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:
Image.fromarray(newimg).save(
'process/img_%s_%s_%s.png' %
(name, extra, it[0]))
])
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])
elif p == 2:
newimg, nb = aug.rotate_image_and_boxes(
newimg, deg, center, new_coords)
#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 % 100 == 0 and SAVE_IMAGES:
#debug