sld_simple

siwatlanedetect.py

@@ -94,13 +94,14 @@ while True:
     '''##### DETECTING lane lines in image ######'''
 
     rv, image1 = camera.read()
+    image1 = cv2.resize(image1,[1280,720])
     plt.imshow(image1)
     plt.show()
 
 
     copy = np.copy(image1)
     edges = cv2.Canny(copy,50,150)
-    isolated = region(edges)
+    isolated = edges #region(edges)
     print(edges)
     plt.imshow(edges)
     plt.imshow(isolated)

siwatld_simply.py

@@ -0,0 +1,60 @@
+import cv2
+import numpy as np
+import math
+import serial
+# import picamera
+# import picamera.array
+
+threshold1 = 85
+threshold2 = 85
+theta=0
+r_width = 500
+r_height = 300
+minLineLength = 5
+maxLineGap = 10
+k_width = 5
+k_height = 5
+max_slider = 10
+
+# Linux System Serial Port
+# ser = serial.Serial("/dev/ttyACM0", 115200, timeout=1)           # linux
+
+# Read Image
+camera = cv2.VideoCapture(1)
+# image = cv2.imread(r'C:\Users\aasai\Desktop\new1.jpeg')
+
+ff, image = camera.read()
+
+# Resize width=500 height=300 incase of inputting raspi captured image
+image = cv2.resize(image,(r_width,r_height))
+# Convert the image to gray-scale
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+# given input image, kernel width =5 height = 5, Gaussian kernel standard deviation
+blurred = cv2.GaussianBlur(gray, (k_width, k_height), 0)
+# Find the edges in the image using canny detector
+edged = cv2.Canny(blurred, threshold1, threshold2)
+# Detect points that form a line
+lines = cv2.HoughLinesP(edged,1,np.pi/180,max_slider,minLineLength,maxLineGap)
+print(lines[0])
+for x in range(0, len(lines)):
+    for x1,y1,x2,y2 in lines[x]:
+        cv2.line(image,(x1,y1),(x2,y2),(255,0,0),3)
+        theta=theta+math.atan2((y2-y1),(x2-x1))
+        print(theta)
+threshold=5
+if(theta>threshold):
+
+       print("Go left")
+if(theta<-threshold):
+
+    print("Go right")
+if(abs(theta)<threshold):
+
+    print("Go straight")
+theta=0
+cv2.imshow("Gray Image",gray)
+cv2.imshow("blurred",blurred)
+cv2.imshow("Edged",edged)
+cv2.imshow("Line Detection",image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()