# ------------------------------------
# Display PC resonstruction over time
# ------------------------------------
empty_wavelet = np.zeros(39 * 39)
if n_com == 0 or n_com == 9:
num_to_see = 400
else:
num_to_see = 80
for frame_num in range(num_to_see):
empty_wavelet[
relevant_wavelet_features] = wavelet_array_relevant_features_recon[
frame_num, :]
wavelet = np.reshape(empty_wavelet, (39, 39))
#reconstruct image from wavelet transform
reconstruction_from_wavelet = reconstruct_from_wavelet(
wavelet, coeff_slices, level, discard_scale)
reconstruction_from_wavelet[
reconstruction_from_wavelet > 255] = 255
reconstruction_from_wavelet = cv2.resize(
abs(reconstruction_from_wavelet).astype(np.uint8), (450, 450))
cv2.imshow('PC / wavelet reconstruction',
reconstruction_from_wavelet)
vid.write(reconstruction_from_wavelet)
if cv2.waitKey(int(1000 / display_frame_rate)) & 0xFF == ord('q'):
break
if cv2.waitKey(500) & 0xFF == ord('q'):
break
vid.release()
''' -------------------------------------------------------------------------------------------------------------------------------------
for t in range(2 * windows_to_look_at + 1):
# Get the mean features for that pose
mean_features = mean_features_model[n, t * features_used:(t + 1) *
features_used]
# Reconstruct wavelet-transformed data from the PCs
mean_wavelet_relevant_features = pca.inverse_transform(
np.append(mean_features[0:num_PCs_used],
np.zeros(pca.n_components_ - num_PCs_used)))
mean_wavelet = np.zeros(39 * 39)
mean_wavelet[
relevant_wavelet_features] = mean_wavelet_relevant_features
mean_wavelet_array = np.reshape(mean_wavelet, (39, 39))
# Reconstruct image in pixel space from wavelet-transformed reconstruction
reconstruction_from_wavelet = reconstruct_from_wavelet(
mean_wavelet_array, coeff_slices, level, discard_scale)
reconstruction_from_wavelet[
(reconstruction_from_wavelet <= 0) + twoD *
(reconstruction_from_wavelet >= 250)] = 0 + twoD * 250
reconstruction_image = cv2.resize(
reconstruction_from_wavelet.astype(np.uint8),
(trajectory_pose_size, trajectory_pose_size))
reconstruction_image = cv2.cvtColor(reconstruction_image,
cv2.COLOR_GRAY2BGR)
reconstruction_image = (reconstruction_image *
np.squeeze(color_array[:, :, :, n])).astype(
np.uint8)
# rotate image
M = cv2.getRotationMatrix2D(
# Open up the selected data wavelet array
file_location_session = save_folder_location + session_name_tags[
session_to_examine] + '\\' + session_name_tags[session_to_examine]
wavelet_array_session = np.load(
file_location_session + '_wavelet.npy')[:, :, ::downsample_every_other]
#show in one video the unflipped frames, and in another, those that the model would flip
level = 5
discard_scale = 4 # these must be parameters taken from original wavelet transform
flip_ind_single_video = flip_ind[flip_ind < wavelet_array_session.shape[2]]
for j in range(len(flip_ind_single_video)):
#reconstruct images from wavelet transform
wavelet_up = wavelet_array_session[:, :, keep_ind[j]]
wavelet_down = wavelet_array_session[:, :, flip_ind[j]]
reconstruction_from_wavelet_up = reconstruct_from_wavelet(
wavelet_up, coeff_slices, level, discard_scale)
reconstruction_from_wavelet_down = reconstruct_from_wavelet(
wavelet_down, coeff_slices, level, discard_scale)
cv2.imshow('right-side up',
reconstruction_from_wavelet_up.astype(np.uint8))
cv2.imshow('up-side down',
reconstruction_from_wavelet_down.astype(np.uint8))
if (cv2.waitKey(int(1000 / display_frame_rate)) & 0xFF == ord('q')):
break
if j % 500 == 0:
print(
str(j) + ' out of ' + str(len(flip_ind_single_video)) +
' flip frames complete')
# ---------------