mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-09-26 23:09:28 +08:00
194 lines
7.3 KiB
Python
194 lines
7.3 KiB
Python
"""
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Algorithm App Implementations By: Prohurtz
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Copyright (c) 2025 EyeTrackVR <3
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LICENSE: Babble Software Distribution License 1.0
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------------------------------------------------------------------------------------------------------
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"""
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import os
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import onnxruntime
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import numpy as np
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import cv2
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import time
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import math
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from queue import Queue
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import threading
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from config import EyeTrackCameraConfig, EyeTrackConfig
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from one_euro_filter import OneEuroFilter
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import psutil
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from utils.misc_utils import resource_path
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from pathlib import Path
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os.environ["OMP_NUM_THREADS"] = "1"
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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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if frame is None:
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break
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img_np = np.array(frame, dtype=np.float32) / 255.0
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gray_img = 0.299 * img_np[:, :, 0] + 0.587 * img_np[:, :, 1] + 0.114 * img_np[:, :, 2]
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gray_img = np.expand_dims(np.expand_dims(gray_img, axis=0), axis=0)
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ort_inputs = {session.get_inputs()[0].name: gray_img}
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pre_landmark = session.run(None, ort_inputs)
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pre_landmark = np.reshape(pre_landmark, (-1, 2))
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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 queue in queues:
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if not queue.full():
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queue.put(frame)
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break
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class LEAP_C:
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def __init__(self, eye_config: EyeTrackCameraConfig, config: EyeTrackConfig):
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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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self.num_threads = 1
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self.queue_max_size = 1
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self.model_path = resource_path(models / "pfld-sim.onnx")
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self.print_fps = False
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self.frames = 0
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self.queues = [Queue(maxsize=self.queue_max_size) for _ in range(self.num_threads)]
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self.threads = []
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self.model_output = np.zeros((12, 2))
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self.output_queue = Queue(maxsize=self.queue_max_size)
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self.start_time = time.time()
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opts = onnxruntime.SessionOptions()
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opts.inter_op_num_threads = 1
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opts.intra_op_num_threads = 1
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opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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opts.enable_mem_pattern = False
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self.one_euro_filter_float = OneEuroFilter(np.random.rand(1, 2), min_cutoff=0.0004, beta=0.9)
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self.dmax = 0
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self.dmin = 0
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self.openlist = []
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self.maxlist = []
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self.previous_time = None
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self.old_matrix = None
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self.total_velocity_new = 0
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self.total_velocity_avg = 0
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self.total_velocity_old = 0
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self.old_per = 0.0
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self.delta_per_neg = 0.0
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self.ort_session1 = onnxruntime.InferenceSession(self.model_path, opts, providers=["CPUExecutionProvider"])
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self.eye_config: EyeTrackCameraConfig = eye_config
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self.config: EyeTrackConfig = config
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for i in range(self.num_threads):
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thread = threading.Thread(
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target=run_model,
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args=(self.queues[i], self.output_queue, self.ort_session1),
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name=f"Thread {i}",
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)
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self.threads.append(thread)
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thread.start()
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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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img_height, img_width = img.shape[:2]
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frame = cv2.resize(img, (112, 112))
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imgvis = self.current_image_gray.copy()
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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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for point in pre_landmark:
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x, y = point
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x = int(x * img_width)
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y = int(y * img_height)
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cv2.circle(imgvis, (x, y), 3, (255, 255, 0), -1)
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cv2.circle(imgvis, (x, y), 1, (0, 0, 255), -1)
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d1 = math.dist(pre_landmark[1], pre_landmark[3])
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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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if self.calib == 0:
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self.openlist = []
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self.eye_config.leap_calibrated = False
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if not self.eye_config.leap_calibrated:
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self.openlist.append(d)
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self.eye_config.leap_calibration_percentile_90 = np.percentile(self.openlist, 90) if len(self.openlist) >= 10 else 0.8
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self.eye_config.leap_calibration_percentile_2 = np.percentile(self.openlist, 2) - self.eye_config.leap_calibration_percentile_90
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if len(self.openlist) >= self.config.settings.leap_calibration_samples:
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self.eye_config.leap_calibrated = True
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self.config.save()
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print(f"[INFO] {'Left' if self.eye_config is self.config.left_eye else 'Right'} eye calibrated")
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try:
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if len(self.openlist) > 0 or self.eye_config.leap_calibrated:
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per = (d - self.eye_config.leap_calibration_percentile_90) / self.eye_config.leap_calibration_percentile_2
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per = 1 - per
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per = np.clip(per, 0.0, 1.0)
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else:
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per = 0.8
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except:
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per = 0.8
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x = pre_landmark[6][0]
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y = pre_landmark[6][1]
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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)[0][0]
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if per <= 0.25:
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per = 0.0
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return imgvis, float(x*img_width), float(y*img_height), per
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imgvis = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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return imgvis, 0, 0, 0
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class External_Run_LEAP:
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def __init__(self, eye_config: EyeTrackCameraConfig, config: EyeTrackConfig):
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self.algo = LEAP_C(eye_config, config)
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def run(self, current_image_gray, current_image_gray_clean, calib):
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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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self.algo.calib = calib
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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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