智能交通-场景数据增强（Python CV）
先看变换效果，代码在博客下文：
1. 增大图片的亮度 （图片变亮）
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption 
Caption
7. 代码包括Automold.py 和 test.py, test.py调用Automold.py进行测试
import Automold as am
import cv2
img = cv2.imread('test_augmentation/image1.jpg')
img= cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i in range(20):
# 测试亮度变化
#brightness_coeff = i * 0.05
#bright_img = am.brighten(img, brightness_coeff)
#bright_img = cv2.cvtColor(bright_img, cv2.COLOR_RGB2BGR)
#cv2.imwrite("test_imgs/bright_%s.jpg" % i, bright_img)
# 测试暗度变化
#darkness_coeff = i * 0.05
#dark_img = am.darken(img, darkness_coeff)
#dark_img = cv2.cvtColor(dark_img, cv2.COLOR_RGB2BGR)
#cv2.imwrite("test_imgs/dark_%s.jpg" % i, dark_img)
# 测试雪量变化
#snow_coeff = i * 0.05
#snow_img = am.add_snow(img, snow_coeff)
#snow_img = cv2.cvtColor(snow_img, cv2.COLOR_RGB2BGR)
#cv2.imwrite("test_imgs/snow_%s.jpg" % i, snow_img)
# 测试速度变化
#speed_coeff = i * 0.05
#speed_img = am.add_speed(img, speed_coeff)
#speed_img = cv2.cvtColor(speed_img, cv2.COLOR_RGB2BGR)
#cv2.imwrite("test_imgs/speed_%s.jpg" % i, speed_img)
# 测试雾量变化
#fog_coeff = (i+1) * 0.05
#fog_img = am.add_fog(img, fog_coeff=fog_coeff)
#fog_img = cv2.cvtColor(fog_img, cv2.COLOR_RGB2BGR)
#cv2.imwrite("test_imgs/fog_%s.jpg" % i, fog_img)
# 测试下雨变化
if i 14:
rain_type = "torrential"
else:
rain_type = "heavy"
rain_img = am.add_rain(img, rain_type=rain_type)
rain_img = cv2.cvtColor(rain_img, cv2.COLOR_RGB2BGR)
cv2.imwrite("test_imgs/rain_%s.jpg" % i, rain_img)
# import glob
import cv2 as cv2
import numpy as np
# import matplotlib.pyplot as plt
import random
import math
###################### HLS #############################
def hls(image,src='RGB'):
verify_image(image)
if(is_list(image)):
image_HLS=[]
image_list=image
for img in image_list:
eval('image_HLS.append(cv2.cvtColor(img,cv2.COLOR_'+src.upper()+'2HLS))')
else:
image_HLS = eval('cv2.cvtColor(image,cv2.COLOR_'+src.upper()+'2HLS)')
return image_HLS
def hue(image,src='RGB'):
verify_image(image)
if(is_list(image)):
image_Hue=[]
image_list=image
for img in image_list:
image_Hue.append(hls(img,src)[:,:,0])
else:
image_Hue= hls(image,src)[:,:,0]
return image_Hue
def lightness(image,src='RGB'):
verify_image(image)
if(is_list(image)):
image_lightness=[]
image_list=image
for img in image_list:
image_lightness.append(hls(img,src)[:,:,1])
else:
image_lightness= hls(image,src)[:,:,1]
return image_lightness
def saturation(image,src='RGB'):
verify_image(image)
if(is_list(image)):
image_saturation=[]
image_list=image
for img in image_list:
image_saturation.append(hls(img,src)[:,:,2])
else:
image_saturation= hls(image,src)[:,:,2]
return image_saturation
###################### HSV #############################
def hsv(image,src='RGB'):
verify_image(image)
if(is_list(image)):
image_HSV=[]
image_list=image
for img in image_list:
eval('image_HSV.append(cv2.cvtColor(img,cv2.COLOR_'+src.upper()+'2HSV))')
else:
image_HSV = eval('cv2.cvtColor(image,cv2.COLOR_'+src.upper()+'2HSV)')
return image_HSV
def value(image,src='RGB'):
verify_image(image)
if(is_list(image)):
image_value=[]
image_list=image
for img in image_list:
image_value.append(hsv(img,src)[:,:,2])
else:
image_value= hsv(image,src)[:,:,2]
return image_value
###################### BGR #############################
def bgr(image, src='RGB'):
verify_image(image)
if(is_list(image)):
image_BGR=[]
image_list=image
for img in image_list:
eval('image_BGR.append(cv2.cvtColor(img,cv2.COLOR_'+src.upper()+'2BGR))')
else:
image_BGR= eval('cv2.cvtColor(image,cv2.COLOR_'+src.upper()+'2BGR)')
return image_BGR
###################### RGB #############################
def rgb(image, src='BGR'):
verify_image(image)
if(is_list(image)):
image_RGB=[]
image_list=image
for img in image_list:
eval('image_RGB.append(cv2.cvtColor(img,cv2.COLOR_'+src.upper()+'2RGB))')
else:
image_RGB= eval('cv2.cvtColor(image,cv2.COLOR_'+src.upper()+'2RGB)')
return image_RGB
def red(image,src='BGR'):
verify_image(image)
if(is_list(image)):
image_red=[]
image_list=image
for img in image_list:
i= eval('cv2.cvtColor(img,cv2.COLOR_'+src.upper()+'2RGB)')
image_red.append(i[:,:,0])
else:
image_red= eval('cv2.cvtColor(image,cv2.COLOR_'+src.upper()+'2RGB)[:,:,0]')
return image_red
def green(image,src='BGR'):
verify_image(image)
if(is_list(image)):
image_green=[]
image_list=image
for img in image_list:
i= eval('cv2.cvtColor(img,cv2.COLOR_'+src.upper()+'2RGB)')
image_green.append(i[:,:,1])
else:
image_green= eval('cv2.cvtColor(image,cv2.COLOR_'+src.upper()+'2RGB)[:,:,1]')
return image_green
def blue(image,src='BGR'):
verify_image(image)
if(is_list(image)):
image_blue=[]
image_list=image
for img in image_list:
i=eval('cv2.cvtColor(img,cv2.COLOR_'+src.upper()+'2RGB)')
image_blue.append(i[:,:,2])
else:
image_blue= eval('cv2.cvtColor(image,cv2.COLOR_'+src.upper()+'2RGB)[:,:,2]')
return image_blue
err_not_np_img= "not a numpy array or list of numpy array"
err_img_arr_empty="Image array is empty"
err_row_zero="No. of rows can't be <=0"
err_column_zero="No. of columns can't be 1):
image_HLS[:,:,1][image_HLS[:,:,1]>255] = 255 ##Sets all values above 255 to 255
else:
image_HLS[:,:,1][image_HLS[:,:,1]<0]=0
image_HLS = np.array(image_HLS, dtype = np.uint8)
image_RGB = cv2.cvtColor(image_HLS,cv2.COLOR_HLS2RGB) ## Conversion to RGB
return image_RGB
def verify_image(image):
if is_numpy_array(image):
pass
elif(is_list(image)):
image_list=image
for img in image_list:
if not is_numpy_array(img):
raise Exception(err_not_np_img)
else:
raise Exception(err_not_np_img)
def brighten(image, brightness_coeff=-1): ##function to brighten the image
verify_image(image)
if(brightness_coeff!=-1):
if(brightness_coeff1.0):
raise Exception(err_brightness_coeff)
if(is_list(image)):
image_RGB=[]
image_list=image
for img in image_list:
if(brightness_coeff==-1):
brightness_coeff_t=1+ random.uniform(0,1) ## coeff between 1.0 and 1.5
else:
brightness_coeff_t=1+ brightness_coeff ## coeff between 1.0 and 2.0
image_RGB.append(change_light(img,brightness_coeff_t))
else:
if(brightness_coeff==-1):
brightness_coeff_t=1+ random.uniform(0,1) ## coeff between 1.0 and 1.5
else:
brightness_coeff_t=1+ brightness_coeff ## coeff between 1.0 and 2.0
image_RGB= change_light(image,brightness_coeff_t)
return image_RGB
def darken(image, darkness_coeff=-1): ##function to darken the image
verify_image(image)
if(darkness_coeff!=-1):
if(darkness_coeff1.0):
raise Exception(err_darkness_coeff)
if(is_list(image)):
image_RGB=[]
image_list=image
for img in image_list:
if(darkness_coeff==-1):
darkness_coeff_t=1- random.uniform(0,1)
else:
darkness_coeff_t=1- darkness_coeff
image_RGB.append(change_light(img,darkness_coeff_t))
else:
if(darkness_coeff==-1):
darkness_coeff_t=1- random.uniform(0,1)
else:
darkness_coeff_t=1- darkness_coeff
image_RGB= change_light(image,darkness_coeff_t)
return image_RGB
def random_brightness(image):
verify_image(image)
if(is_list(image)):
image_RGB=[]
image_list=image
for img in image_list:
random_brightness_coefficient = 2* np.random.uniform(0,1) ## generates value between 0.0 and 2.0
image_RGB.append(change_light(img,random_brightness_coefficient))
else:
random_brightness_coefficient = 2* np.random.uniform(0,1) ## generates value between 0.0 and 2.0
image_RGB= change_light(image,random_brightness_coefficient)
return image_RGB
err_shadow_count="only 1-10 shadows can be introduced in an image"
err_invalid_rectangular_roi="Rectangular ROI dimensions are not valid"
err_shadow_dimension="polygons with dim10 take time to plot"
def generate_shadow_coordinates(imshape, no_of_shadows, rectangular_roi, shadow_dimension):
vertices_list=[]
x1=rectangular_roi[0]
y1=rectangular_roi[1]
x2=rectangular_roi[2]
y2=rectangular_roi[3]
for index in range(no_of_shadows):
vertex=[]
for dimensions in range(shadow_dimension): ## Dimensionality of the shadow polygon
vertex.append((random.randint(x1, x2),random.randint(y1, y2)))
vertices = np.array([vertex], dtype=np.int32) ## single shadow vertices
vertices_list.append(vertices)
return vertices_list ## List of shadow vertices
def shadow_process(image,no_of_shadows,x1,y1,x2,y2, shadow_dimension):
image_HLS = cv2.cvtColor(image,cv2.COLOR_RGB2HLS) ## Conversion to HLS
mask = np.zeros_like(image)
imshape = image.shape
vertices_list= generate_shadow_coordinates(imshape, no_of_shadows,(x1,y1,x2,y2), shadow_dimension) #3 getting list of shadow vertices
for vertices in vertices_list:
cv2.fillPoly(mask, vertices, 255) ## adding all shadow polygons on empty mask, single 255 denotes only red channel
image_HLS[:,:,1][mask[:,:,0]==255] = image_HLS[:,:,1][mask[:,:,0]==255]*0.5 ## if red channel is hot, image's "Lightness" channel's brightness is lowered
image_RGB = cv2.cvtColor(image_HLS,cv2.COLOR_HLS2RGB) ## Conversion to RGB
return image_RGB
def add_shadow(image,no_of_shadows=1,rectangular_roi=(-1,-1,-1,-1), shadow_dimension=5):## ROI:(top-left x1,y1, bottom-right x2,y2), shadow_dimension=no. of sides of polygon generated
verify_image(image)
if not(is_numeric(no_of_shadows) and no_of_shadows>=1 and no_of_shadows=3 and shadow_dimension<=10):
raise Exception(err_shadow_dimension)
if is_tuple(rectangular_roi) and is_numeric_list_or_tuple(rectangular_roi) and len(rectangular_roi)==4:
x1=rectangular_roi[0]
y1=rectangular_roi[1]
x2=rectangular_roi[2]
y2=rectangular_roi[3]
else:
raise Exception(err_invalid_rectangular_roi)
if rectangular_roi==(-1,-1,-1,-1):
x1=0
if(is_numpy_array(image)):
y1=image.shape[0]//2
x2=image.shape[1]
y2=image.shape[0]
else:
y1=image[0].shape[0]//2
x2=image[0].shape[1]
y2=image[0].shape[0]
elif x1==-1 or y1==-1 or x2==-1 or y2==-1 or x2<=x1 or y2<=y1:
raise Exception(err_invalid_rectangular_roi)
if(is_list(image)):
image_RGB=[]
image_list=image
for img in image_list:
output=shadow_process(img,no_of_shadows,x1,y1,x2,y2, shadow_dimension)
image_RGB.append(output)
else:
output=shadow_process(image,no_of_shadows,x1,y1,x2,y2, shadow_dimension)
image_RGB = output
return image_RGB
err_snow_coeff="Snow coeff can only be between 0 and 1"
def snow_process(image,snow_coeff):
image_HLS = cv2.cvtColor(image,cv2.COLOR_RGB2HLS) ## Conversion to HLS
image_HLS = np.array(image_HLS, dtype = np.float64)
brightness_coefficient = 2.5
imshape = image.shape
snow_point=snow_coeff ## increase this for more snow
image_HLS[:,:,1][image_HLS[:,:,1]<snow_point] = image_HLS[:,:,1][image_HLS[:,:,1]255] = 255 ##Sets all values above 255 to 255
image_HLS = np.array(image_HLS, dtype = np.uint8)
image_RGB = cv2.cvtColor(image_HLS,cv2.COLOR_HLS2RGB) ## Conversion to RGB
return image_RGB
def add_snow(image, snow_coeff=-1):
verify_image(image)
if(snow_coeff!=-1):
if(snow_coeff1.0):
raise Exception(err_snow_coeff)
else:
snow_coeff=random.uniform(0,1)
snow_coeff*=255/2
snow_coeff+=255/3
if(is_list(image)):
image_RGB=[]
image_list=image
for img in image_list:
output= snow_process(img,snow_coeff)
image_RGB.append(output)
else:
output= snow_process(image,snow_coeff)
image_RGB=output
return image_RGB
err_rain_slant="Numeric value between -20 and 20 is allowed"
err_rain_width="Width value between 1 and 5 is allowed"
err_rain_length="Length value between 0 and 100 is allowed"
def generate_random_lines(imshape,slant,drop_length,rain_type):
drops=[]
area=imshape[0]*imshape[1]
no_of_drops=area//600
if rain_type.lower()=='drizzle':
no_of_drops=area//770
drop_length=10
elif rain_type.lower()=='heavy':
drop_length=30
elif rain_type.lower()=='torrential':
no_of_drops=area//500
drop_length=60
for i in range(no_of_drops): ## If You want heavy rain, try increasing this
if slant=-20 and slant_extreme=1 and drop_width=0 and drop_length-hw or midy>-hw):
for i in range(hw//10*index):
x= np.random.randint(midx,imshape[1]-midx-hw)
y= np.random.randint(midy,imshape[0]-midy-hw)
blur_points.append((x,y))
midx-=3*hw*imshape[1]//sum(imshape)
midy-=3*hw*imshape[0]//sum(imshape)
index+=1
return blur_points
def add_fog(image, fog_coeff=-1):
verify_image(image)
if(fog_coeff!=-1):
if(fog_coeff1.0):
raise Exception(err_fog_coeff)
if(is_list(image)):
image_RGB=[]
image_list=image
imshape = image[0].shape
for img in image_list:
if fog_coeff==-1:
fog_coeff_t=random.uniform(0.3,1)
else:
fog_coeff_t=fog_coeff
hw=int(imshape[1]//3*fog_coeff_t)
haze_list= generate_random_blur_coordinates(imshape,hw)
for haze_points in haze_list:
img= add_blur(img, haze_points[0],haze_points[1], hw,fog_coeff_t) ## adding all shadow polygons on empty mask, single 255 denotes only red channel
img = cv2.blur(img ,(hw//10,hw//10))
image_RGB.append(img)
else:
imshape = image.shape
if fog_coeff==-1:
fog_coeff_t=random.uniform(0.3,1)
else:
fog_coeff_t=fog_coeff
hw=int(imshape[1]//3*fog_coeff_t)
haze_list= generate_random_blur_coordinates(imshape,hw)
for haze_points in haze_list:
image= add_blur(image, haze_points[0],haze_points[1], hw,fog_coeff_t)
image = cv2.blur(image ,(hw//10,hw//10))
image_RGB = image
return image_RGB
def generate_gravel_patch(rectangular_roi):
x1=rectangular_roi[0]
y1=rectangular_roi[1]
x2=rectangular_roi[2]
y2=rectangular_roi[3]
gravels=[]
area= abs((x2-x1)*(y2-y1))
for i in range((int)(area//10)):
x= np.random.randint(x1,x2)
y= np.random.randint(y1,y2)
gravels.append((x,y))
return gravels
def gravel_process(image,x1,x2,y1,y2,no_of_patches):
x=image.shape[1]
y=image.shape[0]
rectangular_roi_default=[]
for i in range(no_of_patches):
xx1=random.randint(x1, x2)
xx2=random.randint(x1, xx1)
yy1=random.randint(y1, y2)
yy2=random.randint(y1, yy1)
rectangular_roi_default.append((xx2,yy2,min(xx1,xx2+200),min(yy1,yy2+30)))
img_hls=hls(image)
for roi in rectangular_roi_default:
gravels= generate_gravel_patch(roi)
for gravel in gravels:
x=gravel[0]
y=gravel[1]
r=random.randint(1, 4)
r1=random.randint(0, 255)
img_hls[max(y-r,0):min(y+r,y),max(x-r,0):min(x+r,x),1]=r1
image_RGB= rgb(img_hls,'hls')
return image_RGB
def add_gravel(image,rectangular_roi=(-1,-1,-1,-1), no_of_patches=8):
verify_image(image)
if is_tuple(rectangular_roi) and is_numeric_list_or_tuple(rectangular_roi) and len(rectangular_roi)==4:
x1=rectangular_roi[0]
y1=rectangular_roi[1]
x2=rectangular_roi[2]
y2=rectangular_roi[3]
else:
raise Exception(err_invalid_rectangular_roi)
if rectangular_roi==(-1,-1,-1,-1):
if(is_numpy_array(image)):
x1=0
y1=int(image.shape[0]*3/4)
x2=image.shape[1]
y2=image.shape[0]
else:
x1=0
y1=int(image[0].shape[0]*3/4)
x2=image[0].shape[1]
y2=image[0].shape[0]
elif x1==-1 or y1==-1 or x2==-1 or y2==-1 or x2<=x1 or y2=0 and no_of_flare_circles<=20):
raise Exception(err_flare_circle_count)
if(is_list(image)):
image_RGB=[]
image_list=image
imshape=image_list[0].shape
for img in image_list:
if(angle==-1):
angle_t=random.uniform(0,2*math.pi)
if angle_t==math.pi/2:
angle_t=0
else:
angle_t=angle
if flare_center==-1:
flare_center_t=(random.randint(0,imshape[1]),random.randint(0,imshape[0]//2))
else:
flare_center_t=flare_center
x,y= add_sun_flare_line(flare_center_t,angle_t,imshape)
output= add_sun_process(img, no_of_flare_circles,flare_center_t,src_radius,x,y,src_color)
image_RGB.append(output)
else:
imshape=image.shape
if(angle==-1):
angle_t=random.uniform(0,2*math.pi)
if angle_t==math.pi/2:
angle_t=0
else:
angle_t=angle
if flare_center==-1:
flare_center_t=(random.randint(0,imshape[1]),random.randint(0,imshape[0]//2))
else:
flare_center_t=flare_center
x,y= add_sun_flare_line(flare_center_t,angle_t,imshape)
output= add_sun_process(image, no_of_flare_circles,flare_center_t,src_radius,x,y,src_color)
image_RGB = output
return image_RGB
err_speed_coeff="Speed coeff can only be between 0 and 1"
def apply_motion_blur(image,count):
image_t=image.copy()
imshape=image_t.shape
size=15
kernel_motion_blur = np.zeros((size, size))
kernel_motion_blur[int((size-1)/2), :] = np.ones(size)
kernel_motion_blur = kernel_motion_blur / size
i= imshape[1]*3//4 - 10*count
while(i<=imshape[1]):
image_t[:,i:,:] = cv2.filter2D(image_t[:,i:,:], -1, kernel_motion_blur)
image_t[:,:imshape[1]-i,:] = cv2.filter2D(image_t[:,:imshape[1]-i,:], -1, kernel_motion_blur)
i+=imshape[1]//25-count
count+=1
image_RGB=image_t
return image_RGB
def add_speed(image, speed_coeff=-1):
verify_image(image)
if(speed_coeff !=-1):
if(speed_coeff1.0):
raise Exception(err_speed_coeff)
if(is_list(image)):
image_RGB=[]
image_list=image
for img in image_list:
if(speed_coeff==-1):
count_t=int(15*random.uniform(0,1))
else:
count_t=int(15*speed_coeff)
img=apply_motion_blur(img,count_t)
image_RGB.append(img)
else:
if(speed_coeff==-1):
count_t=int(15*random.uniform(0,1))
else:
count_t=int(15*speed_coeff)
image_RGB= apply_motion_blur(image,count_t)
return image_RGB
# In[159]:
def autumn_process(image):
image_t=image.copy()
imshape=image_t.shape
image_hls= hls(image_t)
step=8
aut_colors=[1,5,9,11]
col= aut_colors[random.randint(0,3)]
for i in range(0,imshape[1],step):
for j in range(0,imshape[0],step):
avg=np.average(image_hls[j:j+step,i:i+step,0])
# print(avg)
if(avg >20 and avg< 100 and np.average(image[j:j+step,i:i+step,1])0.5):
image_RGB.append(cv2.flip(img,0))
else:
image_RGB.append(cv2.flip(img,1))
else:
p= random.uniform(0,1)
if(p>0.5):
image_RGB=cv2.flip(image,0)
else:
image_RGB=cv2.flip(image,1)
return image_RGB
# def edges(image,threshold1=100, threshold2=150): ##function to flip the image on horizontal axis
# verify_image(image)
# if(is_list(image)):
# image_RGB=[]
# image_list=image
# for img in image_list:
# image_RGB.append(cv2.Canny(img,threshold1,threshold2))
# else:
# image_RGB=cv2.Canny(img,threshold1,threshold2)
# return image_RGB
def manhole_process(image,center,height,width,src_color=(0,0,0)):
overlay= image.copy()
output= image.copy()
# cv2.ellipse(overlay, center =center,box=None,color =src_color)
cv2.ellipse(overlay, center, (width,height), 0, 0, 360, src_color, -1)
# cv2.circle(overlay, center, radius, src_color, -1)
alp=1
cv2.addWeighted(overlay, alp, output, 1 -alp ,0, output)
return output
err_invalid_center_manhole="center should be in the format (x,y)"
err_invalid_height_width_manhole="height and width should be positive integers."
def add_manhole(image,center=-1,color=(120,120,120),height=1,width=1, type='closed'): ##function to flip the image on horizontal axis
verify_image(image)
if(center!=-1):
if not(is_tuple(center) and is_numeric_list_or_tuple(center) and len(center)==2):
raise Exception(err_invalid_center_manhole)
if not (is_numeric(height) and is_numeric(width) and height>0 and width>0):
raise Exception(err_invalid_height_width_manhole)
if color==(120,120,120):
if type=='closed':
color=(67,70,75)
elif type=='open':
color=(0,0,0)
if(is_list(image)):
image_RGB=[]
image_list=image
for img in image_list:
height_t=height
width_t=width
center_t=center
if height==1:
height_t=img.shape[0]//25
if width==1:
width_t=int(img.shape[0]*3//25)
if center==-1:
center_t= (img.shape[0]-100, img.shape[1]//2)
image_RGB.append(manhole_process(img,center_t,height_t,width_t,color))
else:
height_t=height
width_t=width
center_t=center
if height==1:
height_t=image.shape[0]//25
if width==1:
width_t=int(image.shape[0]*3//25)
if center==-1:
center= (image.shape[0]-100, image.shape[1]//2)
image_RGB= manhole_process(image,center_t,height_t,width_t,color)
return image_RGB
def exposure_process(image):
image= np.copy(image)
img_yuv = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(4,4))
ones= np.ones(img_yuv[:,:,0].shape)
ones[img_yuv[:,:,0]>150]= 0.85
img_yuv[:,:,0]= img_yuv[:,:,0]*ones
img_yuv[:,:,0] = clahe.apply(img_yuv[:,:,0])
img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])
img_yuv[:,:,0] = clahe.apply(img_yuv[:,:,0])
image_res = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)
image_res= cv2.fastNlMeansDenoisingColored(image_res,None,3,3,7,21)
return image_res
def correct_exposure(image):
verify_image(image)
if(is_list(image)):
image_RGB=[]
image_list=image
for img in image_list:
image_RGB.append(exposure_process(img))
else:
image_RGB= exposure_process(image)
return image_RGB
err_aug_type='wrong augmentation function is defined'
err_aug_list_type='aug_types should be a list of string function names'
err_aug_volume='volume type can only be "same" or "expand"'
def augment_random(image, aug_types="", volume='expand' ):
aug_types_all=["random_brightness","add_shadow","add_snow","add_rain","add_fog","add_gravel","add_sun_flare","add_speed","add_autumn","random_flip","add_manhole"]
if aug_types=="":
aug_types=aug_types_all
output=[]
if not(is_list(aug_types)):
raise Exception(err_aug_list_type)
if volume=='expand':
for aug_type in aug_types:
if not(aug_type in aug_types_all):
raise Exception(err_aug_type)
command=aug_type+'(image)'
result=eval(command)
if(is_list(result)):
output+=result
else:
output.append(result)
elif volume=='same':
verify_image(image)
for aug_type in aug_types:
if not(aug_type in aug_types_all):
raise Exception(err_aug_type)
if(is_list(image)):
image_list=image
for img in image_list:
selected_aug=aug_types[random.randint(0,len(aug_types)-1)]
command=selected_aug+'(img)'
output.append(eval(command))
else:
selected_aug=aug_types[random.randint(0,len(aug_types)-1)]
command=selected_aug+'(image)'
output=eval(command)
else:
raise Exception(err_aug_volume)
return output
作者：CV-deeplearning
