import torch
import numpy as np
from PIL import Image
from torchvision import transforms
import torch.nn.functional as F
from .dsine.dsine import DSINE
from .dsine import utils as dsine_utils


class NormalDetector:
    def __init__(self, model_path):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = DSINE()
        self.model = dsine_utils.load_checkpoint(model_path, self.model)
        self.normalize = transforms.Normalize(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
        )
        self.fov = 60

    @torch.no_grad()
    def __call__(self, image):
        self.model.to(self.device)
        self.model.pixel_coords = self.model.pixel_coords.to(self.device)

        img = np.array(image).astype(np.float32) / 255.0
        img = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).to(self.device)
        _, _, orig_H, orig_W = img.shape
        l, r, t, b = dsine_utils.pad_input(orig_H, orig_W)
        img = F.pad(img, (l, r, t, b), mode="constant", value=0.0)
        img = self.normalize(img)
        intrinsics = dsine_utils.get_intrins_from_fov(
            new_fov=self.fov, H=orig_H, W=orig_W, device=self.device
        ).unsqueeze(0)

        intrinsics[:, 0, 2] += l
        intrinsics[:, 1, 2] += t

        pred_norm = self.model(img, intrins=intrinsics)[-1]
        pred_norm = pred_norm[:, :, t : t + orig_H, l : l + orig_W]
        pred_norm_np = (
            pred_norm.cpu().detach().numpy()[0, :, :, :].transpose(1, 2, 0)
        )  # (H, W, 3)
        pred_norm_np = ((pred_norm_np + 1.0) / 2.0 * 255.0).astype(np.uint8)
        normal_img = Image.fromarray(pred_norm_np).resize((orig_W, orig_H))

        self.model.to("cpu")
        self.model.pixel_coords = self.model.pixel_coords.to("cpu")
        return normal_img


if __name__ == "__main__":
    from diffusers.utils import load_image

    image = load_image(
        "https://qhstaticssl.kujiale.com/image/jpeg/1716177580588/9AAA49344B9CE33512C4EBD0A287495F.jpg"
    )
    image = np.asarray(image)
    normal_detector = NormalDetector(
        model_path="/juicefs/training/models/open_source/dsine/dsine.pt",
        efficientnet_path="/juicefs/training/models/open_source/dsine/tf_efficientnet_b5_ap-9e82fae8.pth",
    )
    normal_image = normal_detector(image)
    normal_image.save("normal_image.jpg")