a GLTF file
temp_dir = Path.cwd() / "models"
temp_dir.mkdir(exist_ok=True)
gltf_path = str(temp_dir / f"{image_path.stem}.gltf")
o3d.io.write_triangle_mesh(gltf_path, mesh_crop, write_triangle_uvs=True)
return gltf_path
def depth_process_image(image_path, resized_width=800, z_scale=208):
"""
Processes the input image to generate a depth map and a 3D mesh reconstruction.
Args:
image_path (str): The file path to the input image.
Returns:
list: A list containing the depth image, 3D mesh reconstruction, and GLTF file path.
"""
image_path = Path(image_path)
if not image_path.exists():
raise ValueError("Image file not found")
# Load and resize the image
image_raw = Image.open(image_path).convert("RGB")
print(f"Original size: {image_raw.size}")
resized_height = int(resized_width * image_raw.size[1] / image_raw.size[0])
image = image_raw.resize((resized_width, resized_height), Image.Resampling.LANCZOS)
print(f"Resized size: {image.size}")
# Prepare image for the model
encoding = image_processor(image, return_tensors="pt")
# Perform depth estimation
with torch.no_grad():
outputs = depth_model(**encoding)
predicted_depth = outputs.predicted_depth
# Interpolate depth to match the image size
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=(image.height, image.width),
mode="bicubic",
align_corners=False,
).squeeze()
# Normalize the depth image to 8-bit
if torch.cuda.is_available():
prediction = prediction.numpy()
else:
prediction = prediction.cpu().numpy()
depth_min, depth_max = prediction.min(), prediction.max()
depth_image = ((prediction - depth_min) / (depth_max - depth_min) * 255).astype("uint8")
try:
gltf_path = create_3d_obj(np.array(image), prediction, image_path, depth=10, z_scale=z_scale)
except Exception:
gltf_path = create_3d_obj(np.array(image), prediction, image_path, depth=8, z_scale=z_scale)
img = Image.fromarray(depth_image)
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
return [img, gltf_path, gltf_path]
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