数据处理——图像处理

引入库

import os
import cv2
import random
import torch
import numpy as np
import pandas as pd
from torch.utils.data import Dataset
from imgaug import augmenters as iaa
from utils.process_labels import encode_labels, decode_labels, decode_color_labels

imgaug是一个图像数据扩增工具

 剪裁图像

def crop_resize_data(image, label=None, image_size=(1024, 384), offset=690):
    roi_image = image[offset:, :]
    if label is not None:
        roi_label = label[offset:, :]
        train_image = cv2.resize(roi_image, image_size, interpolation=cv2.INTER_LINEAR) #image做的是线性插值
        train_label = cv2.resize(roi_label, image_size, interpolation=cv2.INTER_NEAREST) #label做的是最邻近插值
        return train_image, train_label
    else:
        train_image = cv2.resize(roi_image, image_size, interpolation=cv2.INTER_LINEAR)
        return train_image

图像可能会有部分是没用的，比如说，我们想实现车道线分割任务，但是，图像中天空占比很大，这时候需要将天空剪裁掉来节省我们的显存资源。 

 图像扩增

Guassian Noise, Sharpen Augmentation

class ImageAug(object):
    image, mask = sample
    
    if np.random.uniform(0,1) > 0.5:
        seq = iaa.Sequential([iaa.OneOf([
            iaa.AdditiveGaussianNoise(scale=(0, 0.2 * 255)), # 加高斯白噪声
            iaa.Sharpen(alpha=(alpha=(0.1,0.3), lightness=(0.7,1.3)), # 锐化
            iaa.GaussianBlur(sigma=(o,1.0))])]) # 高斯模糊

        image = seq.augment_image(image)
    return image, mask

Deformation Augmentation

class DeformAug(object):
    def __call__(self, sample):
        image, mask = sample
        seq = iaa.Sequential([iaa.CropAndPad(percent=(-0.05,0.1))])
        seg_to = seq.to_deterministic()
        image = seg_to.augment_image(image)
        mask = seg_to.augment_image(mask)
        return image, mask

Scale Augmentation

class ScaleAug(object):
    def __cal__(self, sample):
        image, mask = sample
        scale = random.uniform(0.7,1.5) # 按照均匀分布在0.7~1.5之间取值
        h,w,_ = image.shape

        aug_image = image.copy() # 复制一下，不要修改原数据
        aug_mask = mask.copy()

        aug_image = cv2.resize(aug_image, (int(scale * w), int(scale * h)))
        aug_mask = cv2.resize(aug_mask, (int(scale * w, int(scale * h)))
        if (scale < 1.0):
            new_h, new_w, _ = aug_image.shape
            pre_h_pad = int((h - new_h) / 2)
            pre_w_pad = int((w - new_w) / 2)
            pad_list = [[pre_h_pad, h - new_h - pre_h_pad], [pre_w_pad, w - new_w - pre_w_pad], [0, 0]]
            aug_image = np.pad(aug_image, pad_list, mode="constant")
            aug_mask = np.pad(aug_mask, pad_list[:2], mode="constant")
        if (scale > 1.0):
            new_h, new_w, _ = aug_image.shape
            pre_h_crop = int ((new_h - h) / 2)
            pre_w_crop = int ((new_w - w) / 2)
            post_h_crop = h + pre_h_crop
            post_w_crop = w + pre_w_crop
            aug_image = aug_image[pre_h_crop:post_h_crop, pre_w_crop:post_w_crop]
            aug_mask = aug_mask[pre_h_crop:post_h_crop, pre_w_crop:post_w_crop]
        return aug_image, aug_mask            

向量化

class ToTensor(object):
    def __call__(self, sample):
        image, mask = sample
        image = np.transpose(image, (2,0,1))
        image = image.astype(np.float32)
        mask = mask.astype(np.long)
        return {'image':torch.from_numpy(image.copy()),
                'mask':torch.from_numpy(mask.copy())}