def data_generator(annotation_lines, batch_size, anchors, num_classes, max_bbox_per_scale, annotation_type):
'''data generator for fit_generator'''
n = len(annotation_lines)
i = 0
#多尺度训练
train_input_sizes = [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
strides = np.array([8, 16, 32])
while True:
train_input_size = random.choice(train_input_sizes)
# 输出的网格数
train_output_sizes = train_input_size // strides
batch_image = np.zeros((batch_size, train_input_size, train_input_size, 3))
batch_label_sbbox = np.zeros((batch_size, train_output_sizes[0], train_output_sizes[0],
3, 5 + num_classes))
batch_label_mbbox = np.zeros((batch_size, train_output_sizes[1], train_output_sizes[1],
3, 5 + num_classes))
batch_label_lbbox = np.zeros((batch_size, train_output_sizes[2], train_output_sizes[2],
3, 5 + num_classes))
batch_sbboxes = np.zeros((batch_size, max_bbox_per_scale, 4))
batch_mbboxes = np.zeros((batch_size, max_bbox_per_scale, 4))
batch_lbboxes = np.zeros((batch_size, max_bbox_per_scale, 4))
for num in range(batch_size):
if i == 0:
np.random.shuffle(annotation_lines)
image, bboxes, exist_boxes = parse_annotation(annotation_lines[i], train_input_size, annotation_type)
label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = preprocess_true_boxes(bboxes, train_output_sizes, strides, num_classes, max_bbox_per_scale, anchors)
batch_image[num, :, :, :] = image
if exist_boxes:
batch_label_sbbox[num, :, :, :, :] = label_sbbox
batch_label_mbbox[num, :, :, :, :] = label_mbbox
batch_label_lbbox[num, :, :, :, :] = label_lbbox
batch_sbboxes[num, :, :] = sbboxes
batch_mbboxes[num, :, :] = mbboxes
batch_lbboxes[num, :, :] = lbboxes
i = (i + 1) % n
yield [batch_image, batch_label_sbbox, batch_label_mbbox, batch_label_lbbox, batch_sbboxes, batch_mbboxes, batch_lbboxes], np.zeros(batch_size)
def data_generator(annotation_lines, batch_size, input_shape, anchors,
num_classes):
'''data generator for fit_generator'''
n = len(annotation_lines)
i = 0
while True:
image_data = []
box_data = []
for b in range(batch_size):
if i == 0:
np.random.shuffle(annotation_lines)
image, box = get_random_data(annotation_lines[i],
input_shape,
random=True)
image_data.append(image)
box_data.append(box)
i = (i + 1) % n
image_data = np.array(image_data)
box_data = np.array(box_data)
y_true = preprocess_true_boxes(box_data, input_shape, anchors,
num_classes)
yield [image_data, *y_true], np.zeros(batch_size)