def test_happy_2():
filename = get_absolute_path(current_directory + "test_face_emotion_extr" +
"action_data/happy2.png")
img = cv2.imread(filename)
face_data = classify_faces([img])
assert len(face_data[0]) == 7
assert face_data[0][3] > 0.5
def test_neutral_1():
filename = get_absolute_path(current_directory +
"test_face_emotion_extra" +
"ction_data/neutral1.png")
img = cv2.imread(filename)
face_data = classify_faces([img])
assert len(face_data[0]) == 7
assert face_data[0][6] > 0.5
def classify_video(video_path: str, time_range=None):
"""Classifies the emotions in a given video
Parameters
----------
video_path:str
The path of the video that is to be analyzed
time_range:Dict[str:int]
The time range to analyze in as a dictionary of type string:int
Returns
-------
string
Returns a csv of the emotions in the emotion set
"""
faces = analyze_video(video_path, time_range)
angry_sum =\
disgust_sum =\
fear_sum =\
happy_sum =\
sad_sum =\
surprise_sum =\
neutral_sum = 0.0
number_of_faces = 0
realFaces = []
for _, value in faces.items():
for face in value:
realFaces.append(face)
faces = classify_faces(realFaces)
for emotions in faces:
angry_sum += emotions[0]
disgust_sum += emotions[1]
fear_sum += emotions[2]
happy_sum += emotions[3]
sad_sum += emotions[4]
surprise_sum += emotions[5]
neutral_sum += emotions[6]
number_of_faces = number_of_faces + 1
return {
"angry": angry_sum / number_of_faces,
"disgust": disgust_sum / number_of_faces,
"fear": fear_sum / number_of_faces,
"happy": happy_sum / number_of_faces,
"sad": sad_sum / number_of_faces,
"surprise": surprise_sum / number_of_faces,
"neutral": neutral_sum / number_of_faces
}
def test_Many_Faces():
face_list = []
filename1 = get_absolute_path(current_directory +
"test_face_emotion_extr" +
"action_data/happy1.png")
filename2 = get_absolute_path(current_directory +
"test_face_emotion_extr" +
"action_data/happy2.png")
filename3 = get_absolute_path(current_directory +
"test_face_emotion_extr" +
"action_data/happy3.png")
img1 = cv2.imread(filename1)
img2 = cv2.imread(filename2)
img3 = cv2.imread(filename3)
face_list.append(img1)
face_list.append(img2)
face_list.append(img3)
list_with_faces = classify_faces(face_list)
assert list_with_faces[0][3] > 0.5
assert list_with_faces[1][3] > 0.5
assert list_with_faces[2][3] > 0.3