def main_realtime_traj_dict():
#slices = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/data_for_personality_exp/traj_org_by_ID_10fps.txt')
skeleton_data_in_tasks_and_time_slices = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/skeleton_data_in_tasks_time_slices_30fps.txt')#'C:/Users/dario.dotti/Desktop/data_recordings_master/master_skeleton_data_in_tasks_time_slices_30fps.txt')
### Staatistics on data #####
# matrix_dist = []
# for participant in skeleton_data_in_tasks_and_time_slices:
#
# max_dist,max_step = get_distances_between_points(participant)
#
# matrix_dist.append(max_dist)
#
# matrix_dist = np.array(matrix_dist).reshape(-1,1)
# hs.determine_number_k_kMeans(matrix_dist)
# visualize_cluster(matrix_dist)
max_dist = 140
max_step = 15
final_matrix = []
final_orig_points = []
final_matrix_realcoord =[]
for participant in skeleton_data_in_tasks_and_time_slices:
def main_laban_posture_RAW():
## load features from determined tasks
# features_participants_orig_1 = data_organizer.load_matrix_pickle(
# 'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/experiment_upperBody_pathPlanning/RAWpostureUpperBody_path_features_2sec_skeletonF_task45.txt')#'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/experiment_upperBody_pathPlanning/pca_RAWpostureUpperBody_path_features.txt')
# features_participants_orig_2 = data_organizer.load_matrix_pickle(
# 'C:/Users/dario.dotti/Desktop/data_recordings_master/data_personality/RAWpostureUpperBody_path_features_master_2sec_skeletonF_task45.txt') #'C:/Users/dario.dotti/Desktop/data_recordings_master/data_personality/pca_RAWpostureUpperBody_path_features_master.txt')
# features_participants_orig = features_participants_orig_1 + features_participants_orig_2
# del features_participants_orig[44]
## load features from all tasks
features_participants_orig = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/experiment_upperBody_pathPlanning/RAWpostureUpperBody_path_features_2sec_skeletonF_task0123.txt')
##
## separate feature vector again to see what is more informative ##
features_participants_3 = np.concatenate(features_participants_orig,axis=0)
#hot_keyA = features_participants_3[:, :30]
sk_f = features_participants_3[:, 30:72]
#posture = features_participants_3[:,72:]
#print posture.shape
# #
# start = 0
# f_participant = []
# for i_p in range(0, len(features_participants_orig)):
# f_participant.append(posture[start:(start + len(features_participants_orig[i_p]))])
# start += len(features_participants_orig[i_p])
##
## visualize distribution of skeleton features ##
labels_angles = dist_skeleton_angles_features(sk_f)
def create_cluster_labels_participant_task(raw_features, AE_weights_level_2):
cluster_model = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/head_joint_id1/10_cluster_model_layer2_new.txt'
)
final_labels = []
for participant in raw_features:
participant_label = []
for task in participant:
task_label = []
for f_vector in task:
hd = np.dot(f_vector, AE_weights_level_2[0][0])
act = sigmoid_function(hd + AE_weights_level_2[1])
label = cluster_model.predict(act.reshape((1, -1)))
task_label.append(label[0])
participant_label.append(task_label)
final_labels.append(participant_label)
data_organizer.save_matrix_pickle(
final_labels,
'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/head_joint_id1/10clusters_labels_l2_participants_tasks_new.txt'
)
def experiment_as():
as_matrix_6_tasks = np.array(
data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/bow_experiment_data/AS_activation_6_labels.txt'
)).tolist()
as_matrix_5_tasks_transformed = np.array(
data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/bow_experiment_data/AS_activation_5_labels_transformed.txt'
)).tolist()
as_matrix = np.vstack((as_matrix_6_tasks, as_matrix_5_tasks_transformed))
#as_matrix = np.array(as_matrix_6_tasks)
#as_classification_experiment(as_matrix)
print as_matrix.shape
return as_matrix
def main_demo_pecs():
##get raw data for displaying
task_skeleton_data = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/pecs_data_review/skeletons_repetitive_behavior_02082017.txt'
)
##HOT features
HOT_16_subject_6_tasks = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/pecs_data_review/HOT_repetitive_behavior_02082017.txt'
)
##BOW computed on HOT
# bow_data = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/bow_experiment_data/test_PECS/BOW_3_kmeans_16subject_2sec_without_outlier.txt')
# labels_bow_data = data_organizer.load_matrix_pickle(
# 'C:/Users/dario.dotti/Documents/bow_experiment_data/test_PECS/BOW_3_kmeans_labels_16subject_2sec_without_outlier.txt')
bow_data = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/bow_experiment_data/BOW_30_kmeans_16subject_2sec.txt'
)
labels_bow_data = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/bow_experiment_data/BOW_30_kmeans_labels_16subject_2sec.txt'
)
lr = LogisticRegression()
lr.fit(bow_data, np.ravel(labels_bow_data))
##cluster data
# cluster_model = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/bow_experiment_data/test_PECS/cl_30_kmeans_model_2secWindow_without_outliers.txt')
# labels_cluster = data_organizer.load_matrix_pickle(
# 'C:/Users/dario.dotti/Documents/bow_experiment_data/test_PECS/cluster_3_kmeans_word__without_outliers.txt')
cluster_model = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/bow_experiment_data/cl_30_kmeans_model_2secWindow_newVersion.txt'
)
labels_cluster = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/bow_experiment_data/cluster_30_kmeans_word_newVersion.txt'
)
key_labels = map(lambda x: x[0], labels_cluster)
##shared variable between threads
q = multiprocessing.Queue()
current_time_shared = multiprocessing.Queue()
##launch different processes in the same time
display_joints_traj = multiprocessing.Process(
target=draw_joints_and_tracks,
args=(task_skeleton_data, current_time_shared))
display_confidence_classifier = multiprocessing.Process(
target=plot_classifier_confidence,
args=(HOT_16_subject_6_tasks, cluster_model, key_labels, lr, q))
##Start threads
display_joints_traj.start()
display_confidence_classifier.start()
##call plot initializer
basic_plot()
update_figures_in_threads(q)
##launch main window loop
window.geometry('800x700')
window.mainloop()
def main_laban_posture_ID():
features_participants_orig = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/experiment_upperBody_pathPlanning/IDpostureUpperBody_path_features.txt')
#features_participants = np.concatenate(features_participants_orig, axis=0)
#n_minute = features_participants[:,:30]
#posture_l = features_participants[:,30:]
## compare the first n minute ##
n_minute = [np.array(p[:20]).reshape((1,-1)) for p in features_participants_orig]
n_minute = np.concatenate(n_minute,axis=0)
n_minute_similarity = np.concatenate([cdist(p[:20].reshape((1,-1)), n_minute, 'cosine') for p in features_participants_orig],axis=0)
##
Z = hierarchy.linkage(n_minute, method='average', metric='cosine')
#check if metric preserve original distance
c, coph_dists = cophenet(Z, pdist(n_minute))
#print c
y_tr = hierarchy.fcluster(Z, 0.2, criterion="distance")
def visualize_cluster_pred():
with open('C:/Users/dario.dotti/Documents/content_6labels.txt', 'r') as f:
images_name = f.read().split('\n')
pred = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/cl_prediction_2secWindow_band03.txt')
class_counter = Counter(pred)
print class_counter
for k in class_counter.keys()[:20]:
print k
index = np.where(pred == k)[0]
for i in index[:20]:
path = 'C:/Users/dario.dotti/Documents/time_windows_HOT/' + images_name[
i].split(' ')[3]
img = cv2.imread(path)
cv2.imshow('ciao', img)
cv2.waitKey(0)
def main_pecs_data():
##get raw data for displaying
task_skeleton_data = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/pecs_data_review/skeletons_repetitive_behavior_08082017.txt'
)
##if data contains multiple skeletons here I sort them cronologically
sort_skeletons(task_skeleton_data)
my_room = np.zeros((424, 512, 3), dtype=np.uint8)
my_room += 255
list_poly = img_processing.divide_image(my_room)
#draw_joints_and_tracks(task_skeleton_data,my_room)
#return 0
skeleton_data_in_time_slices = org_data_in_timeIntervals(
task_skeleton_data, [0, 0, 2])
HOT_data, patient_ID = histograms_of_oriented_trajectories(
list_poly, skeleton_data_in_time_slices)
data_organizer.save_matrix_pickle(
HOT_data,
'C:/Users/dario.dotti/Documents/pecs_data_review/HOT_repetitive_behavior_08082017.txt'
)
##down
feature_img[int(p[1]), int(p[0]) + 1] = 0.99
feature_img[int(p[1]), int(p[0]) + 2] = 0.99
# cv2.imshow('feature_img',feature_img)
# cv2.waitKey(0)
if len(imgs) > 0: imgs = np.vstack((imgs, feature_img.reshape((1, -1))))
else: imgs = feature_img.reshape((1, -1))
return imgs
AE_weights_level_1 = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/deep_AE_900_225_weights_008hd1_noNoise.txt')
hd_weights = AE_weights_level_1[0][0]
bias_1_level1 = AE_weights_level_1[1]
#pca = decomposition.PCA(n_components=100) # 2-dimensional PCA whiten=True, svd_solver='randomized'
#pca = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/clustering_posture_5sec/100pca_deep900225AE_5sec_data.txt')
#cluster_model = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/clustering_posture_5sec/linearSVM_agglomerative15c_5sec_100pca.txt')
def compute_hot_f(xs,ys):
orientation_intervals = [[range(0, 45)], [range(45, 90)], [range(90, 135)], [range(135, 180)], [range(180, 225)],
[range(225, 270)], \
[range(270, 315)], [range(315, 360)]]
magnitude_intervals = [[range(0, 4)], [range(4, 10)], [range(10, 200)]]
hot_matrix = np.zeros((len(orientation_intervals), len(magnitude_intervals)))
def experiment_video():
#video_classification_experiments()
#as_classification_experiment()
HOT_matrix_5_tasks = np.array(
data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/bow_experiment_data/test_PECS/hot_spatial_grid_4x4x3_5_tasks_2secWindow_without_outliers.txt'
)).tolist()
HOT_matrix_6_tasks = np.array(
data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/hot_spatial_grid_4x4x3_6_tasks_2secWindow.txt'
)
).tolist(
) # 'C:/Users/dario.dotti/Documents/bow_experiment_data/test_PECS/hot_spatial_grid_4x4x3_6_tasks_2secWindow_without_outliers.txt'
#HOT_matrix_6_tasks = np.array(data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/bow_experiment_data/hot_spatial_grid_4x4x3_6_tasks_2secWindow.txt')).tolist()
#HOT_matrix_5_tasks = np.array(data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/bow_experiment_data/hot_spatial_grid_4x4x3_5_tasks_2secWindow.txt')).tolist()
length_task3 = [45, 51, 33, 51, 62]
##modify 5 tasks to make it 6 tasks and merge the two matrices
for n_subj, subject in enumerate(HOT_matrix_5_tasks):
new_subject = []
for n_task, task in enumerate(subject):
if n_task == 3:
new_subject.append(task[:length_task3[n_subj]])
new_subject.append(task[length_task3[n_subj]:])
else:
new_subject.append(task)
HOT_matrix_6_tasks.append(new_subject)
#data_organizer.save_matrix_pickle(HOT_matrix_6_tasks,'C:/Users/dario.dotti/Documents/bow_experiment_data/final_HOT_matrix_6_tasks.txt')
##transform matrix for clustering
HOT_matrix_for_cluster = []
for s in xrange(0, len(HOT_matrix_6_tasks)):
for t in xrange(0, len(HOT_matrix_6_tasks[s])):
for time_slice in xrange(0, len(HOT_matrix_6_tasks[s][t])):
if len(HOT_matrix_for_cluster) > 0:
HOT_matrix_for_cluster = np.vstack(
(HOT_matrix_for_cluster,
HOT_matrix_6_tasks[s][t][time_slice]))
else:
HOT_matrix_for_cluster = HOT_matrix_6_tasks[s][t][
time_slice]
print np.array(HOT_matrix_for_cluster).shape
# #Clustering Meanshift
##video_clustering_fit(concatenated_matrix,'C:/Users/dario.dotti/Documents/cl_model_2secWindow_band03.txt')
#cluster_model = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/bow_experiment_data/cl_model_2secWindow_band03.txt')
#labels = cluster_model.predict(HOT_matrix_for_cluster)
# #Clustering KMeans
# determine_number_k_kMeans(HOT_matrix_for_cluster)
# cluster_model = KMeans(n_clusters=30, n_jobs=-1)
# cluster_model.fit(HOT_matrix_for_cluster)
# data_organizer.save_matrix_pickle(cluster_model,
# 'C:/Users/dario.dotti/Documents/bow_experiment_data/cl_30_kmeans_model_2secWindow_newVersion.txt')
# data_organizer.save_matrix_pickle(cluster_model,
# 'C:/Users/dario.dotti/Documents/cl_30_kmeans_model_2secWindow_newVersion.txt')
cluster_model = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/bow_experiment_data/test_PECS/cl_30_kmeans_model_2secWindow_without_outliers.txt'
)
labels = cluster_model.predict(HOT_matrix_for_cluster)
labels_counter = Counter(labels).most_common(30)
#data_organizer.save_matrix_pickle(labels_counter,'C:/Users/dario.dotti/Documents/cluster_3_kmeans_word__without_outliers.txt')
matrix_training, labels, tasks_dict = test_hist_task(
cluster_model, labels_counter, HOT_matrix_6_tasks)
return matrix_training, labels, tasks_dict
=======
from sklearn.metrics import classification_report,precision_recall_fscore_support,accuracy_score
from sklearn.cluster import KMeans
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from pomegranate import *
>>>>>>> 9348384985d2847c272133ff77ce6181ca1fa082
import data_organizer
import img_processing
import AE_rec
feature_p_1 = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Desktop/Hier_AE_deliverable/head_joint_id1/feature_matrix_participant_task_l2_new_realCoordinates.txt')
<<<<<<< HEAD
feature_p_2 =data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Desktop/Hier_AE_deliverable/head_joint_id1/feature_matrix_participant_master_task_l2_new_realCoordinates.txt')
feature_p = feature_p_1[:19]+feature_p_2[:27]
real_coord = feature_p_1[19:]+feature_p_2[27:]
list_poly = img_processing.divide_image(np.zeros((414,512),dtype=np.uint8))
space_features = []
for i_p in xrange(0,len(real_coord)):
for i_task in xrange(0,len(real_coord[i_p])):
for i_slice in xrange(0,len(real_coord[i_p][i_task])):
votes = np.zeros((16, 3))
def main_laban_posture_ID():
cl_model_posture = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/data_for_personality_exp/after_2_data_recording/posture/kmeans_50_posture_allTasks.txt')
features_participants_orig = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/experiment_upperBody_pathPlanning/RAWpostureUpperBody_path_features_2sec_skeletonF_ALLTASKS.txt') # 'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/experiment_upperBody_pathPlanning/pca_RAWpostureUpperBody_path_features.txt')
# features_participants_orig_2 = data_organizer.load_matrix_pickle(
# 'C:/Users/dario.dotti/Desktop/data_recordings_master/data_personality/RAWpostureUpperBody_path_features_master_2sec_skeletonF_ALLTASKS.txt') # 'C:/Users/dario.dotti/Desktop/data_recordings_master/data_personality/pca_RAWpostureUpperBody_path_features_master.txt')
# features_participants_orig = features_participants_orig_1 + features_participants_orig_2
l_per_participant = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_2_data_recording/clusters_on_pearson_corr_personality_scores.txt')
## separate feature vector again to see what is more informative ##
features_participants_3 = np.concatenate(features_participants_orig, axis=0)
# hot_keyA = features_participants_3[:, :30]
skeleton_angles = features_participants_3[:, 30:72]
posture = features_participants_3[:, 72:]
print posture.shape
## feature and label vectors with all data ##
start = 0
labels_data = np.zeros((posture.shape[0], 1), dtype=int)
for i_p in xrange(0, len(features_participants_orig)):
for i in xrange(0, len(features_participants_orig[i_p])):
labels_data[start + i, 0] = int(l_per_participant[i_p])
start += len(features_participants_orig[i_p])
labels_data = labels_data.ravel()
y_tr = cl_model_posture.predict(posture)
## visualize posture distribution ##
hist = np.zeros((3, 50))
for pers_label in xrange(1, 4):
for i_l in xrange(0, len(y_tr)):
if labels_data[i_l] == pers_label: hist[(pers_label - 1), y_tr[i_l]] += 1
fig = plt.figure()
ax = fig.add_subplot(111)
width = 0.25
ind = np.arange(0, 50)
##normalized heights
rect1 = plt.bar(ind, hist[0,:]/len(np.where(labels_data==1)[0]), width)
rect2 = plt.bar(ind+width, hist[1, :]/len(np.where(labels_data==2)[0]), width, color='red')
rect3 = plt.bar(ind+(width*2), hist[2, :]/len(np.where(labels_data==3)[0]), width, color='green')
ax.legend((rect1[0], rect2[0], rect3[0]), ('class1', 'class2','class3'), fontsize=11)
ax.set_xticks(ind + width)
ax.set_xticklabels(ind)
plt.show()
###
## check posture differences using skeleton raw angles ##
for pers_label in xrange(1,4):
print pers_label
angles_per_cl = []
for i_l in xrange(0,len(y_tr)):
if labels_data[i_l] == pers_label:
angles_per_cl.append(skeleton_angles[i_l])
## show mean per joints ##
## matrix 7 joints x 6 stat of angles
angles_per_cl = np.array(angles_per_cl).reshape((7*len(angles_per_cl),6))
for i_joint in xrange(7):
joint_stat = []
for i_value in xrange(i_joint,angles_per_cl.shape[0],7):
joint_stat.append(angles_per_cl[i_value])
print np.mean(np.array(joint_stat),axis=0)
def main_laban_posture_RAW():
features_participants_orig_1 = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/experiment_upperBody_pathPlanning/RAWpostureUpperBody_path_features_2sec_skeletonF.txt')#'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/experiment_upperBody_pathPlanning/pca_RAWpostureUpperBody_path_features.txt')
features_participants_orig_2 = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Desktop/data_recordings_master/data_personality/RAWpostureUpperBody_path_features_master_2sec_skeletonF.txt') #'C:/Users/dario.dotti/Desktop/data_recordings_master/data_personality/pca_RAWpostureUpperBody_path_features_master.txt')
features_participants_orig = features_participants_orig_1 + features_participants_orig_2
##
## separate feature vector again to see what is more informative ##
#features_participants_3 = np.concatenate(features_participants_orig,axis=0)
# hot = features_participants_3[:, :24]
# key_areas = features_participants_3[:,24:30]
#posture = features_participants_3[:,30:130]
# #
# start = 0
# f_participant = []
# for i_p in range(0, len(features_participants_orig)):
# f_participant.append(posture[start:(start + len(features_participants_orig[i_p]))])
# start += len(features_participants_orig[i_p])
##
##concatenate features to form n_minute feature vectors
# t = 3
# feature_p_n_minute=[]
# for p in features_participants_orig:
# n_minute_p = []
# for n_slice in range(0, len(p) - (t-1), t/2):
# n_minute_p.append(p[n_slice:(n_slice + t)].reshape((1,-1)))
# feature_p_n_minute.append(np.concatenate(n_minute_p,axis=0))
feature_p_n_minute = features_participants_orig
n_minute = np.concatenate(feature_p_n_minute,axis=0)
#print n_minute.shape
#pca_on_data(n_minute)
### clustering on data ###
#hs.determine_number_k_kMeans(n_minute)
#Z = hierarchy.linkage(n_minute, method='average', metric='euclidean')
# check if metric preserve original distance
#c, coph_dists = cophenet(Z, pdist(n_minute))
#print c
#y_tr = hierarchy.fcluster(Z, 5,criterion="distance") #cosine = 0.5
#
# ##print y_tr
#print Counter(y_tr)
# data_organizer.save_matrix_pickle(y_tr,'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_2_data_recording/label_features_clustering.txt')
#y_tr = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/data_for_personality_exp/after_2_data_recording/label_features_clustering.txt')
##
#### classification on data #######
l_per_participant = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/data_for_personality_exp/after_2_data_recording/clusters_on_pearson_corr_personality_scores.txt')
extrovert_label_per_p =[2, 0, 1, 0, 2, 2, 1, 1, 2, 1, 2, 1, 0, 1, 1, 1, 0, 2, 2, 2, 2, 1, 1, 2, 2, 0, 2, 0, 1, 0, 0, 1, 1, 2, 1, 0, 2, 1, 0, 1, 0, 0, 1, 2, 0, 1]
consc_label_per_p = [2, 2, 1, 2, 2, 2, 2, 1, 0, 2, 2, 0, 1, 0, 1, 2, 1, 0, 2, 0, 1, 0, 2, 1, 2, 1, 0, 1, 0, 2, 0, 2, 2, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 2]
nevrotic_label_per_p = [0, 2, 1, 1, 1, 2, 2, 1, 0, 0, 2, 2, 2, 1, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 1, 1, 1, 2, 2, 2, 0, 1, 1, 0, 1, 0]
## baseline classifications
raw_feature_classification(n_minute, feature_p_n_minute, l_per_participant)
def main_demo():
##get raw data for displaying
body_joints = video_traj.xml_parser(
'C:/Users/dario.dotti/Documents/pilot_abnormal_behavior_indoor/joints/subject7_points.xml'
)
##HOT features organized per subjects and tasks
HOT_16_subject_6_tasks = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_demo/HOT_matrix_16_subject_6_tasks.txt'
)
##BOW computed on HOT
bow_data = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_demo/BOW_16subject_2sec.txt')
labels_bow_data = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_demo/BOW_labels_16subject_2sec.txt'
)
lr = LogisticRegression()
lr.fit(bow_data, np.ravel(labels_bow_data))
##cluster data
cluster_model = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/cl_model_2secWindow_band03.txt')
labels_cluster = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_demo/cluster_labels.txt')
labels_cluster_counter = Counter(labels_cluster).most_common(40)
keys_labels = map(lambda x: x[0], labels_cluster_counter)
##load binary data for displaying
entrance_door_str = ambient_sensors.org_data_ID(
'C:/Users/dario.dotti/Documents/pilot_abnormal_behavior_indoor/binary/18-10-16_sensors_subject7.txt'
)['entrance']
entrance_door = []
signal_entrance_door = []
#converting entrance door from string to time
for i, s in enumerate(entrance_door_str):
date = s.split(' ')[1]
time = s.split(' ')[2].split('-')
entrance_door.append(
dt.strptime(date + ' ' + time[0] + ':' + time[1] + ':' + time[2],
'%y-%m-%d %H:%M:%S'))
signal_entrance_door.append(s.split(' ')[0])
subj = 3 #n_subject order: 4,5,6,7,10,11,12,13,14,19,20,15,3,16,17,18
task = 0 #n_task order confusion: 0,1,2 repetitive:3 house_activity: 4,5
##shared variable between threads
q = multiprocessing.Queue()
current_time_shared = multiprocessing.Queue()
##launch different processes in the same time
display_joints_traj = multiprocessing.Process(
target=draw_joints_and_tracks, args=(body_joints, current_time_shared))
display_confidence_classifier = multiprocessing.Process(
target=plot_classifier_confidence,
args=(HOT_16_subject_6_tasks[subj][task], cluster_model, keys_labels,
lr, q))
display_ambient_sensor = multiprocessing.Process(
target=show_binary_sensor,
args=(entrance_door, signal_entrance_door, q, current_time_shared))
display_joints_traj.start()
display_confidence_classifier.start()
display_ambient_sensor.start()
##call plot initializer
basic_plot()
update_figures_in_threads(q)
##launch main window loop
window.geometry('800x700')
window.mainloop()
import video_traj
import hierarchical_ae_learning_methods as hs
import AE_rec
<<<<<<< HEAD
# AE_weights_level_1 = data_organizer.load_matrix_pickle(
# 'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/ae/head_joint_id1/144weights_l1_hd1002.txt')
#
# cluster_model_l1 = data_organizer.load_matrix_pickle(
# 'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/head_joint_id1/20_cluster_model_layer1.txt')
=======
AE_weights_level_1 = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Desktop/Hier_AE_deliverable/ae/head_joint_id1/144weights_l1_hd1002.txt')
cluster_model_l1 = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Desktop/Hier_AE_deliverable/40_cluster_model_layer2_new.txt')
>>>>>>> 9348384985d2847c272133ff77ce6181ca1fa082
def encode_features_using_AE_layer1_cluster_activation(feature_matrix,layer):
##check visually the reconstruction
#AE_rec.AE_reconstruction_level1(feature_matrix, AE_weights_level_1)
for test_traj in feature_matrix:
if sum(test_traj) != 0:
##compute AE reconstruction
def main():
###### Layer 1 #########
#raw_features = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/posture_shoulder_arms_3fps.txt')
AE_weights_level_1 = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Desktop/Hier_AE_deliverable/ae/head_joint_id1/144weights_l1_hd1002.txt'
) #'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/deep_AE_900_225_weights_008hd1_noNoise.txt')#625_weights_008hd1
# ############
#
#data_new = [f for part in raw_features for task in part for f in task]
#data_new= []
#map(lambda part: map(lambda task: data_new.append(task), part),raw_features)
####Visually check weights
plot_layers_l1(AE_weights_level_1[0], tied_weights=True)
plt.show()
#plt.show()
# ##Visually check the reconstruction
#AE_reconstruction_level1(data_new,AE_weights_level_1)
#
# ##create activation matrix
#matrix_activations_data_l1 = hid_unit_activation_allLayers(data_new, AE_weights_level_1)
#data_organizer.save_matrix_pickle(matrix_activations_data_l1,'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/matrix_act_400_weights.txt')
#matrix_activations_data_l1 = data_organizer.load_matrix_pickle('C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/matrix_act_625_weights_6fps.txt')
matrix_activations_data_l1 = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/posture_data/upperBody/matrix_deep_act_225_weights.txt'
)
# ####Clustering on the activation matrix
cluster_activation_allLayers(matrix_activations_data_l1,
AE_weights_level_1, [],
n_layer=1)
return 0
# ################
#### Layer 2 temporal reconstruction####
raw_features = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/head_joint_id1/feature_matrix_participant_task_l2_new.txt'
)
AE_weights_level_1 = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/ae/head_joint_id1/144weights_l1_hd1002.txt'
)
original_points_grid_level_2 = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/head_joint_id1/orig_points_participant_task_l2_new.txt'
)
AE_weights_level_2 = data_organizer.load_matrix_pickle(
'C:/Users/dario.dotti/Documents/data_for_personality_exp/after_data_cleaning/ae/head_joint_id1/169weights_l2_001_new.txt'
)
data_new = [f for part in raw_features for task in part for f in task]
## Visualize weights layer 2 temporal##
# AE_showWeights_level2_temporalExperiment(AE_weights_level_2, AE_weights_level_1)
# original_points_grid_level_2_new = [f for part in original_points_grid_level_2 for task in part for f in task]
# reconstruction_AE_weights_level2_temporal(data_new, original_points_grid_level_2_new, AE_weights_level_1, AE_weights_level_2)
#### Get activations and cluster ####
#matrix_activations_data_l2 = hid_unit_activation_allLayers(data_new,AE_weights_level_2)
#cluster_activation_allLayers(matrix_activations_data_l2, AE_weights_level_1, AE_weights_level_2,n_layer=2 )
#####
create_cluster_labels_participant_task(raw_features, AE_weights_level_2)
