Why is Opencv/Hough Transform not finding the whole line?

Question

I finished a tutorial on OpenCv for finding lanes, and I am trying to apply it to finding a piece of tape on the floor. I got the code running and set the region of interest but it only finds a few edges of the tape. I think it has to do with the thickness but I am not 100% sure. Any help would be appreciated.

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

def canny(image):
    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    blur = cv2.GaussianBlur(gray, (5,5), 0)
    canny = cv2.Canny(blur, 50, 150)
    return canny

def display_lines(image, lines):
    line_image = np.zeros_like(image)
    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line.reshape(4)
            cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 10)
    return line_image

def region_of_interest(image):
    height = image.shape[0]
    polygons = np.array([
        [(200, height), (400, height), (355, 0)]
        ])
    mask = np.zeros_like(image)
    cv2.fillPoly(mask, polygons, 255)
    masked_image = cv2.bitwise_and(image, mask)
    return masked_image

image = cv2.imread('tape3.jpg')
lane_image = np.copy(image)
canny_image = canny(image)
cropped_image = region_of_interest(canny_image)
lines = cv2.HoughLinesP(cropped_image, 2, np.pi/180, 100, np.array([]), minLineLength=40, maxLineGap=5)
line_image = display_lines(lane_image, lines)
combo_image = cv2.addWeighted(lane_image, 0.8, line_image, 1, 1)
# cv2 print image
print(region_of_interest(image))
cv2.imshow("result", combo_image)
cv2.waitKey(0)

Answer 1

This may not answer your original question, but this could be an alternate way to achieve what you're looking for.

I started by thresholding the grayscale of the image to try and isolate the tape

Then I used opencv's findContours to get the segmentation points of each white blob

The thresholding method I used is sensitive to light and shadow so you may have to find some other thresholding method if this isn't a workable constraint. If different colored tape is a concern, you can threshold off of other values (convert to HSV or LAB and threshold off of the H or B channels respectively to look for red).

Edit:

If you still want to use HoughLinesP, here's a working example with your picture.

First I applied canny:

Then I used the HoughLinesP function:

I've never used houghLinesP before so I'm not sure of the potential pitfalls, but it seems to work, though it actually creates a bunch of overlapping lines with these parameters, you'll have to play around with it a bit.

Relevant Code:

# canny
canned = cv2.Canny(gray, 591, 269);

# dilate
kernel = np.ones((3,3), np.uint8);
canned = cv2.dilate(canned, kernel, iterations = 1);

# hough
lines = cv2.HoughLinesP(canned, rho = 1, theta = 1*np.pi/180, threshold = 30, minLineLength = 10, maxLineGap = 20);

Edit 2:

I looked at the documentation for the function and the third parameter (theta) refers to the angle resolution. I think it might not have worked in your code because you didn't run dilation on the image after Canny. With a one-degree search resolution it's not hard to imagine that we could miss the very thin line that canny returns. It might even be worth dilating the lines more than I did in the example by using a larger kernel (or dilating multiple times).