Improve quality of extracted image in OpenCV

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#Segmenting the red pointer
img = cv2.imread('flatmap.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

lower_red = np.array([140, 110, 0])
upper_red = np.array([255, 255 , 255])

# Threshold with inRange() get only specific colors
mask_red = cv2.inRange(hsv, lower_red, upper_red)

# Perform bitwise operation with the masks and original image
red_pointer = cv2.bitwise_and(img,img, mask= mask_red)

# Display results
cv2.imshow('Red pointer', red_pointer)
cv2.imwrite('redpointer.jpg', red_pointer)
cv2.waitKey(0)
cv2.destroyAllWindows()

I have a map and need to extract the red arrow. The code works but the arrow has black patches in it. How would I go about altering the code to improve the output of the arrow so it's a solid shape?

Map

Arrow

hsv opencv python
2 Answers

You could use:

  1. dilate to fill up the internal noise in the shape
  2. external contour finding to get the outline of the triangle
  3. convex hull to further smooth it out
import cv2
import numpy as np

img = cv2.imread('dCkpC.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

lower_red = np.array([140, 60, 0])
upper_red = np.array([255, 255, 255])

mask_red = cv2.inRange(hsv, lower_red, upper_red)
element = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
mask_red = cv2.dilate(mask_red, element)

contours, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
hull_list = [cv2.convexHull(contour) for contour in contours]
drawing = np.zeros_like(img)
for hull in hull_list:
    cv2.fillConvexPoly(img, hull, (255, 0, 0))

cv2.imshow('Image', img)
cv2.imwrite('out.jpg', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

out.jpg ends up looking like

enter image description here

where the triangle has been filled in with blue.

AKX_516850

I've looked at the channels in HSL/HSV space.

The arrows are the only stuff in the picture that has any saturation. That would be one required (but insufficient) aspect to get a lock on the desired arrow. I've picked those pixels and they appear to have a bit more than 50% saturation, so I'll use a lower bound of 25% (64).

saturation

That red arrow's hue dithers around 0 degrees (red)... that means some of its pixels are on the negative side of 0, i.e. something like 359 degrees.

hue hue hue

You need to use two inRange calls to collect all hues from 0 up, and all hues from 359 down. Since OpenCV encodes hues in 2-degree steps, that'll be a value of 180 and down. I'll select 0 +- 20 degrees (0 .. 10 and 170 .. 180).

In summary:

hsv_im = cv.cvtColor(im, cv.COLOR_BGR2HSV)

mask1 = cv.inRange(hsv_im, np.array([0, 64, 0]), np.array([10, 255, 255]))
mask2 = cv.inRange(hsv_im, np.array([170, 64, 0]), np.array([180, 255, 255]))
mask = mask1 | mask2

cv.imshow("mask", mask)
cv.waitKey()

mask

user_26028770
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