115 lines
4.2 KiB
Python
115 lines
4.2 KiB
Python
import sys
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sys.path.append(".")
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import random
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import cv2
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import numpy as np
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import torch
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from decord import VideoReader, cpu
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from pytorchvideo.transforms import ShortSideScale
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from torchvision.io import read_video
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from torchvision.transforms import Lambda, Compose
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from torchvision.transforms._transforms_video import RandomCropVideo
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from torch.nn import functional as F
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from opensora.models.ae import VQVAEModel
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import argparse
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def array_to_video(image_array, fps=30.0, output_file='output_video.mp4'):
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height, width, channels = image_array[0].shape
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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video_writer = cv2.VideoWriter(output_file, fourcc, float(fps), (width, height))
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for image in image_array:
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image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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video_writer.write(image_rgb)
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video_writer.release()
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def custom_to_video(x, fps=2.0, output_file='output_video.mp4'):
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x = x.detach().cpu()
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x = torch.clamp(x, -0.5, 0.5)
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x = (x + 0.5)
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x = x.permute(1, 2, 3, 0).numpy() # (C, T, H, W) -> (T, H, W, C)
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x = (255*x).astype(np.uint8)
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array_to_video(x, fps=fps, output_file=output_file)
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return
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def read_video(video_path, num_frames, sample_rate):
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decord_vr = VideoReader(video_path, ctx=cpu(0))
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total_frames = len(decord_vr)
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sample_frames_len = sample_rate * num_frames
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if total_frames > sample_frames_len:
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s = random.randint(0, total_frames - sample_frames_len - 1)
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e = s + sample_frames_len
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num_frames = num_frames
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else:
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s = 0
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e = total_frames
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num_frames = int(total_frames / sample_frames_len * num_frames)
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print(f'sample_frames_len {sample_frames_len}, only can sample {num_frames * sample_rate}', video_path,
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total_frames)
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frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
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video_data = decord_vr.get_batch(frame_id_list).asnumpy()
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video_data = torch.from_numpy(video_data)
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video_data = video_data.permute(3, 0, 1, 2) # (T, H, W, C) -> (C, T, H, W)
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return video_data
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def preprocess(video_data, short_size=128, crop_size=None):
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transform = Compose(
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[
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# UniformTemporalSubsample(num_frames),
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Lambda(lambda x: ((x / 255.0) - 0.5)),
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# NormalizeVideo(mean=OPENAI_DATASET_MEAN, std=OPENAI_DATASET_STD),
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ShortSideScale(size=short_size),
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RandomCropVideo(size=crop_size) if crop_size is not None else Lambda(lambda x: x),
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# RandomHorizontalFlipVideo(p=0.5),
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]
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)
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video_outputs = transform(video_data)
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video_outputs = torch.unsqueeze(video_outputs, 0)
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return video_outputs
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def main(args):
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video_path = args.video_path
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num_frames = args.num_frames
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resolution = args.resolution
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crop_size = args.crop_size
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sample_fps = args.sample_fps
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sample_rate = args.sample_rate
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device = torch.device('cuda')
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if args.ckpt in ['bair_stride4x2x2', 'ucf101_stride4x4x4', 'kinetics_stride4x4x4', 'kinetics_stride2x4x4']:
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vqvae = VQVAEModel.download_and_load_model(args.ckpt)
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else:
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vqvae = VQVAEModel.load_from_checkpoint(args.ckpt)
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vqvae.eval()
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vqvae = vqvae.to(device)
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with torch.no_grad():
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x_vae = preprocess(read_video(video_path, num_frames, sample_rate), resolution, crop_size)
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x_vae = x_vae.to(device)
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encodings, embeddings = vqvae.encode(x_vae, include_embeddings=True)
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video_recon = vqvae.decode(encodings)
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# custom_to_video(x_vae[0], fps=sample_fps/sample_rate, output_file='origin_input.mp4')
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custom_to_video(video_recon[0], fps=sample_fps/sample_rate, output_file=args.rec_path)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--video-path', type=str, default='')
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parser.add_argument('--rec-path', type=str, default='')
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parser.add_argument('--ckpt', type=str, default='ucf101_stride4x4x4')
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parser.add_argument('--sample-fps', type=int, default=30)
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parser.add_argument('--resolution', type=int, default=336)
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parser.add_argument('--crop-size', type=int, default=None)
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parser.add_argument('--num-frames', type=int, default=100)
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parser.add_argument('--sample-rate', type=int, default=1)
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args = parser.parse_args()
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main(args)
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