def test_back_ground_reset(self):
"""Test if background model is being reset correctly.
resetting background model is via keyboard input,
in Controller's flags_handler.
"""
# setup
# Input from camera.
cv2.namedWindow('test')
cap = cv2.VideoCapture(0)
flags_handler = FlagsHandler()
back_ground_remover = BackGroundRemover(flags_handler)
# run
while flags_handler.quit_flag is False:
"""
Inside loop, remove back ground from frame using back_ground_remover,
here we are testing for background model resetting.
the reset flag is changed within Controller's flags_handler.
Pressing 'b': will rest background.
Pressing esc: break loop.
"""
ret, frame = cap.read()
if ret is True:
back_ground_remover.detected_frame = frame
if back_ground_remover.detected_frame is not None:
cv2.imshow('test', back_ground_remover.detected_frame)
flags_handler.keyboard_input = cv2.waitKey(1)
# teardown
cap.release()
cv2.destroyAllWindows()
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_5_second_calibration_time(self):
"""Test if 5 second calibration time works correctly according to flags_handler.
Usage:
1. press 'b': to calibrate back_ground_remover.
2. insert hand into frame, center palms_center (white dot) with axes crossing.
3. wait for #calibration_time (default 5 sec).
4. press esc
test: after calibration_time, center circle should be green.
"""
# setup
# 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
# run
image = extractor.get_drawn_extreme_contour_points()
cv2.imshow('test_contour_extreme_point_tracking', image)
flags_handler.keyboard_input = cv2.waitKey(1)
def test_palm_angle_calculation(self):
"""Test if angle is calculated correctly.
Usage:
1. press 'b': to calibrate back_ground_remover.
2. insert hand into frame, so that middle_finger is aligned with the Y axe.
3. rotate hand 15 degrees left. (degrees should go above 90).
4. rotate hand 15 degrees right. (degrees should go below 90).
5. press esc when done.
"""
# setup
# 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
# run
image = extractor.get_drawn_extreme_contour_points()
cv2.imshow('test_contour_extreme_point_tracking', image)
print(extractor.palm_angle_in_degrees)
flags_handler.keyboard_input = cv2.waitKey(1)
def test_drawn_correctly(self):
"""Test if zero point is drawn correctly.
zero point is the point that responsible for forward/backward commands extraction.
"""
# setup
# 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
# run
image = extractor.get_drawn_extreme_contour_points()
cv2.imshow('test_drawn_correctly', image)
flags_handler.keyboard_input = cv2.waitKey(1)
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_max_distance_between_top_ext_point_and_palm_center_point(self):
"""Test if max distance is found correctly. """
# setup
# 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
# run
image = extractor.get_drawn_extreme_contour_points()
cv2.line(image, extractor.palm_center_point, (extractor.ext_top[0], extractor.palm_center_point[
1] - extractor.max_distance_from_ext_top_point_to_palm_center), (255, 255, 255), thickness=2)
cv2.imshow('test_max_distance_between_top_ext_point_and_palm_center_point', image)
flags_handler.keyboard_input = cv2.waitKey(1)
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
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_track(self):
"""Test if tracker object tracks correctly after given set of points to track, and a frame."""
# setup
cv2.namedWindow('test_track')
flags_handler = FlagsHandler()
tracker = None
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
max_area_contour = ImageTestTool.get_max_area_contour(
back_ground_removed_clipped)
extLeft, extRight, extTop, extBot = ImageTestTool.get_contour_extreme_points(
max_area_contour)
palm_center = ImageTestTool.get_center_of_mass(max_area_contour)
if tracker is None:
points = np.array([extTop, palm_center])
else:
points = tracker.points_to_track
tracker.track(points, back_ground_removed_clipped)
points = tracker.points_to_track
ImageTestTool.draw_tracking_points(back_ground_removed_clipped,
points)
cv2.circle(back_ground_removed_clipped,
palm_center,
8, (255, 255, 255),
thickness=-1)
cv2.imshow('test_track', back_ground_removed_clipped)
keyboard_input = cv2.waitKey(1)
flags_handler.keyboard_input = keyboard_input
# run
if flags_handler.background_capture_required is True:
tracker = None
if keyboard_input == ord('t'):
tracker = Tracker(flags_handler, points,
back_ground_removed_clipped)
# teardown
cap.release()
cv2.destroyAllWindows()