human
I am working on the file "P1.ipynb".
The first task is:
Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are:
cv2.inRange() for color selection
cv2.fillPoly() for regions selection
cv2.line() to draw lines on an image given endpoints
cv2.addWeighted() to coadd / overlay two images
cv2.cvtColor() to grayscale or change color
cv2.imwrite() to output images to file
cv2.bitwise_and() to apply a mask to an image
Check out the OpenCV documentation to learn about these and discover even more awesome functionality!
Below are some helper functions to help get you started. They should look familiar from the lesson!
Can you write Python code for it?
assistant
#Here is the Python code you requested.

import math
import code
from itertools import cycle

class Line_with_info:
    def __init__(self, x1, y1,x2,y2):
        self.x1 = x1
        self.y1 = y1
        self.x2=x2
        self.y2=y2
        self.slope=(self.y2-self.y1)/(self.x2-self.x1)
        self.slope_angle=math.atan(self.slope)
    def get_line(self):
        points_data=[[self.x1,self.y1,self.x2,self.y2]];
        #code.interact(local=locals())
        #Use raise SystemExit
        return points_data
    def get_slope(self):
        return self.slope
    def get_slope_angle(self):
        return self.slope_angle

def get_distance(x1,y1,x2,y2):
    return math.sqrt(math.pow((x2-x1),2)+math.pow((y2-y1,2)))
def get_slope(x1,y1,x2,y2):
    return abs(math.atan(abs((y2-y1))/abs((x2-x1))))
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
   ...