以下是为了训练识别，轻微的数据增强方法
import os
import cv2
import numpy as np
import random
def colorjitter(img):
'''
### Different Color Jitter ###
img: image
cj_type: {b: brightness, s: saturation, c: constast}
'''
print("==========colorjitter====================")
list_type = ["b","s","c"]
cj_type =random.choice(list_type)
if cj_type == "b":
# value = random.randint(-50, 50)
value = np.random.choice(np.array([-50, -40, -30, 30, 40, 50]))
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
if value >= 0:
lim = 255 - value
v[v > lim] = 255
v[v <= lim] += value
else:
lim = np.absolute(value)
v[v < lim] = 0
v[v >= lim] -= np.absolute(value)
final_hsv = cv2.merge((h, s, v))
img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
return img
elif cj_type == "s":
# value = random.randint(-50, 50)
value = np.random.choice(np.array([-50, -40, -30, 30, 40, 50]))
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
if value >= 0:
lim = 255 - value
s[s > lim] = 255
s[s <= lim] += value
else:
lim = np.absolute(value)
s[s < lim] = 0
s[s >= lim] -= np.absolute(value)
final_hsv = cv2.merge((h, s, v))
img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
return img
elif cj_type == "c":
brightness = 10
contrast = random.randint(40, 100)
dummy = np.int16(img)
dummy = dummy * (contrast / 127 + 1) - contrast + brightness
dummy = np.clip(dummy, 0, 255)
img = np.uint8(dummy)
return img
def noisy(img):
'''
### Adding Noise ###
img: image
cj_type: {gauss: gaussian, sp: salt & pepper}
'''
print("==========noisy====================")
list_type = ["gauss", "sp"]
noise_type = random.choice(list_type)
if noise_type == "gauss":
image = img.copy()
mean = 0
st = 0.7
gauss = np.random.normal(mean, st, image.shape)
gauss = gauss.astype('uint8')
image = cv2.add(image, gauss)
return image
elif noise_type == "sp":
image = img.copy()
prob = 0.05
if len(image.shape) == 2:
black = 0
white = 255
else:
colorspace = image.shape[2]
if colorspace == 3: # RGB
black = np.array([0, 0, 0], dtype='uint8')
white = np.array([255, 255, 255], dtype='uint8')
else: # RGBA
black = np.array([0, 0, 0, 255], dtype='uint8')
white = np.array([255, 255, 255, 255], dtype='uint8')
probs = np.random.random(image.shape[:2])
image[probs < (prob / 2)] = black
image[probs > 1 - (prob / 2)] = white
return image
def filters(img):
'''
### Filtering ###
img: image
f_type: {blur: blur, gaussian: gaussian, median: median}
'''
print("==========filters====================")
list_type = ["blur", "gaussian", "median"]
f_type = random.choice(list_type)
# print(f_type)
if f_type == "blur":
image = img.copy()
fsize = 5
return cv2.blur(image, (fsize, fsize))
elif f_type == "gaussian":
image = img.copy()
fsize = 5
return cv2.GaussianBlur(image, (fsize, fsize), 0)
elif f_type == "median":
image = img.copy()
fsize = 5
return cv2.medianBlur(image, fsize)
def gaussain_noise(img):
print("==========gaussain_noise====================")
img = img.astype(np.uint8)
h, w, c = img.shape
list_var = [0.4,0.38,0.22,20,26,2,3,4,5,6,12,14,16,17,9]
var = random.choice(list_var)
list_mean = [0,0.5,0.08,0.5,15,1,2,3,4,5,6,7,8]
mean = random.choice(list_mean)
# print(var,mean)
sigma = var ** 0.5
gauss = np.random.normal(mean, sigma, (h, w, c))
gauss = gauss.reshape(h, w, c).astype(np.uint8)
noisy = img + gauss
return noisy
def img_contrast(img):
print("==========img_contrast====================")
min_s, max_s, min_v, max_v = 0,25,0,30
img = img.astype(np.uint8)
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
_s = random.randint(min_s, max_s)
_v = random.randint(min_v, max_v)
if _s >= 0 :
hsv_img[:, :, 1] += _s
else :
_s = - _s
hsv_img[:, :, 1] -= _s
if _v >= 0 :
hsv_img[:, :, 2] += _v
else :
_v = - _v
hsv_img[:, :, 2] += _v
out = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR)
return out
def rotate_func(img):
'''
like PIL, rotate by degree, not radians
'''
print("==========rotate_func====================")
fill = (255, 255, 255)
list_ang = [0.4,-0.4,0.25,-0.25,0.3,-0.3,0.45,-0.45,0.6,-0.6,0.7,-0.7,0.8,-0.8,0.9,-0.9,0.168,-0.852,0.5,-0.5,1,-1,0.8,-0.8,1.5,-1.5,1.2,-1.2,1.4,-1.4]
degree = random.choice(list_ang)
# print(degree)
H, W = img.shape[0], img.shape[1]
center = W / 2, H / 2
M = cv2.getRotationMatrix2D(center, degree, 1)
out = cv2.warpAffine(img, M, (W, H), borderValue=fill)
return out
dir_img = "/data_1/everyday/0507/123/"
list_img = os.listdir(dir_img)
for img_name in list_img:
path_img = dir_img + img_name
path_img = "/data_1/everyday/0507/123/0105_None_LBVTZ0100KST87851_B48B20D_1998_20190402_宝马牌_BMW6475JX_165_156_5"
img = cv2.imread(path_img)
while True:
fun_apply_list = [colorjitter,noisy,filters,gaussain_noise,img_contrast,rotate_func]
fun_apply = random.choice(fun_apply_list)
img_aug = fun_apply(img)
cv2.imshow("img_src",img)
cv2.imshow("img_aug", img_aug)
cv2.waitKey(0)
参考github
https://github.com/Canjie-Luo/Text-Image-Augmentation
https://github.com/AISangam/Image-Augmentation-Using-OpenCV-and-Python
https://github.com/FengYen-Chang/Data-Augmentation/blob/master/DataAugmentation/DataAugment.py
https://github.com/CoinCheung/AutoAugment_opencv/blob/master/AA_classification/functions.py