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| # Adapted from https://github.com/dajes/frame-interpolation-pytorch | |
| import os | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import bisect | |
| import shutil | |
| import pdb | |
| from tqdm import tqdm | |
| def init_frame_interpolation_model(): | |
| print("Initializing frame interpolation model") | |
| checkpoint_name = os.path.join("./pretrained_model/film_net_fp16.pt") | |
| model = torch.jit.load(checkpoint_name, map_location='cpu') | |
| model.eval() | |
| model = model.half() | |
| model = model.to(device="cuda") | |
| return model | |
| def batch_images_interpolation_tool(input_tensor, model, inter_frames=1): | |
| video_tensor = [] | |
| frame_num = input_tensor.shape[2] # bs, channel, frame, height, width | |
| for idx in tqdm(range(frame_num-1)): | |
| image1 = input_tensor[:,:,idx] | |
| image2 = input_tensor[:,:,idx+1] | |
| results = [image1, image2] | |
| inter_frames = int(inter_frames) | |
| idxes = [0, inter_frames + 1] | |
| remains = list(range(1, inter_frames + 1)) | |
| splits = torch.linspace(0, 1, inter_frames + 2) | |
| for _ in range(len(remains)): | |
| 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] | |
| 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] | |
| for sub_idx in range(len(results)-1): | |
| video_tensor.append(results[sub_idx].unsqueeze(2)) | |
| video_tensor.append(input_tensor[:,:,-1].unsqueeze(2)) | |
| video_tensor = torch.cat(video_tensor, dim=2) | |
| return video_tensor |