hough.py

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


# Read in and grayscale the image
image = mpimg.imread('img/canny.png',0)
gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)

# Define a kernel size and apply Gaussian smoothing
kernel_size = 5
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)

# Define our parameters for Canny and apply
low_threshold = 50
high_threshold = 150
edges = cv2.Canny(blur_gray, low_threshold, high_threshold)

# Next we'll create a masked edges image using cv2.fillPoly()
mask = np.zeros_like(edges)
ignore_mask_color = 255

# This time we are defining a four sided polygon to mask
imshape = image.shape
#vertices = np.array([[(0,imshape[0]),(0, 0), (imshape[1], 0), (imshape[1],imshape[0])]], dtype=np.int32)
vertices = np.array([[(0,imshape[0]),(450, 290), (490, 290), (imshape[1],imshape[0])]], dtype=np.int32)
cv2.fillPoly(mask, vertices, ignore_mask_color)
masked_edges = cv2.bitwise_and(edges, mask)

# Define the Hough transform parameters
# Make a blank the same size as our image to draw on

#image, rho=1, theta=np.pi/180, threshold=20, minLineLength=20, maxLineGap=300
rho = 2 # distance resolution in pixels of the Hough grid
theta = np.pi/180 # angular resolution in radians of the Hough grid
#threshold = 1     # minimum number of votes (intersections in Hough grid cell)
#min_line_length = 5 #minimum number of pixels making up a line
#max_line_gap = 1    # maximum gap in pixels between connectable line segments
threshold = 15     # minimum number of votes (intersections in Hough grid cell)
min_line_length = 40 #minimum number of pixels making up a line
max_line_gap = 20    # maximum gap in pixels between connectable line segments
line_image = np.copy(image)*0 # creating a blank to draw lines on

# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line segments
lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]),
                            min_line_length, max_line_gap)

# Iterate over the output "lines" and draw lines on a blank image
for line in lines:
    for x1,y1,x2,y2 in line:
        #print("x1: ", x1, "y1: ", y1, "x2:", x2, "y2: ", y2)
        cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),2)

# Create a "color" binary image to combine with line imag,x2
color_edges = np.dstack((edges, edges, edges))

# Draw the lines on the edge image
print("line_image >>>", line_image.shape)
print("color_edges >>>> ", color_edges.shape)