def test_detect_face_and_smile_on_picture(self):
"Test the detection of a smile in a face on a static picture"
image = cv2.imread(ASSETS_FOLDER + self.TRUMP_PHOTO_SMILE, 0)
face_cascade_object = cv2.CascadeClassifier(self.CASCADE_FOLDER +
self.FACE_CASCADE_MODEL)
smile_cascade_object = cv2.CascadeClassifier(self.CASCADE_FOLDER +
self.SMILE_CASCADE_MODEL)
faces = []
smiles = []
for i in face_recognition.find_cascade(face_cascade_object,
image,
draw=True,
draw_frame=image):
faces.append(i)
ROI = image[i[1]:i[1] + i[3], i[0]:i[0] + i[2]]
ROI = ROI[len(ROI) // 2:]
for j in face_recognition.find_cascade(smile_cascade_object,
ROI,
draw=True,
draw_frame=image,
start_x=i[0],
start_y=i[1] + len(ROI)):
smiles.append(j)
self.assertGreaterEqual(
len(faces), 1,
"ERROR(test_detect_face_and_smile_on_picture): No faces found")
self.assertGreaterEqual(
len(smiles), 1,
"ERROR(test_detect_face_and_smile_on_picture): No smiles found")
def test_process_images_both(self): # average = 0.06 seconds
"""process face recognition and the contours of 100 images, and calculate the average processing time"""
path_to_image_contour = "flower_contour_crop.jpg"
path_to_image_cascade = "face_trump.jpg"
# preparation
SETTINGS = (19, 35, 225, 255, 82, 255)
cascade_object = cv2.CascadeClassifier(
CASCADE_FOLDER + FACE_CASCADE_MODEL)
faces = []
start = time.time()
for _ in range(AMOUNT):
image = cv2.imread(ASSETS_FOLDER + path_to_image_contour)
contours = face_recognition.find_contour(
image, 2, pos=True, settings=SETTINGS, return_all_contours=True)
filtered_contour_list = face_recognition.filter_contour(contours)
image = cv2.imread(ASSETS_FOLDER + path_to_image_cascade, 0)
for i in face_recognition.find_cascade(cascade_object, image, draw=True, draw_frame=image):
faces.append(i)
end = time.time()
average_time = PerformanceTest.calculate_average_time(
start, end, AMOUNT)
self.assertLessEqual(
average_time, THRESHOLD, "The average time was %f seconds, but needed to be at least %f seconds" % (average_time, THRESHOLD))
def test_detect_smile_on_picture(self):
"Test the detection of a smile on a static picture"
image = cv2.imread(ASSETS_FOLDER + self.TRUMP_PHOTO_SMILE, 0)
cascade_object = cv2.CascadeClassifier(self.CASCADE_FOLDER +
self.SMILE_CASCADE_MODEL)
smiles = []
for i in face_recognition.find_cascade(cascade_object,
image,
draw=True,
draw_frame=image):
smiles.append(i)
self.assertGreaterEqual(
len(smiles), 1,
"ERROR(test_detect_smile_on_picture): No Smiles found")
def test_detect_face_on_picture_false_postives(self):
"Test the detection of a face on a static picture on false postives"
image = cv2.imread(ASSETS_FOLDER + self.TRUMP_PHOTO, 0)
cascade_object = cv2.CascadeClassifier(self.CASCADE_FOLDER +
self.FACE_CASCADE_MODEL)
faces = []
for i in face_recognition.find_cascade(cascade_object,
image,
draw=True,
draw_frame=image):
faces.append(i)
self.assertGreaterEqual(
len(faces), 1,
"ERROR(test_detect_face_on_picture_false_postives): No faces found"
)
self.assertEqual(
len(faces), 1,
"ERROR(test_detect_face_on_picture_false_postives): To many faces detected"
)
def test_process_images_cascade(self): # average = 0.05 seconds
"""execute face recognition on a 100 images, and calculate the average processing time"""
path_to_image = "face_trump.jpg"
# preparation
cascade_object = cv2.CascadeClassifier(
CASCADE_FOLDER + FACE_CASCADE_MODEL)
faces = []
start = time.time()
for _ in range(AMOUNT):
image = cv2.imread(ASSETS_FOLDER + path_to_image, 0)
for i in face_recognition.find_cascade(cascade_object, image, draw=True, draw_frame=image):
faces.append(i)
end = time.time()
average_time = PerformanceTest.calculate_average_time(
start, end, AMOUNT)
self.assertLessEqual(
average_time, THRESHOLD, "The average time was %f seconds, but needed to be at least %f seconds" % (average_time, THRESHOLD))