How to detect if a specific region of a picture is a certain solid color using OpenCV

To detect if a specific region of a picture is a certain solid color using OpenCV, you can follow these general steps:

Read the Image:

Use OpenCV to read the image

import cv2

image = cv2.imread('your_image.jpg')

Define the Region of Interest (ROI):

Define the specific region of the image where you want to check for the solid color.

# Define the coordinates of the region (x, y, width, height)
roi_coordinates = (x, y, width, height)
roi = image[y:y+height, x:x+width]

Convert the ROI to HSV color space:

Convert the ROI to the HSV (Hue, Saturation, Value) color space for better color representation.

roi_hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)

Define the Range for the Solid Color:

Specify the range of HSV values that correspond to the solid color you are looking for.

# Example: Define a range for the color red
lower_red = np.array([0, 100, 100])
upper_red = np.array([10, 255, 255])

Create a Mask:

Create a binary mask based on the specified color range.

mask = cv2.inRange(roi_hsv, lower_red, upper_red)

Check if the Region is the Solid Color:

Check if the majority of pixels in the mask belong to the solid color.

percentage_solid_color = (cv2.countNonZero(mask) / (width * height)) * 100

# Set a threshold percentage for considering it as a solid color
threshold_percentage = 90

if percentage_solid_color >= threshold_percentage:
    print("The region is a solid color.")
else:
    print("The region is not a solid color.")

Adjust the parameters, such as the color range and threshold percentage, based on your specific requirements. This example assumes that the solid color is within a specific range in the HSV color space; you may need to adjust these values depending on the color you are looking for.

Full code:

import cv2
import numpy as np

def is_solid_color_region(image_path, roi_coordinates, color_lower, color_upper, threshold_percentage=90):
    # Step 1: Read the image
    image = cv2.imread(image_path)

    # Step 2: Define the Region of Interest (ROI)
    x, y, width, height = roi_coordinates
    roi = image[y:y+height, x:x+width]

    # Step 3: Convert the ROI to HSV color space
    roi_hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)

    # Step 4: Define the Range for the Solid Color
    lower_color = np.array(color_lower)
    upper_color = np.array(color_upper)

    # Step 5: Create a Mask
    mask = cv2.inRange(roi_hsv, lower_color, upper_color)

    # Step 6: Check if the Region is the Solid Color
    percentage_solid_color = (cv2.countNonZero(mask) / (width * height)) * 100

    # Display the result
    cv2.imshow('Original Image', image)
    cv2.imshow('ROI', roi)
    cv2.imshow('Mask', mask)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    # Check if the percentage of solid color exceeds the threshold
    return percentage_solid_color >= threshold_percentage

# Example Usage
image_path = 'your_image.jpg'
roi_coordinates = (100, 100, 50, 50)  # Example coordinates (x, y, width, height)
color_lower = [0, 100, 100]  # Example lower HSV values for the color
color_upper = [10, 255, 255]  # Example upper HSV values for the color

result = is_solid_color_region(image_path, roi_coordinates, color_lower, color_upper)

if result:
    print("The region is a solid color.")
else:
    print("The region is not a solid color.")

Make sure to replace ‘your_image.jpg’ with the actual path to your image file. Adjust the roi_coordinates, color_lower, color_upper, and threshold_percentage parameters as needed for your specific case.

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