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import torch |
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import numpy as np |
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import argparse |
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from .utils import convert_to_numpy |
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class FlowAnnotator: |
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def __init__(self, cfg, device=None): |
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try: |
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from raft import RAFT |
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from raft.utils.utils import InputPadder |
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from raft.utils import flow_viz |
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except: |
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import warnings |
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warnings.warn( |
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"ignore raft import, please pip install raft package. you can refer to models/VACE-Annotators/flow/raft-1.0.0-py3-none-any.whl") |
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params = { |
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"small": False, |
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"mixed_precision": False, |
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"alternate_corr": False |
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} |
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params = argparse.Namespace(**params) |
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pretrained_model = cfg['PRETRAINED_MODEL'] |
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device |
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self.model = RAFT(params) |
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self.model.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(pretrained_model, map_location="cpu", weights_only=True).items()}) |
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self.model = self.model.to(self.device).eval() |
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self.InputPadder = InputPadder |
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self.flow_viz = flow_viz |
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def forward(self, frames): |
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frames = [torch.from_numpy(convert_to_numpy(frame).astype(np.uint8)).permute(2, 0, 1).float()[None].to(self.device) for frame in frames] |
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flow_up_list, flow_up_vis_list = [], [] |
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with torch.no_grad(): |
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for i, (image1, image2) in enumerate(zip(frames[:-1], frames[1:])): |
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padder = self.InputPadder(image1.shape) |
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image1, image2 = padder.pad(image1, image2) |
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flow_low, flow_up = self.model(image1, image2, iters=20, test_mode=True) |
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flow_up = flow_up[0].permute(1, 2, 0).cpu().numpy() |
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flow_up_vis = self.flow_viz.flow_to_image(flow_up) |
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flow_up_list.append(flow_up) |
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flow_up_vis_list.append(flow_up_vis) |
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return flow_up_list, flow_up_vis_list |
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class FlowVisAnnotator(FlowAnnotator): |
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def forward(self, frames): |
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flow_up_list, flow_up_vis_list = super().forward(frames) |
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return flow_up_vis_list[:1] + flow_up_vis_list |
