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

def cameraCalibration():
    # 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.
    objpoints = [] # 3d points in real world space
    imgpoints = [] # 2d points in image plane.

    # Make a list of calibration images
    images = glob.glob('camera_cal/calibration*.jpg')

    # Step through the list and search for chessboard corners
    for fname in images:
        img = cv2.imread(fname)

        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        img_size = (gray.shape[1], gray.shape[0])

        # Find the chessboard corners
        ret, corners = cv2.findChessboardCorners(gray, (9,6),None)

        # If found, add object points, image points
        if ret == True:
            objpoints.append(objp)
            imgpoints.append(corners)

    # Use all found corners and image points
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

    return mtx, dist

def undistort(img, mtx, dist):
    # Use generated parameters to undistort each image without recalculating coefficients
    undistorted = cv2.undistort(img, mtx, dist, None, mtx)

    return undistorted

# Define a function that takes an image, number of x and y points, 
# camera matrix and distortion coefficients
def perspectiveTransform(img):
    # Estimated source and destination values for a bird's eye view of the road
    # Values were determined using estimates from two straight line images
    h = img.shape[0]
    w = img.shape[1]
    img_size = (w,h)
    mid_offset = 90

    # Top left, top right, bottom left, bottom right
    src = np.float32([[w/2-mid_offset, 460], [w/2+mid_offset, 460], [0, h-15], [w, h-15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])

    # Given src and dst points, calculate the perspective transform matrix
    M = cv2.getPerspectiveTransform(src, dst)

    # Warp the image using OpenCV warpPerspective()
    transformed = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)

    # Return the resulting image and matrix
    return transformed

def inversePerspectiveTransform(img):
    # Estimated source and destination values for a bird's eye view of the road
    # Values were determined using estimates from two straight line images
    # Values were determined using estimates from two straight line images
    h = img.shape[0]
    w = img.shape[1]
    img_size = (w,h)
    mid_offset = 90
    
    # Top left, top right, bottom left, bottom right
    src = np.float32([[w/2-mid_offset, 460], [w/2+mid_offset, 460], [0, h-15], [w, h-15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])

    # Given src and dst points, calculate the perspective transform matrix
    Minv = cv2.getPerspectiveTransform(dst, src)

    # Warp the image using OpenCV warpPerspective()
    transformed = cv2.warpPerspective(img, Minv, img_size, flags=cv2.INTER_LINEAR)

    # Return the resulting image and matrix
    return transformed

def pipeline(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
    img = np.copy(img)
    # Convert to HLS color space and separate the V channel
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    l_channel = hls[:,:,1]
    s_channel = hls[:,:,2]

    # Sobel x
    sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivative in x
    abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
    scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
    
    # Threshold x gradient
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
    
    # Threshold color channel
    s_binary = np.zeros_like(s_channel)
    s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1

    # Stack each channel
    color_binary = np.zeros_like(s_channel)
    color_binary[(s_binary == 1) | (sxbinary == 1)] = 1
    return color_binary

def measure_curvature_real():
    '''
    Calculates the curvature of polynomial functions in meters.
    '''
    # Define conversions in x and y from pixels space to meters
    ym_per_pix = 30/720 # meters per pixel in y dimension
    xm_per_pix = 3.7/700 # meters per pixel in x dimension
    
    # Start by generating our fake example data
    # Make sure to feed in your real data instead in your project!
    ploty, left_fit_cr, right_fit_cr = generate_data(ym_per_pix, xm_per_pix)
    
    # Define y-value where we want radius of curvature
    # We'll choose the maximum y-value, corresponding to the bottom of the image
    y_eval = np.max(ploty)
    
    ##### TO-DO: Implement the calculation of R_curve (radius of curvature) #####
    left_curverad = ((1 + (2*left_fit_cr[0]*y_eval + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
    right_curverad = ((1 + (2*right_fit_cr[0]*y_eval + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
    
    return left_curverad, right_curverad

def find_lane_pixels(binary_warped):
    # Take a histogram of the bottom half of the image
    histogram = np.sum(binary_warped[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))
    # Find the peak of the left and right halves of the histogram
    # These will be the starting point for the left and right lines
    midpoint = np.int(histogram.shape[0]//2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint

    # HYPERPARAMETERS
    # Choose the number of sliding windows
    nwindows = 9
    # Set the width of the windows +/- margin
    margin = 100
    # Set minimum number of pixels found to recenter window
    minpix = 50

    # Set height of windows - based on nwindows above and image shape
    window_height = np.int(binary_warped.shape[0]//nwindows)
    # Identify the x and y positions of all nonzero pixels in the image
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    # Current positions to be updated later for each window in nwindows
    leftx_current = leftx_base
    rightx_current = rightx_base

    # Create empty lists to receive left and right lane pixel indices
    left_lane_inds = []
    right_lane_inds = []

    # Step through the windows one by one
    for window in range(nwindows):
        # Identify window boundaries in x and y (and right and left)
        win_y_low = binary_warped.shape[0] - (window+1)*window_height
        win_y_high = binary_warped.shape[0] - window*window_height
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin

        # Draw the windows on the visualization image
        cv2.rectangle(out_img,(win_xleft_low,win_y_low),
        (win_xleft_high,win_y_high),(0,255,0), 2) 
        cv2.rectangle(out_img,(win_xright_low,win_y_low),
        (win_xright_high,win_y_high),(0,255,0), 2) 
        
        ### TO-DO: Identify the nonzero pixels in x and y within the window ###
        good_left_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low) & 
        (nonzerox < win_xleft_high) & (nonzerox >= win_xleft_low)).nonzero()[0]
        good_right_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low) *
        (nonzerox < win_xright_high) & (nonzerox >= win_xright_low)).nonzero()[0]
        
        # Append these indices to the lists
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)

        ### TO-DO: If you found > minpix pixels, recenter next window ###
        if (len(good_left_inds) > minpix):
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if (len(good_right_inds) > minpix):
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))

    # Concatenate the arrays of indices (previously was a list of lists of pixels)
    try:
        left_lane_inds = np.concatenate(left_lane_inds)
        right_lane_inds = np.concatenate(right_lane_inds)
    except ValueError:
        # Avoids an error if the above is not implemented fully
        pass

    # Extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

    return leftx, lefty, rightx, righty, out_img


def fit_polynomial(binary_warped):
    # Find our lane pixels first
    leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)

    ### TO-DO: Fit a second order polynomial to each using `np.polyfit` ###
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)

    # Generate x and y values for plotting
    ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
    try:
        left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
        right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    except TypeError:
        # Avoids an error if `left` and `right_fit` are still none or incorrect
        print('The function failed to fit a line!')
        left_fitx = 1*ploty**2 + 1*ploty
        right_fitx = 1*ploty**2 + 1*ploty

    ## Visualization ##
    # Colors in the left and right lane regions
    out_img[lefty, leftx] = [255, 0, 0]
    out_img[righty, rightx] = [0, 0, 255]

    # Plots the left and right polynomials on the lane lines
    #plt.plot(left_fitx, ploty, color='yellow')
    #plt.plot(right_fitx, ploty, color='yellow')

    return out_img, left_fit, right_fit

def fit_poly(img_shape, leftx, lefty, rightx, righty):
    ### TO-DO: Fit a second order polynomial to each with np.polyfit() ###
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    # Generate x and y values for plotting
    ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
    ### TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
    left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
    right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    
    return left_fitx, right_fitx, ploty

def search_around_poly(binary_warped, left_fit, right_fit):
    # HYPERPARAMETER
    # Choose the width of the margin around the previous polynomial to search
    # The quiz grader expects 100 here, but feel free to tune on your own!
    margin = 50

    # Grab activated pixels
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])

    ### TO-DO: Set the area of search based on activated x-values ###
    ### within the +/- margin of our polynomial function ###
    ### Hint: consider the window areas for the similarly named variables ###
    ### in the previous quiz, but change the windows to our new search area ###
    left_margins = left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2]
    right_margins = right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2]
    left_lane_inds = ((nonzerox < left_margins + margin) & (nonzerox > left_margins
    - margin))
    right_lane_inds = ((nonzerox < right_margins + margin) & (nonzerox > right_margins
    - margin))

    # Again, extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

    # Fit new polynomials
    left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)

    ## Visualization ##
    # Create an image to draw on and an image to show the selection window (previouvly multiplied by 255)
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*0
    window_img = np.zeros_like(out_img)
    # Color in left and right line pixels
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]

    # Generate a polygon to illustrate the search window area
    # And recast the x and y points into usable format for cv2.fillPoly()
    left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
    left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, 
                              ploty])))])
    left_line_pts = np.hstack((left_line_window1, left_line_window2))
    right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
    right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, 
                              ploty])))])
    right_line_pts = np.hstack((right_line_window1, right_line_window2))
    #Create center window using a similar technique
    center_window1 = np.array([np.transpose(np.vstack([left_fitx+margin, ploty]))])
    center_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx-margin, 
                              ploty])))])
    center_pts = np.hstack((center_window1, center_window2))

    # Draw the lane onto the warped blank image
    cv2.fillPoly(window_img, np.int_([left_line_pts]), (255, 0, 0))
    cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 0, 255))
    cv2.fillPoly(window_img, np.int_([center_pts]), (0, 255, 0))
    result = cv2.addWeighted(out_img, 1, window_img, 0.5, 0)
    
    # Plot the polynomial lines onto the image
    #plt.plot(left_fitx, ploty, color='yellow')
    #plt.plot(right_fitx, ploty, color='yellow')
    ## End visualization steps ##
    
    return result, left_fit, right_fit

first = True

def process_image(img):
    global first

    mtx, dist = cameraCalibration()

    calibrated = undistort(img, mtx, dist)

    binary = pipeline(calibrated)

    transformed = perspectiveTransform(binary)

    if (first == True):
        curve, left_fit, right_fit = fit_polynomial(transformed)
        first = False
    else:
        curve, left_fit, right_fit = search_around_poly(transformed, left_fit, right_fit)
    print(type(left_fit))
    inverse = inversePerspectiveTransform(curve)

    result = cv2.addWeighted(img, 1, inverse, 1, 0)

    return result

img = cv2.imread("test_images/test1.jpg")

result = process_image(img)
plt.imshow(result)
plt.show()
#white_output = 'project_video_output.mp4'

#clip1 = VideoFileClip("project_video.mp4")
#white_clip = clip1.fl_image(process_image)
#%time white_clip.write_videofile(white_output, audio=False)

564e785e29364ec646aafaf7d0c3e5acd698146b
<mask token>


def cameraCalibration():
    objp = np.zeros((6 * 9, 3), np.float32)
    objp[:, :2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2)
    objpoints = []
    imgpoints = []
    images = glob.glob('camera_cal/calibration*.jpg')
    for fname in images:
        img = cv2.imread(fname)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        img_size = gray.shape[1], gray.shape[0]
        ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)
        if ret == True:
            objpoints.append(objp)
            imgpoints.append(corners)
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
        gray.shape[::-1], None, None)
    return mtx, dist


def undistort(img, mtx, dist):
    undistorted = cv2.undistort(img, mtx, dist, None, mtx)
    return undistorted


def perspectiveTransform(img):
    h = img.shape[0]
    w = img.shape[1]
    img_size = w, h
    mid_offset = 90
    src = np.float32([[w / 2 - mid_offset, 460], [w / 2 + mid_offset, 460],
        [0, h - 15], [w, h - 15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])
    M = cv2.getPerspectiveTransform(src, dst)
    transformed = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
    return transformed


def inversePerspectiveTransform(img):
    h = img.shape[0]
    w = img.shape[1]
    img_size = w, h
    mid_offset = 90
    src = np.float32([[w / 2 - mid_offset, 460], [w / 2 + mid_offset, 460],
        [0, h - 15], [w, h - 15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])
    Minv = cv2.getPerspectiveTransform(dst, src)
    transformed = cv2.warpPerspective(img, Minv, img_size, flags=cv2.
        INTER_LINEAR)
    return transformed


def pipeline(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
    img = np.copy(img)
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    l_channel = hls[:, :, 1]
    s_channel = hls[:, :, 2]
    sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0)
    abs_sobelx = np.absolute(sobelx)
    scaled_sobel = np.uint8(255 * abs_sobelx / np.max(abs_sobelx))
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])
        ] = 1
    s_binary = np.zeros_like(s_channel)
    s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
    color_binary = np.zeros_like(s_channel)
    color_binary[(s_binary == 1) | (sxbinary == 1)] = 1
    return color_binary


def measure_curvature_real():
    """
    Calculates the curvature of polynomial functions in meters.
    """
    ym_per_pix = 30 / 720
    xm_per_pix = 3.7 / 700
    ploty, left_fit_cr, right_fit_cr = generate_data(ym_per_pix, xm_per_pix)
    y_eval = np.max(ploty)
    left_curverad = (1 + (2 * left_fit_cr[0] * y_eval + left_fit_cr[1]) ** 2
        ) ** 1.5 / np.absolute(2 * left_fit_cr[0])
    right_curverad = (1 + (2 * right_fit_cr[0] * y_eval + right_fit_cr[1]) ** 2
        ) ** 1.5 / np.absolute(2 * right_fit_cr[0])
    return left_curverad, right_curverad


def find_lane_pixels(binary_warped):
    histogram = np.sum(binary_warped[binary_warped.shape[0] // 2:, :], axis=0)
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))
    midpoint = np.int(histogram.shape[0] // 2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint
    nwindows = 9
    margin = 100
    minpix = 50
    window_height = np.int(binary_warped.shape[0] // nwindows)
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    leftx_current = leftx_base
    rightx_current = rightx_base
    left_lane_inds = []
    right_lane_inds = []
    for window in range(nwindows):
        win_y_low = binary_warped.shape[0] - (window + 1) * window_height
        win_y_high = binary_warped.shape[0] - window * window_height
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin
        cv2.rectangle(out_img, (win_xleft_low, win_y_low), (win_xleft_high,
            win_y_high), (0, 255, 0), 2)
        cv2.rectangle(out_img, (win_xright_low, win_y_low), (
            win_xright_high, win_y_high), (0, 255, 0), 2)
        good_left_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low) &
            (nonzerox < win_xleft_high) & (nonzerox >= win_xleft_low)).nonzero(
            )[0]
        good_right_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low
            ) * (nonzerox < win_xright_high) & (nonzerox >= win_xright_low)
            ).nonzero()[0]
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
    try:
        left_lane_inds = np.concatenate(left_lane_inds)
        right_lane_inds = np.concatenate(right_lane_inds)
    except ValueError:
        pass
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds]
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]
    return leftx, lefty, rightx, righty, out_img


def fit_polynomial(binary_warped):
    leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])
    try:
        left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2
            ]
        right_fitx = right_fit[0] * ploty ** 2 + right_fit[1
            ] * ploty + right_fit[2]
    except TypeError:
        print('The function failed to fit a line!')
        left_fitx = 1 * ploty ** 2 + 1 * ploty
        right_fitx = 1 * ploty ** 2 + 1 * ploty
    out_img[lefty, leftx] = [255, 0, 0]
    out_img[righty, rightx] = [0, 0, 255]
    return out_img, left_fit, right_fit


<mask token>


def search_around_poly(binary_warped, left_fit, right_fit):
    margin = 50
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    left_margins = left_fit[0] * nonzeroy ** 2 + left_fit[1
        ] * nonzeroy + left_fit[2]
    right_margins = right_fit[0] * nonzeroy ** 2 + right_fit[1
        ] * nonzeroy + right_fit[2]
    left_lane_inds = (nonzerox < left_margins + margin) & (nonzerox > 
        left_margins - margin)
    right_lane_inds = (nonzerox < right_margins + margin) & (nonzerox > 
        right_margins - margin)
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds]
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]
    left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx,
        lefty, rightx, righty)
    out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 0
    window_img = np.zeros_like(out_img)
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
    left_line_window1 = np.array([np.transpose(np.vstack([left_fitx -
        margin, ploty]))])
    left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([
        left_fitx + margin, ploty])))])
    left_line_pts = np.hstack((left_line_window1, left_line_window2))
    right_line_window1 = np.array([np.transpose(np.vstack([right_fitx -
        margin, ploty]))])
    right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([
        right_fitx + margin, ploty])))])
    right_line_pts = np.hstack((right_line_window1, right_line_window2))
    center_window1 = np.array([np.transpose(np.vstack([left_fitx + margin,
        ploty]))])
    center_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx -
        margin, ploty])))])
    center_pts = np.hstack((center_window1, center_window2))
    cv2.fillPoly(window_img, np.int_([left_line_pts]), (255, 0, 0))
    cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 0, 255))
    cv2.fillPoly(window_img, np.int_([center_pts]), (0, 255, 0))
    result = cv2.addWeighted(out_img, 1, window_img, 0.5, 0)
    return result, left_fit, right_fit


<mask token>


def process_image(img):
    global first
    mtx, dist = cameraCalibration()
    calibrated = undistort(img, mtx, dist)
    binary = pipeline(calibrated)
    transformed = perspectiveTransform(binary)
    if first == True:
        curve, left_fit, right_fit = fit_polynomial(transformed)
        first = False
    else:
        curve, left_fit, right_fit = search_around_poly(transformed,
            left_fit, right_fit)
    print(type(left_fit))
    inverse = inversePerspectiveTransform(curve)
    result = cv2.addWeighted(img, 1, inverse, 1, 0)
    return result


<mask token>

<mask token>


def cameraCalibration():
    objp = np.zeros((6 * 9, 3), np.float32)
    objp[:, :2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2)
    objpoints = []
    imgpoints = []
    images = glob.glob('camera_cal/calibration*.jpg')
    for fname in images:
        img = cv2.imread(fname)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        img_size = gray.shape[1], gray.shape[0]
        ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)
        if ret == True:
            objpoints.append(objp)
            imgpoints.append(corners)
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
        gray.shape[::-1], None, None)
    return mtx, dist


def undistort(img, mtx, dist):
    undistorted = cv2.undistort(img, mtx, dist, None, mtx)
    return undistorted


def perspectiveTransform(img):
    h = img.shape[0]
    w = img.shape[1]
    img_size = w, h
    mid_offset = 90
    src = np.float32([[w / 2 - mid_offset, 460], [w / 2 + mid_offset, 460],
        [0, h - 15], [w, h - 15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])
    M = cv2.getPerspectiveTransform(src, dst)
    transformed = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
    return transformed


def inversePerspectiveTransform(img):
    h = img.shape[0]
    w = img.shape[1]
    img_size = w, h
    mid_offset = 90
    src = np.float32([[w / 2 - mid_offset, 460], [w / 2 + mid_offset, 460],
        [0, h - 15], [w, h - 15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])
    Minv = cv2.getPerspectiveTransform(dst, src)
    transformed = cv2.warpPerspective(img, Minv, img_size, flags=cv2.
        INTER_LINEAR)
    return transformed


def pipeline(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
    img = np.copy(img)
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    l_channel = hls[:, :, 1]
    s_channel = hls[:, :, 2]
    sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0)
    abs_sobelx = np.absolute(sobelx)
    scaled_sobel = np.uint8(255 * abs_sobelx / np.max(abs_sobelx))
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])
        ] = 1
    s_binary = np.zeros_like(s_channel)
    s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
    color_binary = np.zeros_like(s_channel)
    color_binary[(s_binary == 1) | (sxbinary == 1)] = 1
    return color_binary


def measure_curvature_real():
    """
    Calculates the curvature of polynomial functions in meters.
    """
    ym_per_pix = 30 / 720
    xm_per_pix = 3.7 / 700
    ploty, left_fit_cr, right_fit_cr = generate_data(ym_per_pix, xm_per_pix)
    y_eval = np.max(ploty)
    left_curverad = (1 + (2 * left_fit_cr[0] * y_eval + left_fit_cr[1]) ** 2
        ) ** 1.5 / np.absolute(2 * left_fit_cr[0])
    right_curverad = (1 + (2 * right_fit_cr[0] * y_eval + right_fit_cr[1]) ** 2
        ) ** 1.5 / np.absolute(2 * right_fit_cr[0])
    return left_curverad, right_curverad


def find_lane_pixels(binary_warped):
    histogram = np.sum(binary_warped[binary_warped.shape[0] // 2:, :], axis=0)
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))
    midpoint = np.int(histogram.shape[0] // 2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint
    nwindows = 9
    margin = 100
    minpix = 50
    window_height = np.int(binary_warped.shape[0] // nwindows)
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    leftx_current = leftx_base
    rightx_current = rightx_base
    left_lane_inds = []
    right_lane_inds = []
    for window in range(nwindows):
        win_y_low = binary_warped.shape[0] - (window + 1) * window_height
        win_y_high = binary_warped.shape[0] - window * window_height
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin
        cv2.rectangle(out_img, (win_xleft_low, win_y_low), (win_xleft_high,
            win_y_high), (0, 255, 0), 2)
        cv2.rectangle(out_img, (win_xright_low, win_y_low), (
            win_xright_high, win_y_high), (0, 255, 0), 2)
        good_left_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low) &
            (nonzerox < win_xleft_high) & (nonzerox >= win_xleft_low)).nonzero(
            )[0]
        good_right_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low
            ) * (nonzerox < win_xright_high) & (nonzerox >= win_xright_low)
            ).nonzero()[0]
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
    try:
        left_lane_inds = np.concatenate(left_lane_inds)
        right_lane_inds = np.concatenate(right_lane_inds)
    except ValueError:
        pass
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds]
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]
    return leftx, lefty, rightx, righty, out_img


def fit_polynomial(binary_warped):
    leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])
    try:
        left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2
            ]
        right_fitx = right_fit[0] * ploty ** 2 + right_fit[1
            ] * ploty + right_fit[2]
    except TypeError:
        print('The function failed to fit a line!')
        left_fitx = 1 * ploty ** 2 + 1 * ploty
        right_fitx = 1 * ploty ** 2 + 1 * ploty
    out_img[lefty, leftx] = [255, 0, 0]
    out_img[righty, rightx] = [0, 0, 255]
    return out_img, left_fit, right_fit


def fit_poly(img_shape, leftx, lefty, rightx, righty):
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    ploty = np.linspace(0, img_shape[0] - 1, img_shape[0])
    left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2]
    right_fitx = right_fit[0] * ploty ** 2 + right_fit[1] * ploty + right_fit[2
        ]
    return left_fitx, right_fitx, ploty


def search_around_poly(binary_warped, left_fit, right_fit):
    margin = 50
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    left_margins = left_fit[0] * nonzeroy ** 2 + left_fit[1
        ] * nonzeroy + left_fit[2]
    right_margins = right_fit[0] * nonzeroy ** 2 + right_fit[1
        ] * nonzeroy + right_fit[2]
    left_lane_inds = (nonzerox < left_margins + margin) & (nonzerox > 
        left_margins - margin)
    right_lane_inds = (nonzerox < right_margins + margin) & (nonzerox > 
        right_margins - margin)
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds]
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]
    left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx,
        lefty, rightx, righty)
    out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 0
    window_img = np.zeros_like(out_img)
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
    left_line_window1 = np.array([np.transpose(np.vstack([left_fitx -
        margin, ploty]))])
    left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([
        left_fitx + margin, ploty])))])
    left_line_pts = np.hstack((left_line_window1, left_line_window2))
    right_line_window1 = np.array([np.transpose(np.vstack([right_fitx -
        margin, ploty]))])
    right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([
        right_fitx + margin, ploty])))])
    right_line_pts = np.hstack((right_line_window1, right_line_window2))
    center_window1 = np.array([np.transpose(np.vstack([left_fitx + margin,
        ploty]))])
    center_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx -
        margin, ploty])))])
    center_pts = np.hstack((center_window1, center_window2))
    cv2.fillPoly(window_img, np.int_([left_line_pts]), (255, 0, 0))
    cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 0, 255))
    cv2.fillPoly(window_img, np.int_([center_pts]), (0, 255, 0))
    result = cv2.addWeighted(out_img, 1, window_img, 0.5, 0)
    return result, left_fit, right_fit


<mask token>


def process_image(img):
    global first
    mtx, dist = cameraCalibration()
    calibrated = undistort(img, mtx, dist)
    binary = pipeline(calibrated)
    transformed = perspectiveTransform(binary)
    if first == True:
        curve, left_fit, right_fit = fit_polynomial(transformed)
        first = False
    else:
        curve, left_fit, right_fit = search_around_poly(transformed,
            left_fit, right_fit)
    print(type(left_fit))
    inverse = inversePerspectiveTransform(curve)
    result = cv2.addWeighted(img, 1, inverse, 1, 0)
    return result


<mask token>

<mask token>


def cameraCalibration():
    objp = np.zeros((6 * 9, 3), np.float32)
    objp[:, :2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2)
    objpoints = []
    imgpoints = []
    images = glob.glob('camera_cal/calibration*.jpg')
    for fname in images:
        img = cv2.imread(fname)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        img_size = gray.shape[1], gray.shape[0]
        ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)
        if ret == True:
            objpoints.append(objp)
            imgpoints.append(corners)
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
        gray.shape[::-1], None, None)
    return mtx, dist


def undistort(img, mtx, dist):
    undistorted = cv2.undistort(img, mtx, dist, None, mtx)
    return undistorted


def perspectiveTransform(img):
    h = img.shape[0]
    w = img.shape[1]
    img_size = w, h
    mid_offset = 90
    src = np.float32([[w / 2 - mid_offset, 460], [w / 2 + mid_offset, 460],
        [0, h - 15], [w, h - 15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])
    M = cv2.getPerspectiveTransform(src, dst)
    transformed = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
    return transformed


def inversePerspectiveTransform(img):
    h = img.shape[0]
    w = img.shape[1]
    img_size = w, h
    mid_offset = 90
    src = np.float32([[w / 2 - mid_offset, 460], [w / 2 + mid_offset, 460],
        [0, h - 15], [w, h - 15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])
    Minv = cv2.getPerspectiveTransform(dst, src)
    transformed = cv2.warpPerspective(img, Minv, img_size, flags=cv2.
        INTER_LINEAR)
    return transformed


def pipeline(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
    img = np.copy(img)
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    l_channel = hls[:, :, 1]
    s_channel = hls[:, :, 2]
    sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0)
    abs_sobelx = np.absolute(sobelx)
    scaled_sobel = np.uint8(255 * abs_sobelx / np.max(abs_sobelx))
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])
        ] = 1
    s_binary = np.zeros_like(s_channel)
    s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
    color_binary = np.zeros_like(s_channel)
    color_binary[(s_binary == 1) | (sxbinary == 1)] = 1
    return color_binary


def measure_curvature_real():
    """
    Calculates the curvature of polynomial functions in meters.
    """
    ym_per_pix = 30 / 720
    xm_per_pix = 3.7 / 700
    ploty, left_fit_cr, right_fit_cr = generate_data(ym_per_pix, xm_per_pix)
    y_eval = np.max(ploty)
    left_curverad = (1 + (2 * left_fit_cr[0] * y_eval + left_fit_cr[1]) ** 2
        ) ** 1.5 / np.absolute(2 * left_fit_cr[0])
    right_curverad = (1 + (2 * right_fit_cr[0] * y_eval + right_fit_cr[1]) ** 2
        ) ** 1.5 / np.absolute(2 * right_fit_cr[0])
    return left_curverad, right_curverad


def find_lane_pixels(binary_warped):
    histogram = np.sum(binary_warped[binary_warped.shape[0] // 2:, :], axis=0)
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))
    midpoint = np.int(histogram.shape[0] // 2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint
    nwindows = 9
    margin = 100
    minpix = 50
    window_height = np.int(binary_warped.shape[0] // nwindows)
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    leftx_current = leftx_base
    rightx_current = rightx_base
    left_lane_inds = []
    right_lane_inds = []
    for window in range(nwindows):
        win_y_low = binary_warped.shape[0] - (window + 1) * window_height
        win_y_high = binary_warped.shape[0] - window * window_height
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin
        cv2.rectangle(out_img, (win_xleft_low, win_y_low), (win_xleft_high,
            win_y_high), (0, 255, 0), 2)
        cv2.rectangle(out_img, (win_xright_low, win_y_low), (
            win_xright_high, win_y_high), (0, 255, 0), 2)
        good_left_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low) &
            (nonzerox < win_xleft_high) & (nonzerox >= win_xleft_low)).nonzero(
            )[0]
        good_right_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low
            ) * (nonzerox < win_xright_high) & (nonzerox >= win_xright_low)
            ).nonzero()[0]
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
    try:
        left_lane_inds = np.concatenate(left_lane_inds)
        right_lane_inds = np.concatenate(right_lane_inds)
    except ValueError:
        pass
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds]
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]
    return leftx, lefty, rightx, righty, out_img


def fit_polynomial(binary_warped):
    leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])
    try:
        left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2
            ]
        right_fitx = right_fit[0] * ploty ** 2 + right_fit[1
            ] * ploty + right_fit[2]
    except TypeError:
        print('The function failed to fit a line!')
        left_fitx = 1 * ploty ** 2 + 1 * ploty
        right_fitx = 1 * ploty ** 2 + 1 * ploty
    out_img[lefty, leftx] = [255, 0, 0]
    out_img[righty, rightx] = [0, 0, 255]
    return out_img, left_fit, right_fit


def fit_poly(img_shape, leftx, lefty, rightx, righty):
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    ploty = np.linspace(0, img_shape[0] - 1, img_shape[0])
    left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2]
    right_fitx = right_fit[0] * ploty ** 2 + right_fit[1] * ploty + right_fit[2
        ]
    return left_fitx, right_fitx, ploty


def search_around_poly(binary_warped, left_fit, right_fit):
    margin = 50
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    left_margins = left_fit[0] * nonzeroy ** 2 + left_fit[1
        ] * nonzeroy + left_fit[2]
    right_margins = right_fit[0] * nonzeroy ** 2 + right_fit[1
        ] * nonzeroy + right_fit[2]
    left_lane_inds = (nonzerox < left_margins + margin) & (nonzerox > 
        left_margins - margin)
    right_lane_inds = (nonzerox < right_margins + margin) & (nonzerox > 
        right_margins - margin)
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds]
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]
    left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx,
        lefty, rightx, righty)
    out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 0
    window_img = np.zeros_like(out_img)
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
    left_line_window1 = np.array([np.transpose(np.vstack([left_fitx -
        margin, ploty]))])
    left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([
        left_fitx + margin, ploty])))])
    left_line_pts = np.hstack((left_line_window1, left_line_window2))
    right_line_window1 = np.array([np.transpose(np.vstack([right_fitx -
        margin, ploty]))])
    right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([
        right_fitx + margin, ploty])))])
    right_line_pts = np.hstack((right_line_window1, right_line_window2))
    center_window1 = np.array([np.transpose(np.vstack([left_fitx + margin,
        ploty]))])
    center_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx -
        margin, ploty])))])
    center_pts = np.hstack((center_window1, center_window2))
    cv2.fillPoly(window_img, np.int_([left_line_pts]), (255, 0, 0))
    cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 0, 255))
    cv2.fillPoly(window_img, np.int_([center_pts]), (0, 255, 0))
    result = cv2.addWeighted(out_img, 1, window_img, 0.5, 0)
    return result, left_fit, right_fit


first = True


def process_image(img):
    global first
    mtx, dist = cameraCalibration()
    calibrated = undistort(img, mtx, dist)
    binary = pipeline(calibrated)
    transformed = perspectiveTransform(binary)
    if first == True:
        curve, left_fit, right_fit = fit_polynomial(transformed)
        first = False
    else:
        curve, left_fit, right_fit = search_around_poly(transformed,
            left_fit, right_fit)
    print(type(left_fit))
    inverse = inversePerspectiveTransform(curve)
    result = cv2.addWeighted(img, 1, inverse, 1, 0)
    return result


img = cv2.imread('test_images/test1.jpg')
result = process_image(img)
plt.imshow(result)
plt.show()

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


def cameraCalibration():
    objp = np.zeros((6 * 9, 3), np.float32)
    objp[:, :2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2)
    objpoints = []
    imgpoints = []
    images = glob.glob('camera_cal/calibration*.jpg')
    for fname in images:
        img = cv2.imread(fname)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        img_size = gray.shape[1], gray.shape[0]
        ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)
        if ret == True:
            objpoints.append(objp)
            imgpoints.append(corners)
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
        gray.shape[::-1], None, None)
    return mtx, dist


def undistort(img, mtx, dist):
    undistorted = cv2.undistort(img, mtx, dist, None, mtx)
    return undistorted


def perspectiveTransform(img):
    h = img.shape[0]
    w = img.shape[1]
    img_size = w, h
    mid_offset = 90
    src = np.float32([[w / 2 - mid_offset, 460], [w / 2 + mid_offset, 460],
        [0, h - 15], [w, h - 15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])
    M = cv2.getPerspectiveTransform(src, dst)
    transformed = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
    return transformed


def inversePerspectiveTransform(img):
    h = img.shape[0]
    w = img.shape[1]
    img_size = w, h
    mid_offset = 90
    src = np.float32([[w / 2 - mid_offset, 460], [w / 2 + mid_offset, 460],
        [0, h - 15], [w, h - 15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])
    Minv = cv2.getPerspectiveTransform(dst, src)
    transformed = cv2.warpPerspective(img, Minv, img_size, flags=cv2.
        INTER_LINEAR)
    return transformed


def pipeline(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
    img = np.copy(img)
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    l_channel = hls[:, :, 1]
    s_channel = hls[:, :, 2]
    sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0)
    abs_sobelx = np.absolute(sobelx)
    scaled_sobel = np.uint8(255 * abs_sobelx / np.max(abs_sobelx))
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])
        ] = 1
    s_binary = np.zeros_like(s_channel)
    s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
    color_binary = np.zeros_like(s_channel)
    color_binary[(s_binary == 1) | (sxbinary == 1)] = 1
    return color_binary


def measure_curvature_real():
    """
    Calculates the curvature of polynomial functions in meters.
    """
    ym_per_pix = 30 / 720
    xm_per_pix = 3.7 / 700
    ploty, left_fit_cr, right_fit_cr = generate_data(ym_per_pix, xm_per_pix)
    y_eval = np.max(ploty)
    left_curverad = (1 + (2 * left_fit_cr[0] * y_eval + left_fit_cr[1]) ** 2
        ) ** 1.5 / np.absolute(2 * left_fit_cr[0])
    right_curverad = (1 + (2 * right_fit_cr[0] * y_eval + right_fit_cr[1]) ** 2
        ) ** 1.5 / np.absolute(2 * right_fit_cr[0])
    return left_curverad, right_curverad


def find_lane_pixels(binary_warped):
    histogram = np.sum(binary_warped[binary_warped.shape[0] // 2:, :], axis=0)
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))
    midpoint = np.int(histogram.shape[0] // 2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint
    nwindows = 9
    margin = 100
    minpix = 50
    window_height = np.int(binary_warped.shape[0] // nwindows)
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    leftx_current = leftx_base
    rightx_current = rightx_base
    left_lane_inds = []
    right_lane_inds = []
    for window in range(nwindows):
        win_y_low = binary_warped.shape[0] - (window + 1) * window_height
        win_y_high = binary_warped.shape[0] - window * window_height
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin
        cv2.rectangle(out_img, (win_xleft_low, win_y_low), (win_xleft_high,
            win_y_high), (0, 255, 0), 2)
        cv2.rectangle(out_img, (win_xright_low, win_y_low), (
            win_xright_high, win_y_high), (0, 255, 0), 2)
        good_left_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low) &
            (nonzerox < win_xleft_high) & (nonzerox >= win_xleft_low)).nonzero(
            )[0]
        good_right_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low
            ) * (nonzerox < win_xright_high) & (nonzerox >= win_xright_low)
            ).nonzero()[0]
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
    try:
        left_lane_inds = np.concatenate(left_lane_inds)
        right_lane_inds = np.concatenate(right_lane_inds)
    except ValueError:
        pass
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds]
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]
    return leftx, lefty, rightx, righty, out_img


def fit_polynomial(binary_warped):
    leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])
    try:
        left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2
            ]
        right_fitx = right_fit[0] * ploty ** 2 + right_fit[1
            ] * ploty + right_fit[2]
    except TypeError:
        print('The function failed to fit a line!')
        left_fitx = 1 * ploty ** 2 + 1 * ploty
        right_fitx = 1 * ploty ** 2 + 1 * ploty
    out_img[lefty, leftx] = [255, 0, 0]
    out_img[righty, rightx] = [0, 0, 255]
    return out_img, left_fit, right_fit


def fit_poly(img_shape, leftx, lefty, rightx, righty):
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    ploty = np.linspace(0, img_shape[0] - 1, img_shape[0])
    left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2]
    right_fitx = right_fit[0] * ploty ** 2 + right_fit[1] * ploty + right_fit[2
        ]
    return left_fitx, right_fitx, ploty


def search_around_poly(binary_warped, left_fit, right_fit):
    margin = 50
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    left_margins = left_fit[0] * nonzeroy ** 2 + left_fit[1
        ] * nonzeroy + left_fit[2]
    right_margins = right_fit[0] * nonzeroy ** 2 + right_fit[1
        ] * nonzeroy + right_fit[2]
    left_lane_inds = (nonzerox < left_margins + margin) & (nonzerox > 
        left_margins - margin)
    right_lane_inds = (nonzerox < right_margins + margin) & (nonzerox > 
        right_margins - margin)
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds]
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]
    left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx,
        lefty, rightx, righty)
    out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 0
    window_img = np.zeros_like(out_img)
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
    left_line_window1 = np.array([np.transpose(np.vstack([left_fitx -
        margin, ploty]))])
    left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([
        left_fitx + margin, ploty])))])
    left_line_pts = np.hstack((left_line_window1, left_line_window2))
    right_line_window1 = np.array([np.transpose(np.vstack([right_fitx -
        margin, ploty]))])
    right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([
        right_fitx + margin, ploty])))])
    right_line_pts = np.hstack((right_line_window1, right_line_window2))
    center_window1 = np.array([np.transpose(np.vstack([left_fitx + margin,
        ploty]))])
    center_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx -
        margin, ploty])))])
    center_pts = np.hstack((center_window1, center_window2))
    cv2.fillPoly(window_img, np.int_([left_line_pts]), (255, 0, 0))
    cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 0, 255))
    cv2.fillPoly(window_img, np.int_([center_pts]), (0, 255, 0))
    result = cv2.addWeighted(out_img, 1, window_img, 0.5, 0)
    return result, left_fit, right_fit


first = True


def process_image(img):
    global first
    mtx, dist = cameraCalibration()
    calibrated = undistort(img, mtx, dist)
    binary = pipeline(calibrated)
    transformed = perspectiveTransform(binary)
    if first == True:
        curve, left_fit, right_fit = fit_polynomial(transformed)
        first = False
    else:
        curve, left_fit, right_fit = search_around_poly(transformed,
            left_fit, right_fit)
    print(type(left_fit))
    inverse = inversePerspectiveTransform(curve)
    result = cv2.addWeighted(img, 1, inverse, 1, 0)
    return result


img = cv2.imread('test_images/test1.jpg')
result = process_image(img)
plt.imshow(result)
plt.show()

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

def cameraCalibration():
    # 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.
    objpoints = [] # 3d points in real world space
    imgpoints = [] # 2d points in image plane.

    # Make a list of calibration images
    images = glob.glob('camera_cal/calibration*.jpg')

    # Step through the list and search for chessboard corners
    for fname in images:
        img = cv2.imread(fname)

        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        img_size = (gray.shape[1], gray.shape[0])

        # Find the chessboard corners
        ret, corners = cv2.findChessboardCorners(gray, (9,6),None)

        # If found, add object points, image points
        if ret == True:
            objpoints.append(objp)
            imgpoints.append(corners)

    # Use all found corners and image points
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

    return mtx, dist

def undistort(img, mtx, dist):
    # Use generated parameters to undistort each image without recalculating coefficients
    undistorted = cv2.undistort(img, mtx, dist, None, mtx)

    return undistorted

# Define a function that takes an image, number of x and y points, 
# camera matrix and distortion coefficients
def perspectiveTransform(img):
    # Estimated source and destination values for a bird's eye view of the road
    # Values were determined using estimates from two straight line images
    h = img.shape[0]
    w = img.shape[1]
    img_size = (w,h)
    mid_offset = 90

    # Top left, top right, bottom left, bottom right
    src = np.float32([[w/2-mid_offset, 460], [w/2+mid_offset, 460], [0, h-15], [w, h-15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])

    # Given src and dst points, calculate the perspective transform matrix
    M = cv2.getPerspectiveTransform(src, dst)

    # Warp the image using OpenCV warpPerspective()
    transformed = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)

    # Return the resulting image and matrix
    return transformed

def inversePerspectiveTransform(img):
    # Estimated source and destination values for a bird's eye view of the road
    # Values were determined using estimates from two straight line images
    # Values were determined using estimates from two straight line images
    h = img.shape[0]
    w = img.shape[1]
    img_size = (w,h)
    mid_offset = 90
    
    # Top left, top right, bottom left, bottom right
    src = np.float32([[w/2-mid_offset, 460], [w/2+mid_offset, 460], [0, h-15], [w, h-15]])
    dst = np.float32([[0, 0], [w, 0], [0, h], [w, h]])

    # Given src and dst points, calculate the perspective transform matrix
    Minv = cv2.getPerspectiveTransform(dst, src)

    # Warp the image using OpenCV warpPerspective()
    transformed = cv2.warpPerspective(img, Minv, img_size, flags=cv2.INTER_LINEAR)

    # Return the resulting image and matrix
    return transformed

def pipeline(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
    img = np.copy(img)
    # Convert to HLS color space and separate the V channel
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    l_channel = hls[:,:,1]
    s_channel = hls[:,:,2]

    # Sobel x
    sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivative in x
    abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
    scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
    
    # Threshold x gradient
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
    
    # Threshold color channel
    s_binary = np.zeros_like(s_channel)
    s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1

    # Stack each channel
    color_binary = np.zeros_like(s_channel)
    color_binary[(s_binary == 1) | (sxbinary == 1)] = 1
    return color_binary

def measure_curvature_real():
    '''
    Calculates the curvature of polynomial functions in meters.
    '''
    # Define conversions in x and y from pixels space to meters
    ym_per_pix = 30/720 # meters per pixel in y dimension
    xm_per_pix = 3.7/700 # meters per pixel in x dimension
    
    # Start by generating our fake example data
    # Make sure to feed in your real data instead in your project!
    ploty, left_fit_cr, right_fit_cr = generate_data(ym_per_pix, xm_per_pix)
    
    # Define y-value where we want radius of curvature
    # We'll choose the maximum y-value, corresponding to the bottom of the image
    y_eval = np.max(ploty)
    
    ##### TO-DO: Implement the calculation of R_curve (radius of curvature) #####
    left_curverad = ((1 + (2*left_fit_cr[0]*y_eval + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
    right_curverad = ((1 + (2*right_fit_cr[0]*y_eval + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
    
    return left_curverad, right_curverad

def find_lane_pixels(binary_warped):
    # Take a histogram of the bottom half of the image
    histogram = np.sum(binary_warped[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))
    # Find the peak of the left and right halves of the histogram
    # These will be the starting point for the left and right lines
    midpoint = np.int(histogram.shape[0]//2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint

    # HYPERPARAMETERS
    # Choose the number of sliding windows
    nwindows = 9
    # Set the width of the windows +/- margin
    margin = 100
    # Set minimum number of pixels found to recenter window
    minpix = 50

    # Set height of windows - based on nwindows above and image shape
    window_height = np.int(binary_warped.shape[0]//nwindows)
    # Identify the x and y positions of all nonzero pixels in the image
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    # Current positions to be updated later for each window in nwindows
    leftx_current = leftx_base
    rightx_current = rightx_base

    # Create empty lists to receive left and right lane pixel indices
    left_lane_inds = []
    right_lane_inds = []

    # Step through the windows one by one
    for window in range(nwindows):
        # Identify window boundaries in x and y (and right and left)
        win_y_low = binary_warped.shape[0] - (window+1)*window_height
        win_y_high = binary_warped.shape[0] - window*window_height
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin

        # Draw the windows on the visualization image
        cv2.rectangle(out_img,(win_xleft_low,win_y_low),
        (win_xleft_high,win_y_high),(0,255,0), 2) 
        cv2.rectangle(out_img,(win_xright_low,win_y_low),
        (win_xright_high,win_y_high),(0,255,0), 2) 
        
        ### TO-DO: Identify the nonzero pixels in x and y within the window ###
        good_left_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low) & 
        (nonzerox < win_xleft_high) & (nonzerox >= win_xleft_low)).nonzero()[0]
        good_right_inds = ((nonzeroy < win_y_high) & (nonzeroy >= win_y_low) *
        (nonzerox < win_xright_high) & (nonzerox >= win_xright_low)).nonzero()[0]
        
        # Append these indices to the lists
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)

        ### TO-DO: If you found > minpix pixels, recenter next window ###
        if (len(good_left_inds) > minpix):
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if (len(good_right_inds) > minpix):
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))

    # Concatenate the arrays of indices (previously was a list of lists of pixels)
    try:
        left_lane_inds = np.concatenate(left_lane_inds)
        right_lane_inds = np.concatenate(right_lane_inds)
    except ValueError:
        # Avoids an error if the above is not implemented fully
        pass

    # Extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

    return leftx, lefty, rightx, righty, out_img


def fit_polynomial(binary_warped):
    # Find our lane pixels first
    leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)

    ### TO-DO: Fit a second order polynomial to each using `np.polyfit` ###
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)

    # Generate x and y values for plotting
    ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
    try:
        left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
        right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    except TypeError:
        # Avoids an error if `left` and `right_fit` are still none or incorrect
        print('The function failed to fit a line!')
        left_fitx = 1*ploty**2 + 1*ploty
        right_fitx = 1*ploty**2 + 1*ploty

    ## Visualization ##
    # Colors in the left and right lane regions
    out_img[lefty, leftx] = [255, 0, 0]
    out_img[righty, rightx] = [0, 0, 255]

    # Plots the left and right polynomials on the lane lines
    #plt.plot(left_fitx, ploty, color='yellow')
    #plt.plot(right_fitx, ploty, color='yellow')

    return out_img, left_fit, right_fit

def fit_poly(img_shape, leftx, lefty, rightx, righty):
    ### TO-DO: Fit a second order polynomial to each with np.polyfit() ###
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    # Generate x and y values for plotting
    ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
    ### TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
    left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
    right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    
    return left_fitx, right_fitx, ploty

def search_around_poly(binary_warped, left_fit, right_fit):
    # HYPERPARAMETER
    # Choose the width of the margin around the previous polynomial to search
    # The quiz grader expects 100 here, but feel free to tune on your own!
    margin = 50

    # Grab activated pixels
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])

    ### TO-DO: Set the area of search based on activated x-values ###
    ### within the +/- margin of our polynomial function ###
    ### Hint: consider the window areas for the similarly named variables ###
    ### in the previous quiz, but change the windows to our new search area ###
    left_margins = left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2]
    right_margins = right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2]
    left_lane_inds = ((nonzerox < left_margins + margin) & (nonzerox > left_margins
    - margin))
    right_lane_inds = ((nonzerox < right_margins + margin) & (nonzerox > right_margins
    - margin))

    # Again, extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

    # Fit new polynomials
    left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)

    ## Visualization ##
    # Create an image to draw on and an image to show the selection window (previouvly multiplied by 255)
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*0
    window_img = np.zeros_like(out_img)
    # Color in left and right line pixels
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]

    # Generate a polygon to illustrate the search window area
    # And recast the x and y points into usable format for cv2.fillPoly()
    left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
    left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, 
                              ploty])))])
    left_line_pts = np.hstack((left_line_window1, left_line_window2))
    right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
    right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, 
                              ploty])))])
    right_line_pts = np.hstack((right_line_window1, right_line_window2))
    #Create center window using a similar technique
    center_window1 = np.array([np.transpose(np.vstack([left_fitx+margin, ploty]))])
    center_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx-margin, 
                              ploty])))])
    center_pts = np.hstack((center_window1, center_window2))

    # Draw the lane onto the warped blank image
    cv2.fillPoly(window_img, np.int_([left_line_pts]), (255, 0, 0))
    cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 0, 255))
    cv2.fillPoly(window_img, np.int_([center_pts]), (0, 255, 0))
    result = cv2.addWeighted(out_img, 1, window_img, 0.5, 0)
    
    # Plot the polynomial lines onto the image
    #plt.plot(left_fitx, ploty, color='yellow')
    #plt.plot(right_fitx, ploty, color='yellow')
    ## End visualization steps ##
    
    return result, left_fit, right_fit

first = True

def process_image(img):
    global first

    mtx, dist = cameraCalibration()

    calibrated = undistort(img, mtx, dist)

    binary = pipeline(calibrated)

    transformed = perspectiveTransform(binary)

    if (first == True):
        curve, left_fit, right_fit = fit_polynomial(transformed)
        first = False
    else:
        curve, left_fit, right_fit = search_around_poly(transformed, left_fit, right_fit)
    print(type(left_fit))
    inverse = inversePerspectiveTransform(curve)

    result = cv2.addWeighted(img, 1, inverse, 1, 0)

    return result

img = cv2.imread("test_images/test1.jpg")

result = process_image(img)
plt.imshow(result)
plt.show()
#white_output = 'project_video_output.mp4'

#clip1 = VideoFileClip("project_video.mp4")
#white_clip = clip1.fl_image(process_image)
#%time white_clip.write_videofile(white_output, audio=False)
