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| import torch | |
| import numpy as np | |
| from PIL import Image | |
| import cv2 | |
| import matplotlib.pyplot as plt | |
| import torch.nn.functional as F | |
| def numpy2tensor(img): | |
| x0 = torch.from_numpy(img.copy()).float().cuda() / 255.0 * 2.0 - 1. | |
| x0 = torch.stack([x0], dim=0) | |
| # einops.rearrange(x0, 'b h w c -> b c h w').clone() | |
| return x0.permute(0, 3, 1, 2) | |
| def pil2tensor(img): | |
| return numpy2tensor(np.array(img)) | |
| def tensor2numpy(img): | |
| image = (img / 2 + 0.5).clamp(0, 1) | |
| image = image.detach().cpu().permute(0, 2, 3, 1).numpy() | |
| images = (image * 255).round().astype("uint8") | |
| return images | |
| def tensor2pil(img): | |
| return Image.fromarray(tensor2numpy(img)[0]) | |
| def cv2sod(img): | |
| in_ = np.array(img, dtype=np.float32) | |
| in_ -= np.array((104.00699, 116.66877, 122.67892)) | |
| in_ = in_.transpose((2,0,1)) | |
| image = torch.Tensor(in_) | |
| return F.interpolate(image.unsqueeze(0), scale_factor=0.5, mode='bilinear') | |
| def get_frame_count(video_path: str): | |
| video = cv2.VideoCapture(video_path) | |
| frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) | |
| video.release() | |
| return frame_count | |
| def resize_image(input_image, resolution): | |
| H, W, C = input_image.shape | |
| H = float(H) | |
| W = float(W) | |
| k = float(resolution) / max(H, W) | |
| H *= k | |
| W *= k | |
| H = int(np.round(H / 64.0)) * 64 | |
| W = int(np.round(W / 64.0)) * 64 | |
| img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA) | |
| return img | |
| def visualize(img_arr, dpi): | |
| plt.figure(figsize=(10,10),dpi=dpi) | |
| plt.imshow(((img_arr.detach().cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8)) | |
| plt.axis('off') | |
| plt.show() | |
| def calc_mean_std(feat, eps=1e-5, chunk=1): | |
| size = feat.size() | |
| assert (len(size) == 4) | |
| if chunk == 2: | |
| feat = torch.cat(feat.chunk(2), dim=3) | |
| N, C = size[:2] | |
| feat_var = feat.view(N//chunk, C, -1).var(dim=2) + eps | |
| feat_std = feat_var.sqrt().view(N, C, 1, 1) | |
| feat_mean = feat.view(N//chunk, C, -1).mean(dim=2).view(N//chunk, C, 1, 1) | |
| return feat_mean.repeat(chunk,1,1,1), feat_std.repeat(chunk,1,1,1) | |
| def adaptive_instance_normalization(content_feat, style_feat, chunk=1): | |
| assert (content_feat.size()[:2] == style_feat.size()[:2]) | |
| size = content_feat.size() | |
| style_mean, style_std = calc_mean_std(style_feat, chunk) | |
| content_mean, content_std = calc_mean_std(content_feat) | |
| normalized_feat = (content_feat - content_mean.expand( | |
| size)) / content_std.expand(size) | |
| return normalized_feat * style_std.expand(size) + style_mean.expand(size) | |
| class Dilate(): | |
| def __init__(self, kernel_size=7, channels=1, device='cpu'): | |
| self.kernel_size=kernel_size | |
| self.channels = channels | |
| gaussian_kernel = torch.ones(1, 1, self.kernel_size, self.kernel_size) | |
| gaussian_kernel = gaussian_kernel.repeat(self.channels, 1, 1, 1) | |
| self.mean = (self.kernel_size - 1)//2 | |
| gaussian_kernel = gaussian_kernel.to(device) | |
| self.gaussian_filter = gaussian_kernel | |
| def __call__(self, x): | |
| x = F.pad(x, (self.mean,self.mean,self.mean,self.mean), "replicate") | |
| return torch.clamp(F.conv2d(x, self.gaussian_filter, bias=None), 0, 1) | |
| def get_saliency(imgs, sod_model, dilate): | |
| imgs_sod = torch.cat([cv2sod(img) for img in imgs], dim=0).cuda() | |
| _, _, up_sal_f = sod_model(imgs_sod) | |
| saliency = 1-dilate(np.squeeze(torch.sigmoid(up_sal_f[-1])).unsqueeze(1)) | |
| del up_sal_f | |
| torch.cuda.empty_cache() | |
| return saliency | |