How to detect the exact color of the images using hsv color model and opencv-CodePudding

I have datasets of images of gemstones.I have categorised the gemstones into folders based on their colours.(That is Red, Blue, Pink,Purple,Yellow).

What I want to do is:

I want to train a model using hsv model and opencv to detect the colour of the gemstone.That is whether its blue,purple,pink,yellow or Red and any other colour except to these 5 colours to define as undefined colour

Source Code:(referred https://www.kaggle.com)

import os
import matplotlib.pyplot as plt
import seaborn as sn

import cv2
from random import randint

import numpy as np

CLASSES, gems = [], [] # names of classes, count of images for each class

for root, dirs, files in os.walk('C:/Users/User/Desktop/Research Project/images'):
    f = os.path.basename(root)    # get class name - Red,Blue etc    
        
    if len(files) > 0:
        gems.append(len(files))
        if f not in CLASSES:
            CLASSES.append(f) # add folder name
    
gems_count = len(CLASSES) # 6 = number of classes
print('{} classes with {} images in total'.format(len(CLASSES), sum(gems)))

img_w, img_h = 220, 220    # width and height of image
train_dir = 'C:/Users/User/Desktop/Gem/images/train'

def read_imgs_lbls(_dir):
    Images, Labels = [], []
    for root, dirs, files in os.walk(_dir):
        f = os.path.basename(root)  # get class name - Red, Blue, etc       
        for file in files:
            Labels.append(f)
            try:
                image = cv2.imread(root '/' file)              # read the image (OpenCV)
                image = cv2.resize(image,(int(img_w*1.5), int(img_h*1.5)))       # resize the image (images are different sizes)
                image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # converts an image from BGR color space to HSV
                Images.append(image)
            except Exception as e:
                print(e)
    Images = np.array(Images)
    return (Images, Labels)

def get_class_index(Labels):
    for i, n in enumerate(Labels):
        for j, k in enumerate(CLASSES):    # foreach CLASSES
            if n == k:
                Labels[i] = j
    Labels = np.array(Labels)
    return Labels

Train_Imgs, Train_Lbls = read_imgs_lbls(train_dir)
Train_Lbls = get_class_index(Train_Lbls)
print('Shape of train images: {}'.format(Train_Imgs.shape))
print('Shape of train labels: {}'.format(Train_Lbls.shape))

dim = 4 

f,ax = plt.subplots(dim,dim) 
f.subplots_adjust(0,0,2,2)
for i in range(0,dim):
    for j in range(0,dim):
        rnd_number = randint(0,len(Train_Imgs))
        cl = Train_Lbls[rnd_number]
        ax[i,j].imshow(Train_Imgs[rnd_number])
        ax[i,j].set_title(CLASSES[cl] ': '   str(cl))
        ax[i,j].axis('off')

It read the values from the folder names.But I want to add the lower and upper values of each colour to the training model as in the link below.(Referred how to know if a color is detected on opencv)

import cv2
import numpy as np

img = cv2.imread("img.jpg")

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

lower_val = np.array([37,42,0]) 
upper_val = np.array([84,255,255]) 

# Threshold the HSV image - any green color will show up as white
mask = cv2.inRange(hsv, lower_val, upper_val)

# if there are any white pixels on mask, sum will be > 0
hasGreen = np.sum(mask)
if hasGreen > 0:
    print('Green detected!')

I found the lower and upper limit hsv values of the colours I want as well

          'red': [[9, 255, 255], [0, 50, 70]],             
          'blue': [[128, 255, 255], [90, 50, 70]],
          'yellow': [[35, 255, 255], [25, 50, 70]],
          'purple': [[158, 255, 255], [129, 50, 70]]

can anyone please let me know how can i combine detecting the colour using the hsv values(as in the how to know if a color is detected on opencv ) to the my source code.

I am new to image processing and any help is appreciated.

Thankyou.

CodePudding user response：

This code demonstrates how to walk through all files in folder ./images and return the detected colours:

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('./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)}")

Output with three sample images in subfolder images:

ruby_3.jpg: red
sapphire blue_18.jpg: blue
sapphire pink_18.jpg: pink
sapphire purple_28.jpg: purple
sapphire yellow_9.jpg: yellow

Credits: