def random_flip_tf(img, out_np=True):
# -img: np array or Tensor, (H,W,C) or (N,H,W,C)
# -out_np: whether output is np array or Tensor
if tf.is_tensor(img) == False:
img = tf.convert_to_tensor(img)
img = tfimage.random_flip_left_right(img)
img = tfimage.random_flip_up_down(img)
if out_np == True:
img = img.numpy()
return img
def random_img_preproc(x,
bright=True,
contrast=True,
lr_flip=True,
hue=True,
saturn=True):
if x is not None:
rv = None
num = x.shape[0]
counter = 1
blue("Kreprocessing: started to process " + str(num) + ' images')
config = tf.ConfigProto(
device_count={
"CPU": 6,
"GPU": 0
}, # limit to num_cpu_core CPU usage
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=12,
log_device_placement=True)
sess = tf.Session(config=config)
for i in x:
img = kimg.array_to_img(i)
if bright:
brighten = tfimg.random_brightness(img, 0.2)
img = sess.run(brighten)
if contrast:
contr = tfimg.random_contrast(img, 0.5, 1.5)
img = sess.run(contr)
if lr_flip:
flip = tfimg.random_flip_left_right(img)
img = sess.run(flip)
if hue:
adj_hue = tfimg.random_hue(img, 0.2)
img = sess.run(adj_hue)
if saturn:
sat = tfimg.random_saturation(img, 0.3, 1.7)
img = sess.run(sat)
raw = np.asarray(img).reshape(1, 64, 64, 3)
''' if rv is None:
rv = raw.copy()
else:
rv = np.concatenate((rv, raw), axis=0)'''
if rv is None:
rv = [raw.copy()]
else:
rv.append(raw)
flush(counter, ' / ', num)
counter += 1
print()
sess.close()
return np.concatenate(tuple(rv), axis=0)
def random_img_preproc_gpu(x,
bright=True,
contrast=True,
lr_flip=True,
hue=True,
saturn=True):
if x is not None:
rv = None
num = x.shape[0]
counter = 1
blue("Kreprocessing: started to process " + str(num) + ' images')
sess = tf.Session()
for i in x:
img = kimg.array_to_img(i)
if bright:
brighten = tfimg.random_brightness(img, 0.2)
img = sess.run(brighten)
if contrast:
contr = tfimg.random_contrast(img, 0.5, 1.5)
img = sess.run(contr)
if lr_flip:
flip = tfimg.random_flip_left_right(img)
img = sess.run(flip)
if hue:
adj_hue = tfimg.random_hue(img, 0.2)
img = sess.run(adj_hue)
if saturn:
sat = tfimg.random_saturation(img, 0.3, 1.7)
img = sess.run(sat)
raw = np.asarray(img).reshape(1, 64, 64, 3)
''' if rv is None:
rv = raw.copy()
else:
rv = np.concatenate((rv, raw), axis=0)'''
if rv is None:
rv = [raw.copy()]
else:
rv.append(raw)
flush(counter, ' / ', num)
counter += 1
print()
sess.close()
return np.concatenate(tuple(rv), axis=0)
def preprocess_train_image(img: Tensor, label: Any) -> Tensor:
img = random_flip_left_right(img)
img = resize(img, [*ORIG_IMG_SIZE])
img = random_crop(img, [*INPUT_IMG_SIZE])
return normalize_img(img)