def transform(img, boxes, labels):
img, boxes = resize(img, boxes, size=(opt.img_size, opt.img_size))
img = transforms.Compose([
transforms.ToTensor(),
caffe_normalize
])(img)
return img, boxes, labels
def train_(img, boxes, labels):
img = random_distort(img)
if random.random() < 0.5:
img, boxes = random_paste(img,
boxes,
max_ratio=4,
fill=(123, 116, 103))
img, boxes, labels = random_crop(img, boxes, labels)
img, boxes = resize(img,
boxes,
size=(opt.img_size, opt.img_size),
random_interpolation=True)
img, boxes = random_flip(img, boxes)
img = transforms.Compose([transforms.ToTensor(), caffe_normalize])(img)
boxes, labels = box_coder.encode(boxes, labels)
return img, boxes, labels
def test_(img, boxes, labels):
img, boxes = resize(img, boxes, size=(opt.img_size, opt.img_size))
img = transforms.Compose([transforms.ToTensor(), caffe_normalize])(img)
boxes, labels = box_coder.encode(boxes, labels)
return img, boxes, labels