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fix python raspberry

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deploy/raspberry/python/__init__.py
浏览文件 @ 3a238aa0
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
\ No newline at end of file
deploy/raspberry/python/demo.py
浏览文件 @ 3a238aa0
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......
deploy/raspberry/python/transforms/__init__.py
浏览文件 @ 3a238aa0
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......
deploy/raspberry/python/transforms/box_utils.py 已删除 100644 → 0
浏览文件 @ 2af6a45f
# 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
import random
import math
import cv2
import scipy
def bbox_area(src_bbox):
if src_bbox[2] < src_bbox[0] or src_bbox[3] < src_bbox[1]:
return 0.
else:
width = src_bbox[2] - src_bbox[0]
height = src_bbox[3] - src_bbox[1]
return width * height
def jaccard_overlap(sample_bbox, object_bbox):
if sample_bbox[0] >= object_bbox[2] or \
sample_bbox[2] <= object_bbox[0] or \
sample_bbox[1] >= object_bbox[3] or \
sample_bbox[3] <= object_bbox[1]:
return 0
intersect_xmin = max(sample_bbox[0], object_bbox[0])
intersect_ymin = max(sample_bbox[1], object_bbox[1])
intersect_xmax = min(sample_bbox[2], object_bbox[2])
intersect_ymax = min(sample_bbox[3], object_bbox[3])
intersect_size = (intersect_xmax - intersect_xmin) * (
intersect_ymax - intersect_ymin)
sample_bbox_size = bbox_area(sample_bbox)
object_bbox_size = bbox_area(object_bbox)
overlap = intersect_size / (
sample_bbox_size + object_bbox_size - intersect_size)
return overlap
def iou_matrix(a, b):
tl_i = np.maximum(a[:, np.newaxis, :2], b[:, :2])
br_i = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
area_i = np.prod(br_i - tl_i, axis=2) * (tl_i < br_i).all(axis=2)
area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
area_o = (area_a[:, np.newaxis] + area_b - area_i)
return area_i / (area_o + 1e-10)
def crop_box_with_center_constraint(box, crop):
cropped_box = box.copy()
cropped_box[:, :2] = np.maximum(box[:, :2], crop[:2])
cropped_box[:, 2:] = np.minimum(box[:, 2:], crop[2:])
cropped_box[:, :2] -= crop[:2]
cropped_box[:, 2:] -= crop[:2]
centers = (box[:, :2] + box[:, 2:]) / 2
valid = np.logical_and(crop[:2] <= centers, centers < crop[2:]).all(axis=1)
valid = np.logical_and(
valid, (cropped_box[:, :2] < cropped_box[:, 2:]).all(axis=1))
return cropped_box, np.where(valid)[0]
def is_poly(segm):
if not isinstance(segm, (list, dict)):
raise Exception("Invalid segm type: {}".format(type(segm)))
return isinstance(segm, list)
def crop_image(img, crop):
x1, y1, x2, y2 = crop
return img[y1:y2, x1:x2, :]
def crop_segms(segms, valid_ids, crop, height, width):
def _crop_poly(segm, crop):
xmin, ymin, xmax, ymax = crop
crop_coord = [xmin, ymin, xmin, ymax, xmax, ymax, xmax, ymin]
crop_p = np.array(crop_coord).reshape(4, 2)
crop_p = Polygon(crop_p)
crop_segm = list()
for poly in segm:
poly = np.array(poly).reshape(len(poly) // 2, 2)
polygon = Polygon(poly)
if not polygon.is_valid:
exterior = polygon.exterior
multi_lines = exterior.intersection(exterior)
polygons = shapely.ops.polygonize(multi_lines)
polygon = MultiPolygon(polygons)
multi_polygon = list()
if isinstance(polygon, MultiPolygon):
multi_polygon = copy.deepcopy(polygon)
else:
multi_polygon.append(copy.deepcopy(polygon))
for per_polygon in multi_polygon:
inter = per_polygon.intersection(crop_p)
if not inter:
continue
if isinstance(inter, (MultiPolygon, GeometryCollection)):
for part in inter:
if not isinstance(part, Polygon):
continue
part = np.squeeze(
np.array(part.exterior.coords[:-1]).reshape(1, -1))
part[0::2] -= xmin
part[1::2] -= ymin
crop_segm.append(part.tolist())
elif isinstance(inter, Polygon):
crop_poly = np.squeeze(
np.array(inter.exterior.coords[:-1]).reshape(1, -1))
crop_poly[0::2] -= xmin
crop_poly[1::2] -= ymin
crop_segm.append(crop_poly.tolist())
else:
continue
return crop_segm
def _crop_rle(rle, crop, height, width):
if 'counts' in rle and type(rle['counts']) == list:
rle = mask_util.frPyObjects(rle, height, width)
mask = mask_util.decode(rle)
mask = mask[crop[1]:crop[3], crop[0]:crop[2]]
rle = mask_util.encode(np.array(mask, order='F', dtype=np.uint8))
return rle
crop_segms = []
for id in valid_ids:
segm = segms[id]
if is_poly(segm):
import copy
import shapely.ops
import logging
from shapely.geometry import Polygon, MultiPolygon, GeometryCollection
logging.getLogger("shapely").setLevel(logging.WARNING)
# Polygon format
crop_segms.append(_crop_poly(segm, crop))
else:
# RLE format
import pycocotools.mask as mask_util
crop_segms.append(_crop_rle(segm, crop, height, width))
return crop_segms
def expand_segms(segms, x, y, height, width, ratio):
def _expand_poly(poly, x, y):
expanded_poly = np.array(poly)
expanded_poly[0::2] += x
expanded_poly[1::2] += y
return expanded_poly.tolist()
def _expand_rle(rle, x, y, height, width, ratio):
if 'counts' in rle and type(rle['counts']) == list:
rle = mask_util.frPyObjects(rle, height, width)
mask = mask_util.decode(rle)
expanded_mask = np.full((int(height * ratio), int(width * ratio)),
0).astype(mask.dtype)
expanded_mask[y:y + height, x:x + width] = mask
rle = mask_util.encode(
np.array(expanded_mask, order='F', dtype=np.uint8))
return rle
expanded_segms = []
for segm in segms:
if is_poly(segm):
# Polygon format
expanded_segms.append([_expand_poly(poly, x, y) for poly in segm])
else:
# RLE format
import pycocotools.mask as mask_util
expanded_segms.append(
_expand_rle(segm, x, y, height, width, ratio))
return expanded_segms
def box_horizontal_flip(bboxes, width):
oldx1 = bboxes[:, 0].copy()
oldx2 = bboxes[:, 2].copy()
bboxes[:, 0] = width - oldx2 - 1
bboxes[:, 2] = width - oldx1 - 1
if bboxes.shape[0] != 0 and (bboxes[:, 2] < bboxes[:, 0]).all():
raise ValueError(
"RandomHorizontalFlip: invalid box, x2 should be greater than x1")
return bboxes
def segms_horizontal_flip(segms, height, width):
def _flip_poly(poly, width):
flipped_poly = np.array(poly)
flipped_poly[0::2] = width - np.array(poly[0::2]) - 1
return flipped_poly.tolist()
def _flip_rle(rle, height, width):
if 'counts' in rle and type(rle['counts']) == list:
rle = mask_util.frPyObjects([rle], height, width)
mask = mask_util.decode(rle)
mask = mask[:, ::-1]
rle = mask_util.encode(np.array(mask, order='F', dtype=np.uint8))
return rle
flipped_segms = []
for segm in segms:
if is_poly(segm):
# Polygon format
flipped_segms.append([_flip_poly(poly, width) for poly in segm])
else:
# RLE format
import pycocotools.mask as mask_util
flipped_segms.append(_flip_rle(segm, height, width))
return flipped_segms
deploy/raspberry/python/transforms/cls_transforms.py
浏览文件 @ 3a238aa0
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -18,7 +18,6 @@ import random
import os.path as osp
import numpy as np
from PIL import Image, ImageEnhance
import utils.logging as logging
class ClsTransform:
......@@ -49,14 +48,7 @@ class Compose(ClsTransform):
'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):
"""
......@@ -79,18 +71,10 @@ class Compose(ClsTransform):
raise TypeError('Can\'t read The image file {}!'.format(im))
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
for op in self.transforms:
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)
outputs = (im, )
if label is not None:
outputs = (im, label)
return outputs
def add_augmenters(self, augmenters):
......@@ -100,109 +84,10 @@ class Compose(ClsTransform):
transform_names = [type(x).__name__ for x in self.transforms]
for aug in augmenters:
if type(aug).__name__ in transform_names:
logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
print("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
self.transforms = augmenters + self.transforms
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。
2. 根据随机剪裁的高、宽随机选取剪裁的起始点。
3. 剪裁图像。
4. 调整剪裁后的图像的大小到crop_size*crop_size。
Args:
crop_size (int): 随机裁剪后重新调整的目标边长。默认为224。
lower_scale (float): 裁剪面积相对原面积比例的最小限制。默认为0.08。
lower_ratio (float): 宽变换比例的最小限制。默认为3. / 4。
upper_ratio (float): 宽变换比例的最大限制。默认为4. / 3。
"""
def __init__(self,
crop_size=224,
lower_scale=0.08,
lower_ratio=3. / 4,
upper_ratio=4. / 3):
self.crop_size = crop_size
self.lower_scale = lower_scale
self.lower_ratio = lower_ratio
self.upper_ratio = upper_ratio
def __call__(self, im, label=None):
"""
Args:
im (np.ndarray): 图像np.ndarray数据。
label (int): 每张图像所对应的类别序号。
Returns:
tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
"""
im = random_crop(im, self.crop_size, self.lower_scale,
self.lower_ratio, self.upper_ratio)
if label is None:
return (im, )
else:
return (im, label)
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:
prob (float): 随机水平翻转的概率。默认为0.5。
"""
def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, im, label=None):
"""
Args:
im (np.ndarray): 图像np.ndarray数据。
label (int): 每张图像所对应的类别序号。
Returns:
tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
"""
if random.random() < self.prob:
im = horizontal_flip(im)
if label is None:
return (im, )
else:
return (im, label)
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:
prob (float): 随机垂直翻转的概率。默认为0.5。
"""
def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, im, label=None):
"""
Args:
im (np.ndarray): 图像np.ndarray数据。
label (int): 每张图像所对应的类别序号。
Returns:
tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
"""
if random.random() < self.prob:
im = vertical_flip(im)
if label is None:
return (im, )
else:
return (im, label)
class Normalize(ClsTransform):
"""对图像进行标准化。
......@@ -315,131 +200,6 @@ class CenterCrop(ClsTransform):
return (im, label)
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。
Args:
rotate_range (int): 旋转度数的范围。默认为30。
prob (float): 随机旋转的概率。默认为0.5。
"""
self.rotate_range = rotate_range
self.prob = prob
def __call__(self, im, label=None):
"""
Args:
im (np.ndarray): 图像np.ndarray数据。
label (int): 每张图像所对应的类别序号。
Returns:
tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
"""
rotate_lower = -self.rotate_range
rotate_upper = self.rotate_range
im = im.astype('uint8')
im = Image.fromarray(im)
if np.random.uniform(0, 1) < self.prob:
im = rotate(im, rotate_lower, rotate_upper)
im = np.asarray(im).astype('float32')
if label is None:
return (im, )
else:
return (im, label)
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。
2. 按照1中的顺序以一定的概率对图像在范围[-range, range]内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0.9。
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0.9。
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0.9。
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=0.9,
brightness_prob=0.5,
contrast_range=0.9,
contrast_prob=0.5,
saturation_range=0.9,
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, label=None):
"""
Args:
im (np.ndarray): 图像np.ndarray数据。
label (int): 每张图像所对应的类别序号。
Returns:
tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
"""
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(len(ops)):
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 is None:
return (im, )
else:
return (im, label)
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
......@@ -510,12 +270,7 @@ class ComposedClsTransforms(Compose):
)
if mode == 'train':
# 训练时的transforms，包含数据增强
transforms = [
RandomCrop(crop_size=width), RandomHorizontalFlip(prob=0.5),
Normalize(
mean=mean, std=std)
]
pass
else:
# 验证/预测时的transforms
transforms = [
......
deploy/raspberry/python/transforms/imgaug_support.py 已删除 100644 → 0
浏览文件 @ 2af6a45f
# 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
import copy
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.kps as kps
import imgaug.augmentables.bbs as bbs
aug_im = im.astype('uint8')
aug_im = augmenter.augment(image=aug_im).astype('float32')
return aug_im
# TODO imgaug的标注处理逻辑与paddlex已存的transform存在部分差异
# 目前仅支持对原图进行处理，因此只能使用pixlevel的imgaug增强操作
# 以下代码暂不会执行
aug_bboxes = None
if bboxes is not None:
aug_bboxes = list()
for i in range(len(bboxes)):
x1 = bboxes[i, 0]
y1 = bboxes[i, 1]
x2 = bboxes[i, 2]
y2 = bboxes[i, 3]
aug_bboxes.append(bbs.BoundingBox(x1, y1, x2, y2))
aug_points = None
if polygons is not None:
aug_points = list()
for i in range(len(polygons)):
num = len(polygons[i])
for j in range(num):
tmp = np.reshape(polygons[i][j], (-1, 2))
for k in range(len(tmp)):
aug_points.append(kps.Keypoint(tmp[k, 0], tmp[k, 1]))
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")
unnormalized_batch = augmenter.augment(
image=aug_im,
bounding_boxes=aug_bboxes,
keypoints=aug_points,
segmentation_maps=aug_segment_map,
return_batch=True)
aug_im = unnormalized_batch.images_aug[0]
aug_bboxes = unnormalized_batch.bounding_boxes_aug
aug_points = unnormalized_batch.keypoints_aug
aug_seg_map = unnormalized_batch.segmentation_maps_aug
aug_im = aug_im.astype('float32')
if aug_bboxes is not None:
converted_bboxes = list()
for i in range(len(aug_bboxes)):
converted_bboxes.append([
aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
aug_bboxes[i].y2
])
aug_bboxes = converted_bboxes
aug_polygons = None
if aug_points is not None:
aug_polygons = copy.deepcopy(polygons)
idx = 0
for i in range(len(aug_polygons)):
num = len(aug_polygons[i])
for j in range(num):
num_points = len(aug_polygons[i][j]) // 2
for k in range(num_points):
aug_polygons[i][j][k * 2] = aug_points[idx].x
aug_polygons[i][j][k * 2 + 1] = aug_points[idx].y
idx += 1
result = [aug_im]
if aug_bboxes is not None:
result.append(np.array(aug_bboxes))
if aug_polygons is not None:
result.append(aug_polygons)
if aug_seg_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
deploy/raspberry/python/transforms/ops.py
浏览文件 @ 3a238aa0
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......
deploy/raspberry/python/transforms/seg_transforms.py
浏览文件 @ 3a238aa0
# coding: utf8
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -14,14 +14,12 @@
# 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
import cv2
from collections import OrderedDict
import utils.logging as logging
class SegTransform:
......@@ -53,14 +51,7 @@ class Compose(SegTransform):
'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):
"""
......@@ -116,7 +107,7 @@ class Compose(SegTransform):
transform_names = [type(x).__name__ for x in self.transforms]
for aug in augmenters:
if type(aug).__name__ in transform_names:
logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
print("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
self.transforms = augmenters + self.transforms
......@@ -828,76 +819,6 @@ class RandomBlur(SegTransform):
return (im, im_info, label)
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。
Args:
rotate_range (float): 最大旋转角度。默认为15度。
im_padding_value (list): 图像padding的值。默认为[127.5, 127.5, 127.5]。
label_padding_value (int): 标注图像padding的值。默认为255。
"""
def __init__(self,
rotate_range=15,
im_padding_value=[127.5, 127.5, 127.5],
label_padding_value=255):
self.rotate_range = rotate_range
self.im_padding_value = im_padding_value
self.label_padding_value = label_padding_value
def __call__(self, im, im_info=None, label=None):
"""
Args:
im (np.ndarray): 图像np.ndarray数据。
im_info (list): 存储图像reisze或padding前的shape信息，如
[('resize', [200, 300]), ('padding', [400, 600])]表示
图像在过resize前shape为(200, 300)， 过padding前shape为
(400, 600)
label (np.ndarray): 标注图像np.ndarray数据。
Returns:
tuple: 当label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不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
存储与图像相关信息的字典和标注图像np.ndarray数据。
"""
if self.rotate_range > 0:
(h, w) = im.shape[:2]
do_rotation = np.random.uniform(-self.rotate_range,
self.rotate_range)
pc = (w // 2, h // 2)
r = cv2.getRotationMatrix2D(pc, do_rotation, 1.0)
cos = np.abs(r[0, 0])
sin = np.abs(r[0, 1])
nw = int((h * sin) + (w * cos))
nh = int((h * cos) + (w * sin))
(cx, cy) = pc
r[0, 2] += (nw / 2) - cx
r[1, 2] += (nh / 2) - cy
dsize = (nw, nh)
im = cv2.warpAffine(
im,
r,
dsize=dsize,
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=self.im_padding_value)
label = cv2.warpAffine(
label,
r,
dsize=dsize,
flags=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=self.label_padding_value)
if label is None:
return (im, im_info)
else:
return (im, im_info, label)
class RandomScaleAspect(SegTransform):
......@@ -1125,11 +1046,7 @@ class ComposedSegTransforms(Compose):
std=[0.5, 0.5, 0.5]):
if mode == 'train':
# 训练时的transforms，包含数据增强
transforms = [
RandomHorizontalFlip(prob=0.5), ResizeStepScaling(),
RandomPaddingCrop(crop_size=train_crop_size), Normalize(
mean=mean, std=std)
]
pass
else:
# 验证/预测时的transforms
transforms = [Normalize(mean=mean, std=std)]
......
deploy/raspberry/python/utils/__init__.py 已删除 100644 → 0
浏览文件 @ 2af6a45f
# 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.
deploy/raspberry/python/utils/logging.py 已删除 100755 → 0
浏览文件 @ 2af6a45f
# 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 time
import os
import sys
import colorama
from colorama import init
init(autoreset=True)
levels = {0: 'ERROR', 1: 'WARNING', 2: 'INFO', 3: 'DEBUG'}
log_level = 2
def log(level=2, message="", use_color=False):
current_time = time.time()
time_array = time.localtime(current_time)
current_time = time.strftime("%Y-%m-%d %H:%M:%S", time_array)
if log_level >= level:
if use_color:
print("\033[1;31;40m{} [{}]\t{}\033[0m".format(current_time, levels[
level], message).encode("utf-8").decode("latin1"))
else:
print("{} [{}]\t{}".format(current_time, levels[level], message)
.encode("utf-8").decode("latin1"))
sys.stdout.flush()
def debug(message="", use_color=False):
log(level=3, message=message, use_color=use_color)
def info(message="", use_color=False):
log(level=2, message=message, use_color=use_color)
def warning(message="", use_color=True):
log(level=1, message=message, use_color=use_color)
def error(message="", use_color=True, exit=True):
log(level=0, message=message, use_color=use_color)
if exit:
sys.exit(-1)
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