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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 torch | |
| import numpy as np | |
| from huggingface_hub import hf_hub_download | |
| from einops import rearrange | |
| from ...annotator.util import annotator_ckpts_path | |
| 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): | |
| modelpath = os.path.join(annotator_ckpts_path, "ControlNetHED.pth") | |
| if not os.path.exists(modelpath): | |
| modelpath = hf_hub_download("lllyasviel/Annotators", "ControlNetHED.pth") | |
| self.netNetwork = ControlNetHED_Apache2().float().cuda().eval() | |
| self.netNetwork.load_state_dict(torch.load(modelpath)) | |
| def __call__(self, input_image): | |
| assert input_image.ndim == 3 | |
| H, W, C = input_image.shape | |
| with torch.no_grad(): | |
| image_hed = torch.from_numpy(input_image.copy()).float().cuda() | |
| 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))) | |
| edge = (edge * 255.0).clip(0, 255).astype(np.uint8) | |
| return edge | |
| def nms(x, t, s): | |
| x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s) | |
| f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8) | |
| f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8) | |
| f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8) | |
| f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8) | |
| y = np.zeros_like(x) | |
| for f in [f1, f2, f3, f4]: | |
| np.putmask(y, cv2.dilate(x, kernel=f) == x, x) | |
| z = np.zeros_like(y, dtype=np.uint8) | |
| z[y > t] = 255 | |
| return z | |