File size: 3,557 Bytes
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import numpy as np
from pathlib import Path
from PIL import Image
# Select files using glob (for now, only process the first file for testing)
shots_dir = Path('data/shots')
files = sorted(shots_dir.glob('sample-000-0.webp')) # Change pattern to 'sample-*.webp' for batch
if not files:
print('No files found.')
exit(1)
# Process each file serially (for now, just one file)
for webp_path in files:
print(f'Processing {webp_path}')
# Extract all frames from the animated webp using PIL
frames = []
frame_durations = []
with Image.open(webp_path) as im:
try:
while True:
frame = im.convert('RGB')
frames.append(np.array(frame)[:, :, ::-1]) # Convert RGB to BGR for OpenCV
# Get duration in ms for this frame (default to 100ms if not present)
duration = im.info.get('duration', 100)
frame_durations.append(duration)
im.seek(im.tell() + 1)
except EOFError:
pass
# Debug: check extracted frames
print(f"Extracted {len(frames)} frames from {webp_path}")
if len(frames) > 0:
print(f"First frame shape: {frames[0].shape}, dtype: {frames[0].dtype}, min: {frames[0].min()}, max: {frames[0].max()}")
# Compute dense optical flow and overlay visualization
hsv = None
motion_frames = []
for i in range(1, len(frames)):
prev = cv2.cvtColor(frames[i-1], cv2.COLOR_BGR2GRAY)
curr = cv2.cvtColor(frames[i], cv2.COLOR_BGR2GRAY)
flow = cv2.calcOpticalFlowFarneback(prev, curr, None, 0.5, 3, 15, 3, 5, 1.2, 0)
mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])
print(f"Frame {i}: flow mag min={mag.min()}, max={mag.max()}, mean={mag.mean()}")
if np.all(mag == 0):
print(f"Frame {i}: All zero motion, skipping.")
continue
# Draw flow as arrows on a grid
step = 16 # grid size
arrow_color = (0, 255, 0) # green
arrow_thickness = 1
overlay = frames[i].copy()
h, w = prev.shape
for y in range(0, h, step):
for x in range(0, w, step):
fx, fy = flow[y, x]
end_x = int(x + fx * 4)
end_y = int(y + fy * 4)
cv2.arrowedLine(overlay, (x, y), (end_x, end_y), arrow_color, arrow_thickness, tipLength=0.3)
motion_frames.append(overlay)
# Save as mp4
if motion_frames:
height, width, _ = motion_frames[0].shape
# Calculate FPS from frame durations (use mean duration between frames)
if len(frame_durations) > 1:
# Use durations between frames (skip first frame)
mean_duration = np.mean(frame_durations[1:])
else:
mean_duration = 100
fps = 1000.0 / mean_duration if mean_duration > 0 else 10
print(f"Using FPS: {fps:.2f} (mean frame duration: {mean_duration} ms)")
if hasattr(cv2, 'VideoWriter_fourcc'):
fourcc = cv2.VideoWriter_fourcc(*'avc1') # More compatible MP4 codec for macOS
else:
raise RuntimeError('cv2.VideoWriter_fourcc is not available in your OpenCV installation. Please update OpenCV.')
out_path = webp_path.parent / f"{webp_path.stem}.motion.mp4"
out = cv2.VideoWriter(str(out_path), fourcc, fps, (width, height))
for f in motion_frames:
out.write(f)
out.release()
print(f'Saved {out_path}')
else:
print('No motion frames to save for', webp_path)
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