import cv2
import numpy as np
import PIL.Image
import torch
from controlnet_aux.util import HWC3, ade_palette
from transformers import AutoImageProcessor, UperNetForSemanticSegmentation, OneFormerProcessor, OneFormerForUniversalSegmentation

from cv_utils import resize_image


class ImageSegmentor:
    def __init__(self):
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self.image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
        self.image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
        self.image_segmentor.to(self.device)

    @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.to(self.device))
        seg = self.image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0].cpu()
        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)


class ImageSegmentorOneFormer:
    def __init__(self):
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self.image_processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")
        self.image_segmentor = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")
        self.image_segmentor.to(self.device)

    @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)

        inputs = self.image_processor(image, ["semantic"], return_tensors="pt")
        inputs = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
        outputs = self.image_segmentor(**inputs)
        seg = self.image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0].cpu()
        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)