line.py

# -*- coding: utf-8 -*-
"""
Created on Tue Oct 10 19:38:04 2017

@author: yang
"""
import numpy as np

# Define a class to receive the characteristics of each line detection
class Line():
    def __init__(self):
        # was the line detected in the last iteration?
        self.detected = False  
        # x values of the last n fits of the line
        self.recent_fitted = [np.array([False])]
        #average x values of the fitted line over the last n iterations
        self.bestx = None     
        #polynomial coefficients averaged over the last n iterations
        self.best_fit = None  
        #polynomial coefficients for the most recent fit
        self.current_fit = [np.array([False])]  
        #radius of curvature of the line in some units
        self.radius_of_curvature = None 
        #distance in meters of vehicle center from the line
        self.line_base_pos = None 
        #difference in fit coefficients between last and new fits
        self.diffs = np.array([0,0,0,0], dtype='float')
        #x values for detected line pixels
        self.allx = None  
        #y values for detected line pixels
        self.ally = None
    
    def check_detected(self):
        if (self.diffs[0] < 0.01 and self.diffs[1] < 10.0 and self.diffs[2] < 1000.) and len(self.recent_fitted) > 0:
            return True
        else:
            return False
    
        
    def update(self,fit):
        if fit is not None:
            if self.best_fit is not None:
                self.diffs = abs(fit - self.best_fit)
                if self.check_detected():
                    self.detected =True
                    if len(self.recent_fitted)>10:
                        self.recent_fitted = self.recent_fitted[1:]
                        self.recent_fitted.append(fit)
                    else:
                        self.recent_fitted.append(fit)
                    self.best_fit = np.average(self.recent_fitted, axis=0)
                    self.current_fit = fit
                else:
                    self.detected = False
            else:
                self.best_fit = fit
                self.current_fit = fit
                self.detected=True
                self.recent_fitted.append(fit)