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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 PIL import Image
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def convert_to_numpy(image):
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if isinstance(image, Image.Image):
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image = np.array(image)
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elif isinstance(image, torch.Tensor):
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image = image.detach().cpu().numpy()
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elif isinstance(image, np.ndarray):
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image = image.copy()
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else:
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raise f'Unsurpport datatype{type(image)}, only surpport np.ndarray, torch.Tensor, Pillow Image.'
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return image
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class FlowAnnotator:
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def __init__(self, cfg, device=None):
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from .raft.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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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 |
