import cv2
import numpy as np
import os.path
import copy
def rotate(image, angle, center=None, scale=1.0):
(h, w) = image.shape[:2]
# If no rotation center is specified, the center of the image is set as the rotation center
if center is None:
center = (w / 2, h / 2)
M = cv2.getRotationMatrix2D(center, angle, scale)
rotated = cv2.warpAffine(image, M, (w, h))
return rotated
def noiseing(img):
#img = cv2.cvtColor(rgbimg, cv2.COLOR_BGR2GRAY)
param = 30
grayscale = 256
w = img.shape[1]
h = img.shape[0]
newimg = np.zeros((h, w, 3), np.uint8)
#row and col
for x in xrange(0, h):
for y in xrange(0, w, 2): #Avoid exceeding boundaries
r1 = np.random.random_sample()
r2 = np.random.random_sample()
z1 = param * np.cos(2 * np.pi * r2) * np.sqrt((-2) * np.log(r1))
z2 = param * np.sin(2 * np.pi * r2) * np.sqrt((-2) * np.log(r1))
fxy_0 = int(img[x, y, 0] + z1)
fxy_1 = int(img[x, y, 1] + z1)
fxy_2 = int(img[x, y, 2] + z1)
fxy1_0 = int(img[x, y + 1, 0] + z2)
fxy1_1 = int(img[x, y + 1, 1] + z2)
fxy1_2 = int(img[x, y + 1, 2] + z2)
# f(x,y)
if fxy_0 < 0:
fxy_val_0 = 0
elif fxy_0 > grayscale - 1:
fxy_val_0 = grayscale - 1
else:
fxy_val_0 = fxy_0
if fxy_1 < 0:
fxy_val_1 = 0
elif fxy_1 > grayscale - 1:
fxy_val_1 = grayscale - 1
else:
fxy_val_1 = fxy_1
if fxy_2 < 0:
fxy_val_2 = 0
elif fxy_2 > grayscale - 1:
fxy_val_2 = grayscale - 1
else:
fxy_val_2 = fxy_2
# f(x,y+1)
if fxy1_0 < 0:
fxy1_val_0 = 0
elif fxy1_0 > grayscale - 1:
fxy1_val_0 = grayscale - 1
else:
fxy1_val_0 = fxy1_0
if fxy1_1 < 0:
fxy1_val_1 = 0
elif fxy1_1 > grayscale - 1:
fxy1_val_1 = grayscale - 1
else:
fxy1_val_1 = fxy1_1
if fxy1_2 < 0:
fxy1_val_2 = 0
elif fxy1_2 > grayscale - 1:
fxy1_val_2 = grayscale - 1
else:
fxy1_val_2 = fxy1_2
newimg[x, y, 0] = fxy_val_0
newimg[x, y, 1] = fxy_val_1
newimg[x, y, 2] = fxy_val_2
newimg[x, y + 1, 0] = fxy1_val_0
newimg[x, y + 1, 1] = fxy1_val_1
newimg[x, y + 1, 2] = fxy1_val_2
#newimg = cv2.cvtColor(newimg, cv2.COLOR_GRAY2RGB)
cv2.destroyAllWindows()
return newimg
#i = 0
file_dir = "/home/xn/caffe/examples/facetestquestions/ImageDatainc/"
for class_name in os.listdir(file_dir):
#for index,name in enumerate(classes):
class_path = file_dir+class_name+"/"
for img_name in os.listdir(class_path):
img_path = class_path + img_name
image = cv2.imread(img_path)
#Simple rotation 90 degrees
rotated = rotate(image, 90)
cv2.imwrite(class_path + '/' + img_name[0:7] +'_ro90.jpg', rotated)
#Rotate 180 degrees and add Gaussian noise
rotated = rotate(image, 180)
# if __name__ == '__main__':
#print 'load %s ...' % fn
#img = cv2.imread(rotated)
# coutn = 100000
# for k in xrange(0, coutn):
# get the random point
# xi = int(np.random.uniform(0, rotated.shape[1]))
# xj = int(np.random.uniform(0, rotated.shape[0]))
# # add noise
# if rotated.ndim == 2:
# rotated[xj, xi] = 255
# elif rotated.ndim == 3:
# rotated[xj, xi, 0] = 25
# rotated[xj, xi, 1] = 20
# rotated[xj, xi, 2] = 20
#cv2.namedWindow('img')
#cv2.imshow('img', img)
#cv2.waitKey()
# cv2.destroyAllWindows()
#newimg = skimage.util.random_noise(rotated, mode='salt', seed=None, clip=False)
newimg = noiseing(rotated)
#newimg = cv2.cvtColor(newing, cv2.COLOR_GRAY2BGR)
cv2.imwrite(class_path + '/' + img_name[0:7] + '_rono.jpg', newimg)
#Image processing
size = image.shape
#Get an image that is the same as the original image, note this to use deep copy
iLR = copy.deepcopy(image)
h = size[0]
w = size[1]
for i in range(h): # row and col
for j in range(w):
iLR[i, w - 1 - j] = image[i, j] # Mirror formula
cv2.imwrite(class_path + '/' + img_name[0:7] + '_mirr.jpg', iLR)
