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import cv2
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import numpy as np
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from PIL import Image
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import torch
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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 GrayAnnotator:
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def __init__(self, cfg):
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pass
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def forward(self, image):
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image = convert_to_numpy(image)
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gray_map = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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return gray_map[..., None].repeat(3, axis=2)
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class GrayVideoAnnotator(GrayAnnotator):
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def forward(self, frames):
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ret_frames = []
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for frame in frames:
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anno_frame = super().forward(np.array(frame))
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ret_frames.append(anno_frame)
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return ret_frames
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