human
Solve the following python programming problem given the following code:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML

#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')

# # NOTE: The output you return should be a color image (3 channel) for processing video below
# def process_image(image):

#     # grayscale conversion before processing causes more harm than good
#     # because sometimes the lane and road have same amount of luminance
#     # grayscaleImage = grayscale(image)

#     # Blur to avoid edges from noise
#     blurredImage = gaussian_blur(image, 5)
    
#     # Detect edges using canny
#     # high to low threshold factor of 3
#     # it is necessary to keep a linient threshold at the lower end
#     # to continue to detect faded lane markings
#     edgesImage = canny(blurredImage, 50, 100)
    
#     # mark out the trapezium region of interest
#     # dont' be too agressive as the car may drift laterally
#     # while driving, hence ample space is still left on both sides.
#     height = image.shape[0]
#     width = image.shape[1]
#     vertices = np.array([[(0,imshape[0]),(450, 320), (490, 320), (imshape[1],imshape[0])]], dtype=np.int32)
    
#     # mask the canny output with trapezium region of interest
#     regionInterestImage = region_of_interest(edgesImage, vertices)
    
#     # parameters tuned using this method:
#     # threshold 30 by modifying it and seeing where slightly curved 
#     # lane markings are barely detected
#     # min line length 20 by modifying and seeing where broken short
#     # lane markings are barely detected
#     # max line gap as 100 to allow plenty of room for the algo to 
#     # connect spaced out lane markings
#     lineMarkedImage = hough_lines(regionInterestImage, 1,...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Test on Videos
I can test my solution on two provided videos:
solidWhiteRight.mp4
solidYellowLeft.mp4
Note: if you get an import error when you run the next cell, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, consult the forums for more troubleshooting tips.
If you get an error that looks like this:
NeedDownloadError: Need ffmpeg exe. 
You can download it by calling: 
imageio.plugins.ffmpeg.download()