# This is an improved version and model of HED edge detection with Apache License, Version 2.0.
# Please use this implementation in your products
# This implementation may produce slightly different results from Saining Xie's official implementations,
# but it generates smoother edges and is more suitable for ControlNet as well as other image-to-image translations.
# Different from official models and other implementations, this is an RGB-input model (rather than BGR)
# and in this way it works better for gradio's RGB protocol

import os
import cv2
import numpy as np
import torch
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image
from modules import devices
from modules.shared import opts
from modules.control.util import HWC3, nms, resize_image, safe_step


class DoubleConvBlock(torch.nn.Module): # pylint: disable=abstract-method
    def __init__(self, input_channel, output_channel, layer_number):
        super().__init__()
        self.convs = torch.nn.Sequential()
        self.convs.append(torch.nn.Conv2d(in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1))
        for _i in range(1, layer_number):
            self.convs.append(torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1))
        self.projection = torch.nn.Conv2d(in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0)

    def __call__(self, x, down_sampling=False):
        h = x
        if down_sampling:
            h = torch.nn.functional.max_pool2d(h, kernel_size=(2, 2), stride=(2, 2))
        for conv in self.convs:
            h = conv(h)
            h = torch.nn.functional.relu(h)
        return h, self.projection(h)


class ControlNetHED_Apache2(torch.nn.Module): # pylint: disable=abstract-method
    def __init__(self):
        super().__init__()
        self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1)))
        self.block1 = DoubleConvBlock(input_channel=3, output_channel=64, layer_number=2)
        self.block2 = DoubleConvBlock(input_channel=64, output_channel=128, layer_number=2)
        self.block3 = DoubleConvBlock(input_channel=128, output_channel=256, layer_number=3)
        self.block4 = DoubleConvBlock(input_channel=256, output_channel=512, layer_number=3)
        self.block5 = DoubleConvBlock(input_channel=512, output_channel=512, layer_number=3)

    def __call__(self, x):
        h = x - self.norm
        h, projection1 = self.block1(h)
        h, projection2 = self.block2(h, down_sampling=True)
        h, projection3 = self.block3(h, down_sampling=True)
        h, projection4 = self.block4(h, down_sampling=True)
        h, projection5 = self.block5(h, down_sampling=True)
        return projection1, projection2, projection3, projection4, projection5

class HEDdetector:
    def __init__(self, model):
        self.model = model

    @classmethod
    def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None):
        filename = filename or "ControlNetHED.pth"
        if os.path.isdir(pretrained_model_or_path):
            model_path = os.path.join(pretrained_model_or_path, filename)
        else:
            model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir)
        model = ControlNetHED_Apache2()
        model.load_state_dict(torch.load(model_path, map_location='cpu'))
        model.float().eval()
        return cls(model)

    def to(self, device):
        self.model.to(device)
        return self

    def __call__(self, input_image, detect_resolution=512, image_resolution=512, safe=False, output_type="pil", scribble=False, **kwargs):
        self.model.to(devices.device)
        device = next(iter(self.model.parameters())).device
        if not isinstance(input_image, np.ndarray):
            input_image = np.array(input_image, dtype=np.uint8)
        input_image = HWC3(input_image)
        input_image = resize_image(input_image, detect_resolution)
        assert input_image.ndim == 3
        H, W, _C = input_image.shape
        image_hed = torch.from_numpy(input_image.copy()).float().to(device)
        image_hed = rearrange(image_hed, 'h w c -> 1 c h w')
        edges = self.model(image_hed)
        edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
        edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges]
        edges = np.stack(edges, axis=2)
        edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
        if safe:
            edge = safe_step(edge)
        edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
        detected_map = edge
        detected_map = HWC3(detected_map)
        img = resize_image(input_image, image_resolution)
        H, W, _C = img.shape
        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
        if scribble:
            detected_map = nms(detected_map, 127, 3.0)
            detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
            detected_map[detected_map > 4] = 255
            detected_map[detected_map < 255] = 0
        if opts.control_move_processor:
            self.model.to('cpu')
        if output_type == "pil":
            detected_map = Image.fromarray(detected_map)
        return detected_map
