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# -*- coding: utf-8 -*-
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"""
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@File : utils.py
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@Author: csc
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@Date : 2022/7/18
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"""
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import numpy as np
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import torch
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import torchvision.transforms as transforms
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from PIL import Image
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import cv2
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def figure2ndarray(fig):
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"""
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matplotlib.figure.Figure转为np.ndarray
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"""
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fig.canvas.draw()
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w, h = fig.canvas.get_width_height()
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buf_ndarray = np.frombuffer(fig.canvas.tostring_rgb(), dtype='u1')
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img = buf_ndarray.reshape(h, w, 3)
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return img
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def inArea(point: tuple, area: tuple):
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"""
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点是否在区域内
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point: (x0, y0)
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area: ((x1, y1), (x2, y2)) 左上角 右下角
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"""
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return area[0][0] <= point[0] <= area[1][0] and area[0][1] <= point[1] <= area[1][1]
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cnn_normalization_mean = [0.485, 0.456, 0.406]
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cnn_normalization_std = [0.229, 0.224, 0.225]
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tensor_normalizer = transforms.Normalize(mean=cnn_normalization_mean, std=cnn_normalization_std)
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epsilon = 1e-5
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def mean_std(features):
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"""输入 VGG19 计算的四个特征，输出每张特征图的均值和标准差，长度为1920"""
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mean_std_features = []
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for x in features:
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x = x.view(*x.shape[:2], -1)
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x = torch.cat([x.mean(-1), torch.sqrt(x.var(-1) + epsilon)], dim=-1)
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n = x.shape[0]
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x2 = x.view(n, 2, -1).transpose(2, 1).contiguous().view(n, -1) # [mean, ..., std, ...] to [mean, std, ...]
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mean_std_features.append(x2)
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mean_std_features = torch.cat(mean_std_features, dim=-1)
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return mean_std_features
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def preprocess_image(image, target_width=None):
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"""输入 PIL.Image 对象，输出标准化后的四维 tensor"""
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if target_width:
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t = transforms.Compose([
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transforms.Resize(target_width),
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transforms.CenterCrop(target_width),
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transforms.ToTensor(),
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tensor_normalizer,
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])
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else:
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t = transforms.Compose([
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transforms.ToTensor(),
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tensor_normalizer,
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])
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return t(image).unsqueeze(0)
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def image_to_tensor(image, target_width=None):
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"""输入 OpenCV 图像，范围 0~255，BGR 顺序，输出标准化后的四维 tensor"""
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# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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image = Image.fromarray(image)
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return preprocess_image(image, target_width)
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def recover_image(tensor):
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"""输入 GPU 上的四维 tensor，输出 0~255 范围的三维 numpy 矩阵，RGB 顺序"""
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image = tensor.detach().cpu().numpy()
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image = image * np.array(cnn_normalization_std).reshape((1, 3, 1, 1)) + \
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np.array(cnn_normalization_mean).reshape((1, 3, 1, 1))
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return (image.transpose(0, 2, 3, 1) * 255.).clip(0, 255).astype(np.uint8)[0]
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def recover_tensor(tensor):
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m = torch.tensor(cnn_normalization_mean).view(1, 3, 1, 1).to(tensor.device)
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s = torch.tensor(cnn_normalization_std).view(1, 3, 1, 1).to(tensor.device)
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tensor = tensor * s + m
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return tensor.clamp(0, 1)
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