wehub-resource-sync
98 lines
4.2 KiB
Python
98 lines
4.2 KiB
Python
# This is an improved version and model of HED edge detection with Apache License, Version 2.0.
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# Please use this implementation in your products
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# This implementation may produce slightly different results from Saining Xie's official implementations,
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# but it generates smoother edges and is more suitable for ControlNet as well as other image-to-image translations.
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# Different from official models and other implementations, this is an RGB-input model (rather than BGR)
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# and in this way it works better for gradio's RGB protocol
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import os
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import cv2
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import numpy as np
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import torch
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from einops import rearrange
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from tools.controlnet.annotator.util import annotator_ckpts_path, safe_step
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class DoubleConvBlock(torch.nn.Module):
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def __init__(self, input_channel, output_channel, layer_number):
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super().__init__()
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self.convs = torch.nn.Sequential()
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self.convs.append(
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torch.nn.Conv2d(
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in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1
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)
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)
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for i in range(1, layer_number):
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self.convs.append(
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torch.nn.Conv2d(
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in_channels=output_channel,
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out_channels=output_channel,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=1,
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)
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)
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self.projection = torch.nn.Conv2d(
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in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0
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)
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def __call__(self, x, down_sampling=False):
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h = x
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if down_sampling:
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h = torch.nn.functional.max_pool2d(h, kernel_size=(2, 2), stride=(2, 2))
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for conv in self.convs:
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h = conv(h)
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h = torch.nn.functional.relu(h)
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return h, self.projection(h)
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class ControlNetHED_Apache2(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1)))
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self.block1 = DoubleConvBlock(input_channel=3, output_channel=64, layer_number=2)
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self.block2 = DoubleConvBlock(input_channel=64, output_channel=128, layer_number=2)
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self.block3 = DoubleConvBlock(input_channel=128, output_channel=256, layer_number=3)
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self.block4 = DoubleConvBlock(input_channel=256, output_channel=512, layer_number=3)
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self.block5 = DoubleConvBlock(input_channel=512, output_channel=512, layer_number=3)
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def __call__(self, x):
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h = x - self.norm
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h, projection1 = self.block1(h)
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h, projection2 = self.block2(h, down_sampling=True)
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h, projection3 = self.block3(h, down_sampling=True)
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h, projection4 = self.block4(h, down_sampling=True)
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h, projection5 = self.block5(h, down_sampling=True)
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return projection1, projection2, projection3, projection4, projection5
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class HEDdetector:
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def __init__(self):
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remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth"
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modelpath = os.path.join(annotator_ckpts_path, "ControlNetHED.pth")
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if not os.path.exists(modelpath):
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from urllib.request import urlretrieve
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os.makedirs(os.path.dirname(modelpath), exist_ok=True)
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urlretrieve(remote_model_path, modelpath)
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self.netNetwork = ControlNetHED_Apache2().float().cuda().eval()
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self.netNetwork.load_state_dict(torch.load(modelpath))
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def __call__(self, input_image, safe=False):
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assert input_image.ndim == 3
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H, W, C = input_image.shape
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with torch.no_grad():
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image_hed = torch.from_numpy(input_image.copy()).float().cuda()
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image_hed = rearrange(image_hed, "h w c -> 1 c h w")
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edges = self.netNetwork(image_hed)
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edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
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edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges]
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edges = np.stack(edges, axis=2)
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edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
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if safe:
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edge = safe_step(edge)
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edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
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return edge
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