How to extract green objects in an image?


I have ROI’s for the bounding boxes around the objects in an image. The ROI’s are obtained by the Faster R-CNN. Now I want to get the green spike objects contained within the bounding box.

Test Image

After getting the ROI’s, I segment the ROIs from the image onto a black image of the same size which results in the following image

image containing only bounding boxes

As you can see that boxes are rectangular so in some places it covers some background area along with spikes. So, how can I apply thresholding to get only the spikes and change other pixels to black?


Color thresholding using cv2.inRange() should work here. I’m assuming you want to isolate the green area

Here’s the main idea

  • Convert image to HSV format since it is easier to represent color than RBG
  • Perform color segmentation with a lower/upper threshold

You could also perform morphological operations to smooth or remove noise after obtaining the mask

enter image description here

import numpy as np
import cv2

image = cv2.imread('1.jpg')
result = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([18, 0, 0])
upper = np.array([179, 255, 255])
mask = cv2.inRange(image, lower, upper)
result = cv2.bitwise_and(result,result, mask=mask)
cv2.imshow('result', result)
cv2.imwrite('result.png', result)

You can use a HSV color thresholder script to isolate the desired color range

enter image description here

import cv2
import sys
import numpy as np

def nothing(x):

# Create a window

# create trackbars for color change
cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv

# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)

# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0

img = cv2.imread('1.jpg')
output = img
waitTime = 33


    # get current positions of all trackbars
    hMin = cv2.getTrackbarPos('HMin','image')
    sMin = cv2.getTrackbarPos('SMin','image')
    vMin = cv2.getTrackbarPos('VMin','image')

    hMax = cv2.getTrackbarPos('HMax','image')
    sMax = cv2.getTrackbarPos('SMax','image')
    vMax = cv2.getTrackbarPos('VMax','image')

    # Set minimum and max HSV values to display
    lower = np.array([hMin, sMin, vMin])
    upper = np.array([hMax, sMax, vMax])

    # Create HSV Image and threshold into a range.
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower, upper)
    output = cv2.bitwise_and(img,img, mask= mask)

    # Print if there is a change in HSV value
    if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
        print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
        phMin = hMin
        psMin = sMin
        pvMin = vMin
        phMax = hMax
        psMax = sMax
        pvMax = vMax

    # Display output image

    # Wait longer to prevent freeze for videos.
    if cv2.waitKey(waitTime) & 0xFF == ord('q'):


Here’s the result on the original image

enter image description here

Answered By – nathancy

Answer Checked By – Marie Seifert (AngularFixing Admin)

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