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# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
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 / RGB
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 # RGB
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 |