"""VOC Dataset Classes
Original author: Francisco Massa
https://github.com/fmassa/vision/blob/voc_dataset/torchvision/datasets/voc.py
Updated by: Ellis Brown, Max deGroot
"""
from .config import HOME
import os.path as osp
import sys
import torch
import torch.utils.data as data
import cv2
import numpy as np
if sys.version_info[0] == 2:
import xml.etree.cElementTree as ET
else:
import xml.etree.ElementTree as ET
VOC_CLASSES = ( # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
# note: if you used our download scripts, this should be right
VOC_ROOT = osp.join(HOME, "data/VOCdevkit/")
class VOCAnnotationTransform_con_init(object):
"""Transforms a VOC annotation into a Tensor of bbox coords and label index
Initilized with a dictionary lookup of classnames to indexes
Arguments:
class_to_ind (dict, optional): dictionary lookup of classnames -> indexes
(default: alphabetic indexing of VOC's 20 classes)
keep_difficult (bool, optional): keep difficult instances or not
(default: False)
height (int): height
width (int): width
"""
def __init__(self, class_to_ind=None, keep_difficult=False):
self.class_to_ind = class_to_ind or dict(
zip(VOC_CLASSES, range(len(VOC_CLASSES))))
self.keep_difficult = keep_difficult
def __call__(self, target, width, height):
"""
Arguments:
target (annotation) : the target annotation to be made usable
will be an ET.Element
Returns:
a list containing lists of bounding boxes [bbox coords, class name]
"""
res = []
for obj in target.iter('object'):
difficult = int(obj.find('difficult').text) == 1
if not self.keep_difficult and difficult:
continue
name = obj.find('name').text.lower().strip()
bbox = obj.find('bndbox')
pts = ['xmin', 'ymin', 'xmax', 'ymax']
bndbox = []
for i, pt in enumerate(pts):
cur_pt = int(bbox.find(pt).text) - 1
# scale height or width
cur_pt = cur_pt / width if i % 2 == 0 else cur_pt / height
bndbox.append(cur_pt)
label_idx = self.class_to_ind[name]
bndbox.append(label_idx)
res += [bndbox] # [xmin, ymin, xmax, ymax, label_ind]
# img_id = target.find('filename').text[:-4]
return res # [[xmin, ymin, xmax, ymax, label_ind], ... ]
class VOCDetection_con_init(data.Dataset):
"""VOC Detection Dataset Object
input is image, target is annotation
Arguments:
root (string): filepath to VOCdevkit folder.
image_set (string): imageset to use (eg. 'train', 'val', 'test')
transform (callable, optional): transformation to perform on the
input image
target_transform (callable, optional): transformation to perform on the
target `annotation`
(eg: take in caption string, return tensor of word indices)
dataset_name (string, optional): which dataset to load
(default: 'VOC2007')
"""
def __init__(self, root,
image_sets=[('2007', 'trainval')],
transform=None, target_transform=VOCAnnotationTransform_con_init(),
dataset_name='VOC0712'):
self.root = root
self.image_set = image_sets
self.transform = transform
self.target_transform = target_transform
self.name = dataset_name
self._annopath = osp.join('%s', 'Annotations', '%s.xml')
self._imgpath = osp.join('%s', 'JPEGImages', '%s.jpg')
self.ids = list()
for (year, name) in image_sets:
rootpath = osp.join(self.root, 'VOC' + year)
for line in open(osp.join(rootpath, 'ImageSets', 'Main', name + '.txt')):
self.ids.append((rootpath, line.strip()))
def __getitem__(self, index):
# im, gt, h, w = self.pull_item(index)
im, gt, h, w, semi = self.pull_item(index)
# return im, gt
return im, gt, semi
def __len__(self):
return len(self.ids)
def pull_item(self, index):
img_id = self.ids[index]
target = ET.parse(self._annopath % img_id).getroot()
img = cv2.imread(self._imgpath % img_id)
height, width, channels = img.shape
if self.target_transform is not None:
target = self.target_transform(target, width, height)
if self.transform is not None:
target = np.array(target)
img, boxes, labels = self.transform(img, target[:, :4], target[:, 4])
# to rgb
img = img[:, :, (2, 1, 0)]
# img = img.transpose(2, 0, 1)
target = np.hstack((boxes, np.expand_dims(labels, axis=1)))
if(img_id[0][(len(img_id[0]) - 7):]=='VOC2007'):
semi = np.array([1])
else:
semi = np.array([0])
target = np.zeros([1, 5])
return torch.from_numpy(img).permute(2, 0, 1), target, height, width, semi
# return torch.from_numpy(img), target, height, width
def pull_image(self, index):
'''Returns the original image object at index in PIL form
Note: not using self.__getitem__(), as any transformations passed in
could mess up this functionality.
Argument:
index (int): index of img to show
Return:
PIL img
'''
img_id = self.ids[index]
return cv2.imread(self._imgpath % img_id, cv2.IMREAD_COLOR)
def pull_anno(self, index):
'''Returns the original annotation of image at index
Note: not using self.__getitem__(), as any transformations passed in
could mess up this functionality.
Argument:
index (int): index of img to get annotation of
Return:
list: [img_id, [(label, bbox coords),...]]
eg: ('001718', [('dog', (96, 13, 438, 332))])
'''
img_id = self.ids[index]
anno = ET.parse(self._annopath % img_id).getroot()
gt = self.target_transform(anno, 1, 1)
return img_id[1], gt
def pull_tensor(self, index):
'''Returns the original image at an index in tensor form
Note: not using self.__getitem__(), as any transformations passed in
could mess up this functionality.
Argument:
index (int): index of img to show
Return:
tensorized version of img, squeezed
'''
return torch.Tensor(self.pull_image(index)).unsqueeze_(0)
