feat: GUI off, leap tweaks

This commit is contained in:
Prohurtz 2024-07-22 09:58:27 -05:00
parent ed599a1b94
commit c0ac123fde
3 changed files with 84 additions and 74 deletions

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@ -35,6 +35,7 @@ class EyeId(IntEnum):
SETTINGS = 3
ALGOSETTINGS = 4
VRCFTMODULESETTINGS = 5
GUIOFF = 6
class EyeInfoOrigin(Enum):

View File

@ -58,6 +58,7 @@ BOTH_EYE_RADIO_NAME = "-BOTHEYERADIO-"
SETTINGS_RADIO_NAME = "-SETTINGSRADIO-"
ALGO_SETTINGS_RADIO_NAME = "-ALGOSETTINGSRADIO-"
VRCFT_MODULE_SETTINGS_RADIO_NAME = "-VRCFTSETTINGSRADIO-"
GUIOFF_RADIO_NAME = "-GUIOFF-"
page_url = "https://github.com/RedHawk989/EyeTrackVR/releases/latest"
appversion = "EyeTrackApp 0.2.0 BETA 13"
@ -180,13 +181,23 @@ def main():
default=(config.eye_display_id == EyeId.ALGOSETTINGS),
key=ALGO_SETTINGS_RADIO_NAME,
),
sg.Radio(
"VRCFT Module Settings",
"EYESELECTRADIO",
background_color="#292929",
default=(config.eye_display_id == EyeId.VRCFTMODULESETTINGS),
key=VRCFT_MODULE_SETTINGS_RADIO_NAME,
),
],
[
sg.Radio(
"VRCFT Module Settings",
"EYESELECTRADIO",
background_color="#292929",
default=(config.eye_display_id == EyeId.VRCFTMODULESETTINGS),
key=VRCFT_MODULE_SETTINGS_RADIO_NAME,
),
sg.Radio(
"GUI OFF",
"EYESELECTRADIO",
background_color="#292929",
default=(config.eye_display_id == EyeId.GUIOFF),
key=GUIOFF_RADIO_NAME,
),
],
[
sg.Column(
@ -348,6 +359,19 @@ def main():
window[ALGO_SETTINGS_NAME].update(visible=False)
config.eye_display_id = EyeId.VRCFTMODULESETTINGS
config.save()
elif values[GUIOFF_RADIO_NAME] and config.eye_display_id != EyeId.GUIOFF:
# eyes[0].stop()
# eyes[1].stop()
settings[0].stop()
settings[1].stop()
settings[2].stop()
window[RIGHT_EYE_NAME].update(visible=False)
window[LEFT_EYE_NAME].update(visible=False)
window[SETTINGS_NAME].update(visible=False)
window[VRCFT_MODULE_SETTINGS_NAME].update(visible=False)
window[ALGO_SETTINGS_NAME].update(visible=False)
config.eye_display_id = EyeId.GUIOFF
config.save()
else:
# Otherwise, render all

View File

@ -28,7 +28,6 @@ LICENSE: GNU GPLv3
"""
# LEAP = Lightweight Eyelid And Pupil
import os
os.environ["OMP_NUM_THREADS"] = "1"
import onnxruntime
import numpy as np
@ -47,7 +46,6 @@ from pathlib import Path
frames = 0
models = Path("Models")
def run_model(input_queue, output_queue, session):
while True:
frame = input_queue.get()
@ -72,8 +70,41 @@ def run_model(input_queue, output_queue, session):
output_queue.put((frame, pre_landmark))
def run_onnx_model(queues, session, frame):
for i in range(len(queues)):
if not queues[i].full():
queues[i].put(frame)
break
def calculate_velocity_vectors(old_matrix, current_matrix, time_difference):
if len(old_matrix) != len(current_matrix):
raise ValueError("Both matrices must have the same number of points")
velocity_vectors = []
for i in range(len(old_matrix)):
old_point = np.array(old_matrix[i])
current_point = np.array(current_matrix[i])
displacement = current_point - old_point
velocity = displacement / time_difference
velocity_vectors.append(velocity)
total_velocity = np.mean([np.linalg.norm(vector) for vector in velocity_vectors])
return total_velocity
def to_numpy(tensor):
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
class LEAP_C(object):
def __init__(self):
self.last_lid = None
self.current_image_gray = None
self.current_image_gray_clean = None
onnxruntime.disable_telemetry_events()
# Config variables
self.num_threads = 1 # Number of python threads to use (using ~1 more than needed to achieve wanted fps yields lower cpu usage)
@ -101,7 +132,7 @@ class LEAP_C(object):
opts = onnxruntime.SessionOptions()
opts.inter_op_num_threads = 4
opts.intra_op_num_threads = 1 # big perf hit
opts.intra_op_num_threads = 1 # big perf hit
opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
opts.optimized_model_filepath = ""
@ -133,7 +164,6 @@ class LEAP_C(object):
min_cutoff = 0.1
beta = 15.0
self.one_euro_filter = OneEuroFilter(np.random.rand(12, 2), min_cutoff=min_cutoff, beta=beta)
self.one_euro_filter_float = OneEuroFilter(np.random.rand(1, 2), min_cutoff=5, beta=0.007)
self.dmax = 0
self.dmin = 0
@ -144,7 +174,6 @@ class LEAP_C(object):
self.previous_time = None
self.old_matrix = None
self.total_velocity = 0
self.ort_session1 = onnxruntime.InferenceSession(self.model_path, opts, providers=["CPUExecutionProvider"])
threads = []
@ -157,36 +186,7 @@ class LEAP_C(object):
threads.append(thread)
thread.start()
def to_numpy(self, tensor):
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
def run_onnx_model(self, queues, session, frame):
for i in range(len(queues)):
if not queues[i].full():
queues[i].put(frame)
break
def calculate_velocity_vectors(self, old_matrix, current_matrix, time_difference):
if len(old_matrix) != len(current_matrix):
raise ValueError("Both matrices must have the same number of points")
velocity_vectors = []
for i in range(len(old_matrix)):
old_point = np.array(old_matrix[i])
current_point = np.array(current_matrix[i])
displacement = current_point - old_point
velocity = displacement / time_difference
velocity_vectors.append(velocity)
total_velocity = np.mean([np.linalg.norm(vector) for vector in velocity_vectors])
return total_velocity
def leap_run(self):
img = self.current_image_gray_clean.copy()
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
@ -194,37 +194,31 @@ class LEAP_C(object):
frame = cv2.resize(img, (112, 112))
imgvis = self.current_image_gray.copy()
self.run_onnx_model(self.queues, self.ort_session1, frame)
run_onnx_model(self.queues, self.ort_session1, frame)
if not self.output_queue.empty():
frame, pre_landmark = self.output_queue.get()
# pre_landmark = np.reshape(pre_landmark, (-1, 2))
# pre_landmark = self.one_euro_filter(pre_landmark)
for point in pre_landmark:
# x, y = (point*112).astype(int)
x, y = point # Assuming point is a tuple (x, y)
# Scale the coordinates to image width and height
x = int(x * img_width)
y = int(y * img_height)
# x, y = int(x), int(y) # Ensure x and y are integers
cv2.circle(imgvis, (int(x), int(y)), 3, (255, 255, 0), -1)
cv2.circle(imgvis, (int(x), int(y)), 1, (0, 0, 255), -1)
x1, y1 = pre_landmark[1]
x2, y2 = pre_landmark[3]
# x1, y1 = pre_landmark[1]
# x2, y2 = pre_landmark[3]
x3, y3 = pre_landmark[4]
x4, y4 = pre_landmark[2]
# x3, y3 = pre_landmark[4]
# x4, y4 = pre_landmark[2]
d1 = math.dist(pre_landmark[1], pre_landmark[3])
# a more fancy method could be used taking into acount the relative size of the landmarks so that weirdness can be acounted for better
# a more fancy method could be used taking into acount the relative size of the landmarks so that
# weirdness can be acounted for better
d2 = math.dist(pre_landmark[2], pre_landmark[4])
d = (d1 + d2) / 2
# by averaging both sets we can get less error?
@ -233,14 +227,15 @@ class LEAP_C(object):
try:
if d >= np.percentile(
self.openlist, 80 # do not go above 85, but this value can be tuned
): # an aditional approach could be using the place where on average it is most stable, denoting what distance is the most stable "open"
): # an additional approach could be using the place where on average it is most stable, denoting
# what distance is the most stable "open"
self.maxlist.append(d)
if len(self.maxlist) > 2000: # i feel that this is very cpu intensive. think of a better method
if len(self.maxlist) > 2000: # I feel that this is very cpu intensive. think of a better method
self.maxlist.pop(0)
# this should be the average most open value, the average of top 2000 values in rolling calibration
# with this we can use it as the "openstate" (0.7, for expanded squeeze)
# with this we can use it as the "open state" (0.7, for expanded squeeze)
# weighted values to shift slightly to max value
normal_open = ((sum(self.maxlist) / len(self.maxlist)) * 0.90 + max(self.openlist) * 0.10) / (
@ -259,17 +254,15 @@ class LEAP_C(object):
try:
per = (d - normal_open) / (min(self.openlist) - normal_open)
oldper = (d - max(self.openlist)) / (
min(self.openlist) - max(self.openlist)
) # TODO: remove when testing is done
# oldper = (d - max(self.openlist)) / (
# min(self.openlist) - max(self.openlist)
# ) # TODO: remove when testing is done
per = 1 - per
per = per - 0.2 # allow for eye widen? might require a more legit math way but this makes sense.
per = min(per, 1.0) # clamp to 1.0 max
per = max(per, 0.0) # clamp to 1.0 min
# print("new: ", per, "vs old: ", oldper)
except:
per = 0.8
pass
@ -277,9 +270,7 @@ class LEAP_C(object):
x = pre_landmark[6][0]
y = pre_landmark[6][1]
current_time = time.time() # Get the current time
# Extract current matrix
current_matrix = [point[1] for point in pre_landmark]
@ -289,32 +280,25 @@ class LEAP_C(object):
# Calculate velocity vectors if we have old data
if self.old_matrix is not None:
self.total_velocity = self.calculate_velocity_vectors(self.old_matrix, current_matrix, time_difference)
self.total_velocity = calculate_velocity_vectors(self.old_matrix, current_matrix, time_difference)
# print(f"Velocity Vectors:", total_velocity)
# Update old matrix and previous time for the next iteration
self.old_matrix = [point[1] for point in pre_landmark]
self.previous_time = current_time
self.last_lid = per
calib_array = np.array([per, per]).reshape(1, 2)
per = self.one_euro_filter_float(calib_array)
per = per[0][0]
# print(per)
#time.sleep(0.01)
if per <= 0.25: # TODO: EXPOSE AS SETTING
per = 0.0
if self.total_velocity > 1:
per = 0.0
# print('blink', self.total_velocity)
# print('BLINK')
# this should be tuned, i could make this auto calib based on min from a list of per values.
return imgvis, float(x), float(y), per
@ -331,4 +315,5 @@ class External_Run_LEAP(object):
self.algo.current_image_gray = current_image_gray
self.algo.current_image_gray_clean = current_image_gray_clean
img, x, y, per = self.algo.leap_run()
return img, x, y, per