File size: 4,227 Bytes
0220f4d | 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 | #!/usr/bin/env python3
"""Generate camera preview images and timelapse videos from LPBF HDF5 data.
This script processes visible light and NIR camera images, creating:
- PNG images for each layer (5 image types × 1117 layers)
- Timelapse videos showing layer progression for each image type
"""
import h5py
import numpy as np
from pathlib import Path
from PIL import Image
import subprocess
HDF5_PATH = Path(__file__).parent.parent / "source/2024-05-01 M2 AMMTO Fatigue Blanks 05.hdf5"
PREVIEW_DIR = Path(__file__).parent.parent / "previews"
IMAGE_TYPES = [
("visible_0", "slices/camera_data/visible/0", "uint8"), # Post-melt visible
("visible_1", "slices/camera_data/visible/1", "uint8"), # Post-recoat visible
("nir_0", "slices/camera_data/nir/0", "uint16"), # NIR sum
("nir_1", "slices/camera_data/nir/1", "uint16"), # NIR max
("nir_2", "slices/camera_data/nir/2", "uint16"), # NIR argmax
]
def normalize_image(data: np.ndarray, dtype: str) -> np.ndarray:
"""Normalize image data to uint8 for PNG output."""
if dtype == "uint8":
return data
elif dtype == "uint16":
data = data.astype(np.float32)
p_low, p_high = np.percentile(data, [1, 99])
if p_high > p_low:
data = np.clip((data - p_low) / (p_high - p_low) * 255, 0, 255)
else:
data = np.zeros_like(data)
return data.astype(np.uint8)
else:
raise ValueError(f"Unknown dtype: {dtype}")
def generate_pngs():
"""Generate PNG images for each layer and image type."""
with h5py.File(HDF5_PATH, "r") as f:
n_layers = f["slices/camera_data/visible/0"].shape[0]
print(f"Generating PNGs for {n_layers} layers across {len(IMAGE_TYPES)} image types...")
for img_name, hdf5_path, dtype in IMAGE_TYPES:
output_dir = PREVIEW_DIR / img_name
output_dir.mkdir(parents=True, exist_ok=True)
dataset = f[hdf5_path]
print(f"\nProcessing {img_name} ({dataset.shape})...")
for layer_idx in range(n_layers):
output_path = output_dir / f"layer_{layer_idx:04d}.png"
if output_path.exists():
if layer_idx % 100 == 0:
print(f" Layer {layer_idx}/{n_layers} (skipped, exists)")
continue
data = dataset[layer_idx]
normalized = normalize_image(data, dtype)
img = Image.fromarray(normalized, mode="L")
img.save(output_path, optimize=True)
if layer_idx % 100 == 0:
print(f" Layer {layer_idx}/{n_layers}")
print(f" Completed {img_name}: {n_layers} images")
def generate_videos(fps: int = 30):
"""Generate MP4 timelapse videos from PNG sequences."""
print("\nGenerating timelapse videos...")
for img_name, _, _ in IMAGE_TYPES:
input_pattern = PREVIEW_DIR / img_name / "layer_%04d.png"
output_path = PREVIEW_DIR / f"{img_name}.mp4"
if output_path.exists():
print(f" {img_name}.mp4 already exists, skipping...")
continue
print(f" Creating {img_name}.mp4...")
cmd = [
"ffmpeg",
"-y",
"-framerate", str(fps),
"-i", str(input_pattern),
"-c:v", "libx264",
"-preset", "medium",
"-crf", "23",
"-pix_fmt", "yuv420p",
str(output_path)
]
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode != 0:
print(f" Error: {result.stderr}")
else:
print(f" Created {output_path}")
def main():
print("=" * 60)
print("Camera Preview Generator")
print("=" * 60)
try:
subprocess.run(["ffmpeg", "-version"], capture_output=True, check=True)
except (subprocess.CalledProcessError, FileNotFoundError):
print("Warning: ffmpeg not found. Videos will not be generated.")
generate_pngs()
generate_videos()
print("\n" + "=" * 60)
print("Camera preview generation complete!")
print("=" * 60)
if __name__ == "__main__":
main()
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