def tranfun(self, image_and_loc):
image, left, top, right, bottom = image_and_loc
w, h = image.size
left = np.clip(left,0,w-1)
right = np.clip(right,0,w-1)
top = np.clip(top, 0, h-1)
bottom = np.clip(bottom, 0, h-1)
img = trans_utils.getcvimage(image)
try:
# global index
res = getpilimage(img[top:bottom,left:right])
# res.save('test_imgs/crop-debug-{}.jpg'.format(index))
# index+=1
return res
except AttributeError as e:
print('error')
image.save('test_imgs/t.png')
print( left, top, right, bottom)
h = bottom - top
w = right - left
org = np.array([[left - np.random.randint(0, self.maxv_w), top + np.random.randint(-self.maxv_h, self.maxv_h//2)],
[right + np.random.randint(0, self.maxv_w), top + np.random.randint(-self.maxv_h, self.maxv_h//2)],
[left - np.random.randint(0, self.maxv_w), bottom - np.random.randint(-self.maxv_h, self.maxv_h//2)],
[right + np.random.randint(0, self.maxv_w), bottom - np.random.randint(-self.maxv_h, self.maxv_h//2)]], np.float32)
dst = np.array([[0, 0], [w, 0], [0, h], [w, h]], np.float32)
M = cv2.getPerspectiveTransform(org,dst)
res = cv2.warpPerspective(img,M,(w,h))
return getpilimage(res)
def tranfun(self, image):
image = getpilimage(image)
w, h = image.size
rate = np.random.random()*(self.max_rate-self.min_rate)+self.min_rate
w2 = int(w*rate)
image = image.resize((w2, h))
return image
def tranfun(self, image):
image = getpilimage(image)
num_noise = int(image.size[1] * image.size[0] * self.rate)
# assert len(image.split()) == 1
for k in range(num_noise):
i = int(np.random.random() * image.size[1])
j = int(np.random.random() * image.size[0])
image.putpixel((j, i), int(np.random.random() * 255))
return image
def tranfun(self, image):
image = getpilimage(image)
draw =ImageDraw.Draw(image)
h=image.height
w=image.width
y0=random.randint(h//4,h*3//4)
y1=np.clip(random.randint(-3,3)+y0,0,h-1)
color=random.randint(0,30)
draw.line(((0,y0),(w-1,y1)),fill=(color,color,color),width=2)
return image
def tranfun(self, image):
img = trans_utils.getcvimage(image)
h,w = img.shape[:2]
org = np.array([[0,np.random.randint(0,self.maxv)],
[w,np.random.randint(0,self.maxv)],
[0,h-np.random.randint(0,self.maxv)],
[w,h-np.random.randint(0,self.maxv)]],np.float32)
dst = np.array([[0, 0], [w, 0], [0, h], [w, h]], np.float32)
M = cv2.getPerspectiveTransform(org,dst)
res = cv2.warpPerspective(img,M,(w,h))
return getpilimage(res)
def tranfun(self, image):
image = getpilimage(image)
angle=random.randint(0,self.angle)
M = cv2.getRotationMatrix2D((self.degree / 2, self.degree / 2), angle, 1)
motion_blur_kernel = np.diag(np.ones(self.degree))
motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (self.degree, self.degree))
motion_blur_kernel = motion_blur_kernel / self.degree
image = image.filter(ImageFilter.Kernel(size=(self.degree,self.degree),kernel=motion_blur_kernel.reshape(-1)))
# blurred_image = image.filter(ImageFilter.Kernel((3,3), (1,1,1,0,0,0,2,0,2)))
# Kernel
return image
def tranfun(self, image):
image = getpilimage(image)
sha = ImageEnhance.Sharpness(image)
return sha.enhance(random.uniform(self.lower, self.upper))
def tranfun(self, image):
image = getpilimage(image)
col = ImageEnhance.Color(image)
return col.enhance(random.uniform(self.lower, self.upper))
def tranfun(self, image):
image = getpilimage(image)
bri = ImageEnhance.Brightness(image)
return bri.enhance(random.uniform(self.lower, self.upper))
def tranfun(self, image):
image = getpilimage(image)
image = image.filter(ImageFilter.GaussianBlur(radius=1.5))
# blurred_image = image.filter(ImageFilter.Kernel((3,3), (1,1,1,0,0,0,2,0,2)))
# Kernel
return image
def tranfun(self, image):
image = getpilimage(image)
rot = random.uniform(self.lower, self.upper)
trans_img = image.rotate(rot, expand=True)
# trans_img.show()
return trans_img
