Skip to content

P
PaddleX

PaddlePaddle / PaddleX

通知 136
Star 4
Fork 0
P
PaddleX

前往新版Gitcode，体验更适合开发者的 AI 搜索 >>

未验证 提交 9c2f0fe4 编写于 作者: J Jason 提交者： GitHub
浏览文件
操作

Merge pull request #44 from PaddlePaddle/develop_imgaug 

add imgaug support
上级 c7d46407 038e163b
隐藏空白更改
内联 并排
Showing with 319 addition and 72 deletion
paddlex/cv/datasets/coco.py
浏览文件 @ 9c2f0fe4
......@@ -100,7 +100,7 @@ class CocoDetection(VOCDetection):
gt_score = np.ones((num_bbox, 1), dtype=np.float32)
is_crowd = np.zeros((num_bbox, 1), dtype=np.int32)
difficult = np.zeros((num_bbox, 1), dtype=np.int32)
gt_poly = [None] * num_bbox
gt_poly = None
for i, box in enumerate(bboxes):
catid = box['category_id']
......@@ -108,6 +108,8 @@ class CocoDetection(VOCDetection):
gt_bbox[i, :] = box['clean_bbox']
is_crowd[i][0] = box['iscrowd']
if 'segmentation' in box:
if gt_poly is None:
gt_poly = [None] * num_bbox
gt_poly[i] = box['segmentation']
im_info = {
......@@ -119,9 +121,11 @@ class CocoDetection(VOCDetection):
'gt_class': gt_class,
'gt_bbox': gt_bbox,
'gt_score': gt_score,
'gt_poly': gt_poly,
'difficult': difficult
}
if gt_poly is not None:
label_info['gt_poly'] = gt_poly
coco_rec = (im_info, label_info)
self.file_list.append([im_fname, coco_rec])
......
paddlex/cv/datasets/voc.py
浏览文件 @ 9c2f0fe4
......@@ -153,7 +153,6 @@ class VOCDetection(Dataset):
'gt_class': gt_class,
'gt_bbox': gt_bbox,
'gt_score': gt_score,
'gt_poly': [],
'difficult': difficult
}
voc_rec = (im_info, label_info)
......
paddlex/cv/models/utils/visualize.py
浏览文件 @ 9c2f0fe4
......@@ -16,6 +16,7 @@ import os
import cv2
import colorsys
import numpy as np
import time
import paddlex.utils.logging as logging
from .detection_eval import fixed_linspace, backup_linspace, loadRes
......@@ -25,8 +26,12 @@ def visualize_detection(image, result, threshold=0.5, save_dir='./'):
Visualize bbox and mask results
"""
image_name = os.path.split(image)[-1]
image = cv2.imread(image)
if isinstance(image, np.ndarray):
image_name = str(int(time.time())) + '.jpg'
else:
image = cv2.imread(image)
image_name = os.path.split(image)[-1]
image = draw_bbox_mask(image, result, threshold=threshold)
if save_dir is not None:
if not os.path.exists(save_dir):
......@@ -56,13 +61,18 @@ def visualize_segmentation(image, result, weight=0.6, save_dir='./'):
c3 = cv2.LUT(label_map, color_map[:, 2])
pseudo_img = np.dstack((c1, c2, c3))
im = cv2.imread(image)
if isinstance(image, np.ndarray):
im = image
image_name = str(int(time.time())) + '.jpg'
else:
image = cv2.imread(image)
image_name = os.path.split(image)[-1]
vis_result = cv2.addWeighted(im, weight, pseudo_img, 1 - weight, 0)
if save_dir is not None:
if not os.path.exists(save_dir):
os.makedirs(save_dir)
image_name = os.path.split(image)[-1]
out_path = os.path.join(save_dir, 'visualize_{}'.format(image_name))
cv2.imwrite(out_path, vis_result)
logging.info('The visualized result is saved as {}'.format(out_path))
......
paddlex/cv/transforms/cls_transforms.py
浏览文件 @ 9c2f0fe4
......@@ -13,13 +13,22 @@
# limitations under the License.
from .ops import *
from .imgaug_support import execute_imgaug
import random
import os.path as osp
import numpy as np
from PIL import Image, ImageEnhance
class Compose:
class ClsTransform:
"""分类Transform的基类
"""
def __init__(self):
pass
class Compose(ClsTransform):
"""根据数据预处理/增强算子对输入数据进行操作。
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。
......@@ -39,6 +48,15 @@ class Compose:
'must be equal or larger than 1!')
self.transforms = transforms
# 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
for op in self.transforms:
if not isinstance(op, ClsTransform):
import imgaug.augmenters as iaa
if not isinstance(op, iaa.Augmenter):
raise Exception(
"Elements in transforms should be defined in 'paddlex.cls.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
)
def __call__(self, im, label=None):
"""
Args:
......@@ -48,20 +66,34 @@ class Compose:
tuple: 根据网络所需字段所组成的tuple；
字段由transforms中的最后一个数据预处理操作决定。
"""
try:
im = cv2.imread(im).astype('float32')
except:
raise TypeError('Can\'t read The image file {}!'.format(im))
if isinstance(im, np.ndarray):
if len(im.shape) != 3:
raise Exception(
"im should be 3-dimension, but now is {}-dimensions".
format(len(im.shape)))
else:
try:
im = cv2.imread(im).astype('float32')
except:
raise TypeError('Can\'t read The image file {}!'.format(im))
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
for op in self.transforms:
outputs = op(im, label)
im = outputs[0]
if len(outputs) == 2:
label = outputs[1]
if isinstance(op, ClsTransform):
outputs = op(im, label)
im = outputs[0]
if len(outputs) == 2:
label = outputs[1]
else:
import imgaug.augmenters as iaa
if isinstance(op, iaa.Augmenter):
im, = execute_imgaug(op, im)
output = (im, )
if label is not None:
output = (im, label)
return outputs
class RandomCrop:
class RandomCrop(ClsTransform):
"""对图像进行随机剪裁，模型训练时的数据增强操作。
1. 根据lower_scale、lower_ratio、upper_ratio计算随机剪裁的高、宽。
......@@ -104,7 +136,7 @@ class RandomCrop:
return (im, label)
class RandomHorizontalFlip:
class RandomHorizontalFlip(ClsTransform):
"""以一定的概率对图像进行随机水平翻转，模型训练时的数据增强操作。
Args:
......@@ -132,7 +164,7 @@ class RandomHorizontalFlip:
return (im, label)
class RandomVerticalFlip:
class RandomVerticalFlip(ClsTransform):
"""以一定的概率对图像进行随机垂直翻转，模型训练时的数据增强操作。
Args:
......@@ -160,7 +192,7 @@ class RandomVerticalFlip:
return (im, label)
class Normalize:
class Normalize(ClsTransform):
"""对图像进行标准化。
1. 对图像进行归一化到区间[0.0, 1.0]。
......@@ -195,7 +227,7 @@ class Normalize:
return (im, label)
class ResizeByShort:
class ResizeByShort(ClsTransform):
"""根据图像短边对图像重新调整大小（resize）。
1. 获取图像的长边和短边长度。
......@@ -242,7 +274,7 @@ class ResizeByShort:
return (im, label)
class CenterCrop:
class CenterCrop(ClsTransform):
"""以图像中心点扩散裁剪长宽为`crop_size`的正方形
1. 计算剪裁的起始点。
......@@ -272,7 +304,7 @@ class CenterCrop:
return (im, label)
class RandomRotate:
class RandomRotate(ClsTransform):
def __init__(self, rotate_range=30, prob=0.5):
"""以一定的概率对图像在[-rotate_range, rotaterange]角度范围内进行旋转，模型训练时的数据增强操作。
......@@ -306,7 +338,7 @@ class RandomRotate:
return (im, label)
class RandomDistort:
class RandomDistort(ClsTransform):
"""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。
......@@ -397,7 +429,7 @@ class RandomDistort:
return (im, label)
class ArrangeClassifier:
class ArrangeClassifier(ClsTransform):
"""获取训练/验证/预测所需信息。注意：此操作不需用户自己显示调用
Args:
......
paddlex/cv/transforms/det_transforms.py
浏览文件 @ 9c2f0fe4
......@@ -24,11 +24,20 @@ import numpy as np
import cv2
from PIL import Image, ImageEnhance
from .imgaug_support import execute_imgaug
from .ops import *
from .box_utils import *
class Compose:
class DetTransform:
"""检测数据处理基类
"""
def __init__(self):
pass
class Compose(DetTransform):
"""根据数据预处理/增强列表对输入数据进行操作。
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。
......@@ -49,8 +58,16 @@ class Compose:
self.transforms = transforms
self.use_mixup = False
for t in self.transforms:
if t.__class__.__name__ == 'MixupImage':
if type(t).__name__ == 'MixupImage':
self.use_mixup = True
# 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
for op in self.transforms:
if not isinstance(op, DetTransform):
import imgaug.augmenters as iaa
if not isinstance(op, iaa.Augmenter):
raise Exception(
"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/"
)
def __call__(self, im, im_info=None, label_info=None):
"""
......@@ -84,11 +101,18 @@ class Compose:
def decode_image(im_file, im_info, label_info):
if im_info is None:
im_info = dict()
try:
im = cv2.imread(im_file).astype('float32')
except:
raise TypeError(
'Can\'t read The image file {}!'.format(im_file))
if isinstance(im_file, np.ndarray):
if len(im_file.shape) != 3:
raise Exception(
"im should be 3-dimensions, but now is {}-dimensions".
format(len(im_file.shape)))
im = im_file
else:
try:
im = cv2.imread(im_file).astype('float32')
except:
raise TypeError(
'Can\'t read The image file {}!'.format(im_file))
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
# make default im_info with [h, w, 1]
im_info['im_resize_info'] = np.array(
......@@ -117,12 +141,28 @@ class Compose:
for op in self.transforms:
if im is None:
return None
outputs = op(im, im_info, label_info)
im = outputs[0]
if isinstance(op, DetTransform):
outputs = op(im, im_info, label_info)
im = outputs[0]
else:
if label_info is not None:
gt_poly = label_info.get('gt_poly', None)
gt_bbox = label_info['gt_bbox']
if gt_poly is None:
im, aug_bbox = execute_imgaug(op, im, bboxes=gt_bbox)
else:
im, aug_bbox, aug_poly = execute_imgaug(
op, im, bboxes=gt_bbox, polygons=gt_poly)
label_info['gt_poly'] = aug_poly
label_info['gt_bbox'] = aug_bbox
outputs = (im, im_info, label_info)
else:
im, = execute_imgaug(op, im)
outputs = (im, im_info)
return outputs
class ResizeByShort:
class ResizeByShort(DetTransform):
"""根据图像的短边调整图像大小（resize）。
1. 获取图像的长边和短边长度。
......@@ -194,7 +234,7 @@ class ResizeByShort:
return (im, im_info, label_info)
class Padding:
class Padding(DetTransform):
"""1.将图像的长和宽padding至coarsest_stride的倍数。如输入图像为[300, 640],
`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
进行padding，最终输出图像为[320, 640]。
......@@ -290,7 +330,7 @@ class Padding:
return (padding_im, im_info, label_info)
class Resize:
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，
......@@ -369,7 +409,7 @@ class Resize:
return (im, im_info, label_info)
class RandomHorizontalFlip:
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，
......@@ -447,7 +487,7 @@ class RandomHorizontalFlip:
return (im, im_info, label_info)
class Normalize:
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]。
......@@ -491,7 +531,7 @@ class Normalize:
return (im, im_info, label_info)
class RandomDistort:
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。
......@@ -585,7 +625,7 @@ class RandomDistort:
return (im, im_info, label_info)
class MixupImage:
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：
......@@ -714,7 +754,7 @@ class MixupImage:
return (im, im_info, label_info)
class RandomExpand:
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。
......@@ -796,7 +836,7 @@ class RandomExpand:
return (canvas, im_info, label_info)
class RandomCrop:
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。
......@@ -944,7 +984,7 @@ class RandomCrop:
return (im, im_info, label_info)
class ArrangeFasterRCNN:
class ArrangeFasterRCNN(DetTransform):
"""获取FasterRCNN模型训练/验证/预测所需信息。
Args:
......@@ -1019,7 +1059,7 @@ class ArrangeFasterRCNN:
return outputs
class ArrangeMaskRCNN:
class ArrangeMaskRCNN(DetTransform):
"""获取MaskRCNN模型训练/验证/预测所需信息。
Args:
......@@ -1103,7 +1143,7 @@ class ArrangeMaskRCNN:
return outputs
class ArrangeYOLOv3:
class ArrangeYOLOv3(DetTransform):
"""获取YOLOv3模型训练/验证/预测所需信息。
Args:
......
paddlex/cv/transforms/imgaug_support.py 0 → 100644
浏览文件 @ 9c2f0fe4
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
def execute_imgaug(augmenter, im, bboxes=None, polygons=None,
segment_map=None):
# 预处理，将bboxes, polygons转换成imgaug格式
import imgaug.augmentables.polys as polys
import imgaug.augmentables.bbs as bbs
aug_im = im.astype('uint8')
aug_bboxes = None
if bboxes is not None:
aug_bboxes = list()
for i in range(len(bboxes)):
x1 = bboxes[i, 0] - 1
y1 = bboxes[i, 1]
x2 = bboxes[i, 2]
y2 = bboxes[i, 3]
aug_bboxes.append(bbs.BoundingBox(x1, y1, x2, y2))
aug_polygons = None
lod_info = list()
if polygons is not None:
aug_polygons = list()
for i in range(len(polygons)):
num = len(polygons[i])
lod_info.append(num)
for j in range(num):
points = np.reshape(polygons[i][j], (-1, 2))
aug_polygons.append(polys.Polygon(points))
aug_segment_map = None
if segment_map is not None:
if len(segment_map.shape) == 2:
h, w = segment_map.shape
aug_segment_map = np.reshape(segment_map, (1, h, w, 1))
elif len(segment_map.shape) == 3:
h, w, c = segment_map.shape
aug_segment_map = np.reshape(segment_map, (1, h, w, c))
else:
raise Exception(
"Only support 2-dimensions for 3-dimensions for segment_map")
aug_im, aug_bboxes, aug_polygons, aug_seg_map = augmenter.augment(
image=aug_im,
bounding_boxes=aug_bboxes,
polygons=aug_polygons,
segmentation_maps=aug_segment_map)
aug_im = aug_im.astype('float32')
if aug_polygons is not None:
assert len(aug_bboxes) == len(
lod_info
), "Number of aug_bboxes should be equal to number of aug_polygons"
if aug_bboxes is not None:
# 裁剪掉在图像之外的bbox和polygon
for i in range(len(aug_bboxes)):
aug_bboxes[i] = aug_bboxes[i].clip_out_of_image(aug_im)
if aug_polygons is not None:
for i in range(len(aug_polygons)):
aug_polygons[i] = aug_polygons[i].clip_out_of_image(aug_im)
# 过滤掉无效的bbox和polygon，并转换为训练数据格式
converted_bboxes = list()
converted_polygons = list()
poly_index = 0
for i in range(len(aug_bboxes)):
# 过滤width或height不足1像素的框
if aug_bboxes[i].width < 1 or aug_bboxes[i].height < 1:
continue
if aug_polygons is None:
converted_bboxes.append([
aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
aug_bboxes[i].y2
])
continue
# 如若有polygons，将会继续执行下面代码
polygons_this_box = list()
for ps in aug_polygons[poly_index:poly_index + lod_info[i]]:
if len(ps) == 0:
continue
for p in ps:
# 没有3个point的polygon被过滤
if len(p.exterior) < 3:
continue
polygons_this_box.append(p.exterior.flatten().tolist())
poly_index += lod_info[i]
if len(polygons_this_box) == 0:
continue
converted_bboxes.append([
aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
aug_bboxes[i].y2
])
converted_polygons.append(polygons_this_box)
if len(converted_bboxes) == 0:
aug_im = im
converted_bboxes = bboxes
converted_polygons = polygons
result = [aug_im]
if bboxes is not None:
result.append(np.array(converted_bboxes))
if polygons is not None:
result.append(converted_polygons)
if segment_map is not None:
n, h, w, c = aug_seg_map.shape
if len(segment_map.shape) == 2:
aug_seg_map = np.reshape(aug_seg_map, (h, w))
elif len(segment_map.shape) == 3:
aug_seg_map = np.reshape(aug_seg_map, (h, w, c))
result.append(aug_seg_map)
return result
paddlex/cv/transforms/seg_transforms.py
浏览文件 @ 9c2f0fe4
......@@ -14,6 +14,7 @@
# limitations under the License.
from .ops import *
from .imgaug_support import execute_imgaug
import random
import os.path as osp
import numpy as np
......@@ -22,7 +23,15 @@ import cv2
from collections import OrderedDict
class Compose:
class SegTransform:
""" 分割transform基类
"""
def __init__(self):
pass
class Compose(SegTransform):
"""根据数据预处理/增强算子对输入数据进行操作。
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。
......@@ -43,6 +52,14 @@ class Compose:
'must be equal or larger than 1!')
self.transforms = transforms
self.to_rgb = False
# 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
for op in self.transforms:
if not isinstance(op, SegTransform):
import imgaug.augmenters as iaa
if not isinstance(op, iaa.Augmenter):
raise Exception(
"Elements in transforms should be defined in 'paddlex.seg.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
)
def __call__(self, im, im_info=None, label=None):
"""
......@@ -60,26 +77,40 @@ class Compose:
if im_info is None:
im_info = list()
try:
im = cv2.imread(im).astype('float32')
except:
raise ValueError('Can\'t read The image file {}!'.format(im))
if isinstance(im, np.ndarray):
if len(im.shape) != 3:
raise Exception(
"im should be 3-dimensions, but now is {}-dimensions".
format(len(im.shape)))
else:
try:
im = cv2.imread(im).astype('float32')
except:
raise ValueError('Can\'t read The image file {}!'.format(im))
if self.to_rgb:
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
if label is not None:
if not isinstance(label, np.ndarray):
label = np.asarray(Image.open(label))
for op in self.transforms:
outputs = op(im, im_info, label)
im = outputs[0]
if len(outputs) >= 2:
im_info = outputs[1]
if len(outputs) == 3:
label = outputs[2]
if isinstance(op, SegTransform):
outputs = op(im, im_info, label)
im = outputs[0]
if len(outputs) >= 2:
im_info = outputs[1]
if len(outputs) == 3:
label = outputs[2]
else:
if label is not None:
im, label = execute_imgaug(op, im, segment_map=label)
outputs = (im, im_info, label)
else:
im, = execute_imgaug(op, im)
outputs = (im, im_info)
return outputs
class RandomHorizontalFlip:
class RandomHorizontalFlip(SegTransform):
"""以一定的概率对图像进行水平翻转。当存在标注图像时，则同步进行翻转。
Args:
......@@ -115,7 +146,7 @@ class RandomHorizontalFlip:
return (im, im_info, label)
class RandomVerticalFlip:
class RandomVerticalFlip(SegTransform):
"""以一定的概率对图像进行垂直翻转。当存在标注图像时，则同步进行翻转。
Args:
......@@ -150,7 +181,7 @@ class RandomVerticalFlip:
return (im, im_info, label)
class Resize:
class Resize(SegTransform):
"""调整图像大小（resize），当存在标注图像时，则同步进行处理。
- 当目标大小（target_size）类型为int时，根据插值方式，
......@@ -260,7 +291,7 @@ class Resize:
return (im, im_info, label)
class ResizeByLong:
class ResizeByLong(SegTransform):
"""对图像长边resize到固定值，短边按比例进行缩放。当存在标注图像时，则同步进行处理。
Args:
......@@ -301,7 +332,7 @@ class ResizeByLong:
return (im, im_info, label)
class ResizeByShort:
class ResizeByShort(SegTransform):
"""根据图像的短边调整图像大小（resize）。
1. 获取图像的长边和短边长度。
......@@ -378,7 +409,7 @@ class ResizeByShort:
return (im, im_info, label)
class ResizeRangeScaling:
class ResizeRangeScaling(SegTransform):
"""对图像长边随机resize到指定范围内，短边按比例进行缩放。当存在标注图像时，则同步进行处理。
Args:
......@@ -427,7 +458,7 @@ class ResizeRangeScaling:
return (im, im_info, label)
class ResizeStepScaling:
class ResizeStepScaling(SegTransform):
"""对图像按照某一个比例resize，这个比例以scale_step_size为步长
在[min_scale_factor, max_scale_factor]随机变动。当存在标注图像时，则同步进行处理。
......@@ -502,7 +533,7 @@ class ResizeStepScaling:
return (im, im_info, label)
class Normalize:
class Normalize(SegTransform):
"""对图像进行标准化。
1.尺度缩放到 [0,1]。
2.对图像进行减均值除以标准差操作。
......@@ -550,7 +581,7 @@ class Normalize:
return (im, im_info, label)
class Padding:
class Padding(SegTransform):
"""对图像或标注图像进行padding，padding方向为右和下。
根据提供的值对图像或标注图像进行padding操作。
......@@ -642,7 +673,7 @@ class Padding:
return (im, im_info, label)
class RandomPaddingCrop:
class RandomPaddingCrop(SegTransform):
"""对图像和标注图进行随机裁剪，当所需要的裁剪尺寸大于原图时，则进行padding操作。
Args:
......@@ -741,7 +772,7 @@ class RandomPaddingCrop:
return (im, im_info, label)
class RandomBlur:
class RandomBlur(SegTransform):
"""以一定的概率对图像进行高斯模糊。
Args：
......@@ -787,7 +818,7 @@ class RandomBlur:
return (im, im_info, label)
class RandomRotate:
class RandomRotate(SegTransform):
"""对图像进行随机旋转, 模型训练时的数据增强操作。
在旋转区间[-rotate_range, rotate_range]内，对图像进行随机旋转，当存在标注图像时，同步进行，
并对旋转后的图像和标注图像进行相应的padding。
......@@ -859,7 +890,7 @@ class RandomRotate:
return (im, im_info, label)
class RandomScaleAspect:
class RandomScaleAspect(SegTransform):
"""裁剪并resize回原始尺寸的图像和标注图像。
按照一定的面积比和宽高比对图像进行裁剪，并reszie回原始图像的图像，当存在标注图时，同步进行。
......@@ -922,7 +953,7 @@ class RandomScaleAspect:
return (im, im_info, label)
class RandomDistort:
class RandomDistort(SegTransform):
"""对图像进行随机失真。
1. 对变换的操作顺序进行随机化操作。
......@@ -1018,7 +1049,7 @@ class RandomDistort:
return (im, im_info, label)
class ArrangeSegmenter:
class ArrangeSegmenter(SegTransform):
"""获取训练/验证/预测所需的信息。
Args:
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或