def learn(self, video, ty, threshold=0.8):
vp = VideoProcessor(video)
if vp.get_video_view() == "front": # you need 5-dimensional data in front case
if ty is "elbow":
angle_result = vp.compute_left_elbow_angle(threshold)
if ty is "shoulder":
angle_result = vp.compute_left_shoulder_angle(threshold)
if ty is "arm":
angle_result = vp.compute_left_arm_angle_with_floor(threshold)
min_max = self.min_max(angle_result, "front", ty)
if (self.div_zero is 1):
print("training data is erronous, div-zero happend. This data is not added")
self.div_zero = 0
return
self.front_data.append(((min_max), vp.get_video_label()))
elif vp.get_video_view() == "left":
angle_result = vp.compute_left_elbow_angle(threshold)
min_max = self.min_max(angle_result, "left")
self.left_data.append(((min_max), vp.get_video_label()))
elif vp.get_video_view() == "squat":
angle_result = vp.compute_left_knee_angle(threshold)
min_max = self.min_max(angle_result, "squat")
self.left_data.append(((min_max), vp.get_video_label()))
else:
sys.exit("Error: Wrong view type is given")
print(video.get_name() + " has been successfully learned.")
def feedback_kmeans(self, video, ty, threshold=0.8):
# input video may have None label
vp = VideoProcessor(video)
if vp.get_video_view() == "front": # you need 6-dimensional data in front caseW
if ty is "elbow":
angle_result = vp.compute_left_elbow_angle(threshold)
if ty is "shoulder":
angle_result = vp.compute_left_shoulder_angle(threshold)
if ty is "arm":
angle_result = vp.compute_left_arm_angle_with_floor(threshold)
min_max = self.min_max(angle_result, "front", ty)
if (self.div_zero is 1):
#sys.exit("training data is erronous, div-zero happend")
print("div-zero happend")
return [], self.div_zero
training_data = [t[0] for t in self.front_data]
# expect two clusters; one for full reps, another one for partial reps
km = KMeans(n_clusters=8, random_state=0).fit(np.array(training_data))
cluster = km.predict(np.array([np.array(min_max)]))
#label format [partial range or not, elbow flare or not, wide or not]
# majority voting
labels = km.labels_
training_labels = [t[1] for t in self.front_data]
positive_labels_num = 0
negative_labels_num = 0
if ty is "elbow":
label_index = 0
if ty is "shoulder":
label_index = 2
if ty is "arm":
label_index = 1
for i in range(len(labels)):
if labels[i] == cluster:
if training_labels[i][label_index] == 1:
positive_labels_num = positive_labels_num + 1
else:
negative_labels_num = negative_labels_num + 1
result = positive_labels_num > negative_labels_num
return result, self.div_zero
def feedback_kmeans(self, video, threshold=0.8):
# input video may have None label
vp = VideoProcessor(video)
if vp.get_video_view(
) == "front": # you need 6-dimensional data in front case
elbow_angle_result = vp.compute_left_elbow_angle(threshold)
shoulder_angle_result = vp.compute_left_shoulder_angle(threshold)
arm_angle_result = vp.compute_left_arm_angle_with_floor(threshold)
min_max_1 = self.min_max(elbow_angle_result, "front", "elbow")
min_max_2 = self.min_max(shoulder_angle_result, "front",
"shoulder")
min_max_3 = self.min_max(arm_angle_result, "front", "arm")
if (self.div_zero is 1):
sys.exit("training data is erronous, div-zero happend")
min_max = min_max_1 + [min_max_2[0]] + min_max_3
training_data = [t[0] for t in self.front_data]
# expect two clusters; one for full reps, another one for partial reps
km = KMeans(n_clusters=8,
random_state=0).fit(np.array(training_data))
cluster = km.predict(np.array([np.array(min_max)]))
#label format [partial range or not, elbow flare or not, wide or not]
# majority voting
labels = km.labels_
training_labels = [t[1] for t in self.front_data]
positive_labels_num = [0, 0, 0]
negative_labels_num = [0, 0, 0]
for i in range(len(labels)):
if labels[i] == cluster:
print(str(i))
print(training_labels[i])
for j in range(len(positive_labels_num)):
if training_labels[i][j] == 1:
positive_labels_num[j] = positive_labels_num[j] + 1
else:
negative_labels_num[j] = negative_labels_num[j] + 1
result = []
for i in range(len(positive_labels_num)):
result.append(positive_labels_num[i] > negative_labels_num[i])
return result