I am trying to detect the colour of the images using hsv model.

Below is the code that I have used to detect the colour using hsv model.

import os
import numpy as np
import cv2

# map colour names to HSV ranges
color_list = [
    ['red', [0, 160, 70], [10, 250, 250]],
    ['pink', [0, 50, 70], [10, 160, 250]],
    ['yellow', [15, 50, 70], [30, 250, 250]],
    ['green', [40, 50, 70], [70, 250, 250]],
    ['cyan', [80, 50, 70], [90, 250, 250]],
    ['blue', [100, 50, 70], [130, 250, 250]],
    ['purple', [140, 50, 70], [160, 250, 250]],
    ['red', [170, 160, 70], [180, 250, 250]],
    ['pink', [170, 50, 70], [180, 160, 250]]
]


def detect_main_color(hsv_image, colors):
    color_found = 'undefined'
    max_count = 0

    for color_name, lower_val, upper_val in colors:
        # threshold the HSV image - any matching color will show up as white
        mask = cv2.inRange(hsv_image, np.array(lower_val), np.array(upper_val))

        # count white pixels on mask
        count = np.sum(mask)
        if count > max_count:
            color_found = color_name
            max_count = count

    return color_found


for root, dirs, files in os.walk('C:/Users/User/Desktop/images/'):
    f = os.path.basename(root)

    for file in files:
        img = cv2.imread(os.path.join(root, file))
        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
        print(f"{file}: {detect_main_color(hsv, color_list)}")

The output

image_1 : blue
image_3 : pink
image_12: purple
...

How can I check the accuracy rate of this trained model?

Any help is appreciated.

Thank you.

CodePudding user response：

By eys I think, or test you code on some pure color images. I think it is better to clarify the definition of "the colour of the image" firstly. For example, what is the color of this image?

CodePudding user response：

you could compare with colorsys

docs.python.org - colorsys

import colorsys

colorsys.rgb_to_hsv(0.2, 0.4, 0.4) = (0.5, 0.5, 0.4)
colorsys.hsv_to_rgb(0.5, 0.5, 0.4) = (0.2, 0.4, 0.4)