hackintoshrao

import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2)

# Arrays to store object points and image points from all the images.

hackintoshrao

import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2)

# Arrays to store object points and image points from all the images.

hackintoshrao

# Saturation color channel binary threshold.
def s_threshold(img, s_thresh=(0, 255)):
    s_channel = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)[:,:,2]
    # Threshold color channel
    s_binary = np.zeros_like(s_channel)
    s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
    return s_binary


# Define a function that takes an image, gradient orientation,

hackintoshrao

# Define a function to return the magnitude of the gradient
# for a given sobel kernel size and threshold values
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
    # Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # 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)
    # Calculate the gradient magnitude
    gradmag = np.sqrt(sobelx**2 + sobely**2)

hackintoshrao

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


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

hackintoshrao

# Define a function that takes an image, number of x and y points, 
# camera matrix and distortion coefficients
def corners_unwarp(img, nx, ny, mtx, dist):
    # Use the OpenCV undistort() function to remove distortion
    undist = cv2.undistort(img, mtx, dist, None, mtx)
    # Convert undistorted image to grayscale
    gray = cv2.cvtColor(undist, cv2.COLOR_BGR2GRAY)
    # Search for corners in the grayscaled image
    ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)

hackintoshrao

import csv
import cv2
import numpy as np
# Data read from csv. CSV contains the image path and steerig angles recorded at various points of gameplay.
lines = []

csvPath = "/Users/hackintoshrao/Documents/code/self-drive/driving_log.csv"

with open(csvPath) as csvData:
        # read from csv 

hackintoshrao

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

# prepare object points
nx = 8# enter the number of inside corners in x
ny = 6# enter the number of inside corners in y

# Make a list of calibration images

hackintoshrao

import csv
import cv2
import numpy as np
# Data read from csv. CSV contains the image path and steerig angles recorded at various points of gameplay.
lines = []

csvPath = "/Users/hackintoshrao/Documents/code/self-drive/driving_log.csv"

with open(csvPath) as csvData:
        # read from csv 

hackintoshrao

import (
        "github.com/gorilla/mux"
        "net/http/pprof"
        "runtime/debug"
        "runtime"
        runtimepprof "runtime/pprof"
)

const (