mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
273 lines
10 KiB
Python
273 lines
10 KiB
Python
"""
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,@@@. @@@@@@((@ @@@@(
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//@@@ ,, @@@@ @@@@@
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@@@( @@@@@@@
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@@@ @ @@@@@@@@#
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@@@@@@@@@@@@@(
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LEAP by: Prohurtz
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Algorithm App Implementation By: Prohurtz
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Copyright (c) 2023 EyeTrackVR <3
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------------------------------------------------------------------------------------------------------
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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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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 one_euro_filter import OneEuroFilter
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import psutil, os
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import sys
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from utils.misc_utils import resource_path
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import platform
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frames = 0
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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)
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img_np = img_np.astype(np.float32) / 255.0
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img_np = np.transpose(img_np, (2, 0, 1))
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img_np = np.expand_dims(img_np, axis=0)
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ort_inputs = {session.get_inputs()[0].name: img_np}
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pre_landmark = session.run(None, ort_inputs)
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pre_landmark = pre_landmark[1]
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pre_landmark = np.reshape(pre_landmark, (12, 2))
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output_queue.put((frame, pre_landmark))
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class LEAP_C(object):
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def __init__(self):
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onnxruntime.disable_telemetry_events()
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# Config variables
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self.num_threads = 3 # Number of python threads to use (using ~1 more than needed to achieve wanted fps yields lower cpu usage)
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self.queue_max_size = 1 # Optimize for best CPU usage, Memory, and Latency. A maxsize is needed to not create a potential memory leak.
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if platform.system() == "Darwin":
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self.model_path = resource_path(
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"EyeTrackApp/Models/leap123023.onnx"
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) # funny MacOS files issues :P
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else:
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self.model_path = resource_path("Models\leap123023.onnx")
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self.interval = 1 # FPS print update rate
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self.low_priority = True # set process priority to low (may cause issues when unfocusing? reported by one, not reproducable)
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self.low_priority = True # set process priority to low (may cause issues when unfocusing? reported by one, not reproducable)
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self.print_fps = False
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# Init variables
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self.frames = 0
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self.queues = []
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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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for _ in range(self.num_threads):
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self.queue = Queue(maxsize=self.queue_max_size)
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self.queues.append(self.queue)
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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 = (
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onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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)
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opts.optimized_model_filepath = ""
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if self.low_priority:
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process = psutil.Process(os.getpid()) # set process priority to low
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try:
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sys.getwindowsversion()
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except AttributeError:
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process.nice(0) # UNIX: 0 low 10 high
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process.nice()
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else:
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process.nice(psutil.BELOW_NORMAL_PRIORITY_CLASS) # Windows
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process.nice()
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# See https://learn.microsoft.com/en-us/windows/win32/api/processthreadsapi/nf-processthreadsapi-getpriorityclass#return-value for values
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else:
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process = psutil.Process(os.getpid()) # set process priority to low
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try:
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sys.getwindowsversion()
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except AttributeError:
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process.nice(10) # UNIX: 0 low 10 high
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else:
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process.nice(psutil.HIGH_PRIORITY_CLASS) # Windows
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# See https://learn.microsoft.com/en-us/windows/win32/api/processthreadsapi/nf-processthreadsapi-getpriorityclass#return-value for values
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min_cutoff = 0.1
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beta = 15.0
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# print(np.random.rand(22, 2))
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# noisy_point = np.array([1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1])
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self.one_euro_filter = OneEuroFilter(
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np.random.rand(12, 2), min_cutoff=min_cutoff, beta=beta
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)
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# self.one_euro_filter_open = OneEuroFilter(
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# np.random.rand(1, 2), min_cutoff=0.01, beta=0.04
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# )
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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.x = 0
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self.y = 0
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self.ort_session1 = onnxruntime.InferenceSession(
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self.model_path, opts, providers=["CPUExecutionProvider"]
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)
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# ort_session1 = onnxruntime.InferenceSession("C:/Users/beaul/PycharmProjects/EyeTrackVR/EyeTrackApp/Models/mommy062023.onnx", opts, providers=['DmlExecutionProvider'])
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threads = []
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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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threads.append(thread)
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thread.start()
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def to_numpy(self, tensor):
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return (
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tensor.detach().cpu().numpy()
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if tensor.requires_grad
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else tensor.cpu().numpy()
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)
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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 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 = imutils.rotate(img, angle=320)
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img_height, img_width = img.shape[:2] # Move outside the loop
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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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if not self.output_queue.empty():
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frame, pre_landmark = self.output_queue.get()
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pre_landmark = self.one_euro_filter(pre_landmark)
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# frame = cv2.resize(frame, (112, 112))
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for point in pre_landmark:
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x, y = point
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cv2.circle(
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imgvis, (int(x * img_width), int(y * img_height)), 2, (0, 0, 50), -1
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)
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cv2.circle(
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imgvis,
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tuple(int(x * img_width) for x in pre_landmark[2]),
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1,
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(255, 255, 0),
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-1,
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)
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# cv2.circle(img, tuple(int(x*112) for x in pre_landmark[2]), 1, (255, 255, 0), -1)
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cv2.circle(
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imgvis,
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tuple(int(x * img_width) for x in pre_landmark[4]),
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1,
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(255, 255, 255),
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-1,
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)
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# cv2.circle(img, tuple(int(x * 112) for x in pre_landmark[4]), 1, (255, 255, 255), -1)
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# print(pre_landmark)
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x1, y1 = pre_landmark[0]
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x2, y2 = pre_landmark[6]
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euclidean_dist_width = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
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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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euclidean_dist_open = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
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# d = area / euclidean_dist_width
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# print(area)
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eyesize_dist = math.dist(pre_landmark[0], pre_landmark[6])
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distance = math.dist(pre_landmark[1], pre_landmark[3])
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# d = distance / eyesize_dist
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d = 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 = d + d2
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if len(self.openlist) < 5000: # TODO expose as setting?
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self.openlist.append(d)
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else:
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# if d >= np.percentile(self.openlist, 99) or d <= np.percentile(
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# self.openlist, 1
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# ):
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# pass
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# else:
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self.openlist.pop(0)
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self.openlist.append(d)
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try:
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per = (d - max(self.openlist)) / (
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min(self.openlist) - max(self.openlist)
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)
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per = 1 - per
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except:
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per = 0.7
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pass
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# print(d, per)
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x = pre_landmark[6][0]
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y = pre_landmark[6][1]
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frame = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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# per = d - 0.1
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self.last_lid = per
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# pera = np.array([per, per])
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# self.one_euro_filter_open(pera)
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if per <= 0.2: # TODO: EXPOSE AS SETTING
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per == 0.0
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# print(per)
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return imgvis, float(x), float(y), 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(object):
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def __init__(self):
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self.algo = LEAP_C()
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def run(self, current_image_gray, current_image_gray_clean):
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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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