class get_Score(object):
def __init__(self, lookup='lookup.pickle'):
self.a = Pose()
self.s = Score()
self.b = pickle.load(open(lookup, 'rb'))
self.input_test = []
def get_action_coords_from_dict(self,action):
for (k,v) in self.b.items():
if k==action:
(model_array,no_of_frames) = (v,v.shape[0])
return model_array,no_of_frames
def calculate_Score(self,video,action):
with tf.compat.v1.Session() as sess:
model_cfg, model_outputs = posenet.load_model(101, sess)
model_array,j = self.get_action_coords_from_dict(action)
cap = cv2.VideoCapture(video)
i = 0
if cap.isOpened() is False:
print("error in opening video")
while cap.isOpened():
ret_val, image = cap.read()
if ret_val:
input_points= self.a.getpoints(cv2.resize(image,(372,495)),sess,model_cfg,model_outputs)
input_new_coords = np.asarray(self.a.roi(input_points)[0:34]).reshape(17,2)
self.input_test.append(input_new_coords)
i = i + 1
else:
break
cap.release()
final_score,score_list = self.s.compare(np.asarray(self.input_test),np.asarray(model_array),j,i)
return final_score,score_list
class get_Score(object):
def __init__(self, lookup='lookup.pickle'):
self.pose = Pose()
self.score = Score()
self.saved_pose = pickle.load(open(lookup, 'rb'))
self.weights = self.saved_pose["weights"]
self.new_video_coordinates = []
def get_action_coords_from_dict(self, action):
for k, v in self.saved_pose.items():
if k == action:
(model_array, no_of_frames) = (v, v.shape[0])
return model_array, no_of_frames
def calculate_Score(self, video, action):
with tf.compat.v1.Session() as sess:
model_cfg, model_outputs = posenet.load_model(101, sess)
reference_coordinates, reference_video_frames = self.get_action_coords_from_dict(
action)
cap = cv2.VideoCapture(video)
new_video_frames = 0
if cap.isOpened() is False:
print("error in opening video")
while cap.isOpened():
ret_val, image = cap.read()
if ret_val:
input_points = self.pose.getpoints(image, sess, model_cfg,
model_outputs)
if len(input_points) == 0:
continue
input_new_coords = np.asarray(
self.pose.roi(input_points)[0:34]).reshape(17, 2)
self.new_video_coordinates.append(input_new_coords)
new_video_frames = new_video_frames + 1
else:
break
cap.release()
final_score, score_list = self.score.compare_34dim(
np.asarray(self.new_video_coordinates),
np.asarray(reference_coordinates), new_video_frames,
reference_video_frames, self.weights)
return final_score, score_list
