n_train = n - n_test
# ---- Get the dataset ---- #
x, z = generate_dataset(img_shape,
n=n,
image_path=spo_dataset.__path__[0] +
'/source_image/illusion.jpg')
x_test = x[:n_train]
z_test = z[:n_train]
x_train = x[n_train:]
z_train = z[n_train:]
# ---- Initialize ---- #
tf.keras.backend.clear_session()
df = DeepNoisyBayesianFilter(hist, img_shape)
# ---- Load weights ---- #
df.load_weights('model_weights_illusion')
# ---- initialize particle filter ---- #
image_path = spo_dataset.__path__[0] + '/source_image/illusion.jpg'
ref_img = cv2.imread(image_path, 0)
ref_img = cv2.resize(ref_img, img_shape, interpolation=cv2.INTER_AREA)
# %%
pf = ParticleFilter_deep(Np=10000,
No=2,
ref_img=ref_img,
radiuses=[18, 22],
initial_pose=[[14, 18], [108, 104]],
# ---- initialize parameters ---- #
hist = 4
img_shape = (128,128)
noise_rate = 0.2
n = 600
n_test = 300
n_train = n - n_test
# ---- Get the dataset ---- #
# ---- initialize particle filter ---- #
ref_img = np.array(data.checkerboard()).astype(np.float64)
ref_img = cv2.resize(ref_img, img_shape,interpolation = cv2.INTER_AREA)
tf.keras.backend.clear_session()
df = DeepNoisyBayesianFilter(hist,img_shape)
# ---- Load weights ---- #
df.load_weights('model_weights_partially_observed_chekkers')
pf = ParticleFilter_deep(Np = 100,
No = 1,
ref_img = ref_img,
radiuses = [18],
initial_pose = [[20,10]],
beta = 60,
likelihood=df)
# ---- Get the dataset ---- #
x, z = generate_dataset(img_shape = img_shape,
img_shape = (128, 128) noise_rate = 0.2 n = 600 n_test = 300 n_train = n - n_test # ---- Get the dataset ---- # x, z = generate_dataset(img_shape, n=n, image_type="checkers") x_test = x[:n_train] z_test = z[:n_train] x_train = x[n_train:] z_train = z[n_train:] # ---- Initialize ---- # tf.keras.backend.clear_session() df = DeepNoisyBayesianFilter(hist, img_shape) # ---- Train ---- # train_likelihood(df, x_train, z_train, epochs=130) #100 train_predictor(df, x_train, epochs=10, min_img=2) #5 train_predictor(df, x_train, epochs=15, min_img=20) #10 train_update(df, x_train, z_train, epochs=10, min_img=2) #10 train_relax(df, x_train, z_train, epochs=5, min_img=10) # 10 train_relax(df, x_train, z_train, epochs=50, min_img=50) # 10 train_relax(df, x_train, z_train, epochs=50, min_img=None) # 10 # ---- Test and viualize ---- # x_old = x_test[:hist, ...].copy() frames = [] obs_frames = []
x, z = generate_dataset(
img_shape,
n=n,
image_path=spo_dataset.__path__[0] + '/source_image/tree.jpg',
mask=spo_dataset.__path__[0] + '/source_image/tree_masked.jpg',
partial=True)
x_test = x[:n_train]
z_test = z[:n_train]
x_train = x[n_train:]
z_train = z[n_train:]
# ---- Initialize DF ---- #
tf.keras.backend.clear_session()
df = DeepNoisyBayesianFilter(hist, img_shape)
# ---- Load weights ---- #
df.load_weights('model_weights_partially_observed_tree')
# ---- Initialize testing arrays ---- #
# ---- initialize particle filter ---- #
image_path = spo_dataset.__path__[0] + '/source_image/tree.jpg'
ref_img = cv2.imread(image_path, 0)
ref_img = cv2.resize(ref_img, img_shape, interpolation=cv2.INTER_AREA)
pf = ParticleFilter_deep(Np=5000,
No=1,
ref_img=ref_img,
radiuses=[20],
# ---- Get the dataset ---- #
x, z = get_dataset_rotating_objects(image_shape=img_shape,
n=n,
var=0.5,
Ber=True,
partial=True)
x_test = np.array(x[:n_test])
z_test = np.array(z[:n_test])
x_train = np.array(x[n_test:])
z_train = np.array(z[n_test:])
# ---- Initialize ---- #
tf.keras.backend.clear_session()
df = DeepNoisyBayesianFilter(hist, img_shape)
# ---- Train ---- #
train_likelihood(df, x_train, z_train, epochs=130) #100
train_predictor(df, x_train, epochs=20, min_img=2) #5
train_predictor(df, x_train, epochs=30, min_img=20) #10
train_update(df, x_train, z_train, epochs=10, min_img=2) #10
train_relax(df, x_train, z_train, epochs=5, min_img=10) # 10
train_relax(df, x_train, z_train, epochs=50, min_img=50) # 10
train_relax(df, x_train, z_train, epochs=200, min_img=None) # 10
# ---- Initialize testing arrays ---- #
cm_err_df = []
mass_err_df = []
img_err_df = []