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
def main():
pose = Pose()
coordinate_list = []
# importing the module
import json
# Opening JSON file
with open('weights.json') as json_file:
weights = json.load(json_file)["weights"]
s = sum(weights)
weights = [w / s for w in weights]
with tf.compat.v1.Session() as sess:
model_cfg, model_outputs = posenet.load_model(101, sess)
cap = cv2.VideoCapture(args["video"])
i = 1
if cap.isOpened() is False:
print("error in opening video")
while cap.isOpened():
ret_val, image = cap.read()
if ret_val:
input_points, input_black_image = pose.getpoints_vis(
image, sess, model_cfg, model_outputs)
cv2.imwrite('./test_video' + str(i) + '.jpg',
input_black_image)
input_points = input_points[0:34]
# print(input_points)
input_new_coords = pose.roi(input_points)
input_new_coords = input_new_coords[0:34]
input_new_coords = np.asarray(input_new_coords).reshape(17, 2)
coordinate_list.append(input_new_coords)
i = i + 1
else:
break
cap.release()
coordinate_list = np.array(coordinate_list)
# print(coordinate_list)
# print(coordinate_list.shape)
print("Lookup Table Created")
file = open(args["lookup"], 'wb')
pickle.dump({
args["activity"]: coordinate_list,
"weights": weights
}, file)
def main():
a = Pose()
b = []
c = {}
with tf.compat.v1.Session() as sess:
model_cfg, model_outputs = posenet.load_model(101, sess)
cap = cv2.VideoCapture(args["video"])
i = 1
if cap.isOpened() is False:
print("error in opening video")
while cap.isOpened():
ret_val, image = cap.read()
if ret_val:
image = cv2.resize(image,(372,495))
input_points,input_black_image = a.getpoints_vis(image,sess,model_cfg,model_outputs)
input_points = input_points[0:34]
print(input_points)
input_new_coords = a.roi(input_points)
input_new_coords = input_new_coords[0:34]
input_new_coords = np.asarray(input_new_coords).reshape(17,2)
b.append(input_new_coords)
cv2.imshow("black", input_black_image)
cv2.waitKey(1)
i = i + 1
else:
break
cap.release()
b = np.array(b)
cv2.destroyAllWindows
print(b)
print(b.shape)
print("Lookup Table Created")
c[args["activity"]] = b
f = open(args["lookup"],'wb')
pickle.dump(c,f)
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
