"""Miscellaneous utility functions."""
from functools import reduce
from PIL import Image
import numpy as np
from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
# 组合多个函数，上一个函数的输出作为下一个函数的输入
def compose(*funcs):
"""Compose arbitrarily many functions, evaluated left to right.
Reference: https://mathieularose.com/function-composition-in-python/
"""
# return lambda x: reduce(lambda v, f: f(v), funcs, x)
if funcs:
return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)
else:
raise ValueError('Composition of empty sequence not supported.')
# 等⽐例缩放图片
def letterbox_image(image, size):
'''resize image with unchanged aspect ratio using padding'''
iw, ih = image.size
w, h = size
scale = min(w/iw, h/ih)
nw = int(iw*scale)
nh = int(ih*scale)
image = image.resize((nw,nh), Image.BICUBIC)
new_image = Image.new('RGB', size, (128,128,128))
new_image.paste(image, ((w-nw)//2, (h-nh)//2))
return new_image
# 返回[a,b)之间的随机数
def rand(a=0, b=1):
return np.random.rand()*(b-a) + a
# 数据预处理，对图像属性做⼀些变换，模拟实时图像数据,并且把RGB值归⼀化
def get_random_data(annotation_line, input_shape, random=True, max_boxes=20, jitter=.3, hue=.1, sat=1.5, val=1.5, proc_img=True):
'''random preprocessing for real-time data augmentation'''
# annotation_line: 数据集中的某一行对应的图片
# input_shape: yolo网络输入图片的大小416*416
# jitter:控制图片的宽高的扭曲比率,jitter=.5表示在0.5到1.5之间进行扭曲
# hue: 代表hsv色域中三个通道中的色调进行扭曲，色调（H）=.1
# sat: 代表hsv色域中三个通道中的饱和度进行扭曲，饱和度(S)=1.5
# val: 代表hsv色域中三个通道中的明度进行扭曲，明度（V）=1.5
# return:
line = annotation_line.split()
image = Image.open(line[0])
iw, ih = image.size
h, w = input_shape
# 对该行的图片中的目标框进行一个划分
box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
if not random:
# resize image
scale = min(w/iw, h/ih)
nw = int(iw*scale)
nh = int(ih*scale)
dx = (w-nw)//2
dy = (h-nh)//2
image_data=0
if proc_img:
image = image.resize((nw,nh), Image.BICUBIC)
new_image = Image.new('RGB', (w,h), (128,128,128))
new_image.paste(image, (dx, dy))
image_data = np.array(new_image)/255.
# correct boxes
box_data = np.zeros((max_boxes,5))
if len(box)>0:
np.random.shuffle(box)
if len(box)>max_boxes: box = box[:max_boxes]
box[:, [0,2]] = box[:, [0,2]]*scale + dx
box[:, [1,3]] = box[:, [1,3]]*scale + dy
box_data[:len(box)] = box
return image_data, box_data
# 对图像进行缩放并且进行长和宽的扭曲
# resize image
new_ar = w/h * rand(1-jitter,1+jitter)/rand(1-jitter,1+jitter)
# rand(.25, 1)会把原始的图片进行缩小，图片的边缘加上灰条，可以训练网络对小目标的检测能力。rand(1,2)放大图像
scale = rand(.25, 2)
if new_ar < 1:
nh = int(scale*h)
nw = int(nh*new_ar)
else:
nw = int(scale*w)
nh = int(nw/new_ar)
image = image.resize((nw,nh), Image.BICUBIC)
# 将图像多余的部分加上灰条，保证图片的大小为w,h = 416,416
# place image
dx = int(rand(0, w-nw))
dy = int(rand(0, h-nh))
new_image = Image.new('RGB', (w,h), (128,128,128))
new_image.paste(image, (dx, dy))
image = new_image
# 翻转图像
# flip image or not
flip = rand()<.5 # 有50%的几率发生翻转
if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT) # 左右翻转
# 色域扭曲
# 色域扭曲是发生在hsv色域上，hsv色域是有色调H、饱和度S、明度V三者控制，调整这3个值调整色域扭曲的比率
# distort image
hue = rand(-hue, hue)
sat = rand(1, sat) if rand()<.5 else 1/rand(1, sat)
val = rand(1, val) if rand()<.5 else 1/rand(1, val)
x = rgb_to_hsv(np.array(image)/255.) # 将图片从RGB图像调整到hsv色域上之后，再对其色域进行扭曲
x[..., 0] += hue
x[..., 0][x[..., 0]>1] -= 1
x[..., 0][x[..., 0]<0] += 1
x[..., 1] *= sat
x[..., 2] *= val
x[x>1] = 1
x[x<0] = 0
image_data = hsv_to_rgb(x) # numpy array, 0 to 1
# 将box进行调整，对原图片进项扭曲后，也要对原图片中的框也进行相应的调整
# correct boxes
box_data = np.zeros((max_boxes,5))
if len(box)>0:
np.random.shuffle(box)
# 扭曲调整
box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
# 旋转调整
if flip: box[:, [0,2]] = w - box[:, [2,0]]
# 因为调整后不再图像中的目标框的调整
box[:, 0:2][box[:, 0:2]<0] = 0
box[:, 2][box[:, 2]>w] = w
box[:, 3][box[:, 3]>h] = h
box_w = box[:, 2] - box[:, 0]
box_h = box[:, 3] - box[:, 1]
box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box
if len(box)>max_boxes: box = box[:max_boxes]
box_data[:len(box)] = box
return image_data, box_data
