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https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
feat: GUI off, leap tweaks
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@ -35,6 +35,7 @@ class EyeId(IntEnum):
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SETTINGS = 3
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ALGOSETTINGS = 4
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VRCFTMODULESETTINGS = 5
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GUIOFF = 6
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class EyeInfoOrigin(Enum):
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@ -58,6 +58,7 @@ BOTH_EYE_RADIO_NAME = "-BOTHEYERADIO-"
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SETTINGS_RADIO_NAME = "-SETTINGSRADIO-"
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ALGO_SETTINGS_RADIO_NAME = "-ALGOSETTINGSRADIO-"
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VRCFT_MODULE_SETTINGS_RADIO_NAME = "-VRCFTSETTINGSRADIO-"
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GUIOFF_RADIO_NAME = "-GUIOFF-"
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page_url = "https://github.com/RedHawk989/EyeTrackVR/releases/latest"
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appversion = "EyeTrackApp 0.2.0 BETA 13"
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@ -180,13 +181,23 @@ def main():
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default=(config.eye_display_id == EyeId.ALGOSETTINGS),
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key=ALGO_SETTINGS_RADIO_NAME,
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),
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sg.Radio(
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"VRCFT Module Settings",
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"EYESELECTRADIO",
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background_color="#292929",
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default=(config.eye_display_id == EyeId.VRCFTMODULESETTINGS),
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key=VRCFT_MODULE_SETTINGS_RADIO_NAME,
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),
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],
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[
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sg.Radio(
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"VRCFT Module Settings",
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"EYESELECTRADIO",
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background_color="#292929",
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default=(config.eye_display_id == EyeId.VRCFTMODULESETTINGS),
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key=VRCFT_MODULE_SETTINGS_RADIO_NAME,
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),
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sg.Radio(
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"GUI OFF",
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"EYESELECTRADIO",
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background_color="#292929",
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default=(config.eye_display_id == EyeId.GUIOFF),
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key=GUIOFF_RADIO_NAME,
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),
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],
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[
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sg.Column(
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@ -348,6 +359,19 @@ def main():
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window[ALGO_SETTINGS_NAME].update(visible=False)
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config.eye_display_id = EyeId.VRCFTMODULESETTINGS
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config.save()
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elif values[GUIOFF_RADIO_NAME] and config.eye_display_id != EyeId.GUIOFF:
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# eyes[0].stop()
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# eyes[1].stop()
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settings[0].stop()
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settings[1].stop()
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settings[2].stop()
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window[RIGHT_EYE_NAME].update(visible=False)
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window[LEFT_EYE_NAME].update(visible=False)
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window[SETTINGS_NAME].update(visible=False)
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window[VRCFT_MODULE_SETTINGS_NAME].update(visible=False)
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window[ALGO_SETTINGS_NAME].update(visible=False)
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config.eye_display_id = EyeId.GUIOFF
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config.save()
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else:
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# Otherwise, render all
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@ -28,7 +28,6 @@ LICENSE: GNU GPLv3
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"""
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# LEAP = Lightweight Eyelid And Pupil
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import os
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os.environ["OMP_NUM_THREADS"] = "1"
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import onnxruntime
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import numpy as np
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@ -47,7 +46,6 @@ from pathlib import Path
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frames = 0
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models = Path("Models")
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def run_model(input_queue, output_queue, session):
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while True:
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frame = input_queue.get()
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@ -72,8 +70,41 @@ def run_model(input_queue, output_queue, session):
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output_queue.put((frame, pre_landmark))
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def run_onnx_model(queues, session, frame):
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for i in range(len(queues)):
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if not queues[i].full():
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queues[i].put(frame)
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break
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def calculate_velocity_vectors(old_matrix, current_matrix, time_difference):
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if len(old_matrix) != len(current_matrix):
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raise ValueError("Both matrices must have the same number of points")
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velocity_vectors = []
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for i in range(len(old_matrix)):
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old_point = np.array(old_matrix[i])
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current_point = np.array(current_matrix[i])
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displacement = current_point - old_point
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velocity = displacement / time_difference
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velocity_vectors.append(velocity)
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total_velocity = np.mean([np.linalg.norm(vector) for vector in velocity_vectors])
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return total_velocity
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def to_numpy(tensor):
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return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
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class LEAP_C(object):
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def __init__(self):
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self.last_lid = None
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self.current_image_gray = None
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self.current_image_gray_clean = None
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onnxruntime.disable_telemetry_events()
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# Config variables
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self.num_threads = 1 # Number of python threads to use (using ~1 more than needed to achieve wanted fps yields lower cpu usage)
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@ -101,7 +132,7 @@ class LEAP_C(object):
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opts = onnxruntime.SessionOptions()
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opts.inter_op_num_threads = 4
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opts.intra_op_num_threads = 1 # big perf hit
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opts.intra_op_num_threads = 1 # big perf hit
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opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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opts.optimized_model_filepath = ""
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@ -133,7 +164,6 @@ class LEAP_C(object):
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min_cutoff = 0.1
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beta = 15.0
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self.one_euro_filter = OneEuroFilter(np.random.rand(12, 2), min_cutoff=min_cutoff, beta=beta)
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self.one_euro_filter_float = OneEuroFilter(np.random.rand(1, 2), min_cutoff=5, beta=0.007)
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self.dmax = 0
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self.dmin = 0
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@ -144,7 +174,6 @@ class LEAP_C(object):
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self.previous_time = None
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self.old_matrix = None
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self.total_velocity = 0
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self.ort_session1 = onnxruntime.InferenceSession(self.model_path, opts, providers=["CPUExecutionProvider"])
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threads = []
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@ -157,36 +186,7 @@ class LEAP_C(object):
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threads.append(thread)
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thread.start()
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def to_numpy(self, tensor):
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return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
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def run_onnx_model(self, queues, session, frame):
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for i in range(len(queues)):
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if not queues[i].full():
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queues[i].put(frame)
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break
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def calculate_velocity_vectors(self, old_matrix, current_matrix, time_difference):
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if len(old_matrix) != len(current_matrix):
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raise ValueError("Both matrices must have the same number of points")
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velocity_vectors = []
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for i in range(len(old_matrix)):
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old_point = np.array(old_matrix[i])
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current_point = np.array(current_matrix[i])
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displacement = current_point - old_point
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velocity = displacement / time_difference
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velocity_vectors.append(velocity)
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total_velocity = np.mean([np.linalg.norm(vector) for vector in velocity_vectors])
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return total_velocity
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def leap_run(self):
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img = self.current_image_gray_clean.copy()
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
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@ -194,37 +194,31 @@ class LEAP_C(object):
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frame = cv2.resize(img, (112, 112))
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imgvis = self.current_image_gray.copy()
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self.run_onnx_model(self.queues, self.ort_session1, frame)
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run_onnx_model(self.queues, self.ort_session1, frame)
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if not self.output_queue.empty():
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frame, pre_landmark = self.output_queue.get()
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# pre_landmark = np.reshape(pre_landmark, (-1, 2))
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# pre_landmark = self.one_euro_filter(pre_landmark)
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for point in pre_landmark:
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# x, y = (point*112).astype(int)
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x, y = point # Assuming point is a tuple (x, y)
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# Scale the coordinates to image width and height
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x = int(x * img_width)
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y = int(y * img_height)
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# x, y = int(x), int(y) # Ensure x and y are integers
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cv2.circle(imgvis, (int(x), int(y)), 3, (255, 255, 0), -1)
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cv2.circle(imgvis, (int(x), int(y)), 1, (0, 0, 255), -1)
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x1, y1 = pre_landmark[1]
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x2, y2 = pre_landmark[3]
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# x1, y1 = pre_landmark[1]
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# x2, y2 = pre_landmark[3]
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x3, y3 = pre_landmark[4]
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x4, y4 = pre_landmark[2]
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# x3, y3 = pre_landmark[4]
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# x4, y4 = pre_landmark[2]
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d1 = math.dist(pre_landmark[1], pre_landmark[3])
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# a more fancy method could be used taking into acount the relative size of the landmarks so that weirdness can be acounted for better
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# a more fancy method could be used taking into acount the relative size of the landmarks so that
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# weirdness can be acounted for better
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d2 = math.dist(pre_landmark[2], pre_landmark[4])
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d = (d1 + d2) / 2
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# by averaging both sets we can get less error?
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@ -233,14 +227,15 @@ class LEAP_C(object):
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try:
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if d >= np.percentile(
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self.openlist, 80 # do not go above 85, but this value can be tuned
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): # an aditional approach could be using the place where on average it is most stable, denoting what distance is the most stable "open"
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): # an additional approach could be using the place where on average it is most stable, denoting
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# what distance is the most stable "open"
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self.maxlist.append(d)
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if len(self.maxlist) > 2000: # i feel that this is very cpu intensive. think of a better method
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if len(self.maxlist) > 2000: # I feel that this is very cpu intensive. think of a better method
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self.maxlist.pop(0)
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# this should be the average most open value, the average of top 2000 values in rolling calibration
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# with this we can use it as the "openstate" (0.7, for expanded squeeze)
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# with this we can use it as the "open state" (0.7, for expanded squeeze)
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# weighted values to shift slightly to max value
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normal_open = ((sum(self.maxlist) / len(self.maxlist)) * 0.90 + max(self.openlist) * 0.10) / (
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@ -259,17 +254,15 @@ class LEAP_C(object):
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try:
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per = (d - normal_open) / (min(self.openlist) - normal_open)
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oldper = (d - max(self.openlist)) / (
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min(self.openlist) - max(self.openlist)
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) # TODO: remove when testing is done
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# oldper = (d - max(self.openlist)) / (
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# min(self.openlist) - max(self.openlist)
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# ) # TODO: remove when testing is done
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per = 1 - per
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per = per - 0.2 # allow for eye widen? might require a more legit math way but this makes sense.
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per = min(per, 1.0) # clamp to 1.0 max
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per = max(per, 0.0) # clamp to 1.0 min
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# print("new: ", per, "vs old: ", oldper)
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except:
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per = 0.8
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pass
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@ -277,9 +270,7 @@ class LEAP_C(object):
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x = pre_landmark[6][0]
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y = pre_landmark[6][1]
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current_time = time.time() # Get the current time
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# Extract current matrix
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current_matrix = [point[1] for point in pre_landmark]
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@ -289,32 +280,25 @@ class LEAP_C(object):
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# Calculate velocity vectors if we have old data
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if self.old_matrix is not None:
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self.total_velocity = self.calculate_velocity_vectors(self.old_matrix, current_matrix, time_difference)
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self.total_velocity = calculate_velocity_vectors(self.old_matrix, current_matrix, time_difference)
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# print(f"Velocity Vectors:", total_velocity)
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# Update old matrix and previous time for the next iteration
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self.old_matrix = [point[1] for point in pre_landmark]
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self.previous_time = current_time
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self.last_lid = per
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calib_array = np.array([per, per]).reshape(1, 2)
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per = self.one_euro_filter_float(calib_array)
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per = per[0][0]
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# print(per)
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#time.sleep(0.01)
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if per <= 0.25: # TODO: EXPOSE AS SETTING
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per = 0.0
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if self.total_velocity > 1:
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per = 0.0
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# print('blink', self.total_velocity)
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# print('BLINK')
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# this should be tuned, i could make this auto calib based on min from a list of per values.
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return imgvis, float(x), float(y), per
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@ -331,4 +315,5 @@ class External_Run_LEAP(object):
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self.algo.current_image_gray = current_image_gray
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self.algo.current_image_gray_clean = current_image_gray_clean
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img, x, y, per = self.algo.leap_run()
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return img, x, y, per
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