Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,069 Bytes
352b049 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
import tempfile
import os
import cv2
import numpy as np
import imageio
import torch
import torchvision.io as io
from torchvision.transforms import functional as F
from PIL import Image, ImageDraw, ImageFont
import torch.nn.functional as nnf
def convert_to_rgb(frame):
"""Convert frame to RGB format."""
if frame.shape[2] == 4: # RGBA
# Convert RGBA to RGB using alpha compositing with white background
alpha = frame[:, :, 3:4] / 255.0
rgb = frame[:, :, :3]
return (rgb * alpha + (1 - alpha) * 255).astype(np.uint8)
return frame
def process_frames_batch(frames, target_size, device):
"""Process a batch of frames efficiently."""
# Stack frames and move to GPU
frames = torch.stack(frames).to(device)
# Batch resize
frames = nnf.interpolate(frames, size=target_size,
mode='bilinear', align_corners=False)
return frames
def combine_video(obj_dir, output_path, input_frames=None, displayed_preds=3):
"""Combine multiple GIFs into a grid layout using torchvision."""
print("Starting video combination process...")
# Set device for GPU acceleration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
# Get all GIF files from shadow_gif directory
shadow_gif_dir = os.path.join(obj_dir, 'shadow_gif')
gif_files = [f for f in os.listdir(shadow_gif_dir) if f.endswith('_tranp.gif') and not f.startswith('obs')]
gif_files = sorted(gif_files)
# Limit number of GIFs based on displayed_preds
gif_files = gif_files[:displayed_preds]
print(f"Using {len(gif_files)} GIFs for {displayed_preds} predictions")
# Calculate grid dimensions
grid_cols = min(displayed_preds, 3) # Maximum 3 columns
grid_rows = (displayed_preds + grid_cols - 1) // grid_cols
print(f"Grid layout: {grid_rows}x{grid_cols}")
# Load and process all GIFs
gif_frames = []
durations = []
for gif_file in gif_files:
gif_path = os.path.join(shadow_gif_dir, gif_file)
print(f"Loading {gif_file}...")
# Read GIF frames efficiently
with imageio.get_reader(gif_path) as reader:
frames = []
for frame in reader:
# Convert to RGB if needed
frame = convert_to_rgb(frame)
frame = cv2.resize(frame, (frame.shape[1] // 4, frame.shape[0] // 4), interpolation=cv2.INTER_AREA)
# Convert to tensor and normalize
frame = torch.from_numpy(frame).permute(2, 0, 1).float().to(device) / 255.0
frames.append(frame)
# Get duration from the first frame
with Image.open(gif_path) as img:
duration = img.info.get('duration', 100) / 1000.0 # Convert to seconds
gif_frames.append(frames)
durations.append(duration)
if not gif_frames:
raise ValueError("No GIF files found!")
# Get common duration
common_duration = min(durations)
print(f"Common duration: {common_duration}")
# Process input frames if provided
if input_frames is not None:
# Convert BGR to RGB and resize input frames
input_frames = [cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) for frame in input_frames]
input_frames = [cv2.resize(frame, (frame.shape[1]//8, frame.shape[0]//8),
interpolation=cv2.INTER_NEAREST) for frame in input_frames]
input_frames = [torch.from_numpy(frame).permute(2, 0, 1).float().to(device) / 255.0
for frame in input_frames]
# Calculate target size for each GIF in the grid
first_frame = gif_frames[0][0]
target_height = first_frame.shape[1]
target_width = first_frame.shape[2]
# Create grid frames
num_frames = max(len(frames) for frames in gif_frames)
grid_frames = []
# Process frames in batches
batch_size = 4 # Adjust based on GPU memory
for frame_idx in range(0, num_frames, batch_size):
batch_end = min(frame_idx + batch_size, num_frames)
# Create empty grid for the batch
grid = torch.ones((batch_end - frame_idx, 3, target_height * grid_rows, target_width * grid_cols),
device=device)
# Process each GIF in the batch
for i, frames in enumerate(gif_frames):
row = i // grid_cols
col = i % grid_cols
# Get frames for this batch
batch_frames = frames[frame_idx:batch_end]
if batch_frames:
# Process frames in batch
resized_frames = process_frames_batch(batch_frames, (target_height, target_width), device)
# Add to grid
for j, frame in enumerate(resized_frames):
grid[j, :, row*target_height:(row+1)*target_height,
col*target_width:(col+1)*target_width] = frame
# Add input frames if provided
if input_frames is not None:
for i in range(len(gif_frames)):
row = i // grid_cols
col = i % grid_cols
# Get input frames for this batch
batch_input_frames = input_frames[frame_idx:batch_end]
if batch_input_frames:
orig_h, orig_w = batch_input_frames[0].shape[1:3] # (C, H, W)
pip_max_width = target_width // 2
pip_max_height = target_height // 2
aspect = orig_w / orig_h
if pip_max_width / aspect <= pip_max_height:
pip_w = pip_max_width
pip_h = int(pip_max_width / aspect)
else:
pip_h = pip_max_height
pip_w = int(pip_max_height * aspect)
# resize
resized_input_frames = process_frames_batch(batch_input_frames, (pip_h, pip_w), device)
# Add to grid
for j, frame in enumerate(resized_input_frames):
x_pos = col * target_width + target_width - frame.shape[2] - 10
y_pos = row * target_height + 10
grid[j, :, y_pos:y_pos+frame.shape[1], x_pos:x_pos+frame.shape[2]] = frame
# Add batch to grid_frames
grid_frames.extend([frame for frame in grid])
# Convert frames to numpy and save as GIF
print(f"Saving to {output_path}")
frames_np = [(frame.cpu().permute(1, 2, 0).numpy() * 255).astype(np.uint8)
for frame in grid_frames]
# Save as GIF with optimization
imageio.mimsave(output_path, frames_np, fps=30, optimize=True, quantizer=0, loop=0)
print("Video combination completed!")
return output_path
if __name__ == "__main__":
combine_video("./9622_GRAB/", tempfile.NamedTemporaryFile(suffix=".gif", delete=False).name)
|