cipher982

"""
A simple example of an animated Brownian motion plot

By: David Rose
Date : 10/9/2017
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

cipher982

"""
Video animation of stock ticker charts to represent randomness

By: David Rose
Date : 10/9/2017
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from itertools import cycle

cipher982

"""
Plot out grid of 5x5 brownian motion 2D plots

By: David Rose
Date : 10/9/2017
"""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random

cipher982

"""
Video animation of Monte Carlo estimation of a circle's area.
The function (x**2 + y**2 + r**2) can be changed around to create other shapes of choice.

By: David Rose
Date : 10/9/2017
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

cipher982

def toCalibrate(img,nx,ny):
    objpoints = []
    imgpoints = []
    objp = np.zeros((nx*ny, 3), np.float32)
    objp[:,:2] = np.mgrid[0:nx,0:ny].T.reshape(-1,2) # x,y coordinates
    
    # Find chessboard corners
    ret, corners= cv2.findChessboardCorners(img, (nx,ny), None)

    # If found, draw corners

cipher982

def toWarp(img,src,dst):     
    # Grab the image shape
    if len(np.shape(img)) > 2:
        img_size = (toGray(img).shape[1], toGray(img).shape[0])
    else:
        img_size = (img.shape[1], img.shape[0])
    

    #im2 = img.reshape(img.shape[0], img.shape[1])
    plt.suptitle('Before Warped', fontsize=14, fontweight='bold')

cipher982

# for a given sobel kernel size and threshold values
def toMagSobel(img, sobel_kernel=3, mag_thresh=(0, 255)):
    # Convert to grayscale
    if len(np.shape(img)) > 2:
        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    else:
        gray = img
    # Take both Sobel x and y gradients
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)

cipher982

plt.suptitle('After Sobel Binary', fontsize=14, fontweight='bold')
plt.imshow(step2)
plt.show()

histogram = np.sum(step2[step2.shape[0]//2:,:], axis=0)
plt.suptitle('Histogram of Pixels', fontsize=14, fontweight='bold')
plt.plot(histogram)
plt.show()

cipher982

def regressionLanesTracker(src):
    
    binary_warped = src
    # Assuming you have created a warped binary image called "binary_warped"
    # Take a histogram of the bottom half of the image
    histogram = np.sum(binary_warped[int(binary_warped.shape[0]/2):,:], axis=0)
    # Create an output image to draw on and  visualize the result
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
    # Find the peak of the left and right halves of the histogram
    # These will be the starting point for the left and right lines

cipher982

  """
  Add polygon and Overlay and Warp
  """
def polygonOverlay(src):
  
  binary_warped = src
  # Create an image to draw the lines on
  warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
  color_warp = np.dstack((warp_zero, warp_zero, warp_zero))