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import numpy as np
import cv2
import os
import torch
from einops import rearrange
class HEDNetwork(torch.nn.Module):
def __init__(self, model_path):
super().__init__()
self.netVggOne = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
self.netVggTwo = torch.nn.Sequential(
torch.nn.MaxPool2d(kernel_size=2, stride=2),
torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
self.netVggThr = torch.nn.Sequential(
torch.nn.MaxPool2d(kernel_size=2, stride=2),
torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
self.netVggFou = torch.nn.Sequential(
torch.nn.MaxPool2d(kernel_size=2, stride=2),
torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
self.netVggFiv = torch.nn.Sequential(
torch.nn.MaxPool2d(kernel_size=2, stride=2),
torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
self.netScoreOne = torch.nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0)
self.netScoreTwo = torch.nn.Conv2d(in_channels=128, out_channels=1, kernel_size=1, stride=1, padding=0)
self.netScoreThr = torch.nn.Conv2d(in_channels=256, out_channels=1, kernel_size=1, stride=1, padding=0)
self.netScoreFou = torch.nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0)
self.netScoreFiv = torch.nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0)
self.netCombine = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=5, out_channels=1, kernel_size=1, stride=1, padding=0),
torch.nn.Sigmoid()
)
self.load_state_dict({strKey.replace('module', 'net'): tenWeight for strKey, tenWeight in torch.load(model_path).items()})
def forward(self, tenInput):
tenInput = tenInput * 255.0
tenInput = tenInput - torch.tensor(data=[104.00698793, 116.66876762, 122.67891434], dtype=tenInput.dtype, device=tenInput.device).view(1, 3, 1, 1)
tenVggOne = self.netVggOne(tenInput)
tenVggTwo = self.netVggTwo(tenVggOne)
tenVggThr = self.netVggThr(tenVggTwo)
tenVggFou = self.netVggFou(tenVggThr)
tenVggFiv = self.netVggFiv(tenVggFou)
tenScoreOne = self.netScoreOne(tenVggOne)
tenScoreTwo = self.netScoreTwo(tenVggTwo)
tenScoreThr = self.netScoreThr(tenVggThr)
tenScoreFou = self.netScoreFou(tenVggFou)
tenScoreFiv = self.netScoreFiv(tenVggFiv)
tenScoreOne = torch.nn.functional.interpolate(input=tenScoreOne, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
tenScoreTwo = torch.nn.functional.interpolate(input=tenScoreTwo, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
tenScoreThr = torch.nn.functional.interpolate(input=tenScoreThr, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
tenScoreFou = torch.nn.functional.interpolate(input=tenScoreFou, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
tenScoreFiv = torch.nn.functional.interpolate(input=tenScoreFiv, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
return self.netCombine(torch.cat([ tenScoreOne, tenScoreTwo, tenScoreThr, tenScoreFou, tenScoreFiv ], 1))
class HEDdetector:
def __init__(self, network ):
self.netNetwork = network
def __call__(self, input_image):
if isinstance(input_image, torch.Tensor):
# 输入的就是 b c h w的tensor 范围是-1~1，需要转换为0～1
input_image = (input_image + 1) / 2
input_image = input_image.float().cuda()
edge = self.netNetwork(input_image) # 范围也是0～1, 不用转了直接用
return edge
else:
assert input_image.ndim == 3
input_image = input_image[:, :, ::-1].copy()
with torch.no_grad():
image_hed = torch.from_numpy(input_image).float().cuda()
image_hed = image_hed / 255.0
image_hed = rearrange(image_hed, 'h w c -> 1 c h w')
edge = self.netNetwork(image_hed)[0]
edge = (edge.cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8)
return edge[0]
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
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