def test_detected_frame(self):
"""Test if input frames background is being removed correctly. """
# setup
expected_path = utils.get_full_path(
'docs/material_for_testing/back_ground_removed_frame.jpg')
expected = cv2.imread(expected_path)
test_path = utils.get_full_path(
'docs/material_for_testing/face_and_hand_0.avi')
cap = cv2.VideoCapture(test_path)
flags_handler = FlagsHandler()
back_ground_remover = BackGroundRemover(flags_handler)
ret = True
# run
while ret is True:
ret, frame = cap.read()
if ret is True:
back_ground_remover.detected_frame = frame
# write_path = utils.get_full_path('docs')
# cv2.imwrite(write_path+'/back_ground_removed_frame.jpg',back_ground_remover.detected_frame)
ssim = ImageTestTool.compare_imaged(back_ground_remover.detected_frame,
expected)
# print("SSIM: {}".format(ssim))
assert ssim >= 0.95
# teardown
cap.release()
cv2.destroyAllWindows()
def test_draw_contour(self):
"""Test is contour is being drawn accordingly to flags_handles. """
# setup
# Input from camera.
cv2.namedWindow('test_draw_contour')
test_path = utils.get_full_path(
'docs/material_for_testing/back_ground_removed_frame.jpg')
test_image = cv2.imread(test_path)
# Because image loaded from local, and not received from web-cam, a flip is needed.
test_image = cv2.flip(test_image, 1)
expected = test_image.copy()
flags_handler = FlagsHandler()
# Set flags_handler in order to perform the test.
flags_handler.lifted = True
flags_handler.calibrated = True
detector = Detector(flags_handler)
# run
while flags_handler.quit_flag is False:
"""
Inside loop, update self._threshold according to flags_handler,
Pressing 'c': in order to toggle control (suppose to change contour's color between green and red)
Pressing 'l': to raise 'land' flag in flags_handler, in order to be able to break loop (with esc)
Pressing esc: break loop.
"""
detector.input_frame_for_feature_extraction = test_image
cv2.imshow('test_draw_contour',
detector.input_frame_for_feature_extraction)
flags_handler.keyboard_input = cv2.waitKey(1)
# teardown
cv2.destroyAllWindows()
def test_input_frame(self):
"""Test if input frame preprocessed correctly. """
# setup
test_path = utils.get_full_path(
'docs/material_for_testing/face_and_hand.jpg')
test_image = cv2.imread(test_path)
# Because image loaded from local, and not received from web-cam, a flip is needed,
# inside frame_handler, a frame is supposed to be received from web-cam, hence it is flipped after receiving it.
test_image = cv2.flip(test_image, 1) # type: np.ndarray
expected = test_image.copy()
expected = cv2.bilateralFilter(expected, 5, 50,
100) # smoothing filter
expected = cv2.flip(expected, 1)
frame_handler = FrameHandler()
frame_handler.logger.setLevel(logging.DEBUG)
# run
# range [-1, 1] with a value of one being a “perfect match”.
frame_handler.input_frame = test_image
ssim = ImageTestTool.compare_imaged(frame_handler.input_frame,
expected)
# print("SSIM: {}".format(ssim))
assert ssim >= 0.95
def __init__(self):
self.logger = logging.getLogger('face_processor_handler')
self.logger.setLevel(logging.INFO)
self._face_detector = cv2.CascadeClassifier(
utils.get_full_path('hallopy/config/haarcascade_frontalface_default.xml'))
self._face_padding_x = 20
self._face_padding_y = 60
self._preprocessed_input_frame = None
def detect_faces(img):
"""Function for detecting faces.
:returns faces: array with detected faces coordination's.
"""
face_detector = cv2.CascadeClassifier(
utils.get_full_path(
'hallopy/config/haarcascade_frontalface_default.xml'))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return face_detector.detectMultiScale(gray, 1.3, 5)
def test_extract_center_of_mass(self):
"""Test if extract find center of mass. """
# setup
test_path = utils.get_full_path('docs/material_for_testing/back_ground_removed_frame.jpg')
test_image = cv2.imread(test_path)
expected_path = utils.get_full_path(
'docs/material_for_testing/back_ground_removed_and_center_of_mass_discovered.jpg')
expected_image = cv2.imread(expected_path)
# Because image loaded from local, and not received from web-cam, a flip is needed.
test_image = cv2.flip(test_image, 1)
# todo: use mockito here to mock detector
flags_handler = FlagsHandler()
detector = Detector(flags_handler)
extractor = Extractor(flags_handler)
detector.input_frame_for_feature_extraction = test_image
# run
extractor.extract = detector
result_image = test_image.copy()
cv2.circle(result_image, extractor.palm_center_point, 5, (255, 0, 0), thickness=5)
ssim = ImageTestTool.compare_imaged(result_image, expected_image)
# print("SSIM: {}".format(ssim))
assert ssim >= 0.95
def test_find_largest_contours(self):
"""Test if largest contours is found. """
# setup
test_path = utils.get_full_path(
'docs/material_for_testing/back_ground_removed_frame.jpg')
test_image = cv2.imread(test_path)
# Because image loaded from local, and not received from web-cam, a flip is needed.
test_image = cv2.flip(test_image, 1)
test_image = cv2.bitwise_not(test_image)
max_area_contour = ImageTestTool.get_max_area_contour(test_image)
expected_area = ImageTestTool.get_contour_area(max_area_contour)
# Create detector
flags_handler = FlagsHandler()
detector = Detector(flags_handler)
# run
detector.input_frame_for_feature_extraction = test_image
result_area = cv2.contourArea(detector.max_area_contour)
assert result_area == expected_area
def test_face_covered_frame(self):
"""Test if faces are detected and covered. """
# setup
test_path = utils.get_full_path(
'docs/material_for_testing/face_and_hand.jpg')
test_image = cv2.imread(test_path)
expected = test_image.copy()
expected_faces = ImageTestTool.detect_faces(expected)
ImageTestTool.draw_black_recs(expected, expected_faces)
face_processor = FaceProcessor()
face_processor.logger.setLevel(logging.DEBUG)
# Insert image with face.
face_processor.face_covered_frame = expected
# run
ssim = ImageTestTool.compare_imaged(face_processor.face_covered_frame,
expected)
# print("SSIM: {}".format(ssim))
assert ssim >= 0.93
def test_contour_extreme_point_tracking(self):
"""Test for tracking extreme_points without optical flow (e.g until calibrated). """
# setup
test_path = utils.get_full_path('docs/material_for_testing/back_ground_removed_frame.jpg')
test_image = cv2.imread(test_path)
# todo: use mockito here to mock preprocessing elements
flags_handler = FlagsHandler()
detector = Detector(flags_handler)
extractor = Extractor(flags_handler)
# Background model preparations.
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
cap = cv2.VideoCapture(0)
while flags_handler.quit_flag is False:
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
# Remove background from input frame.
fgmask = bg_model.apply(frame, learningRate=0)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
res = cv2.bitwise_and(frame, frame, mask=fgmask)
# Clip frames ROI.
back_ground_removed_clipped = ImageTestTool.clip_roi(res,
{'cap_region_x_begin': 0.6, 'cap_region_y_end': 0.6})
if flags_handler.background_capture_required is True:
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
flags_handler.background_capture_required = False
detector.input_frame_for_feature_extraction = back_ground_removed_clipped
extractor.extract = detector
image = extractor.get_drawn_extreme_contour_points()
cv2.imshow('test_contour_extreme_point_tracking', image)
flags_handler.keyboard_input = cv2.waitKey(1)
def test_draw_axes(self):
"""Test if detected_out_put_center calculated properly. """
# setup
test_path = utils.get_full_path(
'docs/material_for_testing/back_ground_removed_frame.jpg')
test_image = cv2.imread(test_path)
# Because image loaded from local, and not received from web-cam, a flip is needed.
test_image = cv2.flip(test_image, 1)
expected = test_image.copy()
# Create detector
flags_handler = FlagsHandler()
detector = Detector(flags_handler)
expected_detected_out_put_center = (int(expected.shape[1] / 2),
int(expected.shape[0] / 2) +
detector.horiz_axe_offset)
# run
detector.input_frame_for_feature_extraction = test_image
cv2.imshow('expected', expected)
cv2.imshow('result', detector.input_frame_for_feature_extraction)
cv2.waitKey()
assert expected_detected_out_put_center == detector.detected_out_put_center
def test_get_contour_extreme_point(self):
"""Test if middle finger edge was found correctly. """
# setup
test_path = utils.get_full_path('docs/material_for_testing/back_ground_removed_frame.jpg')
test_image = cv2.imread(test_path)
max_area_contour = ImageTestTool.get_max_area_contour(test_image)
expected_extLeft, expected_extRight, expected_extTop, expected_extBot = ImageTestTool.get_contour_extreme_points(
max_area_contour)
# todo: use mockito here to mock detector
flags_handler = FlagsHandler()
detector = Detector(flags_handler)
extractor = Extractor(flags_handler)
detector.input_frame_for_feature_extraction = test_image
# run
extractor.extract = detector
assert expected_extLeft == extractor.ext_left
assert expected_extRight == extractor.ext_right
assert expected_extTop == extractor.ext_top
assert expected_extBot == extractor.ext_bot
