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| # 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 annotator.base_annotator import BaseProcessor | |
| from annotator.util import safe_step | |
| class DoubleConvBlock(torch.nn.Module): | |
| 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): | |
| 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 | |
| netNetwork = None | |
| remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth" | |
| class HedDetector(BaseProcessor): | |
| def __init__(self, **kwargs): | |
| super().__init__(**kwargs) | |
| self.model_dir = os.path.join(self.models_path, "hed") | |
| self.net_work = None | |
| def unload_hed_model(self): | |
| if self.net_work is not None: | |
| self.net_work.cpu() | |
| def load_hed_model(self): | |
| model_path = os.path.join(self.model_dir, "ControlNetHED.pth") | |
| if not os.path.exists(model_path): | |
| from basicsr.utils.download_util import load_file_from_url | |
| load_file_from_url(remote_model_path, model_dir=self.model_dir) | |
| net_work = ControlNetHED_Apache2() | |
| net_work.load_state_dict(torch.load(model_path, map_location='cpu')) | |
| net_work.to(self.device).float().eval() | |
| self.net_work = net_work | |
| def __call__(self, input_image, is_safe=False, **kwargs): | |
| assert input_image.ndim == 3 | |
| H, W, C = input_image.shape | |
| self.load_hed_model() | |
| with torch.no_grad(): | |
| image_hed = torch.from_numpy(input_image.copy()).float().to(self.device) | |
| image_hed = rearrange(image_hed, 'h w c -> 1 c h w') | |
| edges = self.net_work(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 is_safe: | |
| edge = safe_step(edge) | |
| edge = (edge * 255.0).clip(0, 255).astype(np.uint8) | |
| return edge | |