# it: iterator for different resolutions
# start to augment the rest
if AUGMENT and prob < it[0] and MINOR_CLASS_FLAG == False:
for extra in range(3):
center = np.array([
int(image.shape[0] / 2),
int(image.shape[1] / 2)
])
deg = np.random.randint(-10, 10)
#deg = np.random.normal()*30
# changed
# remove and gaussian blur
newimg = aug.gaussian_blur(image)
#newimg = image
#.3 probability for each of shifting vs rotating vs shift(rotate(image))
p = np.random.randint(0, 3)
# debug
# modified to use the removed cloud version of bboxes
# image, new_coords, new_classes
if p == 0:
newimg, nb = aug.shift_image(
newimg, new_coords)
#newimg,nb = aug.shift_image(newimg,box[idx])
elif p == 1:
newimg, nb = aug.rotate_image_and_boxes(
newimg, deg, center, new_coords)
#newimg,nb = aug.rotate_image_and_boxes(newimg,deg,center,box[idx])
#Make augmentation probability proportional to chip size. Lower chip size = less chance.
#This makes the chip-size imbalance less severe.
prob = np.random.randint(0, np.max(res))
#for 200x200: p(augment) = 200/500 ; for 300x300: p(augment) = 300/500 ...
if AUGMENT and prob < it[0]:
for extra in range(3):
center = np.array([
int(image.shape[0] / 2),
int(image.shape[1] / 2)
])
deg = np.random.randint(-10, 10)
#deg = np.random.normal()*30
newimg = aug.salt_and_pepper(
aug.gaussian_blur(image))
#.3 probability for each of shifting vs rotating vs shift(rotate(image))
p = np.random.randint(0, 3)
if p == 0:
newimg, nb = aug.shift_image(newimg, box[idx])
elif p == 1:
newimg, nb = aug.rotate_image_and_boxes(
newimg, deg, center, box[idx])
elif p == 2:
newimg, nb = aug.rotate_image_and_boxes(
newimg, deg, center, box[idx])
newimg, nb = aug.shift_image(newimg, nb)
newimg = (newimg).astype(np.uint8)
train_chips += 1
ind_chips +=1
#Make augmentation probability proportional to chip size. Lower chip size = less chance.
#This makes the chip-size imbalance less severe.
prob = np.random.randint(0,np.max(res))
#for 200x200: p(augment) = 200/500 ; for 300x300: p(augment) = 300/500 ...
if AUGMENT and prob < it[0]:
for extra in range(3):
center = np.array([int(image.shape[0]/2),int(image.shape[1]/2)])
deg = np.random.randint(-10,10)
#deg = np.random.normal()*30
newimg = aug.salt_and_pepper(aug.gaussian_blur(image))
#.3 probability for each of shifting vs rotating vs shift(rotate(image))
p = np.random.randint(0,3)
if p == 0:
newimg,nb = aug.shift_image(newimg,box[idx])
elif p == 1:
newimg,nb = aug.rotate_image_and_boxes(newimg,deg,center,box[idx])
elif p == 2:
newimg,nb = aug.rotate_image_and_boxes(newimg,deg,center,box[idx])
newimg,nb = aug.shift_image(newimg,nb)
newimg = (newimg).astype(np.uint8)
if idx%1000 == 0 and SAVE_IMAGES:
