def _aug_image(self, instance, net_h, net_w):
image_name = instance['filename']
image = cv2.imread(image_name) # BGR image
if image is None:
print('Cannot find ', image_name)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # RGB image
image_h, image_w, _ = image.shape
# determine the amount of scaling and cropping
dw = self.jitter * image_w
dh = self.jitter * image_h
new_ar = (image_w + np.random.uniform(-dw, dw)) / (image_h + np.random.uniform(-dh, dh))
if self.aug == 'scale' and np.random.random_sample()<0.3:
scale = np.random.uniform(0.25, 2)
else:
scale = 1
if new_ar < 1:
new_h = int(scale * net_h)
new_w = int(net_h * new_ar)
else:
new_w = int(scale * net_w)
new_h = int(net_w / new_ar)
dx = int(np.random.uniform(0, net_w - new_w))
dy = int(np.random.uniform(0, net_h - new_h))
# apply scaling and cropping
im_sized = apply_random_scale_and_crop(image, new_w, new_h, net_w, net_h, dx, dy)
# if self.aug == True:
# # randomly distort hsv space
# im_sized = random_distort_image(im_sized)
if self.aug == 'filp' and np.random.random_sample()<0.3:
# randomly flip
flip = np.random.randint(2)
im_sized = random_flip(im_sized, flip)
else:
flip = 0
# correct the size and pos of bounding boxes
all_objs = correct_bounding_boxes(instance['object'], new_w, new_h, net_w, net_h, dx, dy, flip, image_w, image_h)
return im_sized, all_objs
def _aug_image(self, instance, net_h, net_w):
image_name = instance['filename']
image = cv2.imread(image_name) # RGB image
if image is None: print('Cannot find ', image_name)
image = image[:, :, ::-1] # RGB image
image_h, image_w, _ = image.shape
# determine the amount of scaling and cropping
dw = self.jitter * image_w
dh = self.jitter * image_h
new_ar = (image_w + np.random.uniform(-dw, dw)) / (
image_h + np.random.uniform(-dh, dh))
scale = np.random.uniform(0.25, 2)
if (new_ar < 1):
new_h = int(scale * net_h)
new_w = int(net_h * new_ar)
else:
new_w = int(scale * net_w)
new_h = int(net_w / new_ar)
dx = int(np.random.uniform(0, net_w - new_w))
dy = int(np.random.uniform(0, net_h - new_h))
# apply scaling and cropping
im_sized = apply_random_scale_and_crop(image, new_w, new_h, net_w,
net_h, dx, dy)
# randomly distort hsv space
im_sized = random_distort_image(im_sized)
# randomly flip
flip = np.random.randint(2)
im_sized = random_flip(im_sized, flip)
# correct the size and pos of bounding boxes
all_objs = correct_bounding_boxes(instance['object'], new_w, new_h,
net_w, net_h, dx, dy, flip, image_w,
image_h)
return im_sized, all_objs
