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2796071 | 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 | """Project animated point cloud onto image using camera parameters."""
import json
import numpy as np
from PIL import Image
from scipy.ndimage import gaussian_filter
def project_points(
points: np.ndarray, camera: dict, img_size: int
) -> tuple[np.ndarray, np.ndarray]:
"""Project 3D points to pixel coordinates.
Uses Blender camera convention:
X_cam = points @ R^T + T, camera looks along -Z.
Args:
points: (N, 3) world-space points.
camera: dict with R, T, focal_length_ndc, principal_point_ndc.
img_size: output image resolution (square).
Returns:
(x_pixels, y_pixels): each (N,) array of pixel coordinates.
"""
R = np.asarray(camera["R"], dtype=np.float64)
T = np.asarray(camera["T"], dtype=np.float64)
focal = camera["focal_length_ndc"]
pp = camera["principal_point_ndc"]
X_cam = points @ R.T + T
depth = -X_cam[:, 2]
Z = np.clip(depth, 1e-4, None)
x_ndc = focal[0] * X_cam[:, 0] / Z + pp[0]
y_ndc = focal[1] * X_cam[:, 1] / Z + pp[1]
x_px = img_size / 2.0 * (1.0 + x_ndc)
y_px = img_size / 2.0 * (1.0 - y_ndc)
return x_px, y_px
def render_projection(
image: Image.Image, x_px: np.ndarray, y_px: np.ndarray, img_size: int
) -> np.ndarray:
"""Overlay projected points as a heatmap on an image.
Returns:
(H, W, 3) float32 blended image in [0, 1].
"""
valid = (x_px >= 0) & (x_px < img_size) & (y_px >= 0) & (y_px < img_size)
mask = np.zeros((img_size, img_size), dtype=np.float32)
mask[y_px[valid].astype(int), x_px[valid].astype(int)] = 1.0
mask = np.clip(gaussian_filter(mask, sigma=1.5) * 5.0, 0, 1)
img_np = np.array(image.convert("RGB")).astype(np.float32) / 255.0
overlay = np.array([1.0, 0.3, 0.1])
alpha = 0.4 * mask[..., None]
blended = img_np * (1 - alpha) + overlay * alpha
return np.clip(blended, 0, 1)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Project point cloud onto an image.")
parser.add_argument("--image", required=True, help="Path to input image")
parser.add_argument("--points", required=True, help="Path to surfaces.npy (T,V,6)")
parser.add_argument(
"-t",
"--timestep",
type=int,
default=0,
help="Keyframe index to project (default: 0)",
)
parser.add_argument("--camera", required=True, help="Path to camera.json")
parser.add_argument("--output", required=True, help="Path to output image")
args = parser.parse_args()
# (T, V, 6) -> take keyframe t, xyz only
surfaces = np.load(args.points)
points = surfaces[args.timestep, :, :3].astype(np.float64)
with open(args.camera) as f:
camera = json.load(f)
image = Image.open(args.image)
img_size = image.size[0]
x_px, y_px = project_points(points, camera, img_size)
result = render_projection(image, x_px, y_px, img_size)
out = Image.fromarray((result * 255).astype(np.uint8))
out.save(args.output)
print(f"Saved projection to {args.output}")
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