In [6]:

    

#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline


    

In [7]:

    

#reading in an image
image = mpimg.imread('test_images/solidYellowCurve.jpg')

#printing out some stats and plotting
print('This image is:', type(image), 'with dimensions:', image.shape)
plt.imshow(image)  # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')


    

    




This image is: <class 'numpy.ndarray'> with dimensions: (540, 960, 3)






    
Out[7]:





<matplotlib.image.AxesImage at 0x7f429a244828>






    

In [8]:

    

import math

def grayscale(img):
    """Applies the Grayscale transform
    This will return an image with only one color channel
    but NOTE: to see the returned image as grayscale
    (assuming your grayscaled image is called 'gray')
    you should call plt.imshow(gray, cmap='gray')"""
    return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # Or use BGR2GRAY if you read an image with cv2.imread()
    # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
def canny(img, low_threshold, high_threshold):
    """Applies the Canny transform"""
    return cv2.Canny(img, low_threshold, high_threshold)

def gaussian_blur(img, kernel_size):
    """Applies a Gaussian Noise kernel"""
    return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)

def region_of_interest(img, vertices):
    """
    Applies an image mask.
    
    Only keeps the region of the image defined by the polygon
    formed from `vertices`. The rest of the image is set to black.
    """
    #defining a blank mask to start with
    mask = np.zeros_like(img)   
    
    #defining a 3 channel or 1 channel color to fill the mask with depending on the input image
    if len(img.shape) > 2:
        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image
        ignore_mask_color = (255,) * channel_count
    else:
        ignore_mask_color = 255
        
    #filling pixels inside the polygon defined by "vertices" with the fill color    
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    
    #returning the image only where mask pixels are nonzero
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image


def draw_lines(img, lines, color=[255, 0, 0], thickness=2):
    """
    NOTE: this is the function you might want to use as a starting point once you want to 
    average/extrapolate the line segments you detect to map out the full
    extent of the lane (going from the result shown in raw-lines-example.mp4
    to that shown in P1_example.mp4).  
    
    Think about things like separating line segments by their 
    slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left
    line vs. the right line.  Then, you can average the position of each of 
    the lines and extrapolate to the top and bottom of the lane.
    
    This function draws `lines` with `color` and `thickness`.    
    Lines are drawn on the image inplace (mutates the image).
    If you want to make the lines semi-transparent, think about combining
    this function with the weighted_img() function below
    """
    for line in lines:
        for x1,y1,x2,y2 in line:
            cv2.line(img, (x1, y1), (x2, y2), color, thickness)

def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
    """
    `img` should be the output of a Canny transform.
        
    Returns an image with hough lines drawn.
    """
    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
    draw_lines(line_img, lines)
    return line_img

# Python 3 has support for cool math symbols.

def weighted_img(img, initial_img, α=0.8, β=1., λ=0.):
    """
    `img` is the output of the hough_lines(), An image with lines drawn on it.
    Should be a blank image (all black) with lines drawn on it.
    
    `initial_img` should be the image before any processing.
    
    The result image is computed as follows:
    
    initial_img * α + img * β + λ
    NOTE: initial_img and img must be the same shape!
    """
    return cv2.addWeighted(initial_img, α, img, β, λ)


    

In [9]:

    

import os
os.listdir("test_images/")


    

    
Out[9]:





['solidYellowCurve.jpg',
 'solidYellowLeft.jpg',
 'solidWhiteCurve.jpg',
 'solidYellowCurve2.jpg',
 'whiteCarLaneSwitch.jpg',
 'solidWhiteRight.jpg']

In [10]:

    

# TODO: Build your pipeline that will draw lane lines on the test_images
# then save them to the test_images directory.
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2


# Read in and grayscale the image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)

# Define a kernel size and apply Gaussian smoothing
kernel_size = 5
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)

# Define our parameters for Canny and apply
low_threshold = 50 #50
high_threshold = 150 #150
edges = cv2.Canny(blur_gray, low_threshold, high_threshold)

# Next we'll create a masked edges image using cv2.fillPoly()
mask = np.zeros_like(edges)   
ignore_mask_color = 255   

# This time we are defining a four sided polygon to mask
imshape = image.shape
vertices = np.array([[(0,imshape[0]),(450, 290), (490, 290), (imshape[1],imshape[0])]], dtype=np.int32)
cv2.fillPoly(mask, vertices, ignore_mask_color)
masked_edges = cv2.bitwise_and(edges, mask)

# Define the Hough transform parameters
# Make a blank the same size as our image to draw on
rho = 1 #  1 distance resolution in pixels of the Hough grid
theta = np.pi/180 # angular resolution in radians of the Hough grid
threshold = 15    # minimum number of votes (intersections in Hough grid cell)
min_line_length = 150 # 5 minimum number of pixels making up a line
max_line_gap = 50    # 1 maximum gap in pixels between connectable line segments
line_image = np.copy(image)*0 # creating a blank to draw lines on

# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line segments
lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]),
                            min_line_length, max_line_gap)

# Iterate over the output "lines" and draw lines on a blank image
for line in lines:
    for x1,y1,x2,y2 in line:
        cv2.line(image,(x1,y1),(x2,y2),(255,0,0),10)
        slope=(y2-y1)/(x2-x1)
        print (slope)
        if slope > 0:
            print ("leftline")
            leftline =[[x1, y1, x2, y2]]
        else :
            print ("rightline")
            rightline =[[x1, y1, x2, y2]]
# cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)
# Create a "color" binary image to combine with line image
color_edges = np.dstack((edges, edges, edges)) 
#print (x1, y1)
#print (x2, y2)
#print (lines)
#print ("--------")
#print (line)

# Draw the lines on the edge image
lines_edges = cv2.addWeighted(color_edges, 0.8, line_image, 1, 0) 
plt.imshow(lines_edges)

# extrapolate for right line
for x1,y1,x2,y2 in rightline:
    yl=600
    a=np.subtract(yl,y1)
    b=np.subtract(x2,x1)
    c=np.multiply(a,b)
    d=np.subtract(y2, y1)
    e=np.divide(c,d)
    f=np.add(e, x1)
    xl=np.around(f)
print (xl)

print (x1, y1)
print (x2, y2)
#xl=np.around[[np.multiply[(yl-y1),(x2-x1)]/(y2-y1)]+x1]

#print (xl)
rightline =[[x1, y1, xl.astype(int), yl]]
#draw_lines(image, lines, [255, 0, 0], 2)
#
for x1,y1,x2,y2 in rightline:
   cv2.line(image,(x1,y1),(x2,y2),(255,0,0),10)
color_edges = np.dstack((edges, edges, edges)) 
lines_edges = cv2.addWeighted(color_edges, 0.8, line_image, 1, 0) 
plt.imshow(lines_edges)

# extrapolate for left line
for x1,y1,x2,y2 in leftline:
    yl=600
    a=np.subtract(yl,y1)
    b=np.subtract(x2,x1)
    c=np.multiply(a,b)
    d=np.subtract(y2, y1)
    e=np.divide(c,d)
    f=np.add(e, x1)
    xl=np.around(f)
print (xl)

print (x1, y1)
print (x2, y2)
#xl=np.around[[np.multiply[(yl-y1),(x2-x1)]/(y2-y1)]+x1]

#print (xl)
leftline =[[x1, y1, xl.astype(int), yl]]
#draw_lines(image, lines, [255, 0, 0], 2)
#
for x1,y1,x2,y2 in leftline:
   cv2.line(image,(x1,y1),(x2,y2),(255,0,0),10)
color_edges = np.dstack((edges, edges, edges)) 
lines_edges = cv2.addWeighted(color_edges, 0.8, line_image, 1, 0) 
plt.imshow(image)


    

    




0.651234567901
leftline
0.624664879357
leftline
0.624040920716
leftline
0.653266331658
leftline
0.65
leftline
-0.726708074534
rightline
-0.69696969697
rightline
-0.723684210526
rightline
-0.701219512195
rightline
60.0
310 425
474 310
931.0
466 298
626 402






    
Out[10]:





<matplotlib.image.AxesImage at 0x7f429a10f7f0>






    

In [11]:

    

# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML


    

In [88]:

    

def process_image(image):
    # NOTE: The output you return should be a color image (3 channel) for processing video below
    # TODO: put your pipeline here,
    # you should return the final output (image where lines are drawn on lanes)
    gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)
    # Define a kernel size and apply Gaussian smoothing
    kernel_size = 5
    blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)

    # Define our parameters for Canny and apply
    low_threshold = 50 #50
    high_threshold = 150 #150
    edges = cv2.Canny(blur_gray, low_threshold, high_threshold)

    # Next we'll create a masked edges image using cv2.fillPoly()
    mask = np.zeros_like(edges)   
    ignore_mask_color = 255 
    
    # This time we are defining a four sided polygon to mask
    imshape = image.shape
    vertices = np.array([[(0,imshape[0]),(450, 290), (490, 290), (imshape[1],imshape[0])]], dtype=np.int32)
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    masked_edges = cv2.bitwise_and(edges, mask)

    # Define the Hough transform parameters
    # Make a blank the same size as our image to draw on
    rho = 1 #  1 distance resolution in pixels of the Hough grid
    theta = np.pi/180 # angular resolution in radians of the Hough grid
    threshold = 15    # minimum number of votes (intersections in Hough grid cell)
    min_line_length = 150 # 5 minimum number of pixels making up a line
    max_line_gap = 50    # 1 maximum gap in pixels between connectable line segments
    line_image = np.copy(image)*0 # creating a blank to draw lines on

    # Run Hough on edge detected image
    # Output "lines" is an array containing endpoints of detected line segments
    lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]),
                            min_line_length, max_line_gap)
    
    # Iterate over the output "lines" and draw lines on a blank image
    lslopemax=0
    count1=0
    xl_points = []
    yl_points = []
    
    xr_points = []
    yr_points = []
    
    for line in lines:
        for x1,y1,x2,y2 in line:
            #cv2.line(image,(x1,y1),(x2,y2),(255,0,0),10)
            print (x1, y1)
            print (x2, y2)
            
            slope=(y2-y1)/(x2-x1)
            print (slope)
            if slope > 0:
                print ("rightline")
                #leftline =[[x1, y1, x2, y2]]
                #if slope > slopemax :
                xr_points += [x1, x2]
                yr_points += [y1, y2]
                #if slope > lslopemax :
                #lslopemax+=slope
                #count1=count1+1
            else :
                print ("leftline")
                xl_points.extend([x1, x2])
                yl_points.extend([y1, y2])
                #rightline =[[x1, y1, x2, y2]]
                
    # cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)
    # Create a "color" binary image to combine with line image
    #lavgslope=lslopemax/count1;
    
#    print ("Avg left slope is :")
#    print (lavgslope)

    if (len(xr_points)>0) :
        fit_line = np.polyfit(xr_points, yr_points, 1)
       
        min_x = min(xr_points)
        max_x = max(xr_points)

#    min_y = min(yl_points)
#    max_y= max(yl_points)

        min_y = fit_line[0]*min_x + fit_line[1]
        max_y = fit_line[0]*max_x + fit_line[1]
    

 #   min_y = lslopemax * min_x + fit_line[1]
        ypoint=320
    #xpoint= avgX + (ypoint-avgY)/lavgslope
        xpoint = (ypoint - fit_line[1])/fit_line[0]
        xf=np.around(xpoint)

    ##point_1 = (min_x.astype(int), min_y.astype(int))
    #point_1 = (avgX.astype(int), avgY.astype(int))
    #point_2 = (xf.astype(int), ypoint)
        point_1 = (max_x.astype(int), max_y.astype(int))
        point_2 = (xf.astype(int), ypoint)
        cv2.line(image, point_1, point_2, color=(255,0,0),thickness=5)
        print (xl_points)
    
    if (len(xl_points)>0) :
        fit_linel = np.polyfit(xl_points, yl_points, 1)
    
        max_xl = max(xl_points)
        min_xl = min(xl_points)
        
        max_yl = fit_linel[0]*max_xl + fit_linel[1]
        min_yl = fit_linel[0]*min_xl + fit_linel[1]
    
        point_3 = (min_xl.astype(int), min_yl.astype(int))
    
        ylpoint = 320
        xlpoint = (ylpoint - fit_linel[1])/fit_linel[0]
    
        xfl = np.around(xlpoint)
    
        print ("############################")
        print (fit_linel[1])
        point_4 = (xfl.astype(int), ylpoint)
    
##    cv2.line(image, point_1, point_2, color=(255,0,0),thickness=5)

        if (fit_linel[1] > 0) :
            cv2.line(image, point_3, point_4, color=(255,0,0),thickness=5)
    
    color_edges = np.dstack((edges, edges, edges)) 
#print (x1, y1)
#print (x2, y2)
#print (lines)
#print ("--------")
#print (line)
    
# Draw the lines on the edge image
    lines_edges = cv2.addWeighted(color_edges, 0.8, line_image, 1, 0) 
##    plt.imshow(lines_edgesa

    return image


    

In [89]:

    

white_output = 'test_videos_output/solidWhiteRight.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)


    

    




462 293
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rightline
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rightline
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rightline
464 293
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rightline
702 438
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rightline
[]
[MoviePy] >>>> Building video test_videos_output/solidWhiteRight.mp4
[MoviePy] Writing video test_videos_output/solidWhiteRight.mp4






    




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453 293
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0.601965601966
rightline
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rightline
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leftline
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rightline
[282, 469]
############################
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[]
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leftline
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leftline
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rightline
[337, 489, 337, 487]
############################
641.928364689
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rightline
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rightline
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rightline
[330, 485, 322, 481, 327, 483, 323, 488]
############################
653.896395694
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rightline
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0.625
rightline
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-0.723163841808
leftline
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rightline
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309 429
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leftline
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rightline
[311, 488, 308, 480, 309, 486]
############################
651.781352921
631 400
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rightline
462 293
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0.600490196078
rightline
461 293
870 539
0.601466992665
rightline
653 415
804 510
0.629139072848
rightline
[]






    




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462 293
871 538
0.59902200489
rightline
458 293
813 515
0.625352112676
rightline
640 399
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rightline
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rightline
[]
458 293
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rightline
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rightline
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leftline
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484 300
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leftline
509 324
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0.625
rightline
[326, 489, 332, 483, 326, 484]
############################
642.245888851
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rightline
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rightline
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leftline
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rightline
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leftline
[318, 489, 335, 485, 316, 488]
############################
650.649579189
461 295
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0.6
rightline
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rightline
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rightline
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leftline
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leftline
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leftline
[300, 483, 289, 485, 289, 449, 297, 488]
############################
649.532676119
560 357
860 538
0.603333333333
rightline
461 297
693 447
0.646551724138
rightline
272 461
488 304
-0.726851851852
leftline
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484 304
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leftline
605 385
859 538
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rightline
493 317
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rightline
263 458
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-0.7
leftline
271 461
468 318
-0.725888324873
leftline
[272, 488, 263, 484, 263, 483, 271, 468]
############################
650.087937858






    




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450 290
834 539
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rightline
230 483
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-0.7004048583
leftline
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leftline
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rightline
[230, 477, 240, 486, 230, 476, 298, 488]
############################
649.977043888
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[]
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[]
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[]
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627.887348012
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############################
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[MoviePy] Done.
[MoviePy] >>>> Video ready: test_videos_output/solidWhiteRight.mp4 

CPU times: user 15.5 s, sys: 1.25 s, total: 16.8 s
Wall time: 9.65 s

In [90]:

    

HTML("""
<video width="960" height="540" controls>
  <source src="{0}">
</video>
""".format(white_output))


    

    
Out[90]:

In [91]:

    

yellow_output = 'test_videos_output/solidYellowLeft.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5)
clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')
yellow_clip = clip2.fl_image(process_image)
%time yellow_clip.write_videofile(yellow_output, audio=False)


    

    




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############################
601.569679922
[MoviePy] >>>> Building video test_videos_output/solidYellowLeft.mp4
[MoviePy] Writing video test_videos_output/solidYellowLeft.mp4






    




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156





    




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652.759843476






    




  5%|▍         | 34/682 [00:01<00:21, 29.72it/s]





    




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650.510811146






    




  6%|▋         | 43/682 [00:01<00:21, 30.30it/s]





    




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649.006311993






    




  7%|▋         | 47/682 [00:01<00:21, 29.79it/s]





    




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657.644597187
199





    




  8%|▊         | 54/682 [00:01<00:22, 27.82it/s]





    




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  9%|▉         | 60/682 [00:02<00:25, 24.70it/s]





    




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642.493565118






    




  9%|▉         | 63/682 [00:02<00:25, 24.23it/s]





    




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267 450






    




 10%|█         | 69/682 [00:02<00:26, 22.85it/s]





    




457 317
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645.319111316






    




 11%|█         | 72/682 [00:02<00:27, 22.58it/s]





    




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636.85757008






    




 12%|█▏        | 79/682 [00:02<00:24, 24.13it/s]





    




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643.794267826
245





    




 12%|█▏        | 85/682 [00:03<00:26, 22.77it/s]





    




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642.093555832






    




 13%|█▎        | 88/682 [00:03<00:24, 23.85it/s]





    




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############################
638.359061236
131 539






    




 14%|█▍        | 94/682 [00:03<00:25, 23.49it/s]





    




485 300
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491 312
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rightline






    




 15%|█▍        | 100/682 [00:03<00:23, 24.80it/s]





    




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############################
637.634579446






    




 16%|█▌        | 106/682 [00:04<00:21, 27.03it/s]





    




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635.271163565






    




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634.433917263
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 20%|█▉        | 136/682 [00:05<00:23, 23.03it/s]





    




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642.607856427






    




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655.28625254






    




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[MoviePy] Done.
[MoviePy] >>>> Video ready: test_videos_output/solidYellowLeft.mp4 

CPU times: user 49.5 s, sys: 4.7 s, total: 54.2 s
Wall time: 30 s

In [86]:

    

HTML("""
<video width="960" height="540" controls>
  <source src="{0}">
</video>
""".format(yellow_output))


    

    
Out[86]:

In [87]:

    

challenge_output = 'test_videos_output/challenge.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
clip3 = VideoFileClip('test_videos/challenge.mp4').subclip(0,5)
##clip3 = VideoFileClip('test_videos/challenge.mp4')
challenge_clip = clip3.fl_image(process_image)
%time challenge_clip.write_videofile(challenge_output, audio=False)


    

    




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############################
672.955169625
[MoviePy] >>>> Building video test_videos_output/challenge.mp4
[MoviePy] Writing video test_videos_output/challenge.mp4






    




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############################
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580





    




  8%|▊         | 10/126 [00:00<00:07, 15.38it/s]





    




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 10%|▉         | 12/126 [00:00<00:07, 15.55it/s]





    




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644.242245897






    




 11%|█         | 14/126 [00:01<00:16,  6.94it/s]





    




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674.283176104






    




 14%|█▍        | 18/126 [00:02<00:21,  4.98it/s]





    




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665.970912427






    




 17%|█▋        | 22/126 [00:02<00:13,  7.43it/s]





    




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608.940022485






    




 21%|██        | 26/126 [00:02<00:09, 10.50it/s]





    




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560.497999561






    










    




[MoviePy] Done.
[MoviePy] >>>> Video ready: test_videos_output/challenge.mp4 

CPU times: user 12.4 s, sys: 3.06 s, total: 15.4 s
Wall time: 12.8 s

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HTML("""
<video width="960" height="540" controls>
  <source src="{0}">
</video>
""".format(challenge_output))


    

    
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