def stop(self):
# TODO: redundancy between all gaze mappers -> might be moved to parent class
audio.say("Stopping Calibration")
logger.info("Stopping Calibration")
self.screen_marker_state = 0
self.active = False
self.close_window()
self.button.status_text = ""
if self.g_pool.binocular:
cal_pt_cloud = calibrate.preprocess_data(list(self.pupil_list), list(self.ref_list), id_filter=(0, 1))
cal_pt_cloud_eye0 = calibrate.preprocess_data(list(self.pupil_list), list(self.ref_list), id_filter=(0,))
cal_pt_cloud_eye1 = calibrate.preprocess_data(list(self.pupil_list), list(self.ref_list), id_filter=(1,))
logger.info("Collected %s binocular data points." % len(cal_pt_cloud))
logger.info("Collected %s data points for eye 0." % len(cal_pt_cloud_eye0))
logger.info("Collected %s data points for eye 1." % len(cal_pt_cloud_eye1))
else:
cal_pt_cloud = calibrate.preprocess_data(self.pupil_list, self.ref_list)
logger.info("Collected %s data points." % len(cal_pt_cloud))
if (
self.g_pool.binocular
and (len(cal_pt_cloud_eye0) < 20 or len(cal_pt_cloud_eye1) < 20)
or len(cal_pt_cloud) < 20
):
logger.warning("Did not collect enough data.")
return
cal_pt_cloud = np.array(cal_pt_cloud)
map_fn, params = calibrate.get_map_from_cloud(
cal_pt_cloud, self.g_pool.capture.frame_size, return_params=True, binocular=self.g_pool.binocular
)
np.save(os.path.join(self.g_pool.user_dir, "cal_pt_cloud.npy"), cal_pt_cloud)
# replace current gaze mapper with new
if self.g_pool.binocular:
# get monocular models for fallback (if only one pupil is detected)
cal_pt_cloud_eye0 = np.array(cal_pt_cloud_eye0)
cal_pt_cloud_eye1 = np.array(cal_pt_cloud_eye1)
_, params_eye0 = calibrate.get_map_from_cloud(
cal_pt_cloud_eye0, self.g_pool.capture.frame_size, return_params=True
)
_, params_eye1 = calibrate.get_map_from_cloud(
cal_pt_cloud_eye1, self.g_pool.capture.frame_size, return_params=True
)
self.g_pool.plugins.add(
Bilateral_Gaze_Mapper, args={"params": params, "params_eye0": params_eye0, "params_eye1": params_eye1}
)
np.save(os.path.join(self.g_pool.user_dir, "cal_pt_cloud_eye0.npy"), cal_pt_cloud_eye0)
np.save(os.path.join(self.g_pool.user_dir, "cal_pt_cloud_eye1.npy"), cal_pt_cloud_eye1)
else:
self.g_pool.plugins.add(Simple_Gaze_Mapper, args={"params": params})
def stop(self):
audio.say("Stopping Calibration")
logger.info('Stopping Calibration')
self.screen_marker_state = 0
self.active = False
self.close_window()
self.button.status_text = ''
cal_pt_cloud = calibrate.preprocess_data(self.pupil_list,
self.ref_list)
logger.info("Collected %s data points." % len(cal_pt_cloud))
if len(cal_pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
cal_pt_cloud = np.array(cal_pt_cloud)
map_fn, params = calibrate.get_map_from_cloud(cal_pt_cloud,
self.world_size,
return_params=True)
np.save(os.path.join(self.g_pool.user_dir, 'cal_pt_cloud.npy'),
cal_pt_cloud)
#replace current gaze mapper with new
self.g_pool.plugins.add(Simple_Gaze_Mapper(self.g_pool, params))
def stop(self):
# TODO: redundancy between all gaze mappers -> might be moved to parent class
audio.say("Stopping Calibration")
logger.info("Stopping Calibration")
self.active = False
self.button.status_text = ''
#img_size = self.first_img.shape[1],self.first_img.shape[0]
if self.g_pool.binocular:
cal_pt_cloud = calibrate.preprocess_data(list(self.pupil_list),list(self.ref_list),id_filter=(0,1))
cal_pt_cloud_eye0 = calibrate.preprocess_data(list(self.pupil_list),list(self.ref_list),id_filter=(0,))
cal_pt_cloud_eye1 = calibrate.preprocess_data(list(self.pupil_list),list(self.ref_list),id_filter=(1,))
else:
cal_pt_cloud = calibrate.preprocess_data(self.pupil_list,self.ref_list)
if self.g_pool.binocular:
logger.info("Collected %s binocular data points." %len(cal_pt_cloud))
logger.info("Collected %s data points for eye 0." %len(cal_pt_cloud_eye0))
logger.info("Collected %s data points for eye 1." %len(cal_pt_cloud_eye1))
else:
logger.info("Collected %s data points." %len(cal_pt_cloud))
if self.g_pool.binocular and (len(cal_pt_cloud) < 20 or len(cal_pt_cloud_eye0) < 20 or len(cal_pt_cloud_eye1) < 20) or len(cal_pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
cal_pt_cloud = np.array(cal_pt_cloud)
map_fn,params = calibrate.get_map_from_cloud(cal_pt_cloud,self.g_pool.capture.frame_size,return_params=True, binocular=self.g_pool.binocular)
np.save(os.path.join(self.g_pool.user_dir,'cal_pt_cloud.npy'),cal_pt_cloud)
#replace current gaze mapper with new
if self.g_pool.binocular:
# get monocular models for fallback (if only one pupil is detected)
cal_pt_cloud_eye0 = np.array(cal_pt_cloud_eye0)
cal_pt_cloud_eye1 = np.array(cal_pt_cloud_eye1)
_,params_eye0 = calibrate.get_map_from_cloud(cal_pt_cloud_eye0,self.g_pool.capture.frame_size,return_params=True)
_,params_eye1 = calibrate.get_map_from_cloud(cal_pt_cloud_eye1,self.g_pool.capture.frame_size,return_params=True)
self.g_pool.plugins.add(Bilateral_Gaze_Mapper,args={'params':params, 'params_eye0':params_eye0, 'params_eye1':params_eye1})
else:
self.g_pool.plugins.add(Simple_Gaze_Mapper,args={'params':params})
def stop(self):
audio.say("Stopping Calibration")
logger.info("Stopping Calibration")
self.active = False
cal_pt_cloud = calibrate.preprocess_data(self.pupil_list,self.ref_list)
logger.info("Collected %s data points." %len(cal_pt_cloud))
if len(cal_pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
cal_pt_cloud = np.array(cal_pt_cloud)
img_size = self.first_img.shape[1],self.first_img.shape[0]
self.g_pool.map_pupil = calibrate.get_map_from_cloud(cal_pt_cloud,img_size)
np.save(os.path.join(self.g_pool.user_dir,'cal_pt_cloud.npy'),cal_pt_cloud)
def stop(self):
logger.info("Stopping Touchup")
self.smooth_pos = 0.,0.
self.sample_site = -2,-2
self.counter = 0
self.active = False
self.button.status_text = ''
offset_pt_clound = np.array(calibrate.preprocess_data(self.gaze_list,self.ref_list))
if len(offset_pt_clound)<3:
logger.error('Did not sample enough data for touchup please retry.')
return
#Calulate the offset for gaze to target
offset = offset_pt_clound[:,:2]-offset_pt_clound[:,2:]
mean_offset = np.mean(offset,axis=0)
cal_pt_cloud = np.load(os.path.join(self.g_pool.user_dir,'cal_pt_cloud.npy'))
#deduct the offset from the old calibration ref point position. Thus shifiting the calibtation.
# p["norm_pos"][0], p["norm_pos"][1],ref_pt['norm_pos'][0],ref_pt['norm_pos'][1]
cal_pt_cloud[:,-2::] -= mean_offset
if self.g_pool.binocular:
cal_pt_cloud_eye0 = np.load(os.path.join(self.g_pool.user_dir,'cal_pt_cloud_eye0.npy'))
cal_pt_cloud_eye1 = np.load(os.path.join(self.g_pool.user_dir,'cal_pt_cloud_eye1.npy'))
#Do the same for the individual eye in binocular
#p0["norm_pos"][0], p0["norm_pos"][1],p1["norm_pos"][0], p1["norm_pos"][1],ref_pt['norm_pos'][0],ref_pt['norm_pos'][1]
cal_pt_cloud_eye0[:,-2::] -= mean_offset
cal_pt_cloud_eye1[:,-2::] -= mean_offset
#then recalibtate the with old but shifted data.
map_fn,params = calibrate.get_map_from_cloud(cal_pt_cloud,self.g_pool.capture.frame_size,return_params=True, binocular=self.g_pool.binocular)
np.save(os.path.join(self.g_pool.user_dir,'cal_pt_cloud.npy'),cal_pt_cloud)
#replace current gaze mapper with new
if self.g_pool.binocular:
# get monocular models for fallback (if only one pupil is detected)
cal_pt_cloud_eye0 = np.array(cal_pt_cloud_eye0)
cal_pt_cloud_eye1 = np.array(cal_pt_cloud_eye1)
_,params_eye0 = calibrate.get_map_from_cloud(cal_pt_cloud_eye0,self.g_pool.capture.frame_size,return_params=True)
_,params_eye1 = calibrate.get_map_from_cloud(cal_pt_cloud_eye1,self.g_pool.capture.frame_size,return_params=True)
self.g_pool.plugins.add(Bilateral_Gaze_Mapper,args={'params':params, 'params_eye0':params_eye0, 'params_eye1':params_eye1})
np.save(os.path.join(self.g_pool.user_dir,'cal_pt_cloud_eye0.npy'),cal_pt_cloud_eye0)
np.save(os.path.join(self.g_pool.user_dir,'cal_pt_cloud_eye1.npy'),cal_pt_cloud_eye1)
else:
self.g_pool.plugins.add(Simple_Gaze_Mapper,args={'params':params})
def stop(self):
audio.say("Stopping Calibration")
logger.info("Stopping Calibration")
self.active = False
cal_pt_cloud = calibrate.preprocess_data(self.pupil_list,
self.ref_list)
logger.info("Collected %s data points." % len(cal_pt_cloud))
if len(cal_pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
cal_pt_cloud = np.array(cal_pt_cloud)
img_size = self.first_img.shape[1], self.first_img.shape[0]
self.g_pool.map_pupil = calibrate.get_map_from_cloud(
cal_pt_cloud, img_size)
np.save(os.path.join(self.g_pool.user_dir, 'cal_pt_cloud.npy'),
cal_pt_cloud)
def stop(self):
audio.say("Stopping Calibration")
logger.info("Stopping Calibration")
self.smooth_pos = 0,0
self.counter = 0
self.active = False
self.button.status_text = ''
print 'button:', self.button.status_text
cal_pt_cloud = calibrate.preprocess_data(self.pupil_list,self.ref_list)
logger.info("Collected %s data points." %len(cal_pt_cloud))
if len(cal_pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
cal_pt_cloud = np.array(cal_pt_cloud)
map_fn,params = calibrate.get_map_from_cloud(cal_pt_cloud,self.world_size,return_params=True)
np.save(os.path.join(self.g_pool.user_dir,'cal_pt_cloud.npy'),cal_pt_cloud)
self.g_pool.plugins.add(Simple_Gaze_Mapper(self.g_pool,params))
def stop(self):
audio.say("Stopping Calibration")
logger.info('Stopping Calibration')
self.screen_marker_state = 0
self.active = False
self.window_should_close = True
cal_pt_cloud = calibrate.preprocess_data(self.pupil_list,self.ref_list)
logger.info("Collected %s data points." %len(cal_pt_cloud))
if len(cal_pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
cal_pt_cloud = np.array(cal_pt_cloud)
map_fn = calibrate.get_map_from_cloud(cal_pt_cloud,self.world_size)
self.g_pool.map_pupil = map_fn
np.save(os.path.join(self.g_pool.user_dir,'cal_pt_cloud.npy'),cal_pt_cloud)
def stop(self):
audio.say("Stopping Calibration")
logger.info("Stopping Calibration")
self.active = False
self.button.status_text = ''
cal_pt_cloud = calibrate.preprocess_data(self.pupil_list,self.ref_list)
logger.info("Collected %s data points." %len(cal_pt_cloud))
if len(cal_pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
cal_pt_cloud = np.array(cal_pt_cloud)
img_size = self.first_img.shape[1],self.first_img.shape[0]
map_fn,params = calibrate.get_map_from_cloud(cal_pt_cloud,img_size,return_params=True)
np.save(os.path.join(self.g_pool.user_dir,'cal_pt_cloud.npy'),cal_pt_cloud)
#replace gaze mapper
self.g_pool.plugins.add(Simple_Gaze_Mapper(self.g_pool,params))
def stop(self):
audio.say("Stopping Calibration")
logger.info('Stopping Calibration')
self.screen_marker_state = 0
self.active = False
self.window_should_close = True
cal_pt_cloud = calibrate.preprocess_data(self.pupil_list,self.ref_list)
logger.info("Collected %s data points." %len(cal_pt_cloud))
if len(cal_pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
cal_pt_cloud = np.array(cal_pt_cloud)
map_fn = calibrate.get_map_from_cloud(cal_pt_cloud,self.world_size)
self.g_pool.map_pupil = map_fn
np.save(os.path.join(self.g_pool.user_dir,'cal_pt_cloud.npy'),cal_pt_cloud)
def stop(self):
audio.say("Stopping Accuracy Test")
logger.info('Stopping Accuracy_Test')
self.screen_marker_state = 0
self.active = False
self.close_window()
pt_cloud = preprocess_data(self.gaze_list,self.ref_list)
logger.info("Collected %s data points." %len(pt_cloud))
if len(pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
pt_cloud = np.array(pt_cloud)
np.save(os.path.join(self.g_pool.user_dir,'accuracy_test_pt_cloud.npy'),pt_cloud)
gaze,ref = pt_cloud[:,0:2],pt_cloud[:,2:4]
error_lines = np.array([[g,r] for g,r in zip(gaze,ref)])
self.error_lines = error_lines.reshape(-1,2)
self.pt_cloud = pt_cloud
def stop(self):
audio.say("Stopping Calibration")
logger.info('Stopping Calibration')
self.screen_marker_state = 0
self.active = False
self.close_window()
self.button.status_text = ''
cal_pt_cloud = calibrate.preprocess_data(self.pupil_list,self.ref_list)
logger.info("Collected %s data points." %len(cal_pt_cloud))
if len(cal_pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
cal_pt_cloud = np.array(cal_pt_cloud)
map_fn,params = calibrate.get_map_from_cloud(cal_pt_cloud,self.g_pool.capture.frame_size,return_params=True)
np.save(os.path.join(self.g_pool.user_dir,'cal_pt_cloud.npy'),cal_pt_cloud)
#replace current gaze mapper with new
self.g_pool.plugins.add(Simple_Gaze_Mapper,args={'params':params})
def stop(self):
audio.say("Stopping Calibration")
logger.info("Stopping Calibration")
self.smooth_pos = 0, 0
self.counter = 0
self.active = False
self.button.status_text = ''
print 'button:', self.button.status_text
cal_pt_cloud = calibrate.preprocess_data(self.pupil_list,
self.ref_list)
logger.info("Collected %s data points." % len(cal_pt_cloud))
if len(cal_pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
cal_pt_cloud = np.array(cal_pt_cloud)
map_fn, params = calibrate.get_map_from_cloud(cal_pt_cloud,
self.world_size,
return_params=True)
np.save(os.path.join(self.g_pool.user_dir, 'cal_pt_cloud.npy'),
cal_pt_cloud)
self.g_pool.plugins.add(Simple_Gaze_Mapper(self.g_pool, params))
def stop(self):
logger.info("Stopping Touchup")
self.smooth_pos = 0., 0.
self.sample_site = -2, -2
self.counter = 0
self.active = False
self.button.status_text = ''
offset_pt_clound = np.array(
calibrate.preprocess_data(self.gaze_list, self.ref_list))
if len(offset_pt_clound) < 3:
logger.error(
'Did not sample enough data for touchup please retry.')
return
#Calulate the offset for gaze to target
offset = offset_pt_clound[:, :2] - offset_pt_clound[:, 2:]
mean_offset = np.mean(offset, axis=0)
cal_pt_cloud = np.load(
os.path.join(self.g_pool.user_dir, 'cal_pt_cloud.npy'))
#deduct the offset from the old calibration ref point position. Thus shifiting the calibtation.
# p["norm_pos"][0], p["norm_pos"][1],ref_pt['norm_pos'][0],ref_pt['norm_pos'][1]
cal_pt_cloud[:, -2::] -= mean_offset
if self.g_pool.binocular:
cal_pt_cloud_eye0 = np.load(
os.path.join(self.g_pool.user_dir, 'cal_pt_cloud_eye0.npy'))
cal_pt_cloud_eye1 = np.load(
os.path.join(self.g_pool.user_dir, 'cal_pt_cloud_eye1.npy'))
#Do the same for the individual eye in binocular
#p0["norm_pos"][0], p0["norm_pos"][1],p1["norm_pos"][0], p1["norm_pos"][1],ref_pt['norm_pos'][0],ref_pt['norm_pos'][1]
cal_pt_cloud_eye0[:, -2::] -= mean_offset
cal_pt_cloud_eye1[:, -2::] -= mean_offset
#then recalibtate the with old but shifted data.
map_fn, params = calibrate.get_map_from_cloud(
cal_pt_cloud,
self.g_pool.capture.frame_size,
return_params=True,
binocular=self.g_pool.binocular)
np.save(os.path.join(self.g_pool.user_dir, 'cal_pt_cloud.npy'),
cal_pt_cloud)
#replace current gaze mapper with new
if self.g_pool.binocular:
# get monocular models for fallback (if only one pupil is detected)
cal_pt_cloud_eye0 = np.array(cal_pt_cloud_eye0)
cal_pt_cloud_eye1 = np.array(cal_pt_cloud_eye1)
_, params_eye0 = calibrate.get_map_from_cloud(
cal_pt_cloud_eye0,
self.g_pool.capture.frame_size,
return_params=True)
_, params_eye1 = calibrate.get_map_from_cloud(
cal_pt_cloud_eye1,
self.g_pool.capture.frame_size,
return_params=True)
self.g_pool.plugins.add(Bilateral_Gaze_Mapper,
args={
'params': params,
'params_eye0': params_eye0,
'params_eye1': params_eye1
})
np.save(
os.path.join(self.g_pool.user_dir, 'cal_pt_cloud_eye0.npy'),
cal_pt_cloud_eye0)
np.save(
os.path.join(self.g_pool.user_dir, 'cal_pt_cloud_eye1.npy'),
cal_pt_cloud_eye1)
else:
self.g_pool.plugins.add(Simple_Gaze_Mapper,
args={'params': params})
