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@@ -0,0 +1,1366 @@
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+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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+#
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+# Licensed under the Apache License, Version 2.0 (the "License");
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+# you may not use this file except in compliance with the License.
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+# You may obtain a copy of the License at
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+#
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+# http://www.apache.org/licenses/LICENSE-2.0
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+#
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+# Unless required by applicable law or agreed to in writing, software
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+# distributed under the License is distributed on an "AS IS" BASIS,
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+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+# See the License for the specific language governing permissions and
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+# limitations under the License.
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+
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+try:
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+ from collections.abc import Sequence
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+except Exception:
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+ from collections import Sequence
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+
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+import random
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+import os.path as osp
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+import numpy as np
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+
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+import cv2
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+from PIL import Image, ImageEnhance
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+
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+from .imgaug_support import execute_imgaug
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+from .ops import *
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+from .box_utils import *
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+import utils.logging as logging
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+
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+
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+class DetTransform:
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+ """检测数据处理基类
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+ """
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+
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+ def __init__(self):
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+ pass
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+
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+
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+class Compose(DetTransform):
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+ """根据数据预处理/增强列表对输入数据进行操作。
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+ 所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。
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+
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+ Args:
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+ transforms (list): 数据预处理/增强列表。
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+
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+ Raises:
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+ TypeError: 形参数据类型不满足需求。
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+ ValueError: 数据长度不匹配。
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+ """
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+
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+ def __init__(self, transforms):
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+ if not isinstance(transforms, list):
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+ raise TypeError('The transforms must be a list!')
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+ if len(transforms) < 1:
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+ raise ValueError('The length of transforms ' + \
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+ 'must be equal or larger than 1!')
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+ self.transforms = transforms
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+ self.use_mixup = False
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+ for t in self.transforms:
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+ if type(t).__name__ == 'MixupImage':
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+ self.use_mixup = True
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+ # 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
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+ for op in self.transforms:
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+ if not isinstance(op, DetTransform):
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+ import imgaug.augmenters as iaa
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+ if not isinstance(op, iaa.Augmenter):
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+ raise Exception(
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+ "Elements in transforms should be defined in 'paddlex.det.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
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+ )
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+
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+ def __call__(self, im, im_info=None, label_info=None):
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+ """
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+ Args:
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+ im (str/np.ndarray): 图像路径/图像np.ndarray数据。
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+ im_info (dict): 存储与图像相关的信息，dict中的字段如下：
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+ - im_id (np.ndarray): 图像序列号，形状为(1,)。
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+ - image_shape (np.ndarray): 图像原始大小，形状为(2,)，
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+ image_shape[0]为高，image_shape[1]为宽。
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+ - mixup (list): list为[im, im_info, label_info]，分别对应
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+ 与当前图像进行mixup的图像np.ndarray数据、图像相关信息、标注框相关信息；
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+ 注意，当前epoch若无需进行mixup，则无该字段。
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+ label_info (dict): 存储与标注框相关的信息，dict中的字段如下：
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+ - gt_bbox (np.ndarray): 真实标注框坐标[x1, y1, x2, y2]，形状为(n, 4)，
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+ 其中n代表真实标注框的个数。
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+ - gt_class (np.ndarray): 每个真实标注框对应的类别序号，形状为(n, 1)，
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+ 其中n代表真实标注框的个数。
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+ - gt_score (np.ndarray): 每个真实标注框对应的混合得分，形状为(n, 1)，
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+ 其中n代表真实标注框的个数。
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+ - gt_poly (list): 每个真实标注框内的多边形分割区域，每个分割区域由点的x、y坐标组成，
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+ 长度为n，其中n代表真实标注框的个数。
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+ - is_crowd (np.ndarray): 每个真实标注框中是否是一组对象，形状为(n, 1)，
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+ 其中n代表真实标注框的个数。
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+ - difficult (np.ndarray): 每个真实标注框中的对象是否为难识别对象，形状为(n, 1)，
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+ 其中n代表真实标注框的个数。
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+ Returns:
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+ tuple: 根据网络所需字段所组成的tuple；
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+ 字段由transforms中的最后一个数据预处理操作决定。
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+ """
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+
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+ def decode_image(im_file, im_info, label_info):
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+ if im_info is None:
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+ im_info = dict()
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+ if isinstance(im_file, np.ndarray):
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+ if len(im_file.shape) != 3:
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+ raise Exception(
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+ "im should be 3-dimensions, but now is {}-dimensions".
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+ format(len(im_file.shape)))
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+ im = im_file
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+ else:
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+ try:
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+ im = cv2.imread(im_file).astype('float32')
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+ except:
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+ raise TypeError('Can\'t read The image file {}!'.format(
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+ im_file))
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+ im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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+ # make default im_info with [h, w, 1]
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+ im_info['im_resize_info'] = np.array(
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+ [im.shape[0], im.shape[1], 1.], dtype=np.float32)
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+ im_info['image_shape'] = np.array([im.shape[0],
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+ im.shape[1]]).astype('int32')
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+ if not self.use_mixup:
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+ if 'mixup' in im_info:
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+ del im_info['mixup']
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+ # decode mixup image
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+ if 'mixup' in im_info:
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+ im_info['mixup'] = \
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+ decode_image(im_info['mixup'][0],
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+ im_info['mixup'][1],
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+ im_info['mixup'][2])
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+ if label_info is None:
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+ return (im, im_info)
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+ else:
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+ return (im, im_info, label_info)
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+
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+ outputs = decode_image(im, im_info, label_info)
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+ im = outputs[0]
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+ im_info = outputs[1]
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+ if len(outputs) == 3:
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+ label_info = outputs[2]
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+ for op in self.transforms:
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+ if im is None:
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+ return None
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+ if isinstance(op, DetTransform):
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+ outputs = op(im, im_info, label_info)
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+ im = outputs[0]
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+ else:
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+ im = execute_imgaug(op, im)
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+ if label_info is not None:
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+ outputs = (im, im_info, label_info)
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+ else:
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+ outputs = (im, im_info)
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+ return outputs
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+
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+ def add_augmenters(self, augmenters):
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+ if not isinstance(augmenters, list):
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+ raise Exception(
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+ "augmenters should be list type in func add_augmenters()")
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+ transform_names = [type(x).__name__ for x in self.transforms]
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+ for aug in augmenters:
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+ if type(aug).__name__ in transform_names:
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+ logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
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+ self.transforms = augmenters + self.transforms
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+
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+
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+class ResizeByShort(DetTransform):
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+ """根据图像的短边调整图像大小（resize）。
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+
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+ 1. 获取图像的长边和短边长度。
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+ 2. 根据短边与short_size的比例，计算长边的目标长度，
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+ 此时高、宽的resize比例为short_size/原图短边长度。
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+ 3. 如果max_size>0，调整resize比例：
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+ 如果长边的目标长度>max_size，则高、宽的resize比例为max_size/原图长边长度。
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+ 4. 根据调整大小的比例对图像进行resize。
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+
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+ Args:
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+ target_size (int): 短边目标长度。默认为800。
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+ max_size (int): 长边目标长度的最大限制。默认为1333。
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+
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+ Raises:
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+ TypeError: 形参数据类型不满足需求。
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+ """
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+
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+ def __init__(self, short_size=800, max_size=1333):
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+ self.max_size = int(max_size)
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+ if not isinstance(short_size, int):
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+ raise TypeError(
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+ "Type of short_size is invalid. Must be Integer, now is {}".
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+ format(type(short_size)))
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+ self.short_size = short_size
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+ if not (isinstance(self.max_size, int)):
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+ raise TypeError("max_size: input type is invalid.")
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+
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+ def __call__(self, im, im_info=None, label_info=None):
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+ """
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+ Args:
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+ im (numnp.ndarraypy): 图像np.ndarray数据。
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+ im_info (dict, 可选): 存储与图像相关的信息。
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+ label_info (dict, 可选): 存储与标注框相关的信息。
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+
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+ Returns:
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+ tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
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+ 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
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+ 存储与标注框相关信息的字典。
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+ 其中，im_info更新字段为：
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+ - im_resize_info (np.ndarray): resize后的图像高、resize后的图像宽、resize后的图像相对原始图的缩放比例
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+ 三者组成的np.ndarray，形状为(3,)。
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+
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+ Raises:
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+ TypeError: 形参数据类型不满足需求。
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+ ValueError: 数据长度不匹配。
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+ """
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+ if im_info is None:
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+ im_info = dict()
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+ if not isinstance(im, np.ndarray):
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+ raise TypeError("ResizeByShort: image type is not numpy.")
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+ if len(im.shape) != 3:
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+ raise ValueError('ResizeByShort: image is not 3-dimensional.')
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+ im_short_size = min(im.shape[0], im.shape[1])
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+ im_long_size = max(im.shape[0], im.shape[1])
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+ scale = float(self.short_size) / im_short_size
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+ if self.max_size > 0 and np.round(scale *
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+ im_long_size) > self.max_size:
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+ scale = float(self.max_size) / float(im_long_size)
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+ resized_width = int(round(im.shape[1] * scale))
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+ resized_height = int(round(im.shape[0] * scale))
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+ im_resize_info = [resized_height, resized_width, scale]
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+ im = cv2.resize(
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+ im, (resized_width, resized_height),
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+ interpolation=cv2.INTER_LINEAR)
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+ im_info['im_resize_info'] = np.array(im_resize_info).astype(np.float32)
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+ if label_info is None:
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+ return (im, im_info)
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+ else:
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+ return (im, im_info, label_info)
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+
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+
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+class Padding(DetTransform):
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+ """1.将图像的长和宽padding至coarsest_stride的倍数。如输入图像为[300, 640],
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+ `coarest_stride`为32，则由于300不为32的倍数，因此在图像最右和最下使用0值
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+ 进行padding，最终输出图像为[320, 640]。
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+ 2.或者，将图像的长和宽padding到target_size指定的shape，如输入的图像为[300，640]，
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+ a. `target_size` = 960，在图像最右和最下使用0值进行padding，最终输出
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+ 图像为[960, 960]。
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+ b. `target_size` = [640, 960]，在图像最右和最下使用0值进行padding，最终
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+ 输出图像为[640, 960]。
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+
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+ 1. 如果coarsest_stride为1，target_size为None则直接返回。
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+ 2. 获取图像的高H、宽W。
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+ 3. 计算填充后图像的高H_new、宽W_new。
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+ 4. 构建大小为(H_new, W_new, 3)像素值为0的np.ndarray，
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+ 并将原图的np.ndarray粘贴于左上角。
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+
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+ Args:
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+ coarsest_stride (int): 填充后的图像长、宽为该参数的倍数，默认为1。
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+ target_size (int|list|tuple): 填充后的图像长、宽，默认为None，coarset_stride优先级更高。
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+
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+ Raises:
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+ TypeError: 形参`target_size`数据类型不满足需求。
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+ ValueError: 形参`target_size`为(list|tuple)时，长度不满足需求。
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+ """
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+
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+ def __init__(self, coarsest_stride=1, target_size=None):
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+ self.coarsest_stride = coarsest_stride
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+ if target_size is not None:
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+ if not isinstance(target_size, int):
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+ if not isinstance(target_size, tuple) and not isinstance(
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+ target_size, list):
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+ raise TypeError(
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+ "Padding: Type of target_size must in (int|list|tuple)."
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+ )
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+ elif len(target_size) != 2:
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+ raise ValueError(
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+ "Padding: Length of target_size must equal 2.")
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+ self.target_size = target_size
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+
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+ def __call__(self, im, im_info=None, label_info=None):
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+ """
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+ Args:
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+ im (numnp.ndarraypy): 图像np.ndarray数据。
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+ im_info (dict, 可选): 存储与图像相关的信息。
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+ label_info (dict, 可选): 存储与标注框相关的信息。
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+
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+ Returns:
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+ tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
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+ 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
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+ 存储与标注框相关信息的字典。
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+
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+ Raises:
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+ TypeError: 形参数据类型不满足需求。
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+ ValueError: 数据长度不匹配。
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+ ValueError: coarsest_stride，target_size需有且只有一个被指定。
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+ ValueError: target_size小于原图的大小。
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+ """
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+ if im_info is None:
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+ im_info = dict()
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+ if not isinstance(im, np.ndarray):
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+ raise TypeError("Padding: image type is not numpy.")
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+ if len(im.shape) != 3:
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+ raise ValueError('Padding: image is not 3-dimensional.')
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+ im_h, im_w, im_c = im.shape[:]
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+
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+ if isinstance(self.target_size, int):
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+ padding_im_h = self.target_size
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+ padding_im_w = self.target_size
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+ elif isinstance(self.target_size, list) or isinstance(self.target_size,
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+ tuple):
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+ padding_im_w = self.target_size[0]
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+ padding_im_h = self.target_size[1]
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+ elif self.coarsest_stride > 0:
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+ padding_im_h = int(
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+ np.ceil(im_h / self.coarsest_stride) * self.coarsest_stride)
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+ padding_im_w = int(
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+ np.ceil(im_w / self.coarsest_stride) * self.coarsest_stride)
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+ else:
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+ raise ValueError(
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+ "coarsest_stridei(>1) or target_size(list|int) need setting in Padding transform"
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+ )
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+ pad_height = padding_im_h - im_h
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+ pad_width = padding_im_w - im_w
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+ if pad_height < 0 or pad_width < 0:
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+ raise ValueError(
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+ 'the size of image should be less than target_size, but the size of image ({}, {}), is larger than target_size ({}, {})'
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+ .format(im_w, im_h, padding_im_w, padding_im_h))
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+ padding_im = np.zeros(
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+ (padding_im_h, padding_im_w, im_c), dtype=np.float32)
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+ padding_im[:im_h, :im_w, :] = im
|
|
|
+ if label_info is None:
|
|
|
+ return (padding_im, im_info)
|
|
|
+ else:
|
|
|
+ return (padding_im, im_info, label_info)
|
|
|
+
|
|
|
+
|
|
|
+class Resize(DetTransform):
|
|
|
+ """调整图像大小（resize）。
|
|
|
+
|
|
|
+ - 当目标大小（target_size）类型为int时，根据插值方式，
|
|
|
+ 将图像resize为[target_size, target_size]。
|
|
|
+ - 当目标大小（target_size）类型为list或tuple时，根据插值方式，
|
|
|
+ 将图像resize为target_size。
|
|
|
+ 注意：当插值方式为“RANDOM”时，则随机选取一种插值方式进行resize。
|
|
|
+
|
|
|
+ Args:
|
|
|
+ target_size (int/list/tuple): 短边目标长度。默认为608。
|
|
|
+ interp (str): resize的插值方式，与opencv的插值方式对应，取值范围为
|
|
|
+ ['NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM']。默认为"LINEAR"。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ TypeError: 形参数据类型不满足需求。
|
|
|
+ ValueError: 插值方式不在['NEAREST', 'LINEAR', 'CUBIC',
|
|
|
+ 'AREA', 'LANCZOS4', 'RANDOM']中。
|
|
|
+ """
|
|
|
+
|
|
|
+ # The interpolation mode
|
|
|
+ interp_dict = {
|
|
|
+ 'NEAREST': cv2.INTER_NEAREST,
|
|
|
+ 'LINEAR': cv2.INTER_LINEAR,
|
|
|
+ 'CUBIC': cv2.INTER_CUBIC,
|
|
|
+ 'AREA': cv2.INTER_AREA,
|
|
|
+ 'LANCZOS4': cv2.INTER_LANCZOS4
|
|
|
+ }
|
|
|
+
|
|
|
+ def __init__(self, target_size=608, interp='LINEAR'):
|
|
|
+ self.interp = interp
|
|
|
+ if not (interp == "RANDOM" or interp in self.interp_dict):
|
|
|
+ raise ValueError("interp should be one of {}".format(
|
|
|
+ self.interp_dict.keys()))
|
|
|
+ if isinstance(target_size, list) or isinstance(target_size, tuple):
|
|
|
+ if len(target_size) != 2:
|
|
|
+ raise TypeError(
|
|
|
+ 'when target is list or tuple, it should include 2 elements, but it is {}'
|
|
|
+ .format(target_size))
|
|
|
+ elif not isinstance(target_size, int):
|
|
|
+ raise TypeError(
|
|
|
+ "Type of target_size is invalid. Must be Integer or List or tuple, now is {}"
|
|
|
+ .format(type(target_size)))
|
|
|
+
|
|
|
+ self.target_size = target_size
|
|
|
+
|
|
|
+ def __call__(self, im, im_info=None, label_info=None):
|
|
|
+ """
|
|
|
+ Args:
|
|
|
+ im (np.ndarray): 图像np.ndarray数据。
|
|
|
+ im_info (dict, 可选): 存储与图像相关的信息。
|
|
|
+ label_info (dict, 可选): 存储与标注框相关的信息。
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
|
|
|
+ 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
|
|
|
+ 存储与标注框相关信息的字典。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ TypeError: 形参数据类型不满足需求。
|
|
|
+ ValueError: 数据长度不匹配。
|
|
|
+ """
|
|
|
+ if im_info is None:
|
|
|
+ im_info = dict()
|
|
|
+ if not isinstance(im, np.ndarray):
|
|
|
+ raise TypeError("Resize: image type is not numpy.")
|
|
|
+ if len(im.shape) != 3:
|
|
|
+ raise ValueError('Resize: image is not 3-dimensional.')
|
|
|
+ if self.interp == "RANDOM":
|
|
|
+ interp = random.choice(list(self.interp_dict.keys()))
|
|
|
+ else:
|
|
|
+ interp = self.interp
|
|
|
+ im = resize(im, self.target_size, self.interp_dict[interp])
|
|
|
+ if label_info is None:
|
|
|
+ return (im, im_info)
|
|
|
+ else:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+
|
|
|
+
|
|
|
+class RandomHorizontalFlip(DetTransform):
|
|
|
+ """随机翻转图像、标注框、分割信息，模型训练时的数据增强操作。
|
|
|
+
|
|
|
+ 1. 随机采样一个0-1之间的小数，当小数小于水平翻转概率时，
|
|
|
+ 执行2-4步操作，否则直接返回。
|
|
|
+ 2. 水平翻转图像。
|
|
|
+ 3. 计算翻转后的真实标注框的坐标，更新label_info中的gt_bbox信息。
|
|
|
+ 4. 计算翻转后的真实分割区域的坐标，更新label_info中的gt_poly信息。
|
|
|
+
|
|
|
+ Args:
|
|
|
+ prob (float): 随机水平翻转的概率。默认为0.5。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ TypeError: 形参数据类型不满足需求。
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self, prob=0.5):
|
|
|
+ self.prob = prob
|
|
|
+ if not isinstance(self.prob, float):
|
|
|
+ raise TypeError("RandomHorizontalFlip: input type is invalid.")
|
|
|
+
|
|
|
+ def __call__(self, im, im_info=None, label_info=None):
|
|
|
+ """
|
|
|
+ Args:
|
|
|
+ im (np.ndarray): 图像np.ndarray数据。
|
|
|
+ im_info (dict, 可选): 存储与图像相关的信息。
|
|
|
+ label_info (dict, 可选): 存储与标注框相关的信息。
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
|
|
|
+ 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
|
|
|
+ 存储与标注框相关信息的字典。
|
|
|
+ 其中，im_info更新字段为：
|
|
|
+ - gt_bbox (np.ndarray): 水平翻转后的标注框坐标[x1, y1, x2, y2]，形状为(n, 4)，
|
|
|
+ 其中n代表真实标注框的个数。
|
|
|
+ - gt_poly (list): 水平翻转后的多边形分割区域的x、y坐标，长度为n，
|
|
|
+ 其中n代表真实标注框的个数。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ TypeError: 形参数据类型不满足需求。
|
|
|
+ ValueError: 数据长度不匹配。
|
|
|
+ """
|
|
|
+ if not isinstance(im, np.ndarray):
|
|
|
+ raise TypeError(
|
|
|
+ "RandomHorizontalFlip: image is not a numpy array.")
|
|
|
+ if len(im.shape) != 3:
|
|
|
+ raise ValueError(
|
|
|
+ "RandomHorizontalFlip: image is not 3-dimensional.")
|
|
|
+ if im_info is None or label_info is None:
|
|
|
+ raise TypeError(
|
|
|
+ 'Cannot do RandomHorizontalFlip! ' +
|
|
|
+ 'Becasuse the im_info and label_info can not be None!')
|
|
|
+ if 'gt_bbox' not in label_info:
|
|
|
+ raise TypeError('Cannot do RandomHorizontalFlip! ' + \
|
|
|
+ 'Becasuse gt_bbox is not in label_info!')
|
|
|
+ image_shape = im_info['image_shape']
|
|
|
+ gt_bbox = label_info['gt_bbox']
|
|
|
+ height = image_shape[0]
|
|
|
+ width = image_shape[1]
|
|
|
+
|
|
|
+ if np.random.uniform(0, 1) < self.prob:
|
|
|
+ im = horizontal_flip(im)
|
|
|
+ if gt_bbox.shape[0] == 0:
|
|
|
+ if label_info is None:
|
|
|
+ return (im, im_info)
|
|
|
+ else:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+ label_info['gt_bbox'] = box_horizontal_flip(gt_bbox, width)
|
|
|
+ if 'gt_poly' in label_info and \
|
|
|
+ len(label_info['gt_poly']) != 0:
|
|
|
+ label_info['gt_poly'] = segms_horizontal_flip(
|
|
|
+ label_info['gt_poly'], height, width)
|
|
|
+ if label_info is None:
|
|
|
+ return (im, im_info)
|
|
|
+ else:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+
|
|
|
+
|
|
|
+class Normalize(DetTransform):
|
|
|
+ """对图像进行标准化。
|
|
|
+
|
|
|
+ 1. 归一化图像到到区间[0.0, 1.0]。
|
|
|
+ 2. 对图像进行减均值除以标准差操作。
|
|
|
+
|
|
|
+ Args:
|
|
|
+ mean (list): 图像数据集的均值。默认为[0.485, 0.456, 0.406]。
|
|
|
+ std (list): 图像数据集的标准差。默认为[0.229, 0.224, 0.225]。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ TypeError: 形参数据类型不满足需求。
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
|
|
|
+ self.mean = mean
|
|
|
+ self.std = std
|
|
|
+ if not (isinstance(self.mean, list) and isinstance(self.std, list)):
|
|
|
+ raise TypeError("NormalizeImage: input type is invalid.")
|
|
|
+ from functools import reduce
|
|
|
+ if reduce(lambda x, y: x * y, self.std) == 0:
|
|
|
+ raise TypeError('NormalizeImage: std is invalid!')
|
|
|
+
|
|
|
+ def __call__(self, im, im_info=None, label_info=None):
|
|
|
+ """
|
|
|
+ Args:
|
|
|
+ im (numnp.ndarraypy): 图像np.ndarray数据。
|
|
|
+ im_info (dict, 可选): 存储与图像相关的信息。
|
|
|
+ label_info (dict, 可选): 存储与标注框相关的信息。
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
|
|
|
+ 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
|
|
|
+ 存储与标注框相关信息的字典。
|
|
|
+ """
|
|
|
+ mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
|
|
|
+ std = np.array(self.std)[np.newaxis, np.newaxis, :]
|
|
|
+ im = normalize(im, mean, std)
|
|
|
+ if label_info is None:
|
|
|
+ return (im, im_info)
|
|
|
+ else:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+
|
|
|
+
|
|
|
+class RandomDistort(DetTransform):
|
|
|
+ """以一定的概率对图像进行随机像素内容变换，模型训练时的数据增强操作
|
|
|
+
|
|
|
+ 1. 对变换的操作顺序进行随机化操作。
|
|
|
+ 2. 按照1中的顺序以一定的概率在范围[-range, range]对图像进行随机像素内容变换。
|
|
|
+
|
|
|
+ Args:
|
|
|
+ brightness_range (float): 明亮度因子的范围。默认为0.5。
|
|
|
+ brightness_prob (float): 随机调整明亮度的概率。默认为0.5。
|
|
|
+ contrast_range (float): 对比度因子的范围。默认为0.5。
|
|
|
+ contrast_prob (float): 随机调整对比度的概率。默认为0.5。
|
|
|
+ saturation_range (float): 饱和度因子的范围。默认为0.5。
|
|
|
+ saturation_prob (float): 随机调整饱和度的概率。默认为0.5。
|
|
|
+ hue_range (int): 色调因子的范围。默认为18。
|
|
|
+ hue_prob (float): 随机调整色调的概率。默认为0.5。
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self,
|
|
|
+ brightness_range=0.5,
|
|
|
+ brightness_prob=0.5,
|
|
|
+ contrast_range=0.5,
|
|
|
+ contrast_prob=0.5,
|
|
|
+ saturation_range=0.5,
|
|
|
+ saturation_prob=0.5,
|
|
|
+ hue_range=18,
|
|
|
+ hue_prob=0.5):
|
|
|
+ self.brightness_range = brightness_range
|
|
|
+ self.brightness_prob = brightness_prob
|
|
|
+ self.contrast_range = contrast_range
|
|
|
+ self.contrast_prob = contrast_prob
|
|
|
+ self.saturation_range = saturation_range
|
|
|
+ self.saturation_prob = saturation_prob
|
|
|
+ self.hue_range = hue_range
|
|
|
+ self.hue_prob = hue_prob
|
|
|
+
|
|
|
+ def __call__(self, im, im_info=None, label_info=None):
|
|
|
+ """
|
|
|
+ Args:
|
|
|
+ im (np.ndarray): 图像np.ndarray数据。
|
|
|
+ im_info (dict, 可选): 存储与图像相关的信息。
|
|
|
+ label_info (dict, 可选): 存储与标注框相关的信息。
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
|
|
|
+ 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
|
|
|
+ 存储与标注框相关信息的字典。
|
|
|
+ """
|
|
|
+ brightness_lower = 1 - self.brightness_range
|
|
|
+ brightness_upper = 1 + self.brightness_range
|
|
|
+ contrast_lower = 1 - self.contrast_range
|
|
|
+ contrast_upper = 1 + self.contrast_range
|
|
|
+ saturation_lower = 1 - self.saturation_range
|
|
|
+ saturation_upper = 1 + self.saturation_range
|
|
|
+ hue_lower = -self.hue_range
|
|
|
+ hue_upper = self.hue_range
|
|
|
+ ops = [brightness, contrast, saturation, hue]
|
|
|
+ random.shuffle(ops)
|
|
|
+ params_dict = {
|
|
|
+ 'brightness': {
|
|
|
+ 'brightness_lower': brightness_lower,
|
|
|
+ 'brightness_upper': brightness_upper
|
|
|
+ },
|
|
|
+ 'contrast': {
|
|
|
+ 'contrast_lower': contrast_lower,
|
|
|
+ 'contrast_upper': contrast_upper
|
|
|
+ },
|
|
|
+ 'saturation': {
|
|
|
+ 'saturation_lower': saturation_lower,
|
|
|
+ 'saturation_upper': saturation_upper
|
|
|
+ },
|
|
|
+ 'hue': {
|
|
|
+ 'hue_lower': hue_lower,
|
|
|
+ 'hue_upper': hue_upper
|
|
|
+ }
|
|
|
+ }
|
|
|
+ prob_dict = {
|
|
|
+ 'brightness': self.brightness_prob,
|
|
|
+ 'contrast': self.contrast_prob,
|
|
|
+ 'saturation': self.saturation_prob,
|
|
|
+ 'hue': self.hue_prob
|
|
|
+ }
|
|
|
+ for id in range(4):
|
|
|
+ params = params_dict[ops[id].__name__]
|
|
|
+ prob = prob_dict[ops[id].__name__]
|
|
|
+ params['im'] = im
|
|
|
+
|
|
|
+ if np.random.uniform(0, 1) < prob:
|
|
|
+ im = ops[id](**params)
|
|
|
+ if label_info is None:
|
|
|
+ return (im, im_info)
|
|
|
+ else:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+
|
|
|
+
|
|
|
+class MixupImage(DetTransform):
|
|
|
+ """对图像进行mixup操作,模型训练时的数据增强操作，目前仅YOLOv3模型支持该transform。
|
|
|
+
|
|
|
+ 当label_info中不存在mixup字段时，直接返回，否则进行下述操作：
|
|
|
+ 1. 从随机beta分布中抽取出随机因子factor。
|
|
|
+ 2.
|
|
|
+ - 当factor>=1.0时，去除label_info中的mixup字段，直接返回。
|
|
|
+ - 当factor<=0.0时，直接返回label_info中的mixup字段，并在label_info中去除该字段。
|
|
|
+ - 其余情况，执行下述操作：
|
|
|
+ （1）原图像乘以factor，mixup图像乘以(1-factor)，叠加2个结果。
|
|
|
+ （2）拼接原图像标注框和mixup图像标注框。
|
|
|
+ （3）拼接原图像标注框类别和mixup图像标注框类别。
|
|
|
+ （4）原图像标注框混合得分乘以factor，mixup图像标注框混合得分乘以(1-factor)，叠加2个结果。
|
|
|
+ 3. 更新im_info中的image_shape信息。
|
|
|
+
|
|
|
+ Args:
|
|
|
+ alpha (float): 随机beta分布的下限。默认为1.5。
|
|
|
+ beta (float): 随机beta分布的上限。默认为1.5。
|
|
|
+ mixup_epoch (int): 在前mixup_epoch轮使用mixup增强操作；当该参数为-1时，该策略不会生效。
|
|
|
+ 默认为-1。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ ValueError: 数据长度不匹配。
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self, alpha=1.5, beta=1.5, mixup_epoch=-1):
|
|
|
+ self.alpha = alpha
|
|
|
+ self.beta = beta
|
|
|
+ if self.alpha <= 0.0:
|
|
|
+ raise ValueError("alpha shold be positive in MixupImage")
|
|
|
+ if self.beta <= 0.0:
|
|
|
+ raise ValueError("beta shold be positive in MixupImage")
|
|
|
+ self.mixup_epoch = mixup_epoch
|
|
|
+
|
|
|
+ def _mixup_img(self, img1, img2, factor):
|
|
|
+ h = max(img1.shape[0], img2.shape[0])
|
|
|
+ w = max(img1.shape[1], img2.shape[1])
|
|
|
+ img = np.zeros((h, w, img1.shape[2]), 'float32')
|
|
|
+ img[:img1.shape[0], :img1.shape[1], :] = \
|
|
|
+ img1.astype('float32') * factor
|
|
|
+ img[:img2.shape[0], :img2.shape[1], :] += \
|
|
|
+ img2.astype('float32') * (1.0 - factor)
|
|
|
+ return img.astype('float32')
|
|
|
+
|
|
|
+ def __call__(self, im, im_info=None, label_info=None):
|
|
|
+ """
|
|
|
+ Args:
|
|
|
+ im (np.ndarray): 图像np.ndarray数据。
|
|
|
+ im_info (dict, 可选): 存储与图像相关的信息。
|
|
|
+ label_info (dict, 可选): 存储与标注框相关的信息。
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
|
|
|
+ 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
|
|
|
+ 存储与标注框相关信息的字典。
|
|
|
+ 其中，im_info更新字段为：
|
|
|
+ - image_shape (np.ndarray): mixup后的图像高、宽二者组成的np.ndarray，形状为(2,)。
|
|
|
+ im_info删除的字段：
|
|
|
+ - mixup (list): 与当前字段进行mixup的图像相关信息。
|
|
|
+ label_info更新字段为：
|
|
|
+ - gt_bbox (np.ndarray): mixup后真实标注框坐标，形状为(n, 4)，
|
|
|
+ 其中n代表真实标注框的个数。
|
|
|
+ - gt_class (np.ndarray): mixup后每个真实标注框对应的类别序号，形状为(n, 1)，
|
|
|
+ 其中n代表真实标注框的个数。
|
|
|
+ - gt_score (np.ndarray): mixup后每个真实标注框对应的混合得分，形状为(n, 1)，
|
|
|
+ 其中n代表真实标注框的个数。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ TypeError: 形参数据类型不满足需求。
|
|
|
+ """
|
|
|
+ if im_info is None:
|
|
|
+ raise TypeError('Cannot do MixupImage! ' +
|
|
|
+ 'Becasuse the im_info can not be None!')
|
|
|
+ if 'mixup' not in im_info:
|
|
|
+ if label_info is None:
|
|
|
+ return (im, im_info)
|
|
|
+ else:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+ factor = np.random.beta(self.alpha, self.beta)
|
|
|
+ factor = max(0.0, min(1.0, factor))
|
|
|
+ if im_info['epoch'] > self.mixup_epoch \
|
|
|
+ or factor >= 1.0:
|
|
|
+ im_info.pop('mixup')
|
|
|
+ if label_info is None:
|
|
|
+ return (im, im_info)
|
|
|
+ else:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+ if factor <= 0.0:
|
|
|
+ return im_info.pop('mixup')
|
|
|
+ im = self._mixup_img(im, im_info['mixup'][0], factor)
|
|
|
+ if label_info is None:
|
|
|
+ raise TypeError('Cannot do MixupImage! ' +
|
|
|
+ 'Becasuse the label_info can not be None!')
|
|
|
+ if 'gt_bbox' not in label_info or \
|
|
|
+ 'gt_class' not in label_info or \
|
|
|
+ 'gt_score' not in label_info:
|
|
|
+ raise TypeError('Cannot do MixupImage! ' + \
|
|
|
+ 'Becasuse gt_bbox/gt_class/gt_score is not in label_info!')
|
|
|
+ gt_bbox1 = label_info['gt_bbox']
|
|
|
+ gt_bbox2 = im_info['mixup'][2]['gt_bbox']
|
|
|
+ gt_class1 = label_info['gt_class']
|
|
|
+ gt_class2 = im_info['mixup'][2]['gt_class']
|
|
|
+ gt_score1 = label_info['gt_score']
|
|
|
+ gt_score2 = im_info['mixup'][2]['gt_score']
|
|
|
+ if 'gt_poly' in label_info:
|
|
|
+ gt_poly1 = label_info['gt_poly']
|
|
|
+ gt_poly2 = im_info['mixup'][2]['gt_poly']
|
|
|
+ is_crowd1 = label_info['is_crowd']
|
|
|
+ is_crowd2 = im_info['mixup'][2]['is_crowd']
|
|
|
+
|
|
|
+ if 0 not in gt_class1 and 0 not in gt_class2:
|
|
|
+ gt_bbox = np.concatenate((gt_bbox1, gt_bbox2), axis=0)
|
|
|
+ gt_class = np.concatenate((gt_class1, gt_class2), axis=0)
|
|
|
+ gt_score = np.concatenate(
|
|
|
+ (gt_score1 * factor, gt_score2 * (1. - factor)), axis=0)
|
|
|
+ if 'gt_poly' in label_info:
|
|
|
+ label_info['gt_poly'] = gt_poly1 + gt_poly2
|
|
|
+ is_crowd = np.concatenate((is_crowd1, is_crowd2), axis=0)
|
|
|
+ elif 0 in gt_class1:
|
|
|
+ gt_bbox = gt_bbox2
|
|
|
+ gt_class = gt_class2
|
|
|
+ gt_score = gt_score2 * (1. - factor)
|
|
|
+ if 'gt_poly' in label_info:
|
|
|
+ label_info['gt_poly'] = gt_poly2
|
|
|
+ is_crowd = is_crowd2
|
|
|
+ else:
|
|
|
+ gt_bbox = gt_bbox1
|
|
|
+ gt_class = gt_class1
|
|
|
+ gt_score = gt_score1 * factor
|
|
|
+ if 'gt_poly' in label_info:
|
|
|
+ label_info['gt_poly'] = gt_poly1
|
|
|
+ is_crowd = is_crowd1
|
|
|
+ label_info['gt_bbox'] = gt_bbox
|
|
|
+ label_info['gt_score'] = gt_score
|
|
|
+ label_info['gt_class'] = gt_class
|
|
|
+ label_info['is_crowd'] = is_crowd
|
|
|
+ im_info['image_shape'] = np.array([im.shape[0],
|
|
|
+ im.shape[1]]).astype('int32')
|
|
|
+ im_info.pop('mixup')
|
|
|
+ if label_info is None:
|
|
|
+ return (im, im_info)
|
|
|
+ else:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+
|
|
|
+
|
|
|
+class RandomExpand(DetTransform):
|
|
|
+ """随机扩张图像，模型训练时的数据增强操作。
|
|
|
+ 1. 随机选取扩张比例（扩张比例大于1时才进行扩张）。
|
|
|
+ 2. 计算扩张后图像大小。
|
|
|
+ 3. 初始化像素值为输入填充值的图像，并将原图像随机粘贴于该图像上。
|
|
|
+ 4. 根据原图像粘贴位置换算出扩张后真实标注框的位置坐标。
|
|
|
+ 5. 根据原图像粘贴位置换算出扩张后真实分割区域的位置坐标。
|
|
|
+ Args:
|
|
|
+ ratio (float): 图像扩张的最大比例。默认为4.0。
|
|
|
+ prob (float): 随机扩张的概率。默认为0.5。
|
|
|
+ fill_value (list): 扩张图像的初始填充值（0-255）。默认为[123.675, 116.28, 103.53]。
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self,
|
|
|
+ ratio=4.,
|
|
|
+ prob=0.5,
|
|
|
+ fill_value=[123.675, 116.28, 103.53]):
|
|
|
+ super(RandomExpand, self).__init__()
|
|
|
+ assert ratio > 1.01, "expand ratio must be larger than 1.01"
|
|
|
+ self.ratio = ratio
|
|
|
+ self.prob = prob
|
|
|
+ assert isinstance(fill_value, Sequence), \
|
|
|
+ "fill value must be sequence"
|
|
|
+ if not isinstance(fill_value, tuple):
|
|
|
+ fill_value = tuple(fill_value)
|
|
|
+ self.fill_value = fill_value
|
|
|
+
|
|
|
+ def __call__(self, im, im_info=None, label_info=None):
|
|
|
+ """
|
|
|
+ Args:
|
|
|
+ im (np.ndarray): 图像np.ndarray数据。
|
|
|
+ im_info (dict, 可选): 存储与图像相关的信息。
|
|
|
+ label_info (dict, 可选): 存储与标注框相关的信息。
|
|
|
+ Returns:
|
|
|
+ tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
|
|
|
+ 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
|
|
|
+ 存储与标注框相关信息的字典。
|
|
|
+ 其中，im_info更新字段为：
|
|
|
+ - image_shape (np.ndarray): 扩张后的图像高、宽二者组成的np.ndarray，形状为(2,)。
|
|
|
+ label_info更新字段为：
|
|
|
+ - gt_bbox (np.ndarray): 随机扩张后真实标注框坐标，形状为(n, 4)，
|
|
|
+ 其中n代表真实标注框的个数。
|
|
|
+ - gt_class (np.ndarray): 随机扩张后每个真实标注框对应的类别序号，形状为(n, 1)，
|
|
|
+ 其中n代表真实标注框的个数。
|
|
|
+ Raises:
|
|
|
+ TypeError: 形参数据类型不满足需求。
|
|
|
+ """
|
|
|
+ if im_info is None or label_info is None:
|
|
|
+ raise TypeError(
|
|
|
+ 'Cannot do RandomExpand! ' +
|
|
|
+ 'Becasuse the im_info and label_info can not be None!')
|
|
|
+ if 'gt_bbox' not in label_info or \
|
|
|
+ 'gt_class' not in label_info:
|
|
|
+ raise TypeError('Cannot do RandomExpand! ' + \
|
|
|
+ 'Becasuse gt_bbox/gt_class is not in label_info!')
|
|
|
+ if np.random.uniform(0., 1.) < self.prob:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+
|
|
|
+ if 'gt_class' in label_info and 0 in label_info['gt_class']:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+ image_shape = im_info['image_shape']
|
|
|
+ height = int(image_shape[0])
|
|
|
+ width = int(image_shape[1])
|
|
|
+
|
|
|
+ expand_ratio = np.random.uniform(1., self.ratio)
|
|
|
+ h = int(height * expand_ratio)
|
|
|
+ w = int(width * expand_ratio)
|
|
|
+ if not h > height or not w > width:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+ y = np.random.randint(0, h - height)
|
|
|
+ x = np.random.randint(0, w - width)
|
|
|
+ canvas = np.ones((h, w, 3), dtype=np.float32)
|
|
|
+ canvas *= np.array(self.fill_value, dtype=np.float32)
|
|
|
+ canvas[y:y + height, x:x + width, :] = im
|
|
|
+
|
|
|
+ im_info['image_shape'] = np.array([h, w]).astype('int32')
|
|
|
+ if 'gt_bbox' in label_info and len(label_info['gt_bbox']) > 0:
|
|
|
+ label_info['gt_bbox'] += np.array([x, y] * 2, dtype=np.float32)
|
|
|
+ if 'gt_poly' in label_info and len(label_info['gt_poly']) > 0:
|
|
|
+ label_info['gt_poly'] = expand_segms(label_info['gt_poly'], x, y,
|
|
|
+ height, width, expand_ratio)
|
|
|
+ return (canvas, im_info, label_info)
|
|
|
+
|
|
|
+
|
|
|
+class RandomCrop(DetTransform):
|
|
|
+ """随机裁剪图像。
|
|
|
+ 1. 若allow_no_crop为True，则在thresholds加入’no_crop’。
|
|
|
+ 2. 随机打乱thresholds。
|
|
|
+ 3. 遍历thresholds中各元素：
|
|
|
+ (1) 如果当前thresh为’no_crop’，则返回原始图像和标注信息。
|
|
|
+ (2) 随机取出aspect_ratio和scaling中的值并由此计算出候选裁剪区域的高、宽、起始点。
|
|
|
+ (3) 计算真实标注框与候选裁剪区域IoU，若全部真实标注框的IoU都小于thresh，则继续第3步。
|
|
|
+ (4) 如果cover_all_box为True且存在真实标注框的IoU小于thresh，则继续第3步。
|
|
|
+ (5) 筛选出位于候选裁剪区域内的真实标注框，若有效框的个数为0，则继续第3步，否则进行第4步。
|
|
|
+ 4. 换算有效真值标注框相对候选裁剪区域的位置坐标。
|
|
|
+ 5. 换算有效分割区域相对候选裁剪区域的位置坐标。
|
|
|
+
|
|
|
+ Args:
|
|
|
+ aspect_ratio (list): 裁剪后短边缩放比例的取值范围，以[min, max]形式表示。默认值为[.5, 2.]。
|
|
|
+ thresholds (list): 判断裁剪候选区域是否有效所需的IoU阈值取值列表。默认值为[.0, .1, .3, .5, .7, .9]。
|
|
|
+ scaling (list): 裁剪面积相对原面积的取值范围，以[min, max]形式表示。默认值为[.3, 1.]。
|
|
|
+ num_attempts (int): 在放弃寻找有效裁剪区域前尝试的次数。默认值为50。
|
|
|
+ allow_no_crop (bool): 是否允许未进行裁剪。默认值为True。
|
|
|
+ cover_all_box (bool): 是否要求所有的真实标注框都必须在裁剪区域内。默认值为False。
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self,
|
|
|
+ aspect_ratio=[.5, 2.],
|
|
|
+ thresholds=[.0, .1, .3, .5, .7, .9],
|
|
|
+ scaling=[.3, 1.],
|
|
|
+ num_attempts=50,
|
|
|
+ allow_no_crop=True,
|
|
|
+ cover_all_box=False):
|
|
|
+ self.aspect_ratio = aspect_ratio
|
|
|
+ self.thresholds = thresholds
|
|
|
+ self.scaling = scaling
|
|
|
+ self.num_attempts = num_attempts
|
|
|
+ self.allow_no_crop = allow_no_crop
|
|
|
+ self.cover_all_box = cover_all_box
|
|
|
+
|
|
|
+ def __call__(self, im, im_info=None, label_info=None):
|
|
|
+ """
|
|
|
+ Args:
|
|
|
+ im (np.ndarray): 图像np.ndarray数据。
|
|
|
+ im_info (dict, 可选): 存储与图像相关的信息。
|
|
|
+ label_info (dict, 可选): 存储与标注框相关的信息。
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
|
|
|
+ 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
|
|
|
+ 存储与标注框相关信息的字典。
|
|
|
+ 其中，im_info更新字段为：
|
|
|
+ - image_shape (np.ndarray): 扩裁剪的图像高、宽二者组成的np.ndarray，形状为(2,)。
|
|
|
+ label_info更新字段为：
|
|
|
+ - gt_bbox (np.ndarray): 随机裁剪后真实标注框坐标，形状为(n, 4)，
|
|
|
+ 其中n代表真实标注框的个数。
|
|
|
+ - gt_class (np.ndarray): 随机裁剪后每个真实标注框对应的类别序号，形状为(n, 1)，
|
|
|
+ 其中n代表真实标注框的个数。
|
|
|
+ - gt_score (np.ndarray): 随机裁剪后每个真实标注框对应的混合得分，形状为(n, 1)，
|
|
|
+ 其中n代表真实标注框的个数。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ TypeError: 形参数据类型不满足需求。
|
|
|
+ """
|
|
|
+ if im_info is None or label_info is None:
|
|
|
+ raise TypeError(
|
|
|
+ 'Cannot do RandomCrop! ' +
|
|
|
+ 'Becasuse the im_info and label_info can not be None!')
|
|
|
+ if 'gt_bbox' not in label_info or \
|
|
|
+ 'gt_class' not in label_info:
|
|
|
+ raise TypeError('Cannot do RandomCrop! ' + \
|
|
|
+ 'Becasuse gt_bbox/gt_class is not in label_info!')
|
|
|
+
|
|
|
+ if len(label_info['gt_bbox']) == 0:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+ if 'gt_class' in label_info and 0 in label_info['gt_class']:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+
|
|
|
+ image_shape = im_info['image_shape']
|
|
|
+ w = image_shape[1]
|
|
|
+ h = image_shape[0]
|
|
|
+ gt_bbox = label_info['gt_bbox']
|
|
|
+ thresholds = list(self.thresholds)
|
|
|
+ if self.allow_no_crop:
|
|
|
+ thresholds.append('no_crop')
|
|
|
+ np.random.shuffle(thresholds)
|
|
|
+
|
|
|
+ for thresh in thresholds:
|
|
|
+ if thresh == 'no_crop':
|
|
|
+ return (im, im_info, label_info)
|
|
|
+
|
|
|
+ found = False
|
|
|
+ for i in range(self.num_attempts):
|
|
|
+ scale = np.random.uniform(*self.scaling)
|
|
|
+ min_ar, max_ar = self.aspect_ratio
|
|
|
+ aspect_ratio = np.random.uniform(
|
|
|
+ max(min_ar, scale**2), min(max_ar, scale**-2))
|
|
|
+ crop_h = int(h * scale / np.sqrt(aspect_ratio))
|
|
|
+ crop_w = int(w * scale * np.sqrt(aspect_ratio))
|
|
|
+ crop_y = np.random.randint(0, h - crop_h)
|
|
|
+ crop_x = np.random.randint(0, w - crop_w)
|
|
|
+ crop_box = [crop_x, crop_y, crop_x + crop_w, crop_y + crop_h]
|
|
|
+ iou = iou_matrix(
|
|
|
+ gt_bbox, np.array(
|
|
|
+ [crop_box], dtype=np.float32))
|
|
|
+ if iou.max() < thresh:
|
|
|
+ continue
|
|
|
+
|
|
|
+ if self.cover_all_box and iou.min() < thresh:
|
|
|
+ continue
|
|
|
+
|
|
|
+ cropped_box, valid_ids = crop_box_with_center_constraint(
|
|
|
+ gt_bbox, np.array(
|
|
|
+ crop_box, dtype=np.float32))
|
|
|
+ if valid_ids.size > 0:
|
|
|
+ found = True
|
|
|
+ break
|
|
|
+
|
|
|
+ if found:
|
|
|
+ if 'gt_poly' in label_info and len(label_info['gt_poly']) > 0:
|
|
|
+ crop_polys = crop_segms(
|
|
|
+ label_info['gt_poly'],
|
|
|
+ valid_ids,
|
|
|
+ np.array(
|
|
|
+ crop_box, dtype=np.int64),
|
|
|
+ h,
|
|
|
+ w)
|
|
|
+ if [] in crop_polys:
|
|
|
+ delete_id = list()
|
|
|
+ valid_polys = list()
|
|
|
+ for id, crop_poly in enumerate(crop_polys):
|
|
|
+ if crop_poly == []:
|
|
|
+ delete_id.append(id)
|
|
|
+ else:
|
|
|
+ valid_polys.append(crop_poly)
|
|
|
+ valid_ids = np.delete(valid_ids, delete_id)
|
|
|
+ if len(valid_polys) == 0:
|
|
|
+ return (im, im_info, label_info)
|
|
|
+ label_info['gt_poly'] = valid_polys
|
|
|
+ else:
|
|
|
+ label_info['gt_poly'] = crop_polys
|
|
|
+ im = crop_image(im, crop_box)
|
|
|
+ label_info['gt_bbox'] = np.take(cropped_box, valid_ids, axis=0)
|
|
|
+ label_info['gt_class'] = np.take(
|
|
|
+ label_info['gt_class'], valid_ids, axis=0)
|
|
|
+ im_info['image_shape'] = np.array(
|
|
|
+ [crop_box[3] - crop_box[1],
|
|
|
+ crop_box[2] - crop_box[0]]).astype('int32')
|
|
|
+ if 'gt_score' in label_info:
|
|
|
+ label_info['gt_score'] = np.take(
|
|
|
+ label_info['gt_score'], valid_ids, axis=0)
|
|
|
+
|
|
|
+ if 'is_crowd' in label_info:
|
|
|
+ label_info['is_crowd'] = np.take(
|
|
|
+ label_info['is_crowd'], valid_ids, axis=0)
|
|
|
+ return (im, im_info, label_info)
|
|
|
+
|
|
|
+ return (im, im_info, label_info)
|
|
|
+
|
|
|
+
|
|
|
+class ArrangeFasterRCNN(DetTransform):
|
|
|
+ """获取FasterRCNN模型训练/验证/预测所需信息。
|
|
|
+
|
|
|
+ Args:
|
|
|
+ mode (str): 指定数据用于何种用途，取值范围为['train', 'eval', 'test', 'quant']。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ ValueError: mode的取值不在['train', 'eval', 'test', 'quant']之内。
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self, mode=None):
|
|
|
+ if mode not in ['train', 'eval', 'test', 'quant']:
|
|
|
+ raise ValueError(
|
|
|
+ "mode must be in ['train', 'eval', 'test', 'quant']!")
|
|
|
+ self.mode = mode
|
|
|
+
|
|
|
+ def __call__(self, im, im_info=None, label_info=None):
|
|
|
+ """
|
|
|
+ Args:
|
|
|
+ im (np.ndarray): 图像np.ndarray数据。
|
|
|
+ im_info (dict, 可选): 存储与图像相关的信息。
|
|
|
+ label_info (dict, 可选): 存储与标注框相关的信息。
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ tuple: 当mode为'train'时，返回(im, im_resize_info, gt_bbox, gt_class, is_crowd)，分别对应
|
|
|
+ 图像np.ndarray数据、图像相当对于原图的resize信息、真实标注框、真实标注框对应的类别、真实标注框内是否是一组对象；
|
|
|
+ 当mode为'eval'时，返回(im, im_resize_info, im_id, im_shape, gt_bbox, gt_class, is_difficult)，
|
|
|
+ 分别对应图像np.ndarray数据、图像相当对于原图的resize信息、图像id、图像大小信息、真实标注框、真实标注框对应的类别、
|
|
|
+ 真实标注框是否为难识别对象；当mode为'test'或'quant'时，返回(im, im_resize_info, im_shape)，分别对应图像np.ndarray数据、
|
|
|
+ 图像相当对于原图的resize信息、图像大小信息。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ TypeError: 形参数据类型不满足需求。
|
|
|
+ ValueError: 数据长度不匹配。
|
|
|
+ """
|
|
|
+ im = permute(im, False)
|
|
|
+ if self.mode == 'train':
|
|
|
+ if im_info is None or label_info is None:
|
|
|
+ raise TypeError(
|
|
|
+ 'Cannot do ArrangeFasterRCNN! ' +
|
|
|
+ 'Becasuse the im_info and label_info can not be None!')
|
|
|
+ if len(label_info['gt_bbox']) != len(label_info['gt_class']):
|
|
|
+ raise ValueError("gt num mismatch: bbox and class.")
|
|
|
+ im_resize_info = im_info['im_resize_info']
|
|
|
+ gt_bbox = label_info['gt_bbox']
|
|
|
+ gt_class = label_info['gt_class']
|
|
|
+ is_crowd = label_info['is_crowd']
|
|
|
+ outputs = (im, im_resize_info, gt_bbox, gt_class, is_crowd)
|
|
|
+ elif self.mode == 'eval':
|
|
|
+ if im_info is None or label_info is None:
|
|
|
+ raise TypeError(
|
|
|
+ 'Cannot do ArrangeFasterRCNN! ' +
|
|
|
+ 'Becasuse the im_info and label_info can not be None!')
|
|
|
+ im_resize_info = im_info['im_resize_info']
|
|
|
+ im_id = im_info['im_id']
|
|
|
+ im_shape = np.array(
|
|
|
+ (im_info['image_shape'][0], im_info['image_shape'][1], 1),
|
|
|
+ dtype=np.float32)
|
|
|
+ gt_bbox = label_info['gt_bbox']
|
|
|
+ gt_class = label_info['gt_class']
|
|
|
+ is_difficult = label_info['difficult']
|
|
|
+ outputs = (im, im_resize_info, im_id, im_shape, gt_bbox, gt_class,
|
|
|
+ is_difficult)
|
|
|
+ else:
|
|
|
+ if im_info is None:
|
|
|
+ raise TypeError('Cannot do ArrangeFasterRCNN! ' +
|
|
|
+ 'Becasuse the im_info can not be None!')
|
|
|
+ im_resize_info = im_info['im_resize_info']
|
|
|
+ im_shape = np.array(
|
|
|
+ (im_info['image_shape'][0], im_info['image_shape'][1], 1),
|
|
|
+ dtype=np.float32)
|
|
|
+ outputs = (im, im_resize_info, im_shape)
|
|
|
+ return outputs
|
|
|
+
|
|
|
+
|
|
|
+class ArrangeMaskRCNN(DetTransform):
|
|
|
+ """获取MaskRCNN模型训练/验证/预测所需信息。
|
|
|
+
|
|
|
+ Args:
|
|
|
+ mode (str): 指定数据用于何种用途，取值范围为['train', 'eval', 'test', 'quant']。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ ValueError: mode的取值不在['train', 'eval', 'test', 'quant']之内。
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self, mode=None):
|
|
|
+ if mode not in ['train', 'eval', 'test', 'quant']:
|
|
|
+ raise ValueError(
|
|
|
+ "mode must be in ['train', 'eval', 'test', 'quant']!")
|
|
|
+ self.mode = mode
|
|
|
+
|
|
|
+ def __call__(self, im, im_info=None, label_info=None):
|
|
|
+ """
|
|
|
+ Args:
|
|
|
+ im (np.ndarray): 图像np.ndarray数据。
|
|
|
+ im_info (dict, 可选): 存储与图像相关的信息。
|
|
|
+ label_info (dict, 可选): 存储与标注框相关的信息。
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ tuple: 当mode为'train'时，返回(im, im_resize_info, gt_bbox, gt_class, is_crowd, gt_masks)，分别对应
|
|
|
+ 图像np.ndarray数据、图像相当对于原图的resize信息、真实标注框、真实标注框对应的类别、真实标注框内是否是一组对象、
|
|
|
+ 真实分割区域；当mode为'eval'时，返回(im, im_resize_info, im_id, im_shape)，分别对应图像np.ndarray数据、
|
|
|
+ 图像相当对于原图的resize信息、图像id、图像大小信息；当mode为'test'或'quant'时，返回(im, im_resize_info, im_shape)，
|
|
|
+ 分别对应图像np.ndarray数据、图像相当对于原图的resize信息、图像大小信息。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ TypeError: 形参数据类型不满足需求。
|
|
|
+ ValueError: 数据长度不匹配。
|
|
|
+ """
|
|
|
+ im = permute(im, False)
|
|
|
+ if self.mode == 'train':
|
|
|
+ if im_info is None or label_info is None:
|
|
|
+ raise TypeError(
|
|
|
+ 'Cannot do ArrangeTrainMaskRCNN! ' +
|
|
|
+ 'Becasuse the im_info and label_info can not be None!')
|
|
|
+ if len(label_info['gt_bbox']) != len(label_info['gt_class']):
|
|
|
+ raise ValueError("gt num mismatch: bbox and class.")
|
|
|
+ im_resize_info = im_info['im_resize_info']
|
|
|
+ gt_bbox = label_info['gt_bbox']
|
|
|
+ gt_class = label_info['gt_class']
|
|
|
+ is_crowd = label_info['is_crowd']
|
|
|
+ assert 'gt_poly' in label_info
|
|
|
+ segms = label_info['gt_poly']
|
|
|
+ if len(segms) != 0:
|
|
|
+ assert len(segms) == is_crowd.shape[0]
|
|
|
+ gt_masks = []
|
|
|
+ valid = True
|
|
|
+ for i in range(len(segms)):
|
|
|
+ segm = segms[i]
|
|
|
+ gt_segm = []
|
|
|
+ if is_crowd[i]:
|
|
|
+ gt_segm.append([[0, 0]])
|
|
|
+ else:
|
|
|
+ for poly in segm:
|
|
|
+ if len(poly) == 0:
|
|
|
+ valid = False
|
|
|
+ break
|
|
|
+ gt_segm.append(np.array(poly).reshape(-1, 2))
|
|
|
+ if (not valid) or len(gt_segm) == 0:
|
|
|
+ break
|
|
|
+ gt_masks.append(gt_segm)
|
|
|
+ outputs = (im, im_resize_info, gt_bbox, gt_class, is_crowd,
|
|
|
+ gt_masks)
|
|
|
+ else:
|
|
|
+ if im_info is None:
|
|
|
+ raise TypeError('Cannot do ArrangeMaskRCNN! ' +
|
|
|
+ 'Becasuse the im_info can not be None!')
|
|
|
+ im_resize_info = im_info['im_resize_info']
|
|
|
+ im_shape = np.array(
|
|
|
+ (im_info['image_shape'][0], im_info['image_shape'][1], 1),
|
|
|
+ dtype=np.float32)
|
|
|
+ if self.mode == 'eval':
|
|
|
+ im_id = im_info['im_id']
|
|
|
+ outputs = (im, im_resize_info, im_id, im_shape)
|
|
|
+ else:
|
|
|
+ outputs = (im, im_resize_info, im_shape)
|
|
|
+ return outputs
|
|
|
+
|
|
|
+
|
|
|
+class ArrangeYOLOv3(DetTransform):
|
|
|
+ """获取YOLOv3模型训练/验证/预测所需信息。
|
|
|
+
|
|
|
+ Args:
|
|
|
+ mode (str): 指定数据用于何种用途，取值范围为['train', 'eval', 'test', 'quant']。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ ValueError: mode的取值不在['train', 'eval', 'test', 'quant']之内。
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self, mode=None):
|
|
|
+ if mode not in ['train', 'eval', 'test', 'quant']:
|
|
|
+ raise ValueError(
|
|
|
+ "mode must be in ['train', 'eval', 'test', 'quant']!")
|
|
|
+ self.mode = mode
|
|
|
+
|
|
|
+ def __call__(self, im, im_info=None, label_info=None):
|
|
|
+ """
|
|
|
+ Args:
|
|
|
+ im (np.ndarray): 图像np.ndarray数据。
|
|
|
+ im_info (dict, 可选): 存储与图像相关的信息。
|
|
|
+ label_info (dict, 可选): 存储与标注框相关的信息。
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ tuple: 当mode为'train'时，返回(im, gt_bbox, gt_class, gt_score, im_shape)，分别对应
|
|
|
+ 图像np.ndarray数据、真实标注框、真实标注框对应的类别、真实标注框混合得分、图像大小信息；
|
|
|
+ 当mode为'eval'时，返回(im, im_shape, im_id, gt_bbox, gt_class, difficult)，
|
|
|
+ 分别对应图像np.ndarray数据、图像大小信息、图像id、真实标注框、真实标注框对应的类别、
|
|
|
+ 真实标注框是否为难识别对象；当mode为'test'或'quant'时，返回(im, im_shape)，
|
|
|
+ 分别对应图像np.ndarray数据、图像大小信息。
|
|
|
+
|
|
|
+ Raises:
|
|
|
+ TypeError: 形参数据类型不满足需求。
|
|
|
+ ValueError: 数据长度不匹配。
|
|
|
+ """
|
|
|
+ im = permute(im, False)
|
|
|
+ if self.mode == 'train':
|
|
|
+ if im_info is None or label_info is None:
|
|
|
+ raise TypeError(
|
|
|
+ 'Cannot do ArrangeYolov3! ' +
|
|
|
+ 'Becasuse the im_info and label_info can not be None!')
|
|
|
+ im_shape = im_info['image_shape']
|
|
|
+ if len(label_info['gt_bbox']) != len(label_info['gt_class']):
|
|
|
+ raise ValueError("gt num mismatch: bbox and class.")
|
|
|
+ if len(label_info['gt_bbox']) != len(label_info['gt_score']):
|
|
|
+ raise ValueError("gt num mismatch: bbox and score.")
|
|
|
+ gt_bbox = np.zeros((50, 4), dtype=im.dtype)
|
|
|
+ gt_class = np.zeros((50, ), dtype=np.int32)
|
|
|
+ gt_score = np.zeros((50, ), dtype=im.dtype)
|
|
|
+ gt_num = min(50, len(label_info['gt_bbox']))
|
|
|
+ if gt_num > 0:
|
|
|
+ label_info['gt_class'][:gt_num, 0] = label_info[
|
|
|
+ 'gt_class'][:gt_num, 0] - 1
|
|
|
+ if -1 not in label_info['gt_class']:
|
|
|
+ gt_bbox[:gt_num, :] = label_info['gt_bbox'][:gt_num, :]
|
|
|
+ gt_class[:gt_num] = label_info['gt_class'][:gt_num, 0]
|
|
|
+ gt_score[:gt_num] = label_info['gt_score'][:gt_num, 0]
|
|
|
+ # parse [x1, y1, x2, y2] to [x, y, w, h]
|
|
|
+ gt_bbox[:, 2:4] = gt_bbox[:, 2:4] - gt_bbox[:, :2]
|
|
|
+ gt_bbox[:, :2] = gt_bbox[:, :2] + gt_bbox[:, 2:4] / 2.
|
|
|
+ outputs = (im, gt_bbox, gt_class, gt_score, im_shape)
|
|
|
+ elif self.mode == 'eval':
|
|
|
+ if im_info is None or label_info is None:
|
|
|
+ raise TypeError(
|
|
|
+ 'Cannot do ArrangeYolov3! ' +
|
|
|
+ 'Becasuse the im_info and label_info can not be None!')
|
|
|
+ im_shape = im_info['image_shape']
|
|
|
+ if len(label_info['gt_bbox']) != len(label_info['gt_class']):
|
|
|
+ raise ValueError("gt num mismatch: bbox and class.")
|
|
|
+ im_id = im_info['im_id']
|
|
|
+ gt_bbox = np.zeros((50, 4), dtype=im.dtype)
|
|
|
+ gt_class = np.zeros((50, ), dtype=np.int32)
|
|
|
+ difficult = np.zeros((50, ), dtype=np.int32)
|
|
|
+ gt_num = min(50, len(label_info['gt_bbox']))
|
|
|
+ if gt_num > 0:
|
|
|
+ label_info['gt_class'][:gt_num, 0] = label_info[
|
|
|
+ 'gt_class'][:gt_num, 0] - 1
|
|
|
+ gt_bbox[:gt_num, :] = label_info['gt_bbox'][:gt_num, :]
|
|
|
+ gt_class[:gt_num] = label_info['gt_class'][:gt_num, 0]
|
|
|
+ difficult[:gt_num] = label_info['difficult'][:gt_num, 0]
|
|
|
+ outputs = (im, im_shape, im_id, gt_bbox, gt_class, difficult)
|
|
|
+ else:
|
|
|
+ if im_info is None:
|
|
|
+ raise TypeError('Cannot do ArrangeYolov3! ' +
|
|
|
+ 'Becasuse the im_info can not be None!')
|
|
|
+ im_shape = im_info['image_shape']
|
|
|
+ outputs = (im, im_shape)
|
|
|
+ return outputs
|
|
|
+
|
|
|
+
|
|
|
+class ComposedRCNNTransforms(Compose):
|
|
|
+ """ RCNN模型(faster-rcnn/mask-rcnn)图像处理流程，具体如下，
|
|
|
+ 训练阶段：
|
|
|
+ 1. 随机以0.5的概率将图像水平翻转
|
|
|
+ 2. 图像归一化
|
|
|
+ 3. 图像按比例Resize，scale计算方式如下
|
|
|
+ scale = min_max_size[0] / short_size_of_image
|
|
|
+ if max_size_of_image * scale > min_max_size[1]:
|
|
|
+ scale = min_max_size[1] / max_size_of_image
|
|
|
+ 4. 将3步骤的长宽进行padding，使得长宽为32的倍数
|
|
|
+ 验证阶段：
|
|
|
+ 1. 图像归一化
|
|
|
+ 2. 图像按比例Resize，scale计算方式同上训练阶段
|
|
|
+ 3. 将2步骤的长宽进行padding，使得长宽为32的倍数
|
|
|
+
|
|
|
+ Args:
|
|
|
+ mode(str): 图像处理流程所处阶段，训练/验证/预测，分别对应'train', 'eval', 'test'
|
|
|
+ min_max_size(list): 图像在缩放时，最小边和最大边的约束条件
|
|
|
+ mean(list): 图像均值
|
|
|
+ std(list): 图像方差
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self,
|
|
|
+ mode,
|
|
|
+ min_max_size=[800, 1333],
|
|
|
+ mean=[0.485, 0.456, 0.406],
|
|
|
+ std=[0.229, 0.224, 0.225]):
|
|
|
+ if mode == 'train':
|
|
|
+ # 训练时的transforms，包含数据增强
|
|
|
+ transforms = [
|
|
|
+ RandomHorizontalFlip(prob=0.5), Normalize(
|
|
|
+ mean=mean, std=std), ResizeByShort(
|
|
|
+ short_size=min_max_size[0], max_size=min_max_size[1]),
|
|
|
+ Padding(coarsest_stride=32)
|
|
|
+ ]
|
|
|
+ else:
|
|
|
+ # 验证/预测时的transforms
|
|
|
+ transforms = [
|
|
|
+ Normalize(
|
|
|
+ mean=mean, std=std), ResizeByShort(
|
|
|
+ short_size=min_max_size[0], max_size=min_max_size[1]),
|
|
|
+ Padding(coarsest_stride=32)
|
|
|
+ ]
|
|
|
+
|
|
|
+ super(ComposedRCNNTransforms, self).__init__(transforms)
|
|
|
+
|
|
|
+
|
|
|
+class ComposedYOLOv3Transforms(Compose):
|
|
|
+ """YOLOv3模型的图像预处理流程，具体如下，
|
|
|
+ 训练阶段：
|
|
|
+ 1. 在前mixup_epoch轮迭代中，使用MixupImage策略，见https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#mixupimage
|
|
|
+ 2. 对图像进行随机扰动，包括亮度，对比度，饱和度和色调
|
|
|
+ 3. 随机扩充图像，见https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#randomexpand
|
|
|
+ 4. 随机裁剪图像
|
|
|
+ 5. 将4步骤的输出图像Resize成shape参数的大小
|
|
|
+ 6. 随机0.5的概率水平翻转图像
|
|
|
+ 7. 图像归一化
|
|
|
+ 验证/预测阶段：
|
|
|
+ 1. 将图像Resize成shape参数大小
|
|
|
+ 2. 图像归一化
|
|
|
+
|
|
|
+ Args:
|
|
|
+ mode(str): 图像处理流程所处阶段，训练/验证/预测，分别对应'train', 'eval', 'test'
|
|
|
+ shape(list): 输入模型中图像的大小，输入模型的图像会被Resize成此大小
|
|
|
+ mixup_epoch(int): 模型训练过程中，前mixup_epoch会使用mixup策略
|
|
|
+ mean(list): 图像均值
|
|
|
+ std(list): 图像方差
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self,
|
|
|
+ mode,
|
|
|
+ shape=[608, 608],
|
|
|
+ mixup_epoch=250,
|
|
|
+ mean=[0.485, 0.456, 0.406],
|
|
|
+ std=[0.229, 0.224, 0.225]):
|
|
|
+ width = shape
|
|
|
+ if isinstance(shape, list):
|
|
|
+ if shape[0] != shape[1]:
|
|
|
+ raise Exception(
|
|
|
+ "In YOLOv3 model, width and height should be equal")
|
|
|
+ width = shape[0]
|
|
|
+ if width % 32 != 0:
|
|
|
+ raise Exception(
|
|
|
+ "In YOLOv3 model, width and height should be multiple of 32, e.g 224、256、320...."
|
|
|
+ )
|
|
|
+
|
|
|
+ if mode == 'train':
|
|
|
+ # 训练时的transforms，包含数据增强
|
|
|
+ transforms = [
|
|
|
+ MixupImage(mixup_epoch=mixup_epoch), RandomDistort(),
|
|
|
+ RandomExpand(), RandomCrop(), Resize(
|
|
|
+ target_size=width,
|
|
|
+ interp='RANDOM'), RandomHorizontalFlip(), Normalize(
|
|
|
+ mean=mean, std=std)
|
|
|
+ ]
|
|
|
+ else:
|
|
|
+ # 验证/预测时的transforms
|
|
|
+ transforms = [
|
|
|
+ Resize(
|
|
|
+ target_size=width, interp='CUBIC'), Normalize(
|
|
|
+ mean=mean, std=std)
|
|
|
+ ]
|
|
|
+ super(ComposedYOLOv3Transforms, self).__init__(transforms)
|
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