import cv2
import numpy as np
import torch
import os

from einops import rearrange
from .models.mbv2_mlsd_tiny import  MobileV2_MLSD_Tiny
from .models.mbv2_mlsd_large import  MobileV2_MLSD_Large
from .utils import  pred_lines
from modules import devices
from annotator.annotator_path import models_path

mlsdmodel = None
remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/mlsd_large_512_fp32.pth"
old_modeldir = os.path.dirname(os.path.realpath(__file__))
modeldir = os.path.join(models_path, "mlsd")

def unload_mlsd_model():
    global mlsdmodel
    if mlsdmodel is not None:
        mlsdmodel = mlsdmodel.cpu()

def apply_mlsd(input_image, thr_v, thr_d):
    global modelpath, mlsdmodel
    if mlsdmodel is None:
        modelpath = os.path.join(modeldir, "mlsd_large_512_fp32.pth")
        old_modelpath = os.path.join(old_modeldir, "mlsd_large_512_fp32.pth")
        if os.path.exists(old_modelpath):
            modelpath = old_modelpath
        elif not os.path.exists(modelpath):
            from basicsr.utils.download_util import load_file_from_url
            load_file_from_url(remote_model_path, model_dir=modeldir)
        mlsdmodel = MobileV2_MLSD_Large()
        mlsdmodel.load_state_dict(torch.load(modelpath), strict=True)
    mlsdmodel = mlsdmodel.to(devices.get_device_for("controlnet")).eval()
        
    model = mlsdmodel
    assert input_image.ndim == 3
    img = input_image
    img_output = np.zeros_like(img)
    try:
        with torch.no_grad():
            lines = pred_lines(img, model, [img.shape[0], img.shape[1]], thr_v, thr_d)
            for line in lines:
                x_start, y_start, x_end, y_end = [int(val) for val in line]
                cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1)
    except Exception as e:
        pass
    return img_output[:, :, 0]