img = cv2.imread(image_path)
h, w, c = image.shape
img = cv2.resize(img, (self.img_width, self.img_height))
img = img[:,:,::-1]#bgr转rgb

from PIL import Image
img = Image.open(img_path).convert('RGB')
img = Image.fromarray(img)

from skimage import io
img=io.imread(image_path)from skimage import transform
img = transform.resize(img, (C, H * scale, W * scale), mode='reflect',anti_aliasing=False)

import scipy.misc
#或scipy.ndimage.imread
img = scipy.misc.imread(image_path)

import matplotlib.image as mpimg
img = mpimg.imread(image_path)

<annotation><folder>VOC2007</folder><filename>000001.jpg</filename><source><database>The VOC2007 Database</database><annotation>PASCAL VOC2007</annotation><image>flickr</image><flickrid>341012865</flickrid></source><owner><flickrid>Fried Camels</flickrid><name>Jinky the Fruit Bat</name></owner><size><width>353</width><height>500</height><depth>3</depth></size><segmented>0</segmented><object><name>dog</name><pose>Left</pose><truncated>1</truncated><difficult>0</difficult><bndbox><xmin>48</xmin><ymin>240</ymin><xmax>195</xmax><ymax>371</ymax></bndbox></object><object><name>person</name><pose>Left</pose><truncated>1</truncated><difficult>0</difficult><bndbox><xmin>8</xmin><ymin>12</ymin><xmax>352</xmax><ymax>498</ymax></bndbox></object>
</annotation>

继承自最基础的torch.utils.data.Dataset
Dataset可以交给DataLoader，封装batchs和多线程
class PascalVOCDataset(torch.utils.data.Dataset):第一个是背景，其他是目标类别CLASSES = ("__background__ ","aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor",)def __init__(self, data_dir, split, use_difficult=False, transforms=None):
数据根目录self.root = data_dir
指定用哪个数据，test、val、train？self.image_set = split
要不要难以识别的图片？self.keep_difficult = use_difficult
图片处理self.transforms = transforms
对应三个文件加：self._annopath = os.path.join(self.root, "Annotations", "%s.xml")self._imgpath = os.path.join(self.root, "JPEGImages", "%s.jpg")
直接用Main文件夹self._imgsetpath = os.path.join(self.root, "ImageSets", "Main", "%s.txt")
初始化的时候就把文件名全读出来with open(self._imgsetpath % self.image_set) as f:self.ids = f.readlines()self.ids = [x.strip("n") for x in self.ids]
构造id到图片的字典，self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}cls = PascalVOCDataset.CLASSES
类别到序号和序号到类别的两个字典self.class_to_ind = dict(zip(cls, range(len(cls))))self.categories = dict(zip(range(len(cls)), cls))
__getitem__是必须的def __getitem__(self, index):img_id = self.ids[index]
根据id号读取图片，用的是PIL来读img = Image.open(self._imgpath % img_id).convert("RGB")
获取目标框，裁剪target = self.get_groundtruth(index)target = target.clip_to_image(remove_empty=True)
做归一化，缩放等处理if self.transforms is not None:img, target = self.transforms(img, target)
target 前四个表示坐标，第五个值表示类别 return img, target, index
__len__也是必须的def __len__(self):return len(self.ids)def get_groundtruth(self, index):img_id = self.ids[index]
定位到根节点anno = ET.parse(self._annopath % img_id).getroot()
读取图片大小、目标类别、目标位置信息anno = self._preprocess_annotation(anno)
取出高宽信息height, width = anno["im_info"]
按xyxy顺序的坐标信息，这个地方要注意，并不是每个实现都是相同顺序，还是看读取的时候怎么读的，有的是yxyx顺序
BoxList是自定义的处理这几个不目标框的类target = BoxList(anno["boxes"], (width, height), mode="xyxy")
BoxList添加标签，难度target.add_field("labels", anno["labels"])target.add_field("difficult", anno["difficult"])return targetdef _preprocess_annotation(self, target):boxes = []gt_classes = []difficult_boxes = []TO_REMOVE = 1
只遍历object节点for obj in target.iter("object"):
遇到难以识别的，跳过也行，不是强烈要求，就跳过吧difficult = int(obj.find("difficult").text) == 1if not self.keep_difficult and difficult:continue
标签名称name = obj.find("name").text.lower().strip()
矩形坐标bb = obj.find("bndbox")# Make pixel indexes 0-based# Refer to "https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/pascal_voc.py#L208-L211"box = [bb.find("xmin").text,bb.find("ymin").text,bb.find("xmax").text,bb.find("ymax").text,]
两次map：
第一次是把文本转化成int，这里int是函数；
第二次是把int减去1，0-based
得到一个tuplebndbox = tuple(map(lambda x: x - TO_REMOVE, list(map(int, box))))
把这tuple添加进boxesboxes.append(bndbox)
把标签转化成类别idgt_classes.append(self.class_to_ind[name])difficult_boxes.append(difficult)循环结束，一张图片目标有一个或多个，但大小只有一个
看图片大小size = target.find("size")
把高宽从文本转化成int，得到一个tupleim_info = tuple(map(int, (size.find("height").text, size.find("width").text)))
把这张图片的信息用一个字典返回
并把坐标转化成floatres = {"boxes": torch.tensor(boxes, dtype=torch.float32),"labels": torch.tensor(gt_classes),"difficult": torch.tensor(difficult_boxes),"im_info": im_info,}return res

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