120 lines
5.0 KiB
Python
120 lines
5.0 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 PIL import Image
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from ..utils import HWC3, resize_image, nms, 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(torch.nn.Conv2d(in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1))
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for i in range(1, layer_number):
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self.convs.append(torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1))
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self.projection = torch.nn.Conv2d(in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0)
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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, netNetwork):
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self.netNetwork = netNetwork
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@classmethod
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def from_pretrained(cls, pretrained_model_or_path, filename=None):
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filename = filename or "ControlNetHED.pth"
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if os.path.isdir(pretrained_model_or_path):
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model_path = os.path.join(pretrained_model_or_path, filename)
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else:
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model_path = pretrained_model_or_path
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netNetwork = ControlNetHED_Apache2()
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netNetwork.load_state_dict(torch.load(model_path, map_location='cpu'))
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netNetwork.float().eval()
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return cls(netNetwork)
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def to(self, device):
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self.netNetwork.to(device)
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return self
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def __call__(self, input_image, detect_resolution=512, image_resolution=512, safe=False, output_type="np", scribble=False):
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device = next(iter(self.netNetwork.parameters())).device
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if not isinstance(input_image, np.ndarray):
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input_image = np.array(input_image, dtype=np.uint8)
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input_image = HWC3(input_image)
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input_image = resize_image(input_image, detect_resolution)
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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().to(device)
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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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detected_map = edge
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detected_map = HWC3(detected_map)
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img = resize_image(input_image, image_resolution)
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H, W, C = img.shape
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detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
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if scribble:
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detected_map = nms(detected_map, 127, 3.0)
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detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
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detected_map[detected_map > 4] = 255
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detected_map[detected_map < 255] = 0
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if output_type == "pil":
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detected_map = Image.fromarray(detected_map)
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return detected_map
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