def main():
with CustomObjectScope(custom_objects()):
model1 = load_model(SAVED_MODEL1)
model2 = load_model(SAVED_MODEL2)
images = np.load('data/images-224.npy')
masks = np.load('data/masks-224.npy')
# only hair
masks = masks[:, :, :, 0].reshape(-1, size, size)
_, images, _, masks = train_test_split(images,
masks,
test_size=0.2,
random_state=seed)
for img, mask in zip(images, masks):
batched1 = img.reshape(1, size, size, 3).astype(float)
batched2 = img.reshape(1, size, size, 3).astype(float)
t1 = time.time()
pred1 = model1.predict(standardize(batched1)).reshape(size, size)
elapsed = time.time() - t1
print('elapsed1: ', elapsed)
t1 = time.time()
pred2 = model2.predict(standardize(batched2)).reshape(size, size)
elapsed = time.time() - t1
print('elapsed2: ', elapsed)
dice = np_dice_coef(mask.astype(float) / 255, pred1)
print('dice1: ', dice)
dice = np_dice_coef(mask.astype(float) / 255, pred2)
print('dice2: ', dice)
if True:
plt.subplot(2, 2, 1)
plt.imshow(img)
plt.subplot(2, 2, 2)
plt.imshow(mask)
plt.subplot(2, 2, 3)
plt.imshow(pred1)
plt.subplot(2, 2, 4)
plt.imshow(pred2)
plt.show()
def main(img_dir, only_weight, img_size):
if only_weight != 0:
with CustomObjectScope(custom_objects()):
model = load_model(SAVED_MODEL)
print("loading model file: ", SAVED_MODEL, img_size)
else:
weight_file = SAVED_WEIGHT
model = MobileUNet(input_shape=(img_size, img_size, 3),
alpha=1,
alpha_up=0.25)
model.load_weights(weight_file, by_name=True)
print("loading weight file: ", weight_file, img_size)
model.summary()
img_files = glob(img_dir + '/*.jpg')
img_files = sorted(img_files, key=os.path.getmtime)
for img_file in reversed(img_files):
img = imread(img_file)
img = imresize(img, (img_size, img_size))
mask_file = re.sub('img2.jpg$', 'msk1.png', img_file)
mask = imread(mask_file)
mask = imresize(mask, (img_size, img_size), interp='nearest')
mask1 = mask[:, :, 0]
batched1 = img.reshape(1, img_size, img_size, 3).astype(float)
pred1 = model.predict(standardize(batched1)).reshape(
img_size, img_size)
mask1 = mask1.astype(float) / 255
dice = np_dice_coef(mask1, pred1)
print('dice1: ', dice)
print('sum ', np.sum(mask1))
print('shap ', mask1.shape)
pred1 = beautify(pred1)
if True:
plt.subplot(2, 2, 1)
plt.imshow(img)
#plt.subplot(2, 2, 2)
#plt.imshow(mask)
plt.subplot(2, 2, 3)
plt.imshow(pred1)
plt.subplot(2, 2, 4)
plt.imshow(mask1)
plt.show()
def main(weight_file):
model = MobileUNet(input_shape=(128, 128, 3), alpha=1, alpha_up=0.25)
model.summary()
model.load_weights(weight_file, by_name=True)
images = np.load('data/id_pack/images-128.npy')
masks = np.load('data/id_pack/masks-128.npy')
# only hair
masks = masks[:, :, :, 0].reshape(-1, size, size)
_, images, _, masks = train_test_split(images,
masks,
test_size=0.2,
random_state=seed)
for img, mask in zip(images, masks):
batched1 = img.reshape(1, size, size, 3).astype(float)
batched2 = img.reshape(1, size, size, 3).astype(float)
t1 = time.time()
pred1 = model.predict(standardize(batched1)).reshape(size, size)
elapsed = time.time() - t1
print('elapsed1: ', elapsed)
dice = np_dice_coef(mask.astype(float) / 255, pred1)
print('dice1: ', dice)
if True:
plt.subplot(2, 2, 1)
plt.imshow(img)
plt.subplot(2, 2, 2)
plt.imshow(mask)
plt.subplot(2, 2, 3)
plt.imshow(pred1)
plt.show()