How to Detect Green Objects in an Image Using OpenCV Threshold Values?

Detect Green Objects with Threshold Values in OpenCV

Detection of specific color objects is a common task in image processing. This question demonstrates how to define threshold values to isolate green color objects in an image using OpenCV.

Defining the Threshold Value

To detect green objects, you can define a threshold value range in the Hue (H), Saturation (S), and Value (V) color space. The H value determines the color hue, while S and V indicate the saturation and brightness, respectively.

Method 1: HSV Color Space

One approach is to use the HSV color space, which provides a more accurate color representation than RGB. For green, you can specify a range such as:

• H: 36-86
• S: 25-255
• V: 25-255

Method 2: cv2.inRange

Another method is to use the cv2.inRange() function, which takes two arguments: a lower bounding threshold and an upper bounding threshold. For instance, to detect green:

• Lower bound: (36, 25, 25)
• Upper bound: (70, 255, 255)

Example Implementation

The following Python code demonstrates this using OpenCV:

<code class="python">import cv2
import numpy as np

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

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

# Define threshold values
lower_bound = (36, 25, 25)
upper_bound = (70, 255, 255)

# Create mask
mask = cv2.inRange(hsv, lower_bound, upper_bound)

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

# Display
cv2.imshow("Green Objects", green)
cv2.waitKey(0)</code>

This code demonstrates how to define threshold values to isolate green objects from an input image, presenting the resulting image with only the identified green regions.

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