| 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) | |