From 4f2cab466867e96b82b4d3c2fc55eda0b8616bb3 Mon Sep 17 00:00:00 2001 From: jiangjiajun Date: Wed, 13 May 2020 15:08:43 +0800 Subject: [PATCH] add imgaug support --- paddlex/cv/datasets/coco.py | 8 +- paddlex/cv/datasets/voc.py | 1 - paddlex/cv/transforms/cls_transforms.py | 68 ++++++-- paddlex/cv/transforms/det_transforms.py | 82 ++++++--- paddlex/cv/transforms/imgaug_support.py | 220 ++++++++++++++++++++++++ paddlex/cv/transforms/seg_transforms.py | 83 ++++++--- 6 files changed, 394 insertions(+), 68 deletions(-) create mode 100644 paddlex/cv/transforms/imgaug_support.py diff --git a/paddlex/cv/datasets/coco.py b/paddlex/cv/datasets/coco.py index e2d3ac4..ff7c2b2 100644 --- a/paddlex/cv/datasets/coco.py +++ b/paddlex/cv/datasets/coco.py @@ -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]) diff --git a/paddlex/cv/datasets/voc.py b/paddlex/cv/datasets/voc.py index 3a4c9ae..b5f1749 100644 --- a/paddlex/cv/datasets/voc.py +++ b/paddlex/cv/datasets/voc.py @@ -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) diff --git a/paddlex/cv/transforms/cls_transforms.py b/paddlex/cv/transforms/cls_transforms.py index 525fa2d..6773f2e 100644 --- a/paddlex/cv/transforms/cls_transforms.py +++ b/paddlex/cv/transforms/cls_transforms.py @@ -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: diff --git a/paddlex/cv/transforms/det_transforms.py b/paddlex/cv/transforms/det_transforms.py index a3a9454..b9aee9e 100644 --- a/paddlex/cv/transforms/det_transforms.py +++ b/paddlex/cv/transforms/det_transforms.py @@ -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: diff --git a/paddlex/cv/transforms/imgaug_support.py b/paddlex/cv/transforms/imgaug_support.py new file mode 100644 index 0000000..24cf8bc --- /dev/null +++ b/paddlex/cv/transforms/imgaug_support.py @@ -0,0 +1,220 @@ +import numpy as np + +#def execute_imgaug(augmenter, im, bboxes=None, polygons=None, segment_map=None): +# import imgaug.augmentables.polys as polys +# import imgaug.augmentables.bbs as bbs +# +# # 预处理，将bboxes, polygons转换成imgaug格式 +# 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 = im.astype('uint8') +# 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) +# +# if aug_polygons is not None: +# assert len(aug_bboxes) == len(lod_info), "length of bboxes should be equal to the length of 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: +# # 若完成增强后，没有可用的目标，则仍然返回原图进行训练 +# aug_im = im +# converted_bboxes = bboxes +# converted_polygons = polygons +# +# result = [aug_im.astype('float32')] +# 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 + +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 diff --git a/paddlex/cv/transforms/seg_transforms.py b/paddlex/cv/transforms/seg_transforms.py index 89733e3..9eb1222 100644 --- a/paddlex/cv/transforms/seg_transforms.py +++ b/paddlex/cv/transforms/seg_transforms.py @@ -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: -- GitLab