Defining a Threshold Value for Detecting Green Objects in Images using Python OpenCV

To detect green objects in an image, a threshold value must be defined to differentiate between green and non-green pixels. Here's how you can approach this task in Python using OpenCV:

HSV Color Space and Thresholding

One method involves converting the image to the HSV color space. In HSV, the hue component represents the color, and green falls within the range of 36-70 degrees.

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, (36, 25, 25), (70, 255, 255))

This code creates a mask where pixels within the specified HSV range (green) are marked as true.

BGR Color Space and Thresholding

Another approach is to work directly in the BGR color space. Here, you can define a range of green values:

mask = cv2.inRange(img, (0, 100, 0), (100, 255, 100))

This mask assigns true values to pixels where the green channel (G) is between 100 and 255 and the other channels (B and R) are below 100.

Extraction and Display of Green Objects

Using the mask, you can extract only the green objects in the image:

green = cv2.bitwise_and(img, img, mask=mask)

This operation sets all non-green pixels to black while retaining green pixels in their original color.

By defining an appropriate threshold value, you can effectively detect and isolate green objects in an image, facilitating further analysis and processing tasks.