From: https://github.com/PaddlePaddle/PaddleSeg/blob/release/v0.4.0/pdseg/data_aug.py
from __future__ import print_function
import cv2
import numpy as np
def resize(img, grt=None, mode=ModelPhase.TRAIN):
"""
改变图像及标签图像尺寸
AUG.AUG_METHOD为unpadding，所有模式均直接resize到AUG.FIX_RESIZE_SIZE的尺寸
AUG.AUG_METHOD为stepscaling, 按比例resize，训练时比例范围AUG.MIN_SCALE_FACTOR到AUG.MAX_SCALE_FACTOR,间隔为AUG.SCALE_STEP_SIZE，其他模式返回原图
AUG.AUG_METHOD为rangescaling，长边对齐，短边按比例变化，训练时长边对齐范围AUG.MIN_RESIZE_VALUE到AUG.MAX_RESIZE_VALUE，其他模式长边对齐AUG.INF_RESIZE_VALUE
Args：
img(numpy.ndarray): 输入图像
grt(numpy.ndarray): 标签图像，默认为None
mode(string): 模式, 默认训练模式，即ModelPhase.TRAIN
Returns：
resize后的图像和标签图
"""
if cfg.AUG.AUG_METHOD == 'unpadding':
target_size = cfg.AUG.FIX_RESIZE_SIZE
img = cv2.resize(img, target_size, interpolation=cv2.INTER_LINEAR)
if grt is not None:
grt = cv2.resize(grt, target_size, interpolation=cv2.INTER_NEAREST)
elif cfg.AUG.AUG_METHOD == 'stepscaling':
if mode == ModelPhase.TRAIN:
min_scale_factor = cfg.AUG.MIN_SCALE_FACTOR
max_scale_factor = cfg.AUG.MAX_SCALE_FACTOR
step_size = cfg.AUG.SCALE_STEP_SIZE
scale_factor = get_random_scale(min_scale_factor,
max_scale_factor,
step_size)
img, grt = randomly_scale_image_and_label(
img, grt, scale=scale_factor)
elif cfg.AUG.AUG_METHOD == 'rangescaling':
min_resize_value = cfg.AUG.MIN_RESIZE_VALUE
max_resize_value = cfg.AUG.MAX_RESIZE_VALUE
if mode == ModelPhase.TRAIN:
if min_resize_value == max_resize_value:
random_size = min_resize_value
else:
random_size = int(np.random.uniform(min_resize_value, max_resize_value) + 0.5)
else:
random_size = cfg.AUG.INF_RESIZE_VALUE
value = max(img.shape[0], img.shape[1])
scale = float(random_size) / float(value)
img = cv2.resize(img, (0, 0), fx=scale, fy=scale,
interpolation=cv2.INTER_LINEAR)
if grt is not None:
grt = cv2.resize(grt, (0, 0), fx=scale, fy=scale,
interpolation=cv2.INTER_NEAREST)
else:
raise Exception("Unexpect data augmention method: {}".format(
cfg.AUG.AUG_METHOD))
return img, grt
def get_random_scale(min_scale_factor, max_scale_factor, step_size):
"""
在一定范围内得到随机值，范围为min_scale_factor到max_scale_factor，间隔为step_size
Args：
min_scale_factor(float): 随机尺度下限，大于0
max_scale_factor(float): 随机尺度上限，不小于下限值
step_size(float): 尺度间隔，非负, 等于为0时直接返回min_scale_factor到max_scale_factor范围内任一值
Returns：
随机尺度值
"""
if min_scale_factor < 0 or min_scale_factor > max_scale_factor:
raise ValueError('Unexpected value of min_scale_factor.')
if min_scale_factor == max_scale_factor:
return min_scale_factor
if step_size == 0:
return np.random.uniform(min_scale_factor, max_scale_factor)
num_steps = int((max_scale_factor - min_scale_factor) / step_size + 1)
scale_factors = np.linspace(min_scale_factor, max_scale_factor, num_steps).tolist()
np.random.shuffle(scale_factors)
return scale_factors[0]
def randomly_scale_image_and_label(image, label=None, scale=1.0):
"""
按比例resize图像和标签图, 如果scale为1，返回原图
Args：
image(numpy.ndarray): 输入图像
label(numpy.ndarray): 标签图，默认None
sclae(float): 图片resize的比例，非负，默认1.0
Returns：
resize后的图像和标签图
"""
if scale == 1.0:
return image, label
height = image.shape[0]
width = image.shape[1]
new_height = int(height * scale + 0.5)
new_width = int(width * scale + 0.5)
new_image = cv2.resize(image, (new_width, new_height),
interpolation=cv2.INTER_LINEAR)
if label is not None:
height = label.shape[0]
width = label.shape[1]
new_height = int(height * scale + 0.5)
new_width = int(width * scale + 0.5)
new_label = cv2.resize(label, (new_width, new_height),
interpolation=cv2.INTER_NEAREST)
return new_image, new_label
def random_rotation(crop_img, crop_seg, rich_crop_max_rotation, mean_value):
"""
随机旋转图像和标签图
Args：
crop_img(numpy.ndarray): 输入图像
crop_seg(numpy.ndarray): 标签图
rich_crop_max_rotation(int)：旋转最大角度，0-90
mean_value(list)：均值, 对图片旋转产生的多余区域使用均值填充
Returns：
旋转后的图像和标签图
"""
ignore_index = cfg.DATASET.IGNORE_INDEX
if rich_crop_max_rotation > 0:
(h, w) = crop_img.shape[:2]
do_rotation = np.random.uniform(-rich_crop_max_rotation,
rich_crop_max_rotation)
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)
crop_img = cv2.warpAffine(
crop_img,
r,
dsize=dsize,
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=mean_value)
crop_seg = cv2.warpAffine(
crop_seg,
r,
dsize=dsize,
flags=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(ignore_index, ignore_index, ignore_index))
return crop_img, crop_seg
def rand_scale_aspect(crop_img,
crop_seg,
rich_crop_min_scale=0,
rich_crop_aspect_ratio=0):
"""
从输入图像和标签图像中裁取随机宽高比的图像，并reszie回原始尺寸
Args:
crop_img(numpy.ndarray): 输入图像
crop_seg(numpy.ndarray): 标签图像
rich_crop_min_scale(float)：裁取图像占原始图像的面积比，0-1，默认0返回原图
rich_crop_aspect_ratio(float): 裁取图像的宽高比范围，非负，默认0返回原图
Returns:
裁剪并resize回原始尺寸的图像和标签图像
"""
if rich_crop_min_scale == 0 or rich_crop_aspect_ratio == 0:
return crop_img, crop_seg
else:
img_height = crop_img.shape[0]
img_width = crop_img.shape[1]
for i in range(0, 10):
area = img_height * img_width
target_area = area * np.random.uniform(rich_crop_min_scale, 1.0)
aspectRatio = np.random.uniform(rich_crop_aspect_ratio,
1.0 / rich_crop_aspect_ratio)
dw = int(np.sqrt(target_area * 1.0 * aspectRatio))
dh = int(np.sqrt(target_area * 1.0 / aspectRatio))
if (np.random.randint(10) < 5):
tmp = dw
dw = dh
dh = tmp
if (dh < img_height and dw < img_width):
h1 = np.random.randint(0, img_height - dh)
w1 = np.random.randint(0, img_width - dw)
crop_img = crop_img[h1:(h1 + dh), w1:(w1 + dw), :]
crop_seg = crop_seg[h1:(h1 + dh), w1:(w1 + dw)]
crop_img = cv2.resize(
crop_img, (img_width, img_height),
interpolation=cv2.INTER_LINEAR)
crop_seg = cv2.resize(
crop_seg, (img_width, img_height),
interpolation=cv2.INTER_NEAREST)
break
return crop_img, crop_seg
def saturation_jitter(cv_img, jitter_range):
"""
调节图像饱和度
Args:
cv_img(numpy.ndarray): 输入图像
jitter_range(float): 调节程度，0-1
Returns:
饱和度调整后的图像
"""
greyMat = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
greyMat = greyMat[:, :, None] * np.ones(3, dtype=int)[None, None, :]
cv_img = cv_img.astype(np.float32)
cv_img = cv_img * (1 - jitter_range) + jitter_range * greyMat
cv_img = np.where(cv_img > 255, 255, cv_img)
cv_img = cv_img.astype(np.uint8)
return cv_img
def brightness_jitter(cv_img, jitter_range):
"""
调节图像亮度
Args:
cv_img(numpy.ndarray): 输入图像
jitter_range(float): 调节程度，0-1
Returns:
亮度调整后的图像
"""
cv_img = cv_img.astype(np.float32)
cv_img = cv_img * (1.0 - jitter_range)
cv_img = np.where(cv_img > 255, 255, cv_img)
cv_img = cv_img.astype(np.uint8)
return cv_img
def contrast_jitter(cv_img, jitter_range):
"""
调节图像对比度
Args:
cv_img(numpy.ndarray): 输入图像
jitter_range(float): 调节程度，0-1
Returns:
对比度调整后的图像
"""
greyMat = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
mean = np.mean(greyMat)
cv_img = cv_img.astype(np.float32)
cv_img = cv_img * (1 - jitter_range) + jitter_range * mean
cv_img = np.where(cv_img > 255, 255, cv_img)
cv_img = cv_img.astype(np.uint8)
return cv_img
def random_jitter(cv_img, saturation_range, brightness_range, contrast_range):
"""
图像亮度、饱和度、对比度调节，在调整范围内随机获得调节比例，并随机顺序叠加三种效果
Args:
cv_img(numpy.ndarray): 输入图像
saturation_range(float): 饱和对调节范围，0-1
brightness_range(float): 亮度调节范围，0-1
contrast_range(float): 对比度调节范围，0-1
Returns:
亮度、饱和度、对比度调整后图像
"""
saturation_ratio = np.random.uniform(-saturation_range, saturation_range)
brightness_ratio = np.random.uniform(-brightness_range, brightness_range)
contrast_ratio = np.random.uniform(-contrast_range, contrast_range)
order = [0, 1, 2]
np.random.shuffle(order)
for i in range(3):
if order[i] == 0:
cv_img = saturation_jitter(cv_img, saturation_ratio)
if order[i] == 1:
cv_img = brightness_jitter(cv_img, brightness_ratio)
if order[i] == 2:
cv_img = contrast_jitter(cv_img, contrast_ratio)
return cv_img
def hsv_color_jitter(crop_img,
brightness_jitter_ratio=0,
saturation_jitter_ratio=0,
contrast_jitter_ratio=0):
"""
图像亮度、饱和度、对比度调节
Args:
crop_img(numpy.ndarray): 输入图像
brightness_jitter_ratio(float): 亮度调节度最大值，1-0，默认0
saturation_jitter_ratio(float): 饱和度调节度最大值，1-0，默认0
contrast_jitter_ratio(float): 对比度调节度最大值，1-0，默认0
Returns：
亮度、饱和度、对比度调节后图像
"""
if brightness_jitter_ratio > 0 or \
saturation_jitter_ratio > 0 or \
contrast_jitter_ratio > 0:
crop_img = random_jitter(crop_img, saturation_jitter_ratio,
brightness_jitter_ratio, contrast_jitter_ratio)
return crop_img
def rand_crop(crop_img, crop_seg, mode=ModelPhase.TRAIN):
"""
随机裁剪图片和标签图, 若crop尺寸大于原始尺寸，分别使用DATASET.PADDING_VALUE值和DATASET.IGNORE_INDEX值填充再进行crop，
crop尺寸与原始尺寸一致，返回原图，crop尺寸小于原始尺寸直接crop
Args:
crop_img(numpy.ndarray): 输入图像
crop_seg(numpy.ndarray): 标签图
mode(string): 模式, 默认训练模式，验证或预测、可视化模式时crop尺寸需大于原始图片尺寸
Returns：
裁剪后的图片和标签图
"""
img_height = crop_img.shape[0]
img_width = crop_img.shape[1]
if ModelPhase.is_train(mode):
crop_width = cfg.TRAIN_CROP_SIZE[0]
crop_height = cfg.TRAIN_CROP_SIZE[1]
else:
crop_width = cfg.EVAL_CROP_SIZE[0]
crop_height = cfg.EVAL_CROP_SIZE[1]
if not ModelPhase.is_train(mode):
if (crop_height < img_height or crop_width < img_width):
raise Exception(
"Crop size({},{}) must large than img size({},{}) when in EvalPhase."
.format(crop_width, crop_height, img_width, img_height))
if img_height == crop_height and img_width == crop_width:
return crop_img, crop_seg
else:
pad_height = max(crop_height - img_height, 0)
pad_width = max(crop_width - img_width, 0)
if (pad_height > 0 or pad_width > 0):
crop_img = cv2.copyMakeBorder(
crop_img,
0,
pad_height,
0,
pad_width,
cv2.BORDER_CONSTANT,
value=cfg.DATASET.PADDING_VALUE)
if crop_seg is not None:
crop_seg = cv2.copyMakeBorder(
crop_seg,
0,
pad_height,
0,
pad_width,
cv2.BORDER_CONSTANT,
value=cfg.DATASET.IGNORE_INDEX)
img_height = crop_img.shape[0]
img_width = crop_img.shape[1]
if crop_height > 0 and crop_width > 0:
h_off = np.random.randint(img_height - crop_height + 1)
w_off = np.random.randint(img_width - crop_width + 1)
crop_img = crop_img[h_off:(crop_height + h_off), w_off:(
w_off + crop_width), :]
if crop_seg is not None:
crop_seg = crop_seg[h_off:(crop_height + h_off), w_off:(
w_off + crop_width)]
return crop_img, crop_seg