How to define a threshold value to detect only green colour objects in an image :Opencv [duplicate]

Question

I want to detect only green objects from an image captured in a natural environment. How to define it? Because in here I want to pass the threshold value let's say 'x', by using this x I want to get only green colour objects in to one colour (white) others are must appear in another colour (black). Please guide me to do this.

Answer 1

Update:

I make a HSV colormap. It's more easy and accurate to find the color range using this map than before.

And maybe I should change use (40, 40,40) ~ (70, 255,255) in hsv to find the green.

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Original answer:

1. Convert to HSV color-space,
2. Use cv2.inRange(hsv, hsv_lower, hsv_higher) to get the green mask.

We use the range (in hsv): (36,0,0) ~ (86,255,255) for this sunflower.

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The source image:

The masked green regions:

More steps:

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The core source code:

import cv2
import numpy as np

## Read
img = cv2.imread("sunflower.jpg")

## convert to hsv
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

## mask of green (36,25,25) ~ (86, 255,255)
# mask = cv2.inRange(hsv, (36, 25, 25), (86, 255,255))
mask = cv2.inRange(hsv, (36, 25, 25), (70, 255,255))

## slice the green
imask = mask>0
green = np.zeros_like(img, np.uint8)
green[imask] = img[imask]

## save 
cv2.imwrite("green.png", green)

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Similar:

1. Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV)

Answer 2

Intro:

Applying a threshold to detect green color can be performed quite easily using LAB color space.

The LAB color space also has 3 channels but unlike its RGB counterpart (where all 3 are color channels), in LAB there are 2 color channels and 1 brightness channel:

• L-channel: represents the brightness value in the image
• A-channel: represents the red and green color in the image
• B-channel: represents the blue and yellow color in the image

Observing the following diagram:

The green and red color are represented on the extremes of the A-channel. Applying a suitable threshold on either of these extremes on this channel can segment either green or red color.

Demo:

The following images are in the order:

1. Original image -->> 2. A-channel of LAB converted image

3. Threshold -->> 4. Mask on the original image

Sample 1:

Sample 2:

Sample 3:

Code:

The code just has few lines:

# read image in BGR
img = cv2.imread('image_path')
# convert to LAB space
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
# store the a-channel
a_channel = lab[:,:,1]
# Automate threshold using Otsu method
th = cv2.threshold(a_channel,127,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)[1]
# Mask the result with the original image
masked = cv2.bitwise_and(img, img, mask = th)

Exception:

The method above will work perfectly if green color appears distinctly. But applying an automated threshold might not always work, especially when there various shades of green in the same image.

In such cases, one set the threshold manually on the A-channel.

img = cv2.imread('flower.jpg')
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
a_channel = lab[:,:,1]
# manually set threshold value
th = cv2.threshold(a_channel, 105, 255, cv2.THRESH_BINARY_INV)
# perform masking
masked = cv2.bitwise_and(img, img, mask = th)

    Threshold image                              Masked image

Answer 3

You can use a simple HSV color thresholder script to determine the lower/upper color ranges using trackbars for any image on the disk. Simply change the image path in cv2.imread(). Example to isolate green:

import cv2
import numpy as np

def nothing(x):
    pass

# Load image
image = cv2.imread('1.jpg')

# Create a window
cv2.namedWindow('image')

# Create trackbars for color change
# Hue is from 0-179 for Opencv
cv2.createTrackbar('HMin', 'image', 0, 179, nothing)
cv2.createTrackbar('SMin', 'image', 0, 255, nothing)
cv2.createTrackbar('VMin', 'image', 0, 255, nothing)
cv2.createTrackbar('HMax', 'image', 0, 179, nothing)
cv2.createTrackbar('SMax', 'image', 0, 255, nothing)
cv2.createTrackbar('VMax', 'image', 0, 255, nothing)

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

# Initialize HSV min/max values
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0

while(1):
    # 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 maximum HSV values to display
    lower = np.array([hMin, sMin, vMin])
    upper = np.array([hMax, sMax, vMax])

    # Convert to HSV format and color threshold
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower, upper)
    result = cv2.bitwise_and(image, image, 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 result image
    cv2.imshow('image', result)
    if cv2.waitKey(10) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()

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HSV lower/upper color threshold ranges

(hMin = 52 , sMin = 0, vMin = 55), (hMax = 104 , sMax = 255, vMax = 255)

Once you have determined your lower and upper HSV color ranges, you can segment your desired colors like this:

import numpy as np
import cv2

image = cv2.imread('1.png')
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([52, 0, 55])
upper = np.array([104, 255, 255])
mask = cv2.inRange(hsv, lower, upper)
result = cv2.bitwise_and(image, image, mask=mask)

cv2.imshow('result', result)
cv2.waitKey()