gonzaloruizdevilla

import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle


# Read in an image
image = mpimg.imread('signs_vehicles_xygrad.png')

gonzaloruizdevilla

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2

# Read in an image, you can also try test1.jpg or test4.jpg
image = mpimg.imread('test6.jpg') 

# Define a function that thresholds the S-channel of HLS
# Use exclusive lower bound (>) and inclusive upper (<=)

gonzaloruizdevilla

import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg


image = mpimg.imread('bridge_shadow.jpg')

# Edit this function to create your own pipeline.
def pipeline(img, s_thresh=(170, 255), sx_thresh=(20, 100)):

gonzaloruizdevilla

import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt

# Load our image
# `mpimg.imread` will load .jpg as 0-255, so normalize back to 0-1
img = mpimg.imread('warped_example.jpg')/255

def hist(img):
    # TO-DO: Grab only the bottom half of the image

gonzaloruizdevilla

import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import cv2

# Load our image
binary_warped = mpimg.imread('warped_example.jpg')

def find_lane_pixels(binary_warped):
    # Take a histogram of the bottom half of the image

gonzaloruizdevilla

import cv2
import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt

# Load our image - this should be a new frame since last time!
binary_warped = mpimg.imread('warped_example.jpg')

# Polynomial fit values from the previous frame
# Make sure to grab the actual values from the previous step in your project!

gonzaloruizdevilla

import numpy as np

def generate_data():
    '''
    Generates fake data to use for calculating lane curvature.
    In your own project, you'll ignore this function and instead
    feed in the output of your lane detection algorithm to
    the lane curvature calculation.
    '''
    # Set random seed number so results are consistent for grader

gonzaloruizdevilla

import numpy as np

def generate_data(ym_per_pix, xm_per_pix):
    '''
    Generates fake data to use for calculating lane curvature.
    In your own project, you'll ignore this function and instead
    feed in the output of your lane detection algorithm to
    the lane curvature calculation.
    '''
    # Set random seed number so results are consistent for grader

gonzaloruizdevilla

let almost_factorial = f => n => n == 0 ? 1 : n * f(n-1)
//This first version is not yet a combinator
let Y1 = f => f (x => Y1(f)(x))
let factorial = (almost_factorial(x => (Y(almost_factorial)(x))))
console.log(factorial(6))
//720

/*The strict (applicative-order) Y combinator*/
let Y2 = f => (x => x(x))(x => f(y => x(x)(y)))
let factorial2 = Y2(almost_factorial)

gonzaloruizdevilla

# Write a function 'kalman_filter' that implements a multi-
# dimensional Kalman Filter for the example given

from math import *


class matrix:
    
    # implements basic operations of a matrix class