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add new RANSAC method
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188
RANSAC/EyeTrackGUI.py
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188
RANSAC/EyeTrackGUI.py
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import kivy
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from multiprocessing import Process,Queue,Pipe
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kivy.require("1.9.1")
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from kivy.app import App
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from kivy.uix.gridlayout import GridLayout
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from kivy.uix.slider import Slider
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from kivy.uix.label import Label
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from kivy.uix.floatlayout import FloatLayout
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from kivy.properties import NumericProperty
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from kivy.uix.scatter import Scatter
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from kivy.uix.textinput import TextInput
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from kivy.uix.boxlayout import BoxLayout
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from kivy.core.window import Window
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import time
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###############################################################################
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Window.size = (700, 200)
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class WidgetContainer(GridLayout):
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def __init__(self, **kwargs):
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super(WidgetContainer, self).__init__(**kwargs)
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############################################################################### right
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self.cols = 3
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self.xcc = Slider(min = 1, max = 240,
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value_track = True,
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value_track_color =[1, 1, 1, 1])
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self.add_widget(Label(text ='Search Size X R'))
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self.add_widget(self.xcc)
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self.xValue = Label(text ='1')
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self.add_widget(self.xValue)
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self.xcc.bind(value = self.on_value)
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############################################################################### bottom
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self.Y = Slider(min = 1, max = 240,
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value_track = True,
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value_track_color =[1, 1, 1, 1])
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self.add_widget(Label(text ='Search Size Y R'))
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self.add_widget(self.Y)
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self.YV = Label(text ='1')
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self.add_widget(self.YV)
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self.Y.bind(value = self.on_value1)
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############################################################################### left
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self.xlc = Slider(min = 1, max = 240,
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value_track = True,
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value_track_color =[1, 1, 1, 1])
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self.add_widget(Label(text ='Search Size X L'))
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self.add_widget(self.xlc)
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self.xlValue = Label(text ='1')
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self.add_widget(self.xlValue)
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self.xlc.bind(value = self.on_value2)
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############################################################################### top
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self.ylc = Slider(min = 1, max = 240,
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value_track = True,
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value_track_color =[1, 1, 1, 1])
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self.add_widget(Label(text ='Search Size Y L'))
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self.add_widget(self.ylc)
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self.ylValue = Label(text ='1')
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self.add_widget(self.ylValue)
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self.ylc.bind(value = self.on_value3)
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############################################################################### detection
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# self.deth = Slider(min = 1, max = 40,
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# value_track = True,
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#value_track_color =[1, 1, 1, 1])
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#self.add_widget(Label(text ='Detection thresh DEFAULT:18'))
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#self.add_widget(self.deth)
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#self.dethv= Label(text ='1')
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#self.add_widget(self.dethv)
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#self.deth.bind(value = self.on_value4)
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############################################################################### camera input
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self.rota = Slider(min = 0, max = 360,
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value_track = True,
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value_track_color =[1, 1, 1, 1])
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self.add_widget(Label(text ='Rotation'))
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self.add_widget(self.rota)
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self.rotav= Label(text ='Select')
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self.add_widget(self.rotav)
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self.rota.bind(value = self.on_value5)
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###############################################################################
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# self.sav = Slider(min = 0, max = 360,
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#value_track = True,
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#value_track_color =[1, 1, 1, 1])
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#self.add_widget(Label(text ='Rotation'))
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#self.add_widget(self.sav)
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#self.sav= Label(text ='Select')
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#self.add_widget(self.sav)
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#self.rotav.bind(value = self.on_value5)
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def on_value(self, instance, brightness):
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self.xValue.text = "% d"% brightness
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confg.fx = self.xValue.text
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configsave()
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time.sleep(0.1)
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def on_value1(self, instance, brightness,):
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self.YV.text = "% d"% brightness
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confg.fy = self.YV.text
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configsave()
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time.sleep(0.1)
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def on_value2(self, instance, brightness):
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self.xlValue.text = "% d"% brightness
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confg.fxl = self.xlValue.text
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configsave()
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time.sleep(0.1)
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def on_value3(self, instance, brightness,):
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self.ylValue.text = "% d"% brightness
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confg.fyl = self.ylValue.text
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configsave()
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time.sleep(0.1)
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#def on_value4(self, instance, brightness,):
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# self.dethv.text = "% d"% brightness
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# confg.fxl = self.YV.text
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def on_value5(self, instance, brightness,):
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self.rotav.text = "% d"% brightness
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confg.rv = self.rotav.text
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configsave()
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time.sleep(0.1)
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class EyetrackGUI(App):
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def build(self):
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widgetContainer = WidgetContainer()
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print()
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return widgetContainer
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def confg():
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confg.fx = 128
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confg.fy = 128
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confg.fxl = 1
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confg.fyl = 1
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confg.rv = 0
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def configsave():
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with open('config.txt', 'w+') as cw:
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cw.write(str(confg.fx))
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cw.write('\n')
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cw.write(str(confg.fy))
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cw.write('\n')
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cw.write(str(confg.fxl))
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cw.write('\n')
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cw.write(str(confg.fyl))
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cw.write('\n')
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cw.write(str(confg.rv))
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cw.write('\n')
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cw.close()
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confg()
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rootGUI = EyetrackGUI()
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rootGUI.run()
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489
RANSAC/RANcalib.py
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489
RANSAC/RANcalib.py
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from tkinter import E
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.image as mpimg
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from time import time
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import pyttsx3
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engine = pyttsx3.init()
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def vc():
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vc.xmax = 1
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vc.xmin = 6969
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vc.ymax = 1
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vc.ymin = 6969
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vc.cfc = 50
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vc.cc = 1
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vc.cu = 0
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vc.cd = 0
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vc.cl = 0
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vc.cr = 0
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vc.fc = 0
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vc()
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def savecalibvalues(calibcenterx, calibcentery, calibrightx, calibleftx, calibupy, calibdowny):
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with open('eyeconfig.cfg', 'w+') as cw:
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cw.write(str(calibcenterx))
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cw.write('\n')
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cw.write(str(calibcentery))
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cw.write('\n')
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cw.write(str(calibrightx))
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cw.write('\n')
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cw.write(str(calibleftx))
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cw.write('\n')
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cw.write(str(calibupy))
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cw.write('\n')
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cw.write(str(calibdowny))
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cw.close()
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with open("config.txt") as calibratefl:
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lines = calibratefl.readlines()
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vx = float(lines[0].strip())
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vy = float(lines[1].strip())
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vxl = float(lines[2].strip())
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vyl = float(lines[3].strip())
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rv = float(lines[4].strip())
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calibratefl.close()
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def fit_rotated_ellipse_ransac(
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data, iter=90, sample_num=10, offset=80.0
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): # before changing these values, please read up on the ransac algorithm
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# However if you want to change any value just know that higher iterations will make processing frames slower
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count_max = 0
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effective_sample = None
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for i in range(iter):
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sample = np.random.choice(len(data), sample_num, replace=False)
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xs = data[sample][:, 0].reshape(-1, 1)
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ys = data[sample][:, 1].reshape(-1, 1)
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J = np.mat(
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np.hstack((xs * ys, ys**2, xs, ys, np.ones_like(xs, dtype=np.float)))
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)
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Y = np.mat(-1 * xs**2)
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P = (J.T * J).I * J.T * Y
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# fitter a*x**2 + b*x*y + c*y**2 + d*x + e*y + f = 0
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a = 1.0
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b = P[0, 0]
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c = P[1, 0]
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d = P[2, 0]
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e = P[3, 0]
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f = P[4, 0]
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ellipse_model = (
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lambda x, y: a * x**2 + b * x * y + c * y**2 + d * x + e * y + f
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)
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# threshold
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ran_sample = np.array(
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[[x, y] for (x, y) in data if np.abs(ellipse_model(x, y)) < offset]
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)
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if len(ran_sample) > count_max:
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count_max = len(ran_sample)
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effective_sample = ran_sample
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return fit_rotated_ellipse(effective_sample)
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def fit_rotated_ellipse(data):
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xs = data[:, 0].reshape(-1, 1)
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ys = data[:, 1].reshape(-1, 1)
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J = np.mat(np.hstack((xs * ys, ys**2, xs, ys, np.ones_like(xs, dtype=np.float))))
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Y = np.mat(-1 * xs**2)
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P = (J.T * J).I * J.T * Y
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a = 1.0
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b = P[0, 0]
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c = P[1, 0]
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d = P[2, 0]
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e = P[3, 0]
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f = P[4, 0]
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theta = 0.5 * np.arctan(b / (a - c))
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cx = (2 * c * d - b * e) / (b**2 - 4 * a * c)
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cy = (2 * a * e - b * d) / (b**2 - 4 * a * c)
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cu = a * cx**2 + b * cx * cy + c * cy**2 - f
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w = np.sqrt(
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cu
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/ (
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a * np.cos(theta) ** 2
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+ b * np.cos(theta) * np.sin(theta)
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+ c * np.sin(theta) ** 2
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)
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)
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h = np.sqrt(
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cu
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/ (
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a * np.sin(theta) ** 2
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- b * np.cos(theta) * np.sin(theta)
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+ c * np.cos(theta) ** 2
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)
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)
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ellipse_model = lambda x, y: a * x**2 + b * x * y + c * y**2 + d * x + e * y + f
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error_sum = np.sum([ellipse_model(x, y) for x, y in data])
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print("fitting error = %.3f" % (error_sum))
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return (cx, cy, w, h, theta)
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def increase_brightness(img, value):
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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h, s, v = cv2.split(hsv)
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lim = 255 - value
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v[v > lim] = 255
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v[v <= lim] += value
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final_hsv = cv2.merge((h, s, v))
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img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
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return img
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#cap = cv2.VideoCapture("http://192.168.0.202:81/stream") # change this to the video you want to test
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#if cap.isOpened() == False:
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# print("Error opening video stream or file")
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while True:
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if vc.cc == 1:
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engine.say("a saved calibration file was not found.")
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engine.say("Calibration starting, 3. 2. 1. please look straight forward")
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engine.runAndWait()
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vc.cc = 2
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if vc.cc == 2:
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cap = cv2.VideoCapture("http://192.168.0.202:81/stream")
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ret, img = cap.read()
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img = img[int(vxl): int(float(vy)), int(vyl): int(float(vx))]
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if ret == True:
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newImage2 = img.copy()
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
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image_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(
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image_gray, 120, 255, cv2.THRESH_BINARY
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) # this will need to be adjusted everytime hardwere is changed (brightness of IR, Camera postion, etc)
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opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
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closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
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image = 255 - closing
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contours, hierarchy = cv2.findContours(
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image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE
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)
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hull = []
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for i in range(len(contours)):
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hull.append(cv2.convexHull(contours[i], False))
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try:
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cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
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||||||
|
cnt = sorted(hull, key=cv2.contourArea)
|
||||||
|
maxcnt = cnt[-1]
|
||||||
|
ellipse = cv2.fitEllipse(maxcnt)
|
||||||
|
cx, cy, w, h, theta = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2))
|
||||||
|
print(cx, cy)
|
||||||
|
cv2.circle(newImage2, (int(cx), int(cy)), 2, (0, 0, 255), -1)
|
||||||
|
cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
||||||
|
cv2.ellipse(
|
||||||
|
newImage2,
|
||||||
|
(int(cx), int(cy)),
|
||||||
|
(int(w), int(h)),
|
||||||
|
theta * 180.0 / np.pi,
|
||||||
|
0.0,
|
||||||
|
360.0,
|
||||||
|
(50, 250, 200),
|
||||||
|
1,
|
||||||
|
)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
cv2.imshow("Ransac", newImage2)
|
||||||
|
cv2.imshow("gray", image_gray)
|
||||||
|
cv2.imshow("thresh", thresh)
|
||||||
|
|
||||||
|
cap.release()
|
||||||
|
cv2.destroyAllWindows()
|
||||||
|
|
||||||
|
|
||||||
|
calibcenterx = cx
|
||||||
|
calibcentery = cy
|
||||||
|
print(cx, cy)
|
||||||
|
engine.say("center calibration complete, please look right")
|
||||||
|
engine.runAndWait()
|
||||||
|
vc.cr = 1
|
||||||
|
vc.cc = 3
|
||||||
|
|
||||||
|
|
||||||
|
if vc.cr == 1:
|
||||||
|
engine.say("Right calibration starting")
|
||||||
|
engine.runAndWait()
|
||||||
|
vc.cr = 2
|
||||||
|
|
||||||
|
|
||||||
|
if vc.cr == 2:
|
||||||
|
cap = cv2.VideoCapture("http://192.168.0.202:81/stream")
|
||||||
|
ret, img = cap.read()
|
||||||
|
img = img[int(vxl): int(float(vy)), int(vyl): int(float(vx))]
|
||||||
|
if ret == True:
|
||||||
|
newImage2 = img.copy()
|
||||||
|
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
||||||
|
image_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||||
|
ret, thresh = cv2.threshold(
|
||||||
|
image_gray, 120, 255, cv2.THRESH_BINARY
|
||||||
|
) # this will need to be adjusted everytime hardwere is changed (brightness of IR, Camera postion, etc)
|
||||||
|
|
||||||
|
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
|
||||||
|
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
|
||||||
|
image = 255 - closing
|
||||||
|
contours, hierarchy = cv2.findContours(
|
||||||
|
image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE
|
||||||
|
)
|
||||||
|
hull = []
|
||||||
|
for i in range(len(contours)):
|
||||||
|
hull.append(cv2.convexHull(contours[i], False))
|
||||||
|
try:
|
||||||
|
cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
|
||||||
|
cnt = sorted(hull, key=cv2.contourArea)
|
||||||
|
maxcnt = cnt[-1]
|
||||||
|
ellipse = cv2.fitEllipse(maxcnt)
|
||||||
|
cx, cy, w, h, theta = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2))
|
||||||
|
print(cx, cy)
|
||||||
|
cv2.circle(newImage2, (int(cx), int(cy)), 2, (0, 0, 255), -1)
|
||||||
|
cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
||||||
|
cv2.ellipse(
|
||||||
|
newImage2,
|
||||||
|
(int(cx), int(cy)),
|
||||||
|
(int(w), int(h)),
|
||||||
|
theta * 180.0 / np.pi,
|
||||||
|
0.0,
|
||||||
|
360.0,
|
||||||
|
(50, 250, 200),
|
||||||
|
1,
|
||||||
|
)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
cv2.imshow("Ransac", newImage2)
|
||||||
|
cv2.imshow("gray", image_gray)
|
||||||
|
cv2.imshow("thresh", thresh)
|
||||||
|
|
||||||
|
calibrightx = cx
|
||||||
|
calibrighty = cy
|
||||||
|
print(cx, cy)
|
||||||
|
cap.release()
|
||||||
|
cv2.destroyAllWindows()
|
||||||
|
|
||||||
|
engine.say("Right calibration complete, please look left")
|
||||||
|
engine.runAndWait()
|
||||||
|
vc.cl = 1
|
||||||
|
vc.cr = 3
|
||||||
|
|
||||||
|
|
||||||
|
if vc.cl == 1:
|
||||||
|
engine.say("left calibration starting")
|
||||||
|
engine.runAndWait()
|
||||||
|
vc.cl = 2
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
if vc.cl == 2:
|
||||||
|
cap = cv2.VideoCapture("http://192.168.0.202:81/stream")
|
||||||
|
ret, img = cap.read()
|
||||||
|
img = img[int(vxl): int(float(vy)), int(vyl): int(float(vx))]
|
||||||
|
if ret == True:
|
||||||
|
newImage2 = img.copy()
|
||||||
|
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
||||||
|
image_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||||
|
ret, thresh = cv2.threshold(
|
||||||
|
image_gray, 120, 255, cv2.THRESH_BINARY
|
||||||
|
) # this will need to be adjusted everytime hardwere is changed (brightness of IR, Camera postion, etc)
|
||||||
|
|
||||||
|
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
|
||||||
|
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
|
||||||
|
image = 255 - closing
|
||||||
|
contours, hierarchy = cv2.findContours(
|
||||||
|
image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE
|
||||||
|
)
|
||||||
|
hull = []
|
||||||
|
for i in range(len(contours)):
|
||||||
|
hull.append(cv2.convexHull(contours[i], False))
|
||||||
|
try:
|
||||||
|
cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
|
||||||
|
cnt = sorted(hull, key=cv2.contourArea)
|
||||||
|
maxcnt = cnt[-1]
|
||||||
|
ellipse = cv2.fitEllipse(maxcnt)
|
||||||
|
cx, cy, w, h, theta = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2))
|
||||||
|
print(cx, cy)
|
||||||
|
cv2.circle(newImage2, (int(cx), int(cy)), 2, (0, 0, 255), -1)
|
||||||
|
cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
||||||
|
cv2.ellipse(
|
||||||
|
newImage2,
|
||||||
|
(int(cx), int(cy)),
|
||||||
|
(int(w), int(h)),
|
||||||
|
theta * 180.0 / np.pi,
|
||||||
|
0.0,
|
||||||
|
360.0,
|
||||||
|
(50, 250, 200),
|
||||||
|
1,
|
||||||
|
)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
cv2.imshow("Ransac", newImage2)
|
||||||
|
cv2.imshow("gray", image_gray)
|
||||||
|
cv2.imshow("thresh", thresh)
|
||||||
|
|
||||||
|
calibleftx = cx
|
||||||
|
calibclefty = cy
|
||||||
|
print(cx, cy)
|
||||||
|
cap.release()
|
||||||
|
cv2.destroyAllWindows()
|
||||||
|
|
||||||
|
engine.say("left calibration complete, please look up")
|
||||||
|
engine.runAndWait()
|
||||||
|
vc.cl = 3
|
||||||
|
vc.cu = 1
|
||||||
|
|
||||||
|
if vc.cu == 1:
|
||||||
|
engine.say("up calibration starting")
|
||||||
|
engine.runAndWait()
|
||||||
|
vc.cu = 2
|
||||||
|
|
||||||
|
|
||||||
|
if vc.cu == 2:
|
||||||
|
cap = cv2.VideoCapture("http://192.168.0.202:81/stream")
|
||||||
|
ret, img = cap.read()
|
||||||
|
img = img[int(vxl): int(float(vy)), int(vyl): int(float(vx))]
|
||||||
|
if ret == True:
|
||||||
|
newImage2 = img.copy()
|
||||||
|
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
||||||
|
image_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||||
|
ret, thresh = cv2.threshold(
|
||||||
|
image_gray, 120, 255, cv2.THRESH_BINARY
|
||||||
|
) # this will need to be adjusted everytime hardwere is changed (brightness of IR, Camera postion, etc)
|
||||||
|
|
||||||
|
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
|
||||||
|
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
|
||||||
|
image = 255 - closing
|
||||||
|
contours, hierarchy = cv2.findContours(
|
||||||
|
image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE
|
||||||
|
)
|
||||||
|
hull = []
|
||||||
|
for i in range(len(contours)):
|
||||||
|
hull.append(cv2.convexHull(contours[i], False))
|
||||||
|
try:
|
||||||
|
cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
|
||||||
|
cnt = sorted(hull, key=cv2.contourArea)
|
||||||
|
maxcnt = cnt[-1]
|
||||||
|
ellipse = cv2.fitEllipse(maxcnt)
|
||||||
|
cx, cy, w, h, theta = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2))
|
||||||
|
print(cx, cy)
|
||||||
|
cv2.circle(newImage2, (int(cx), int(cy)), 2, (0, 0, 255), -1)
|
||||||
|
cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
||||||
|
cv2.ellipse(
|
||||||
|
newImage2,
|
||||||
|
(int(cx), int(cy)),
|
||||||
|
(int(w), int(h)),
|
||||||
|
theta * 180.0 / np.pi,
|
||||||
|
0.0,
|
||||||
|
360.0,
|
||||||
|
(50, 250, 200),
|
||||||
|
1,
|
||||||
|
)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
cv2.imshow("Ransac", newImage2)
|
||||||
|
cv2.imshow("gray", image_gray)
|
||||||
|
cv2.imshow("thresh", thresh)
|
||||||
|
|
||||||
|
calibupx = cx
|
||||||
|
calibupy = cy
|
||||||
|
print(cx, cy)
|
||||||
|
cap.release()
|
||||||
|
cv2.destroyAllWindows()
|
||||||
|
|
||||||
|
engine.say("up calibration complete, please look down")
|
||||||
|
engine.runAndWait()
|
||||||
|
vc.cd = 1
|
||||||
|
vc.cu = 3
|
||||||
|
|
||||||
|
if vc.cd == 1:
|
||||||
|
engine.say("down calibration starting")
|
||||||
|
engine.runAndWait()
|
||||||
|
vc.cd = 2
|
||||||
|
|
||||||
|
|
||||||
|
if vc.cd == 2:
|
||||||
|
cap = cv2.VideoCapture("http://192.168.0.202:81/stream")
|
||||||
|
ret, img = cap.read()
|
||||||
|
img = img[int(vxl): int(float(vy)), int(vyl): int(float(vx))]
|
||||||
|
if ret == True:
|
||||||
|
newImage2 = img.copy()
|
||||||
|
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
||||||
|
image_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||||
|
ret, thresh = cv2.threshold(
|
||||||
|
image_gray, 120, 255, cv2.THRESH_BINARY
|
||||||
|
) # this will need to be adjusted everytime hardwere is changed (brightness of IR, Camera postion, etc)
|
||||||
|
|
||||||
|
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
|
||||||
|
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
|
||||||
|
image = 255 - closing
|
||||||
|
contours, hierarchy = cv2.findContours(
|
||||||
|
image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE
|
||||||
|
)
|
||||||
|
hull = []
|
||||||
|
for i in range(len(contours)):
|
||||||
|
hull.append(cv2.convexHull(contours[i], False))
|
||||||
|
try:
|
||||||
|
cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
|
||||||
|
cnt = sorted(hull, key=cv2.contourArea)
|
||||||
|
maxcnt = cnt[-1]
|
||||||
|
ellipse = cv2.fitEllipse(maxcnt)
|
||||||
|
cx, cy, w, h, theta = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2))
|
||||||
|
print(cx, cy)
|
||||||
|
cv2.circle(newImage2, (int(cx), int(cy)), 2, (0, 0, 255), -1)
|
||||||
|
cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
||||||
|
cv2.ellipse(
|
||||||
|
newImage2,
|
||||||
|
(int(cx), int(cy)),
|
||||||
|
(int(w), int(h)),
|
||||||
|
theta * 180.0 / np.pi,
|
||||||
|
0.0,
|
||||||
|
360.0,
|
||||||
|
(50, 250, 200),
|
||||||
|
1,
|
||||||
|
)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
cv2.imshow("Ransac", newImage2)
|
||||||
|
cv2.imshow("gray", image_gray)
|
||||||
|
cv2.imshow("thresh", thresh)
|
||||||
|
|
||||||
|
calibdownx = cx
|
||||||
|
calibdowny = cy
|
||||||
|
print(cx, cy)
|
||||||
|
cap.release()
|
||||||
|
cv2.destroyAllWindows()
|
||||||
|
|
||||||
|
engine.say("calibration complete")
|
||||||
|
engine.runAndWait()
|
||||||
|
vc.cd = 3
|
||||||
|
else:
|
||||||
|
print('CALIBCOMPLETE')
|
||||||
|
savecalibvalues(calibcenterx, calibcentery, calibrightx, calibleftx, calibupy, calibdowny)
|
||||||
|
vc.cfc = 2
|
||||||
|
vc.fc = 1
|
||||||
|
print('CALIBCOMPLETE22q2')
|
||||||
|
break
|
||||||
307
RANSAC/pupiltest.py
Normal file
307
RANSAC/pupiltest.py
Normal file
@ -0,0 +1,307 @@
|
|||||||
|
from tkinter import E
|
||||||
|
import cv2
|
||||||
|
import numpy as np
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import matplotlib.image as mpimg
|
||||||
|
from time import time
|
||||||
|
import sys
|
||||||
|
from pythonosc import udp_client
|
||||||
|
import torch
|
||||||
|
|
||||||
|
#model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt',force_reload=True)
|
||||||
|
#model.conf = 0.25 # NMS confidence threshold
|
||||||
|
#model.iou = 0.45 # NMS IoU threshold
|
||||||
|
#model.agnostic = False # NMS class-agnostic
|
||||||
|
#model.multi_label = False # NMS multiple labels per box
|
||||||
|
#model.max_det = 1 # maximum number of detections per image
|
||||||
|
#model.amp = False # Automatic Mixed Precision (AMP) inference
|
||||||
|
|
||||||
|
cx = 0.5
|
||||||
|
cy = 0.5
|
||||||
|
|
||||||
|
def vc():
|
||||||
|
|
||||||
|
vc.lidmax = 1
|
||||||
|
vc.lidmin = 6969 #( ͡° ͜ʖ ͡°) yes i know im stupid
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
vc.cfc = 1
|
||||||
|
vc.cc = 1
|
||||||
|
vc.cu = 0
|
||||||
|
vc.cd = 0
|
||||||
|
vc.cl = 0
|
||||||
|
vc.cr = 0
|
||||||
|
vc.fc = 0
|
||||||
|
|
||||||
|
vc.el = 2
|
||||||
|
vc.eyelidv = 1
|
||||||
|
vc.src = '1'
|
||||||
|
vc()
|
||||||
|
|
||||||
|
OSCip="127.0.0.1"
|
||||||
|
OSCport=9000 #VR Chat OSC port
|
||||||
|
client = udp_client.SimpleUDPClient(OSCip, OSCport)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def fit_rotated_ellipse_ransac(
|
||||||
|
data, iter=90, sample_num=10, offset=80.0
|
||||||
|
): # before changing these values, please read up on the ransac algorithm
|
||||||
|
# However if you want to change any value just know that higher iterations will make processing frames slower
|
||||||
|
count_max = 0
|
||||||
|
effective_sample = None
|
||||||
|
|
||||||
|
for i in range(iter):
|
||||||
|
sample = np.random.choice(len(data), sample_num, replace=False)
|
||||||
|
|
||||||
|
xs = data[sample][:, 0].reshape(-1, 1)
|
||||||
|
ys = data[sample][:, 1].reshape(-1, 1)
|
||||||
|
|
||||||
|
J = np.mat(
|
||||||
|
np.hstack((xs * ys, ys**2, xs, ys, np.ones_like(xs, dtype=np.float)))
|
||||||
|
)
|
||||||
|
Y = np.mat(-1 * xs**2)
|
||||||
|
P = (J.T * J).I * J.T * Y
|
||||||
|
|
||||||
|
# fitter a*x**2 + b*x*y + c*y**2 + d*x + e*y + f = 0
|
||||||
|
a = 1.0
|
||||||
|
b = P[0, 0]
|
||||||
|
c = P[1, 0]
|
||||||
|
d = P[2, 0]
|
||||||
|
e = P[3, 0]
|
||||||
|
f = P[4, 0]
|
||||||
|
ellipse_model = (
|
||||||
|
lambda x, y: a * x**2 + b * x * y + c * y**2 + d * x + e * y + f
|
||||||
|
)
|
||||||
|
|
||||||
|
# threshold
|
||||||
|
ran_sample = np.array(
|
||||||
|
[[x, y] for (x, y) in data if np.abs(ellipse_model(x, y)) < offset]
|
||||||
|
)
|
||||||
|
|
||||||
|
if len(ran_sample) > count_max:
|
||||||
|
count_max = len(ran_sample)
|
||||||
|
effective_sample = ran_sample
|
||||||
|
|
||||||
|
return fit_rotated_ellipse(effective_sample)
|
||||||
|
|
||||||
|
|
||||||
|
def fit_rotated_ellipse(data):
|
||||||
|
|
||||||
|
xs = data[:, 0].reshape(-1, 1)
|
||||||
|
ys = data[:, 1].reshape(-1, 1)
|
||||||
|
|
||||||
|
J = np.mat(np.hstack((xs * ys, ys**2, xs, ys, np.ones_like(xs, dtype=np.float))))
|
||||||
|
Y = np.mat(-1 * xs**2)
|
||||||
|
P = (J.T * J).I * J.T * Y
|
||||||
|
|
||||||
|
a = 1.0
|
||||||
|
b = P[0, 0]
|
||||||
|
c = P[1, 0]
|
||||||
|
d = P[2, 0]
|
||||||
|
e = P[3, 0]
|
||||||
|
f = P[4, 0]
|
||||||
|
theta = 0.5 * np.arctan(b / (a - c))
|
||||||
|
|
||||||
|
cx = (2 * c * d - b * e) / (b**2 - 4 * a * c)
|
||||||
|
cy = (2 * a * e - b * d) / (b**2 - 4 * a * c)
|
||||||
|
|
||||||
|
cu = a * cx**2 + b * cx * cy + c * cy**2 - f
|
||||||
|
w = np.sqrt(
|
||||||
|
cu
|
||||||
|
/ (
|
||||||
|
a * np.cos(theta) ** 2
|
||||||
|
+ b * np.cos(theta) * np.sin(theta)
|
||||||
|
+ c * np.sin(theta) ** 2
|
||||||
|
)
|
||||||
|
)
|
||||||
|
h = np.sqrt(
|
||||||
|
cu
|
||||||
|
/ (
|
||||||
|
a * np.sin(theta) ** 2
|
||||||
|
- b * np.cos(theta) * np.sin(theta)
|
||||||
|
+ c * np.cos(theta) ** 2
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
ellipse_model = lambda x, y: a * x**2 + b * x * y + c * y**2 + d * x + e * y + f
|
||||||
|
|
||||||
|
error_sum = np.sum([ellipse_model(x, y) for x, y in data])
|
||||||
|
print("fitting error = %.3f" % (error_sum))
|
||||||
|
|
||||||
|
return (cx, cy, w, h, theta)
|
||||||
|
|
||||||
|
|
||||||
|
def increase_brightness(img, value):
|
||||||
|
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
||||||
|
h, s, v = cv2.split(hsv)
|
||||||
|
|
||||||
|
lim = 255 - value
|
||||||
|
v[v > lim] = 255
|
||||||
|
v[v <= lim] += value
|
||||||
|
|
||||||
|
final_hsv = cv2.merge((h, s, v))
|
||||||
|
img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
|
||||||
|
return img
|
||||||
|
|
||||||
|
|
||||||
|
cap = cv2.VideoCapture("http://192.168.0.202:81/stream")
|
||||||
|
#cap = cv2.VideoCapture("http://192.168.1.177:4747/video")
|
||||||
|
# change this to the video you want to test
|
||||||
|
if cap.isOpened() == False:
|
||||||
|
print("Error opening video stream or file")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
while cap.isOpened():
|
||||||
|
with open("config.txt") as calibratefl:
|
||||||
|
lines = calibratefl.readlines()
|
||||||
|
vx = float(lines[0].strip())
|
||||||
|
vy = float(lines[1].strip())
|
||||||
|
vxl = float(lines[2].strip())
|
||||||
|
vyl = float(lines[3].strip())
|
||||||
|
rv = float(lines[4].strip())
|
||||||
|
calibratefl.close()
|
||||||
|
|
||||||
|
# try:
|
||||||
|
ret, img = cap.read()
|
||||||
|
img = img[int(vxl): int(float(vy)), int(vyl): int(float(vx))]
|
||||||
|
if ret == True:
|
||||||
|
newImage2 = img.copy()
|
||||||
|
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
||||||
|
image_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||||
|
ret, thresh = cv2.threshold(
|
||||||
|
image_gray, 125, 255, cv2.THRESH_BINARY
|
||||||
|
) # this will need to be adjusted everytime hardwere is changed (brightness of IR, Camera postion, etc)
|
||||||
|
|
||||||
|
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
|
||||||
|
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
|
||||||
|
image = 255 - closing
|
||||||
|
contours, hierarchy = cv2.findContours(
|
||||||
|
image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE
|
||||||
|
)
|
||||||
|
hull = []
|
||||||
|
for i in range(len(contours)):
|
||||||
|
hull.append(cv2.convexHull(contours[i], False))
|
||||||
|
try:
|
||||||
|
cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
|
||||||
|
cnt = sorted(hull, key=cv2.contourArea)
|
||||||
|
maxcnt = cnt[-1]
|
||||||
|
ellipse = cv2.fitEllipse(maxcnt)
|
||||||
|
cx, cy, w, h, theta = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2))
|
||||||
|
print(cx, cy)
|
||||||
|
cv2.circle(newImage2, (int(cx), int(cy)), 2, (0, 0, 255), -1)
|
||||||
|
cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
||||||
|
cv2.ellipse(
|
||||||
|
newImage2,
|
||||||
|
(int(cx), int(cy)),
|
||||||
|
(int(w), int(h)),
|
||||||
|
theta * 180.0 / np.pi,
|
||||||
|
0.0,
|
||||||
|
360.0,
|
||||||
|
(50, 250, 200),
|
||||||
|
1,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
|
||||||
|
if vc.el == 2:
|
||||||
|
print('here')
|
||||||
|
vc.el = 5
|
||||||
|
#results = model(img) # inference
|
||||||
|
#for box in results.xyxy[0]: # box is a list of 4 numbers
|
||||||
|
# if box[5]==0: # if the confidence is 0, then skip
|
||||||
|
# xB = int(box[2]) # xB is the x coordinate of the bottom right corner
|
||||||
|
# xA = int(box[0]) # xA is the x coordinate of the top left corner
|
||||||
|
# yB = int(box[3]) # yB is the y coordinate of the bottom right corner
|
||||||
|
# yA = int(box[1]) # yA is the y coordinate of the top left corner
|
||||||
|
# cv2.rectangle(img, (xA, yA), (xB, yB), (0, 255, 0), 2) # draw a rectangle around the detected object
|
||||||
|
#cv2.circle(img, (int((xA+xB)/2), int((yA+yB)/2)), 2, (0, 0, 255), -1)
|
||||||
|
#cv2.imshow('EYEMODEL',img)
|
||||||
|
print('shown')
|
||||||
|
|
||||||
|
|
||||||
|
vc.el = vc.el - 1
|
||||||
|
|
||||||
|
print(vc.el)
|
||||||
|
|
||||||
|
|
||||||
|
if vc.cfc == 1:
|
||||||
|
try:
|
||||||
|
|
||||||
|
with open("eyeconfig.cfg") as eyecalib:
|
||||||
|
lines = eyecalib.readlines()
|
||||||
|
calibcenterx = float(lines[0].strip())
|
||||||
|
calibcentery = float(lines[1].strip())
|
||||||
|
calibrightx = float(lines[2].strip())
|
||||||
|
calibleftx = float(lines[3].strip())
|
||||||
|
calibupy = float(lines[4].strip())
|
||||||
|
calibdowny = float(lines[5].strip())
|
||||||
|
eyecalib.close()
|
||||||
|
vc.cfc = 2
|
||||||
|
|
||||||
|
except:
|
||||||
|
print('eror')
|
||||||
|
|
||||||
|
sys.exit()
|
||||||
|
|
||||||
|
#percentage = (((input - min) * 100) / (max - min)) / 100 only for reference because im dum and forget stuff
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
xr = float((((cx - calibcenterx) * 100) / (calibrightx - calibcenterx)) / 100)
|
||||||
|
|
||||||
|
xl = float((((cx - calibcenterx) * 100) / (calibleftx - calibcenterx)) / 100)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
yu = float((((cy - calibcentery) * 100) / (calibupy - calibcentery)) / 100)
|
||||||
|
|
||||||
|
yd = float((((cy - calibcentery) * 100) / (calibdowny - calibcentery)) / 100)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
if xr > 0:
|
||||||
|
if xr > 1:
|
||||||
|
xr = 1.0
|
||||||
|
client.send_message("/avatar/parameters/RightEyeX", xr)
|
||||||
|
client.send_message("/avatar/parameters/LeftEyeX", xr)
|
||||||
|
|
||||||
|
print('XR', xr)
|
||||||
|
if xl > 0:
|
||||||
|
if xl > 1:
|
||||||
|
xl = 1.0
|
||||||
|
client.send_message("/avatar/parameters/RightEyeX", -abs(xl))
|
||||||
|
client.send_message("/avatar/parameters/LeftEyeX", -abs(xl))
|
||||||
|
print('XL', xl)
|
||||||
|
|
||||||
|
if yd > 0:
|
||||||
|
if yd > 1:
|
||||||
|
yd = 1.0
|
||||||
|
client.send_message("/avatar/parameters/EyesY", -abs(yd))
|
||||||
|
# print('YD', yd)
|
||||||
|
|
||||||
|
if yu > 0:
|
||||||
|
if yu > 1:
|
||||||
|
yu = 1.0
|
||||||
|
|
||||||
|
client.send_message("/avatar/parameters/EyesY", yu)
|
||||||
|
#print('YU', yu)
|
||||||
|
|
||||||
|
cv2.imshow("Ransac", newImage2)
|
||||||
|
cv2.imshow("gray", image_gray)
|
||||||
|
cv2.imshow("thresh", thresh)
|
||||||
|
if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||||
|
break
|
||||||
|
#except:
|
||||||
|
# print('error')
|
||||||
Loading…
Reference in New Issue
Block a user