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