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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"
@classmethod
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
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