数据集简介
两个数据集
一个是783张图片+对应的xml文件
一个是2482张图片+对应的xml文件
如下图所示：
部分代码：
# 测试数据读取
def test_data_loader(datadir, batch_size= 10, test_image_size=608, mode='test'):"""加载测试用的图片，测试数据没有groundtruth标签"""image_names = os.listdir(datadir)def reader():batch_data = []img_size = test_image_sizefor image_name in image_names:file_path = os.path.join(datadir, image_name)img = cv2.imread(file_path)img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)H = img.shape[0]W = img.shape[1]img = cv2.resize(img, (img_size, img_size))mean = [0.485, 0.456, 0.406]std = [0.229, 0.224, 0.225]mean = np.array(mean).reshape((1, 1, -1))std = np.array(std).reshape((1, 1, -1))out_img = (img / 255.0 - mean) / stdout_img = out_img.astype('float32').transpose((2, 0, 1))img = out_img #np.transpose(out_img, (2,0,1))im_shape = [H, W]batch_data.append((image_name.split('.')[0], img, im_shape))if len(batch_data) == batch_size:yield make_test_array(batch_data)batch_data = []if len(batch_data) > 0:yield make_test_array(batch_data)return reader# 读取数据
import paddle
reader = paddle.io.DataLoader(train_dataset, batch_size=2, shuffle=True, num_workers=1, drop_last=True)
img, gt_boxes, gt_labels, im_shape = next(reader())
img, gt_boxes, gt_labels, im_shape = img.numpy(), gt_boxes.numpy(), gt_labels.numpy(), im_shape.numpy()# 计算出锚框对应的标签
label_objectness, label_location, label_classification, scale_location = get_objectness_label(img,gt_boxes, gt_labels, iou_threshold = 0.7,anchors = [116, 90, 156, 198, 373, 326],num_classes=7, downsample=32)
