obs_frames.append(add_border(normalize_image(z_new)))
state_frames.append(add_border(normalize_image(x_new)))
pf_frames.append(add_border(normalize_image(x_hat_pf)))
# cv2.destroyAllWindows()
# ---- Saves multiple samples as an image ---- #
idxs = np.arange(15,55,4, dtype = np.int16)
obs_img = np.concatenate(tuple(np.array(obs_frames)[idxs]),axis=1)
state_img = np.concatenate(tuple(np.array(state_frames)[idxs]),axis=1)
pf_img = np.concatenate(tuple(np.array(pf_frames)[idxs]),axis=1)
full_img = np.concatenate(( obs_img,state_img, df_img), axis = 0).astype(np.uint8)
matplotlib.image.imsave('samples_pf.png', full_img, cmap='gray')
# ---- Saves a video ---- #
outputdata = np.array(frames).astype(np.uint8)
skvideo.io.vwrite("samples.mp4", frames)
# ---- Save Weights ---- #
df.save_weights('model_weights')
x_hat_df_like = x_hat_df_like[0, :, :, 0]
x_old[:-1, :, :] = x_old[1:, :, :]
x_old[-1, :, :] = x_hat_df
obs_frames.append(add_border(normalize_image(z_new)))
state_frames.append(add_border(normalize_image(x_new)))
df_frames.append(add_border(normalize_image(x_hat_df)))
direct_frames.append(add_border(normalize_image(x_hat_df_like)))
frame1 = np.concatenate((normalize_image(x_new), normalize_image(z_new)),
axis=1)
frame2 = np.concatenate(
(normalize_image(x_hat_df), normalize_image(x_hat_df_like)), axis=1)
frame = np.concatenate((frame1, frame2), axis=0)
frames.append(frame)
# ---- Saves multiple samples as an image ---- #
idxs = np.arange(15, 200, 4, dtype=np.int16)
obs_img = np.concatenate(tuple(np.array(obs_frames)[idxs]), axis=1)
state_img = np.concatenate(tuple(np.array(state_frames)[idxs]), axis=1)
df_img = np.concatenate(tuple(np.array(df_frames)[idxs]), axis=1)
direct_img = np.concatenate(tuple(np.array(direct_frames)[idxs]), axis=1)
full_img = np.concatenate((obs_img, state_img, df_img, direct_img),
axis=0).astype(np.uint8)
matplotlib.image.imsave('samples.png', full_img, cmap='gray')
# ---- Saves a video ---- #
outputdata = np.array(frames).astype(np.uint8)
skvideo.io.vwrite("samples.mp4", frames)
# ---- Save Weights ---- #
df.save_weights('model_weights_inverse_chekkers')
frame2 = np.concatenate((normalize_image(x_hat_df),normalize_image(x_hat_df_like) ),axis = 1)
frame = np.concatenate((frame1,frame2),axis = 0)
frames.append(frame)
# ---- Saves multiple samples as an image ---- #
idxs = np.arange(0,200,4, dtype = np.int16)
obs_img = np.concatenate(tuple(np.array(obs_frames)[idxs]),axis=1)
state_img = np.concatenate(tuple(np.array(state_frames)[idxs]),axis=1)
df_img = np.concatenate(tuple(np.array(df_frames)[idxs]),axis=1)
direct_img = np.concatenate(tuple(np.array(direct_frames)[idxs]),axis=1)
full_img = np.concatenate(( obs_img,state_img, df_img, direct_img), axis = 0).astype(np.uint8)
matplotlib.image.imsave('samples.png', full_img, cmap='gray')
# ---- Saves a video ---- #
outputdata = np.array(frames).astype(np.uint8)
skvideo.io.vwrite("samples.mp4", frames)
# ---- Save Weights ---- #
df.save_weights('model_weights_partially_observed_chekkers')
frame2 = np.concatenate((normalize_image(x_hat_df),normalize_image(x_hat_df_like) ),axis = 1)
frame = np.concatenate((frame1,frame2),axis = 0)
frames.append(frame)
# ---- Saves multiple samples as an image ---- #
idxs = np.arange(15,200,4, dtype = np.int16)
obs_img = np.concatenate(tuple(np.array(obs_frames)[idxs]),axis=1)
state_img = np.concatenate(tuple(np.array(state_frames)[idxs]),axis=1)
df_img = np.concatenate(tuple(np.array(df_frames)[idxs]),axis=1)
direct_img = np.concatenate(tuple(np.array(direct_frames)[idxs]),axis=1)
full_img = np.concatenate(( obs_img,state_img, df_img, direct_img), axis = 0).astype(np.uint8)
matplotlib.image.imsave('samples.png', full_img, cmap='gray')
# ---- Saves a video ---- #
outputdata = np.array(frames).astype(np.uint8)
skvideo.io.vwrite("samples.mp4", frames)
# ---- Save Weights ---- #
df.save_weights('model_weights_illusion')
#x_hat_df_like[x_hat_df_like<0.5] = 0
x_old[:-1, :, :] = x_old[1:, :, :]
x_old[-1, :, :] = x_hat_df
obs_frames.append(add_border(normalize_image(z_new)))
state_frames.append(add_border(normalize_image(x_new)))
df_frames.append(add_border(normalize_image(x_hat_df)))
direct_frames.append(add_border(normalize_image(x_hat_df_like)))
frame1 = np.concatenate((normalize_image(x_new), normalize_image(z_new)),
axis=1)
frame2 = np.concatenate(
(normalize_image(x_hat_df), normalize_image(x_hat_df_like)), axis=1)
frame = np.concatenate((frame1, frame2), axis=0)
frames.append(frame)
# ---- Saves multiple samples as an image ---- #
idxs = np.arange(0, 200, 5, dtype=np.int16)
obs_img = np.concatenate(tuple(np.array(obs_frames)[idxs]), axis=1)
state_img = np.concatenate(tuple(np.array(state_frames)[idxs]), axis=1)
df_img = np.concatenate(tuple(np.array(df_frames)[idxs]), axis=1)
direct_img = np.concatenate(tuple(np.array(direct_frames)[idxs]), axis=1)
full_img = np.concatenate((obs_img, state_img, df_img, direct_img),
axis=0).astype(np.uint8)
matplotlib.image.imsave('t.png', full_img, cmap='gray')
# ---- Saves a video ---- #
outputdata = np.array(frames).astype(np.uint8)
skvideo.io.vwrite("samples.mp4", frames)
# ---- Save Weights ---- #
df.save_weights('model_weights_rectangles_very_noisy')