grad_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
grad_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)

abs_grad_x = cv2.convertScaleAbs(grad_x)
abs_grad_y = cv2.convertScaleAbs(grad_y)

# threshold
thresh_x = cv2.threshold(abs_grad_x, 0, 255,  cv2.THRESH_OTSU)[1]
thresh_y = cv2.threshold(abs_grad_y, 0, 255,  cv2.THRESH_OTSU)[1]

# bluring 
kernel_size = 3
blur_thresh_x = cv2.GaussianBlur(thresh_x,(kernel_size, kernel_size),0)
blur_thresh_y = cv2.GaussianBlur(thresh_y,(kernel_size, kernel_size),0)

# Run Hough on edge detected image

rho = 1  # distance resolution in pixels of the Hough grid   
theta = np.pi / 180  # angular resolution in radians of the Hough grid
threshold = 15  # minimum number of votes (intersections in Hough grid cell)  
min_line_length = 200  # minimum number of pixels making up a line   
max_line_gap = 1  # maximum gap in pixels between connectable line segments   
line_image = np.copy(gray) * 0  # creating a blank to draw lines on

# Vertical lines
vertical_lines = cv2.HoughLinesP(blur_thresh_x, rho, theta, threshold, np.array([]), min_line_length, max_line_gap)

if vertical_lines is not None:
    for line in vertical_lines:
        for x1,y1,x2,y2 in line:
            # here it's possible to add a selection of only vertical lines
            if np.abs(y1-y2)> 0.1 * np.abs(x1-x2):
                cv2.line(line_image,(x1,y1),(x2,y2),255,5)

# Horizontal lines
horizontal_lines = cv2.HoughLinesP(blur_thresh_y, rho, theta, threshold, np.array([]), min_line_length, max_line_gap)

if horizontal_lines is not None:
    for line in horizontal_lines:
        for x1,y1,x2,y2 in line:
            # here it's possible to add a selection of only horizontal lines
            if np.abs(x1-x2)> 0.1 * np.abs(y1-y2):
                cv2.line(line_image,(x1,y1),(x2,y2),255,5)