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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 warnings | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from einops import rearrange | |
| from PIL import Image | |
| from custom_controlnet_aux.util import HWC3, nms, resize_image_with_pad, safe_step, common_input_validate, custom_hf_download, HF_MODEL_NAME | |
| 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 | |
| class HEDdetector: | |
| def __init__(self, netNetwork): | |
| self.netNetwork = netNetwork | |
| self.device = "cpu" | |
| def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="ControlNetHED.pth"): | |
| model_path = custom_hf_download(pretrained_model_or_path, filename) | |
| netNetwork = ControlNetHED_Apache2() | |
| netNetwork.load_state_dict(torch.load(model_path, map_location='cpu')) | |
| netNetwork.float().eval() | |
| return cls(netNetwork) | |
| def to(self, device): | |
| self.netNetwork.to(device) | |
| self.device = device | |
| return self | |
| def __call__(self, input_image, detect_resolution=512, safe=False, output_type="pil", scribble=False, upscale_method="INTER_CUBIC", **kwargs): | |
| input_image, output_type = common_input_validate(input_image, output_type, **kwargs) | |
| input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) | |
| assert input_image.ndim == 3 | |
| H, W, C = input_image.shape | |
| with torch.no_grad(): | |
| image_hed = torch.from_numpy(input_image).float().to(self.device) | |
| image_hed = rearrange(image_hed, 'h w c -> 1 c h w') | |
| edges = self.netNetwork(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 | |
| 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 | |
| detected_map = HWC3(remove_pad(detected_map)) | |
| if output_type == "pil": | |
| detected_map = Image.fromarray(detected_map) | |
| return detected_map | |