| |
| |
| import torch |
| import numpy as np |
| import argparse |
| from PIL import Image |
|
|
| def convert_to_numpy(image): |
| if isinstance(image, Image.Image): |
| image = np.array(image) |
| elif isinstance(image, torch.Tensor): |
| image = image.detach().cpu().numpy() |
| elif isinstance(image, np.ndarray): |
| image = image.copy() |
| else: |
| raise f'Unsurpport datatype{type(image)}, only surpport np.ndarray, torch.Tensor, Pillow Image.' |
| return image |
|
|
| class FlowAnnotator: |
| def __init__(self, cfg, device=None): |
| from .raft.raft import RAFT |
| from .raft.utils.utils import InputPadder |
| from .raft.utils import flow_viz |
|
|
| params = { |
| "small": False, |
| "mixed_precision": False, |
| "alternate_corr": False |
| } |
| params = argparse.Namespace(**params) |
| pretrained_model = cfg['PRETRAINED_MODEL'] |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device |
| self.model = RAFT(params) |
| self.model.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(pretrained_model, map_location="cpu", weights_only=True).items()}) |
| self.model = self.model.to(self.device).eval() |
| self.InputPadder = InputPadder |
| self.flow_viz = flow_viz |
|
|
| def forward(self, frames): |
| |
| frames = [torch.from_numpy(convert_to_numpy(frame).astype(np.uint8)).permute(2, 0, 1).float()[None].to(self.device) for frame in frames] |
| flow_up_list, flow_up_vis_list = [], [] |
| with torch.no_grad(): |
| for i, (image1, image2) in enumerate(zip(frames[:-1], frames[1:])): |
| padder = self.InputPadder(image1.shape) |
| image1, image2 = padder.pad(image1, image2) |
| flow_low, flow_up = self.model(image1, image2, iters=20, test_mode=True) |
| flow_up = flow_up[0].permute(1, 2, 0).cpu().numpy() |
| flow_up_vis = self.flow_viz.flow_to_image(flow_up) |
| flow_up_list.append(flow_up) |
| flow_up_vis_list.append(flow_up_vis) |
| return flow_up_list, flow_up_vis_list |
|
|
|
|
| class FlowVisAnnotator(FlowAnnotator): |
| def forward(self, frames): |
| flow_up_list, flow_up_vis_list = super().forward(frames) |
| return flow_up_vis_list[:1] + flow_up_vis_list |
