BasicLanDetection/all_image.py at master · me-unpredictable/BasicLanDetection · GitHub

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import numpy as np
import matplotlib.pyplot as plt
import cv2
#vid =cv2.VideoCapture('test_videos/challenge.mp4')
#vid =cv2.VideoCapture('test_videos/solidWhiteRight.mp4')


cv2.namedWindow('Road View')
#RGB Threshold Parameters
red_val=0
green_val=200
blue_val=200
#HSV Threshold Parameters
Hue=41
Sat=96
Val=41
#Gaussian Blur Parameters
kernel_size=5
low_threshold=50
high_threshold=150
# Hough transform parameters
rho_ = 8
theta_ = (np.pi/180)
threshold_ = 90
min_line_length =1
max_line_gap =250

pic_set=['test_images/solidWhiteCurve.jpg','test_images/solidWhiteRight.jpg','test_images/solidYellowCurve.jpg',
         'test_images/solidYellowCurve2.jpg','test_images/solidYellowLeft.jpg','test_images/whiteCarLaneSwitch.jpg']
for i in range(0,6):

    pic = cv2.imread(pic_set[i])#load image

    line_image= np.copy(pic)*0 #creating a blank to draw lines on

    #image assets
    vid_img=np.copy(pic)
    y_size=vid_img.shape[0]
    x_size=vid_img.shape[1]
    apex=[490,315]
    image_bottomleft=[0,y_size]
    image_bottomright=[x_size,y_size]
    rm=np.array([apex,image_bottomleft,image_bottomright],np.int32)
    rm=rm.reshape((-1,1,2))

    ##################################################################
    ############ RGB Color Based######################################
    ##################################################################

    rgb_threshold=[red_val,green_val,blue_val]
    pic_m = cv2.fillPoly(pic, [rm], (0, 0, 0))
    pic_m = cv2.bitwise_and(~pic, vid_img)

    hsv_img=np.copy(pic_m)#hasv image copy from masked image

    threshold=(pic_m[:,:,0]<rgb_threshold[0])|(pic_m[:,:,1]<rgb_threshold[1])|(pic_m[:,:,2]<rgb_threshold[2])
    pic_m[threshold]=[0,0,0]
    pic_m_gray=cv2.cvtColor(pic_m,cv2.COLOR_RGB2GRAY)
    pic_blur=cv2.GaussianBlur(pic_m_gray,(kernel_size,kernel_size),0)
    pic_edge=cv2.Canny(pic_blur,low_threshold,high_threshold)
    # Run 1Hough on edge detected image
    lines = cv2.HoughLinesP(pic_edge, rho_, theta_, threshold_,np.array([]),min_line_length,max_line_gap)

    try:
        # Iterate over the output "lines" and draw lines on the blank
        for line in lines:
            for x1, y1, x2, y2 in line:
                cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 3)
    except:
        pass

    ##################################################################
    ############ HSV Color Based######################################
    ##################################################################
    hsv_threshold=[Hue,Sat,Val]
    hsv_img=cv2.cvtColor(hsv_img,cv2.COLOR_RGB2HSV)
    threshold_1=(hsv_img[:,:,0]<hsv_threshold[0])|(hsv_img[:,:,1]<hsv_threshold[1])|(hsv_img[:,:,2]<hsv_threshold[2])
    hsv_img[threshold_1]=[0,0,0]
    hsv_img=cv2.cvtColor(hsv_img,cv2.COLOR_HSV2RGB)
    hsv_gray=cv2.cvtColor(hsv_img,cv2.COLOR_RGB2GRAY)
    hsv_blur=cv2.GaussianBlur(hsv_gray,(kernel_size,kernel_size),0)
    hsv_edge=cv2.Canny(hsv_blur,low_threshold,high_threshold)

    # Run 1Hough on edge detected image
    hsv_lines = cv2.HoughLinesP(hsv_edge, rho_, theta_, threshold_,np.array([]),min_line_length,max_line_gap)

    try:
        # Iterate over the output "lines" and draw lines on the blank
        #p1=hsv_lines[0, 0, 0:2]
        #p2=hsv_lines[0, 0, 2:4]
        #print "(X1,Y1):",p1
        #print "(X2,Y2):",p2
        #print "slop:", (p2[1]-p1[1])/(p2[0]-p1[0])


        for line in hsv_lines:
            for x1, y1, x2, y2 in line:
                cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 3)
    except:
        pass

    # Draw the lines on the edge image

    combo=cv2.addWeighted(vid_img,0.8,line_image,1,0)#draw detection lines on orignal image
    while cv2.waitKey(1)==-1:
        cv2.imshow('Road View',combo)
    cv2.destroyWindow('Road View')