def __getitem__(self, item):
assert item <= len(self), 'index range error'
img_org, bboxes_org = self.__parse_annotation(self.__annotations[item])
img_org = img_org.transpose(2, 0, 1) # HWC->CHW
item_mix = random.randint(0, len(self.__annotations) - 1)
img_mix, bboxes_mix = self.__parse_annotation(
self.__annotations[item_mix])
img_mix = img_mix.transpose(2, 0, 1)
img, bboxes = dataAug.Mixup()(img_org, bboxes_org, img_mix, bboxes_mix)
del img_org, bboxes_org, img_mix, bboxes_mix
label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.__creat_label(
bboxes)
img = torch.from_numpy(img).float()
label_sbbox = torch.from_numpy(label_sbbox).float()
label_mbbox = torch.from_numpy(label_mbbox).float()
label_lbbox = torch.from_numpy(label_lbbox).float()
sbboxes = torch.from_numpy(sbboxes).float()
mbboxes = torch.from_numpy(mbboxes).float()
lbboxes = torch.from_numpy(lbboxes).float()
return img, label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes
def __getitem__(self, item):
img_org, bboxes_org = self.__parse_data(self.img_paths[item],
self.annos[item])
img_org = img_org.transpose(2, 0, 1) # HWC->CHW
item_mix = random.randint(0, len(self.annos) - 1)
img_mix, bboxes_mix = self.__parse_data(self.img_paths[item_mix],
self.annos[item_mix])
img_mix = img_mix.transpose(2, 0, 1)
img, bboxes = dataAug.Mixup(p=0.5)(img_org, bboxes_org, img_mix,
bboxes_mix)
del img_org, bboxes_org, img_mix, bboxes_mix
# Without mixing. or we can set p=1 in the dataAug.Mixup(p=1)
# img = img_org
# bboxes = np.concatenate([bboxes_org, np.full((len(bboxes_org), 1), 1.0)], axis=1)
# del img_org, bboxes_org
# for debuging we will see this 2 times in 1 loop if we set batch = 2
# new_boxes = np.copy(bboxes)
# im = np.copy(img)
# im = im.transpose(1, 2, 0)
# im = im[:,:,[2,1,0]]
# im = im.astype('float32')
# im = cv2.UMat(im)
# for box in new_boxes:
# box = box.astype('float32')
# cv2.rectangle(im, (box[0], box[1]), (box[2], box[3]) , (0,255,0), 2)
# font = cv2.FONT_HERSHEY_SIMPLEX
# color = (255, 0, 0)
# cv2.putText(im , str(box[4]),(int(box[0]-10),int(box[1])), font, .5,color ,2,cv2.LINE_AA)
# cv2.imshow('test mix', im)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.creat_label(
bboxes)
img = torch.from_numpy(img).float()
label_sbbox = torch.from_numpy(label_sbbox).float()
label_mbbox = torch.from_numpy(label_mbbox).float()
label_lbbox = torch.from_numpy(label_lbbox).float()
sbboxes = torch.from_numpy(sbboxes).float()
mbboxes = torch.from_numpy(mbboxes).float()
lbboxes = torch.from_numpy(lbboxes).float()
return img, label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes
def __getitem__(self, item):
img_org, bboxes_org = self.__parse_annotation(self.__annotations[item])
H,W,_ = img_org.shape
if self.do_copy_paste:
resize_transform = A.Compose([A.augmentations.transforms.Resize (H, W, interpolation=1, always_apply=True, p=1)], bbox_params=A.BboxParams(format="coco", min_visibility=0.05))
img_data = self.copy_paste_data[item]
img_data = resize_transform(**img_data)
img_org = img_data['image']
#plt.imshow(img_org)
#plt.show()
bboxes_org = np.array(img_data['bboxes'])[:, :5]
#convert from xywh to xyxy
bboxes_org[:, 2] += bboxes_org[:, 0]
bboxes_org[:, 3] += bboxes_org[:, 1]
if cfg.VIS_COPY_PASTE :
global image_counter
image_counter +=1
plt.imshow(visualize_boxes(img_org, bboxes_org[:,:4].astype(int), bboxes_org[:,4].astype(int), np.ones(bboxes_org.shape[0]), class_labels))
plt.imsave('copy_paste_{}.png'.format(image_counter), visualize_boxes(img_org, bboxes_org[:,:4].astype(int), bboxes_org[:,4].astype(int), np.ones(bboxes_org.shape[0]), class_labels))
plt.show()
img_org = img_org.transpose(2, 0, 1) # HWC->CHW
item_mix = random.randint(0, len(self.__annotations)-1)
img_mix, bboxes_mix = self.__parse_annotation(self.__annotations[item_mix])
img_mix = img_mix.transpose(2, 0, 1)
img, bboxes = dataAug.Mixup()(img_org, bboxes_org, img_mix, bboxes_mix)
del img_org, bboxes_org, img_mix, bboxes_mix
label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.__creat_label(bboxes)
img = torch.from_numpy(img).float()
label_sbbox = torch.from_numpy(label_sbbox).float()
label_mbbox = torch.from_numpy(label_mbbox).float()
label_lbbox = torch.from_numpy(label_lbbox).float()
sbboxes = torch.from_numpy(sbboxes).float()
mbboxes = torch.from_numpy(mbboxes).float()
lbboxes = torch.from_numpy(lbboxes).float()
return img, label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes
def __getitem__(self, item):
assert item <= len(self), 'index range error'
if self.anno_file_type == 'train':
img_org, bboxes_org, img_name = self.__parse_annotation(
self.__annotations[item])
img_org, bboxes_org = self.__data_aug(img_org, bboxes_org)
img_org = img_org.transpose(2, 0, 1) # HWC->CHW
item_mix = random.randint(0, len(self.__annotations) - 1)
img_mix, bboxes_mix, _ = self.__parse_annotation(
self.__annotations[item_mix])
img_mix, bboxes_mix = self.__data_aug(img_mix, bboxes_mix)
img_mix = img_mix.transpose(2, 0, 1)
img, bboxes = dataAug.Mixup()(img_org, bboxes_org, img_mix,
bboxes_mix)
del img_mix, bboxes_mix
img_size = img.shape[1] # img must be square
else:
img_org, bboxes_org, img_name = self.__parse_annotation(
self.__annotations[item])
img_org = img_org.transpose(2, 0, 1)
img = img_org
bboxes = bboxes_org
img_size = 512 #magic, need fix
del img_org, bboxes_org
label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.__creat_label(
bboxes, img_size)
img = torch.from_numpy(img).float()
label_sbbox = torch.from_numpy(label_sbbox).float()
label_mbbox = torch.from_numpy(label_mbbox).float()
label_lbbox = torch.from_numpy(label_lbbox).float()
sbboxes = torch.from_numpy(sbboxes).float()
mbboxes = torch.from_numpy(mbboxes).float()
lbboxes = torch.from_numpy(lbboxes).float()
return img, label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes, img_name
