How to Define a Threshold Value for Detecting Green Objects in Images with Python OpenCV?

How can I define a threshold value to detect only green colour objects in an image with Python OpenCV?

In computer vision, identifying and isolating objects based on specific color characteristics plays a crucial role in various applications. When working with natural environments, it becomes necessary to define threshold values that can accurately detect objects of a particular color, such as green. Python's OpenCV library provides powerful tools for image processing and color detection.

Setting a Threshold for Green Detection

There are two primary methods for setting a threshold value to detect green objects in an image using Python OpenCV:

HSV Color Map

The HSV (Hue, Saturation, Value) color map offers a more accurate and user-friendly way to define color ranges. For green detection, the following range can be used:

(40, 40,40) ~ (70, 255,255) in HSV

HSV Range

Another approach is to use the HSV range directly to create a mask for the green objects. Here's an example:

<code class="python">import cv2

# Convert to HSV color space
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))</code>

Threshold Value Application

Once the mask is created, it can be applied to the original image to extract only the green objects while turning all other pixels black:

<code class="python">imask = mask > 0
green = np.zeros_like(img, np.uint8)
green[imask] = img[imask]</code>

By adjusting the threshold values within the specified ranges, it's possible to fine-tune the detection accuracy for green objects in various lighting conditions and environments. The green variable now contains an image with isolated green objects ready for further processing.

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