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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("openmmlab/upernet-convnext-small")
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self.image_segmentor = UperNetForSemanticSegmentation.from_pretrained("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, 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(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, resolution=image_resolution, interpolation=cv2.INTER_NEAREST)
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return PIL.Image.fromarray(color_seg)
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