Merge pull request #2492 from avinashkranjan/deepsource-transform-dd9234bd

format code with autopep8

Author: avinashkranjan

Line_Detection_HoughLine/HoughLine.py

@@ -3,58 +3,65 @@
 from tkinter.filedialog import *
 import tkinter as tk
 
-window=tk.Tk()
+window = tk.Tk()
 window.title("Line detection using Hough transform")
 window.geometry('380x100')
-label = tk.Label(window, text="Choose an option").grid(row=0,column=0)
+label = tk.Label(window, text="Choose an option").grid(row=0, column=0)
 # displaying a menu window for choosing transforms
 
+
 def hough():
     photo = askopenfilename()
     img = cv2.imread(photo)
-    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
-    edges = cv2.Canny(gray,50,150,apertureSize=3)
-    
-    lines = cv2.HoughLines(edges,1,np.pi/180,500)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    edges = cv2.Canny(gray, 50, 150, apertureSize=3)
+
+    lines = cv2.HoughLines(edges, 1, np.pi/180, 500)
     for line in lines:
-        rho,theta = line[0]
-        # defining lines with the parameters returned by HoughLines() 
-        a=np.cos(theta)
-        b=np.sin(theta)
+        rho, theta = line[0]
+        # defining lines with the parameters returned by HoughLines()
+        a = np.cos(theta)
+        b = np.sin(theta)
         x0 = a*rho
         y0 = b*rho
         x1 = int(x0 + 1000*(-b))
         y1 = int(y0 + 1000*(a))
         x2 = int(x0 - 1000*(-b))
         y2 = int(y0 - 1000*(a))
-        cv2.line(img,(x1,y1),(x2,y2),(0,0,255),4) # drawing lines on image to mark
-    cv2.imwrite('houghlines.jpg',img)
-    cv2.imshow("houghlines",img)   
+        # drawing lines on image to mark
+        cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 4)
+    cv2.imwrite('houghlines.jpg', img)
+    cv2.imshow("houghlines", img)
     cv2.waitKey(5000)
     cv2.destroyAllWindows()
 
+
 def houghP():
     photo = askopenfilename()
     img = cv2.imread(photo)
-    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
-    edges = cv2.Canny(gray,50,150,apertureSize=3)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    edges = cv2.Canny(gray, 50, 150, apertureSize=3)
 
-    lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength=100,maxLineGap=10)
+    lines = cv2.HoughLinesP(edges, 1, np.pi/180, 100,
+                            minLineLength=100, maxLineGap=10)
     for line in lines:
-        x1,y1,x2,y2 = line[0] # endpoints are returned by HoughLinesP()
-        cv2.line(img,(x1,y1),(x2,y2),(0,0,255),4) #drawing lines by joining those
-    cv2.imwrite('houghlinesP.jpg',img)    
-    cv2.imshow("houghlinesP",img)   
+        x1, y1, x2, y2 = line[0]  # endpoints are returned by HoughLinesP()
+        # drawing lines by joining those
+        cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 4)
+    cv2.imwrite('houghlinesP.jpg', img)
+    cv2.imshow("houghlinesP", img)
     cv2.waitKey(5000)
-    cv2.destroyAllWindows()  
+    cv2.destroyAllWindows()
 
 
-rad1 = tk.Radiobutton(window,text='Hough Transform', value=1, command=hough)
-rad2 = tk.Radiobutton(window,text='Probabilistic Hough Transform', value=2, command=houghP)
+rad1 = tk.Radiobutton(window, text='Hough Transform', value=1, command=hough)
+rad2 = tk.Radiobutton(
+    window, text='Probabilistic Hough Transform', value=2, command=houghP)
 
-rad1.grid(row=1,column=0)
-rad2.grid(row=2,column=0)
+rad1.grid(row=1, column=0)
+rad2.grid(row=2, column=0)
 
-label = tk.Label(window, text="Check the output image in this folder you are working in").grid(row=3,column=0)
+label = tk.Label(window, text="Check the output image in this folder you are working in").grid(
+    row=3, column=0)
 
-window.mainloop()
+window.mainloop()