aero是什么意思啊_自动驾驶视觉算法

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数据集介绍

aeroscapes数据集下载链接
AeroScapes 航空语义分割基准包括使用商用无人机在 5 到 50 米的高度范围内捕获的图像。该数据集提供 3269 张 720p 图像和 11 个类别的真实掩码。
在这里插入图片描述

获取Class类别及其RGB值

由于本数据集未提供类别ID对应的RGB值，可以通过以下代码获取：

from PIL import Image
import os

base_dir = "Visualizations/"
base_seg_dir = "SegmentationClass/"
files = os.listdir(base_dir)
list1 = []
for file in files:
    img_dir = base_dir + file
    segimg_dir = base_seg_dir + file
    im = Image.open(img_dir)
    segimg = Image.open(segimg_dir)
    pix = im.load()
    pix_seg = segimg.load() 
    width = im.size[0]
    height = im.size[1]
    for x in range(width):
        for y in range(height):
            r, g, b = pix[x, y]
            c = pix_seg[x,y]
            if [c,r,g,b] not in list1:
                list1.append([c,r,g,b])
                print(list1)
print(list1)

结果如下：

Person [192,128,128]--------------1
Bike [0,128,0]----------------------2
Car [128,128,128]----------------- 3
Drone [128,0,0]--------------------4
Boat [0,0,128]--------------------- 5
Animal [192,0,128]---------------- 6
Obstacle [192,0,0]------------------7
Construction [192,128,0]-----------8
Vegetation [0,64,0]-----------------9
Road [128,128,0]-------------------10
Sky [0,128,128]---------------------11

数据加载dataloder写法（基于pytorch）

由于该数据集提供了掩码图，因此不需要进行掩码图转换。下载完成后，文件结构如下：

ImageSets文件夹：存放了两个txt文件，划分了训练集和验证集。
JPEGImages文件夹：存放了RGB图像。
SegmentationClass：存放了标签的掩模图。
Visualizations：存放了标签图像。
为了使用此数据集，需要根据划分好的txt文件读取图像，然后采用Pytorch的Dataloader模块进行加载。具体代码：

''' dataset.py '''
import torch
import torch.utils.data
import numpy as np
import cv2
import os

# txt_file = open("ImageSets/trn.txt")
# train_filenames = txt_file.readlines()
# for train_filename in train_filenames:
# print(train_filename)

class DatasetTrain(torch.utils.data.Dataset):
    def __init__(self, base_dir):

        
        self.base_dir = base_dir
        self.img_dir = base_dir + "JPEGImages/"
        self.label_dir = base_dir + "SegmentationClass/"

        self.new_img_h = 512
        self.new_img_w = 1024

        self.examples = []
        txt_path = self.base_dir + "ImageSets/trn.txt"
        txt_file = open(txt_path)
        train_filenames = txt_file.readlines()

        train_img_dir_path = self.img_dir
        label_img__dir_path = self.label_dir

        for train_filename in train_filenames:
            train_filename=train_filename.strip('\n')
            img_path = train_img_dir_path + train_filename + '.jpg'
            label_img_path = label_img__dir_path + train_filename + '.png'
            example = { 
   }
            example["img_path"] = img_path
            example["label_img_path"] = label_img_path
            self.examples.append(example)

        self.num_examples = len(self.examples)

    def __getitem__(self, index):
        example = self.examples[index]

        img_path = example["img_path"]
        print(img_path)
        img = cv2.imread(img_path, -1) 
        img = cv2.resize(img, (self.new_img_w, self.new_img_h),
                         interpolation=cv2.INTER_NEAREST) 
        label_img_path = example["label_img_path"]
        print(label_img_path)
        label_img = cv2.imread(label_img_path, cv2.IMREAD_GRAYSCALE) 
        label_img = cv2.resize(label_img, (self.new_img_w, self.new_img_h),
                               interpolation=cv2.INTER_NEAREST) 

        # normalize the img (with the mean and std for the pretrained ResNet):
        img = img/255.0
        img = img - np.array([0.485, 0.456, 0.406])
        img = img/np.array([0.229, 0.224, 0.225]) 
        img = np.transpose(img, (2, 0, 1)) 
        img = img.astype(np.float32)

        # convert numpy -> torch:
        img = torch.from_numpy(img) 
        label_img = torch.from_numpy(label_img) 

        return (img, label_img)

    def __len__(self):
        return self.num_examples

class DatasetVal(torch.utils.data.Dataset):
    def __init__(self, base_dir):

        
        self.base_dir = base_dir
        self.img_dir = base_dir + "JPEGImages/"
        self.label_dir = base_dir + "SegmentationClass/"

        self.new_img_h = 512
        self.new_img_w = 1024

        self.examples = []
        txt_path = self.base_dir + "ImageSets/val.txt"
        txt_file = open(txt_path)
        valid_filenames = txt_file.readlines()

        train_img_dir_path = self.img_dir
        label_img__dir_path = self.label_dir

        for valid_filename in valid_filenames:
            valid_filename=valid_filename.strip('\n')
            img_path = train_img_dir_path + valid_filename + '.jpg'
            label_img_path = label_img__dir_path + valid_filename + '.png'
            example = { 
   }
            example["img_path"] = img_path
            example["label_img_path"] = label_img_path
            self.examples.append(example)

        self.num_examples = len(self.examples)

    def __getitem__(self, index):
        example = self.examples[index]

        img_path = example["img_path"]
        print(img_path)
        img = cv2.imread(img_path, -1) 
        img = cv2.resize(img, (self.new_img_w, self.new_img_h),
                         interpolation=cv2.INTER_NEAREST) 
        label_img_path = example["label_img_path"]
        print(label_img_path)
        label_img = cv2.imread(label_img_path, cv2.IMREAD_GRAYSCALE) 
        label_img = cv2.resize(label_img, (self.new_img_w, self.new_img_h),
                               interpolation=cv2.INTER_NEAREST) 

        # normalize the img (with the mean and std for the pretrained ResNet):
        img = img/255.0
        img = img - np.array([0.485, 0.456, 0.406])
        img = img/np.array([0.229, 0.224, 0.225]) 
        img = np.transpose(img, (2, 0, 1)) 
        img = img.astype(np.float32)

        # convert numpy -> torch:
        img = torch.from_numpy(img) 
        label_img = torch.from_numpy(label_img) 

        return (img, label_img)

    def __len__(self):
        return self.num_examples


''' 以下代码为测试功能，正式使用时需要注释掉 '''

if __name__ == "__main__":
    base_dir = "aeroscapes/"
    train_dataset = DatasetTrain(base_dir = base_dir)
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                            batch_size=3, shuffle=True,
                                            num_workers=1,drop_last=True)
    val_dataset = DatasetVal(base_dir = base_dir)
    val_loader = torch.utils.data.DataLoader(dataset=val_dataset,
                                            batch_size=3, shuffle=True,
                                            num_workers=1,drop_last=True)
    from torch.autograd import Variable
    for step, (imgs, label_imgs) in enumerate(train_loader):
        imgs = Variable(imgs).cuda() # (shape: (batch_size, 3, img_h, img_w))
        print(imgs.shape)
        label_imgs = Variable(label_imgs.type(torch.LongTensor)).cuda() # (shape: (batch_size, img_h, img_w))
        print(label_imgs.shape)
    for step, (imgs, label_imgs) in enumerate(val_loader):
        imgs = Variable(imgs).cuda() # (shape: (batch_size, 3, img_h, img_w))
        print(imgs.shape)
        label_imgs = Variable(label_imgs.type(torch.LongTensor)).cuda() # (shape: (batch_size, img_h, img_w))
        print(label_imgs.shape)