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import os |
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import cv2 |
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import numpy as np |
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import torch |
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import bisect |
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import shutil |
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import pdb |
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from tqdm import tqdm |
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def init_frame_interpolation_model(): |
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print("Initializing frame interpolation model") |
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checkpoint_name = os.path.join("./pretrained_model/film_net_fp16.pt") |
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model = torch.jit.load(checkpoint_name, map_location='cpu') |
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model.eval() |
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model = model.half() |
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model = model.to(device="cuda") |
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return model |
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def batch_images_interpolation_tool(input_tensor, model, inter_frames=1): |
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video_tensor = [] |
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frame_num = input_tensor.shape[2] |
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for idx in tqdm(range(frame_num-1)): |
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image1 = input_tensor[:,:,idx] |
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image2 = input_tensor[:,:,idx+1] |
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results = [image1, image2] |
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inter_frames = int(inter_frames) |
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idxes = [0, inter_frames + 1] |
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remains = list(range(1, inter_frames + 1)) |
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splits = torch.linspace(0, 1, inter_frames + 2) |
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for _ in range(len(remains)): |
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starts = splits[idxes[:-1]] |
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ends = splits[idxes[1:]] |
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distances = ((splits[None, remains] - starts[:, None]) / (ends[:, None] - starts[:, None]) - .5).abs() |
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matrix = torch.argmin(distances).item() |
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start_i, step = np.unravel_index(matrix, distances.shape) |
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end_i = start_i + 1 |
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x0 = results[start_i] |
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x1 = results[end_i] |
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x0 = x0.half() |
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x1 = x1.half() |
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x0 = x0.cuda() |
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x1 = x1.cuda() |
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dt = x0.new_full((1, 1), (splits[remains[step]] - splits[idxes[start_i]])) / (splits[idxes[end_i]] - splits[idxes[start_i]]) |
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with torch.no_grad(): |
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prediction = model(x0, x1, dt) |
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insert_position = bisect.bisect_left(idxes, remains[step]) |
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idxes.insert(insert_position, remains[step]) |
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results.insert(insert_position, prediction.clamp(0, 1).cpu().float()) |
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del remains[step] |
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for sub_idx in range(len(results)-1): |
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video_tensor.append(results[sub_idx].unsqueeze(2)) |
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video_tensor.append(input_tensor[:,:,-1].unsqueeze(2)) |
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video_tensor = torch.cat(video_tensor, dim=2) |
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return video_tensor |
