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L40S
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import bisect
import os
from tqdm import tqdm
import torch
import numpy as np
import cv2
from util import load_image
def inference(model_path, img1, img2, save_path, gpu, inter_frames, fps, half):
model = torch.jit.load(model_path, map_location='cpu')
model.eval()
img_batch_1, crop_region_1 = load_image(img1)
img_batch_2, crop_region_2 = load_image(img2)
img_batch_1 = torch.from_numpy(img_batch_1).permute(0, 3, 1, 2)
img_batch_2 = torch.from_numpy(img_batch_2).permute(0, 3, 1, 2)
if not half:
model.float()
if gpu and torch.cuda.is_available():
if half:
model = model.half()
else:
model.float()
model = model.cuda()
if save_path == 'img1 folder':
save_path = os.path.join(os.path.split(img1)[0], 'output.mp4')
results = [
img_batch_1,
img_batch_2
]
idxes = [0, inter_frames + 1]
remains = list(range(1, inter_frames + 1))
splits = torch.linspace(0, 1, inter_frames + 2)
for _ in tqdm(range(len(remains)), 'Generating in-between frames'):
starts = splits[idxes[:-1]]
ends = splits[idxes[1:]]
distances = ((splits[None, remains] - starts[:, None]) / (ends[:, None] - starts[:, None]) - .5).abs()
matrix = torch.argmin(distances).item()
start_i, step = np.unravel_index(matrix, distances.shape)
end_i = start_i + 1
x0 = results[start_i]
x1 = results[end_i]
if gpu and torch.cuda.is_available():
if half:
x0 = x0.half()
x1 = x1.half()
x0 = x0.cuda()
x1 = x1.cuda()
dt = x0.new_full((1, 1), (splits[remains[step]] - splits[idxes[start_i]])) / (splits[idxes[end_i]] - splits[idxes[start_i]])
with torch.no_grad():
prediction = model(x0, x1, dt)
insert_position = bisect.bisect_left(idxes, remains[step])
idxes.insert(insert_position, remains[step])
results.insert(insert_position, prediction.clamp(0, 1).cpu().float())
del remains[step]
video_folder = os.path.split(save_path)[0]
os.makedirs(video_folder, exist_ok=True)
y1, x1, y2, x2 = crop_region_1
frames = [(tensor[0] * 255).byte().flip(0).permute(1, 2, 0).numpy()[y1:y2, x1:x2].copy() for tensor in results]
w, h = frames[0].shape[1::-1]
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
writer = cv2.VideoWriter(save_path, fourcc, fps, (w, h))
for frame in frames:
writer.write(frame)
for frame in frames[1:][::-1]:
writer.write(frame)
writer.release()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='Test frame interpolator model')
parser.add_argument('model_path', type=str, help='Path to the TorchScript model')
parser.add_argument('img1', type=str, help='Path to the first image')
parser.add_argument('img2', type=str, help='Path to the second image')
parser.add_argument('--save_path', type=str, default='img1 folder', help='Path to save the interpolated frames')
parser.add_argument('--gpu', action='store_true', help='Use GPU')
parser.add_argument('--fp16', action='store_true', help='Use FP16')
parser.add_argument('--frames', type=int, default=18, help='Number of frames to interpolate')
parser.add_argument('--fps', type=int, default=10, help='FPS of the output video')
args = parser.parse_args()
inference(args.model_path, args.img1, args.img2, args.save_path, args.gpu, args.frames, args.fps, args.fp16)
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