def gaze_mapping_result_2d(img_path,
calibration="dummy",
calibration_path="",
calibration_ground_truth=True):
##### prepare data #####
img = cv2.imread(img_path)
frame = Frame(1, img, 1)
u_r = Roi(frame.img.shape)
detector2d = detector_2d.Detector_2D()
pupil_datum = detector2d.detect(frame, user_roi=u_r, visualize=False)
pupil_datum['id'] = 0
#print(pupil_datum)
pool = SimpleNamespace()
pool.active_gaze_mapping_plugin = None
##### prepare data #####
##### map gaze #####
if calibration == "dummy":
pool.active_gaze_mapping_plugin = gaze_mappers.Dummy_Gaze_Mapper(pool)
elif calibration == "monocular":
cal_pt_cloud = read_monocular_calibration_data_from_file(
calibration_path, calibration_ground_truth)
#cal_pt_cloud = read_monocular_calibration_data(calibration_path, calibration_ground_truth)
_, _, params = calibrate.calibrate_2d_polynomial(cal_pt_cloud)
#print(params)
pool.active_gaze_mapping_plugin = gaze_mappers.Monocular_Gaze_Mapper(
pool, params)
gaze_data = pool.active_gaze_mapping_plugin.on_pupil_datum(pupil_datum)
#print(gaze_data)
##### map gaze #####
return gaze_data[0]['norm_pos']
def pupil_detection_result_2d(img_path, visualization=False):
img = cv2.imread(img_path)
frame = Frame(1, img, 1)
#print(frame)
detector2d = detector_2d.Detector_2D()
#print(detector2d)
u_r = Roi(frame.img.shape)
#print(u_r)
result = detector2d.detect(frame, user_roi=u_r, visualize=False)
center_x = result['ellipse']['center'][0]
center_y = result['ellipse']['center'][1]
center = [center_x, center_y]
axes_0 = result['ellipse']['axes'][0]
axes_1 = result['ellipse']['axes'][1]
axes = [axes_0, axes_1]
degree = result['ellipse']['angle']
#print(result['ellipse'])
#print(result['norm_pos'])
#print('')
if visualization:
fitted_ellipse = Ellipse(center, axes[0], axes[1], degree)
frame_img = frame.img
fig = plt.figure()
ax = fig.add_subplot(111)
ax.add_artist(fitted_ellipse)
plt.imshow(frame_img)
plt.scatter(center_x, center_y, color='red', s=10)
return center, degree, axes
def gaze_mapping_with_gt_pupil_2d(img_path,
calibration="dummy",
calibration_path="",
calibration_ground_truth=True):
##### prepare data #####
img = cv2.imread(img_path)
frame = Frame(1, img, 1)
u_r = Roi(frame.img.shape)
detector2d = detector_2d.Detector_2D()
pupil_datum = detector2d.detect(frame, user_roi=u_r, visualize=False)
center, degree, axes = gt_pupil_info(img_path)
pupil_datum['confidence'] = 1.0
pupil_datum['diameter'] = max(axes)
pupil_datum['ellipse']['center'] = center
pupil_datum['ellipse']['angle'] = degree
pupil_datum['ellipse']['axes'] = axes
pupil_datum['norm_pos'] = [center[0] / 320, (240 - center[1]) / 240]
pupil_datum['id'] = 0
#print(pupil_datum)
pool = SimpleNamespace()
pool.active_gaze_mapping_plugin = None
##### prepare data #####
##### map gaze #####
if calibration == "dummy":
pool.active_gaze_mapping_plugin = gaze_mappers.Dummy_Gaze_Mapper(pool)
elif calibration == "monocular":
cal_pt_cloud = read_monocular_calibration_data_from_file(
calibration_path, calibration_ground_truth)
#cal_pt_cloud = read_monocular_calibration_data(calibration_path, calibration_ground_truth)
_, _, params = calibrate.calibrate_2d_polynomial(cal_pt_cloud)
#print(params)
pool.active_gaze_mapping_plugin = gaze_mappers.Monocular_Gaze_Mapper(
pool, params)
gaze_data = pool.active_gaze_mapping_plugin.on_pupil_datum(pupil_datum)
#print(gaze_data)
##### map gaze #####
return gaze_data[0]['norm_pos']
def read_monocular_calibration_data(calibration_path, ground_truth=True):
#cal_data = [
# (*pair["pupil"]["norm_pos"], *pair["ref"]["norm_pos"]) for pair in matched_data
#]
pupil_positions = []
ref_positions = []
detector2d = detector_2d.Detector_2D()
if ground_truth:
pupil_cornea_path = calibration_path + "pupil_cornea\\"
images = [f for f in glob.glob(pupil_cornea_path + "*.png")]
else:
eye_path = calibration_path + "eye\\"
images = [f for f in glob.glob(eye_path + "*.png")]
for i in images:
img = cv2.imread(i)
frame = Frame(1, img, 1)
u_r = Roi(frame.img.shape)
pupil_datum = detector2d.detect(frame, user_roi=u_r, visualize=False)
pupil_norm = (pupil_datum['norm_pos'][0], pupil_datum['norm_pos'][1])
pupil_positions.append(pupil_norm)
f = open(calibration_path + "gaze_in_scene_camera.txt", "r")
for line in f:
if line.startswith(os.path.splitext(i)[0]):
ref_norm = (float(line.split(' ')[1]),
float(line.split(' ')[2]))
ref_positions.append(ref_norm)
break
cal_data = [(pupil_positions[i][0], pupil_positions[i][1],
ref_positions[i][0], ref_positions[i][1])
for i in range(len(ref_positions))]
return cal_data
cap = autoCreateCapture(cap_src, timebase=None)
default_settings = {'frame_size':cap_size,'frame_rate':30}
cap.settings = default_settings
# Test capture
try:
frame = cap.get_frame()
except CameraCaptureError:
print "Could not retrieve image from capture"
cap.close()
Pool = namedtuple('Pool', 'user_dir');
pool = Pool('/')
u_r = Roi(frame.img.shape)
#Our detectors we wanna compare
detector_cpp = detector_2d.Detector_2D()
detector_py = Canny_Detector(pool)
# detector_py.coarse_detection= False
test_file_Folder = '../../../../../Pupil_Test_Files/' # write files to the project root folder
def compareEllipse( ellipse_cpp , ellipse_py):
return \
abs(ellipse_cpp['center'][0] - ellipse_py['center'][0]) <.1 and \
abs(ellipse_cpp['center'][1] - ellipse_py['center'][1]) <.1 and \
abs(ellipse_cpp['major'] - ellipse_py['major'])<.1 and \
abs(ellipse_cpp['minor'] - ellipse_py['minor'])<.1
# abs(ellipse_cpp['angle'] - ellipse_py['angle'])<10.1
def compare_dict(first, second):
