Find Lanes for Self-Driving Cars, code problem

Question

I was self learning the image recognition online. Following the code below.I encountered two problems.

I am using Sypder from Anaconda (python 3.7)

1. I did not get a video output, the cv2.imshow() only showed a picture(maybe the video was stuck due to some bug)

2. error occured under the make_coordinate function:

   slope,intercept = line_parameters

TypeError: cannot unpack non-iterable numpy.float64 object

i

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


def make_coordinate(image,line_parameters):
    slope,intercept = line_parameters
    y1=image.shape[0]
    y2=int(y1*(3/5))
    x1=int((y1-intercept)/slope)
    x2=int((y2-intercept)/slope)
    return np.array([x1,y1,x2,y2])


def average_slope_intercept(image,lines): ##produce the best fit lines
    left_fit=[]
    right_fit=[]
    for line in lines:
        x1,y1,x2,y2=line.reshape(4)
        parameters=np.polyfit((x1,x2),(y1,y2),1)
        slope=parameters[0]
        intercept=parameters[1]
        
        if slope<0:
            left_fit.append((slope,intercept)) ##z left lane has negative slope.
        else:
            right_fit.append((slope,intercept))
        
    left_fit_average=np.average(left_fit,axis=0)
    right_fit_average=np.average(right_fit,axis=0)
    left_line=make_coordinate(image,left_fit_average)
    right_line=make_coordinate(image,right_fit_average)
    
    return np.array([left_line,right_line])


    
    
def Canny(image):
    gray=cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)  ## convert the image to gray color
    blur= cv2.GaussianBlur(gray,(5,5),0) ## blur the image to reduce noise{source,kernel,deviation}
    canny=cv2.Canny(blur,50,150)  ## trace out the lines that have sharp change in color
    return canny

def display_lines(image,lines): ##input slope and intercept to generate lines.
    line_image=np.zeros_like(image)
    if lines is not None:
        for line in lines:
            x1,y1,x2,y2=line.reshape(4)
            cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)  ## color in RGB ,line thickness
            
    return line_image
            
    


def region_of_interest(image):
    height=image.shape[0]
    polygon=np.array([[(200,height),(1100,height),(550,250)]]) ##A triangle with bottem from 200 to 1100 and tip at (550,250)
    mask=np.zeros_like(image)
    cv2.fillPoly(mask,polygon,255)  ##put the triangle into a black background.
    masked_image=cv2.bitwise_and(image,mask)  ## trace the lanes into black background using masking
    return masked_image

#
#
#read_image=cv2.imread('test_image.jpg') ##read the image
#image=np.copy(read_image) ## copy the image
#
#canny_image=Canny(image)  ## trace out the lines that have sharp change in color
#
#cropped_image=region_of_interest(canny_image)  ## trace the interested lines into black background
#
#lines=cv2.HoughLinesP(cropped_image,2,np.pi/180,100,np.array([]),minLineLength=0,maxLineGap=5)  ##2 pixel, 1 degree in radian,threshold 100,array,length of line in pixel will accept,maximum length of distance of pixel can be connect to a line.
#averaged_lines=average_slope_intercept(image,lines)
#line_image=display_lines(image,averaged_lines)
#
#combined_image=cv2.addWeighted(image,0.8,line_image,1,1) ##trace out the ideal path in the original image
#
#
#
#
#plt.imshow(canny_image)
#plt.show()

cap=cv2.VideoCapture('test2.mp4')
while(cap.isOpened()):
    _,frame = cap.read()
    
    canny_image=Canny(frame)  ## trace out the lines that have sharp change in color

    cropped_image=region_of_interest(canny_image)  ## trace the interested lines into black background

    lines=cv2.HoughLinesP(cropped_image,2,np.pi/180,100,np.array([]),minLineLength=0,maxLineGap=5)  ##2 pixel, 1 degree in radian,threshold 100,array,length of line in pixel will accept,maximum length of distance of pixel can be connect to a line.
    averaged_lines=average_slope_intercept(frame,lines)
    line_image=display_lines(frame,averaged_lines)

    combined_image=cv2.addWeighted(frame,0.8,line_image,1,1) ##trace out the ideal path in the original image

cv2.imshow('result',combined_image)  ##display the image
cv2.waitKey(5) ## delay in display

Hope anyone can help me with that

Thank you .

Answer 1

Your first problem is because you put the lines

cv2.imshow('result',combined_image)  ##display the image
cv2.waitKey(5) ## delay in display

outside of your while(cap.isOpened()): loop. This means opencv will show your image only after the whole while is done which means at the end of the video. So it only shows the last frame. To solve this, put the above 2 lines inside the while loop.

The second problem is because slope,intercept = line_parameters assumes line_parameters is a tuple of 2 elements, slope and intercept. However if we look at where line_parameters comes from, we can see that it's going to be a numpy array. You cannot unpack a numpy array like a tuple. Instead you can use slope,intercept = tuple(line_parameters) for example.