


How to Effectively Determine HSV Color Boundaries for Object Detection using cv::inRange?
Choosing Color Boundaries for Object Detection with cv::inRange (OpenCV)
When utilizing the cv::inRange function for color detection, selecting appropriate upper and lower HSV boundaries is crucial. This article addresses the question of how to effectively determine these boundaries based on a specific color of interest.
Background
HSV (Hue, Saturation, Value) is a color space commonly used in image processing. The HSV model represents colors as three components:
- Hue (H): Represents the color shade (e.g., red, blue).
- Saturation (S): Measures the amount of color present in the shade (0-1).
- Value (V): Represents the brightness of the color (0-255).
Choosing Boundaries
Determining proper HSV boundaries is based on the specific color being detected. Here's a step-by-step guide:
-
Determine Hue:
- Use a color picker tool to identify the HSV values of the object of interest.
- Note that different scales might be used for HSV values depending on the application.
-
Adjust Hue Range:
- Account for slight variations in hue by adjusting the range around the identified value.
- For example, if the hue is 22 (out of 179), a range of (11-33) could be appropriate.
-
Set Saturation and Value Ranges:
- Use a reasonable range for saturation (e.g., 50-255).
- For value, choose a range that includes the expected brightness of the object.
-
Consider Format:
- Ensure that the HSV conversion is appropriate for your image format.
- For example, OpenCV uses BGR, not RGB for image representation.
Example
Let's consider the example of detecting an orange lid in an image.
-
HSV Values:
- Using a color picker, we obtain an HSV value of (22, 59, 100).
-
Adjusted Boundaries:
- Hue range: (11-33)
- Saturation range: (50-255)
- Value range: (50-255)
-
Python Code:
import cv2 import numpy as np ORANGE_MIN = np.array([11, 50, 50], np.uint8) ORANGE_MAX = np.array([33, 255, 255], np.uint8) # Read and convert image img = cv2.imread('image.png') hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # Detect orange using inRange mask = cv2.inRange(hsv_img, ORANGE_MIN, ORANGE_MAX) # Display mask cv2.imshow('Mask', mask) cv2.waitKey(0)
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