import numpy as np import cv2 import os import torch from einops import rearrange from annotator.util import annotator_ckpts_path class Network(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): remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/network-bsds500.pth" modelpath = os.path.join(annotator_ckpts_path, "network-bsds500.pth") if not os.path.exists(modelpath): from basicsr.utils.download_util import load_file_from_url load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path) if torch.cuda.is_available(): self.netNetwork = Network(modelpath).cuda().eval() else: self.netNetwork = Network(modelpath).eval() def __call__(self, input_image): assert input_image.ndim == 3 input_image = input_image[:, :, ::-1].copy() with torch.no_grad(): if torch.cuda.is_available(): image_hed = torch.from_numpy(input_image).float().cuda() else: image_hed = torch.from_numpy(input_image).float() 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