def _parse_annotation(self,itemidx,random_trainsize):
rootpath, filename = self._ids[itemidx]
annpath = self._annopath.format(rootpath, filename)
imgpath = self._imgpath.format(rootpath, filename)
fname, bboxes, labels = PascalVocXmlParser(annpath, self.labels).parse()
img = cv2.imread(imgpath, cv2.IMREAD_COLOR)
if self.istrain:
img, bboxes = dataAug.random_horizontal_flip(np.copy(img), np.copy(bboxes))
img, bboxes = dataAug.random_crop(np.copy(img), np.copy(bboxes))
img, bboxes = dataAug.random_translate(np.copy(img), np.copy(bboxes))
ori_shape=img.shape[:2]
img, bboxes = dataAug.img_preprocess2(np.copy(img), np.copy(bboxes),
(random_trainsize, random_trainsize), True)
return img,bboxes,labels,imgpath,ori_shape
def _parse_annotation(self, itemidx, random_trainsize):
img_info = self.img_infos[itemidx]
ann_info = self._load_ann_info(itemidx)
ann = self._parse_ann_info(ann_info)
bboxes = ann['bboxes'] # [x1,y1,x2,y2]
labels = ann['labels']
# load the image.
imgpath = osp.join(self.image_dir, img_info['file_name'])
img = cv2.imread(imgpath, cv2.IMREAD_COLOR)
if self.istrain:
img, bboxes = dataAug.random_horizontal_flip(
np.copy(img), np.copy(bboxes))
img, bboxes = dataAug.random_crop(np.copy(img), np.copy(bboxes))
img, bboxes = dataAug.random_translate(np.copy(img),
np.copy(bboxes))
ori_shape = img.shape[:2]
img, bboxes = dataAug.img_preprocess2(
np.copy(img), np.copy(bboxes),
(random_trainsize, random_trainsize), True)
return img, bboxes, labels, imgpath, ori_shape
