Spaces:
Sleeping
Sleeping
File size: 2,655 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 |
"""
Demo depth generator for testing without ONNX models
Creates synthetic depth maps for demonstration
"""
import numpy as np
import cv2
def generate_demo_depth(image: np.ndarray, method: str = "gradient") -> np.ndarray:
"""
Generate a synthetic depth map for demo purposes
Args:
image: Input RGB image
method: Method to use ('gradient', 'center', 'edges')
Returns:
Synthetic depth map (0-1 range)
"""
h, w = image.shape[:2]
if method == "gradient":
# Simple vertical gradient (top is far, bottom is near)
depth = np.linspace(0, 1, h)
depth = np.tile(depth[:, np.newaxis], (1, w))
elif method == "center":
# Radial gradient from center
y, x = np.ogrid[:h, :w]
cy, cx = h // 2, w // 2
distance = np.sqrt((x - cx)**2 + (y - cy)**2)
depth = distance / distance.max()
depth = 1 - depth # Invert so center is near
elif method == "edges":
# Use edge detection as depth approximation
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
edges = cv2.Canny(gray, 50, 150)
edges = edges.astype(float) / 255.0
# Blur edges to create depth-like effect
depth = cv2.GaussianBlur(edges, (21, 21), 0)
depth = 1 - depth # Invert
else:
# Random depth for testing
depth = np.random.rand(h, w)
# Normalize to 0-1 range
depth = (depth - depth.min()) / (depth.max() - depth.min() + 1e-8)
return depth.astype(np.float32)
def generate_smart_depth(image: np.ndarray) -> np.ndarray:
"""
Generate a smarter synthetic depth using image analysis
Better than simple gradients but still demo quality
"""
h, w = image.shape[:2]
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Use intensity as rough depth proxy (darker = farther)
depth_from_intensity = 1 - (gray.astype(float) / 255.0)
# Get edge information
edges = cv2.Canny(gray, 50, 150).astype(float) / 255.0
edges_blur = cv2.GaussianBlur(edges, (15, 15), 0)
# Combine intensity and edge info
depth = 0.6 * depth_from_intensity + 0.4 * (1 - edges_blur)
# Apply smoothing
depth = cv2.GaussianBlur(depth, (31, 31), 0)
# Add some central bias (center tends to be closer)
y, x = np.ogrid[:h, :w]
cy, cx = h // 2, w // 2
distance = np.sqrt((x - cx)**2 + (y - cy)**2)
radial = distance / distance.max()
radial = 1 - radial
depth = 0.7 * depth + 0.3 * radial
# Normalize
depth = (depth - depth.min()) / (depth.max() - depth.min() + 1e-8)
return depth.astype(np.float32)
|