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import cv2 |
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import numpy as np |
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import PIL.Image |
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import torch |
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from controlnet_aux.util import HWC3, ade_palette |
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from transformers import AutoImageProcessor, UperNetForSemanticSegmentation |
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from cv_utils import resize_image |
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class ImageSegmentor: |
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def __init__(self): |
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self.image_processor = AutoImageProcessor.from_pretrained( |
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'openmmlab/upernet-convnext-small') |
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self.image_segmentor = UperNetForSemanticSegmentation.from_pretrained( |
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'openmmlab/upernet-convnext-small') |
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@torch.inference_mode() |
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def __call__(self, image: np.ndarray, **kwargs) -> PIL.Image.Image: |
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detect_resolution = kwargs.pop('detect_resolution', 512) |
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image_resolution = kwargs.pop('image_resolution', 512) |
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image = HWC3(image) |
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image = resize_image(image, resolution=detect_resolution) |
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image = PIL.Image.fromarray(image) |
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pixel_values = self.image_processor(image, |
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return_tensors='pt').pixel_values |
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outputs = self.image_segmentor(pixel_values) |
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seg = self.image_processor.post_process_semantic_segmentation( |
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outputs, target_sizes=[image.size[::-1]])[0] |
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color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) |
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for label, color in enumerate(ade_palette()): |
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color_seg[seg == label, :] = color |
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color_seg = color_seg.astype(np.uint8) |
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color_seg = resize_image(color_seg, |
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resolution=image_resolution, |
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interpolation=cv2.INTER_NEAREST) |
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return PIL.Image.fromarray(color_seg) |
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