Img_analysis_py_v1.py

"""
Python3

How to Install
apt-get update && apt-get install -y python3-opencv

pip3 install --upgrade pip # Important

pip3 install sklearn matplotlib numpy opencv-python scikit-image 

"""

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import cv2
from collections import Counter
from skimage.color import rgb2lab, deltaE_cie76
import os

''' Comment: '''
def RGB2HEX(color):
    return "#{:02x}{:02x}{:02x}".format(int(color[0]), int(color[1]), int(color[2]))

''' Comment: Get Converted Image'''
def get_image(image_path):
    image = cv2.imread(image_path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    return image
    
''' Get Image Colors '''
def get_colors(image, number_of_colors=4, show_chart):
    
    modified_image = cv2.resize(image, (600, 400), interpolation = cv2.INTER_AREA)
    modified_image = modified_image.reshape(modified_image.shape[0]*modified_image.shape[1], 3)
    
    clf = KMeans(n_clusters = number_of_colors)
    labels = clf.fit_predict(modified_image)
    
    counts = Counter(labels)
    # sort to ensure correct color percentage
    counts = dict(sorted(counts.items()))
    
    center_colors = clf.cluster_centers_
    # We get ordered colors by iterating through the keys
    ordered_colors = [center_colors[i] for i in counts.keys()]
    hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts.keys()]
    rgb_colors = [ordered_colors[i] for i in counts.keys()]

    if (show_chart):
        plt.figure(figsize = (8, 6))
        plt.pie(counts.values(), labels = hex_colors, colors = hex_colors)
    
    return rgb_colors


'''  '''
def match_image_by_color(image, color, threshold = 60, number_of_colors = 10): 
    
    image_colors = get_colors(image, number_of_colors, False)
    selected_color = rgb2lab(np.uint8(np.asarray([[color]])))

    select_image = False
    for i in range(number_of_colors):
        curr_color = rgb2lab(np.uint8(np.asarray([[image_colors[i]]])))
        diff = deltaE_cie76(selected_color, curr_color)
        if (diff < threshold):
            select_image = True
    
    return select_image


def show_selected_images(images, color, threshold, colors_to_match):
    index = 1
    
    for i in range(len(images)):
        selected = match_image_by_color(images[i],                                        color,
                                        threshold,
                                        colors_to_match)
        if (selected):
            plt.subplot(1, 5, index)
            plt.imshow(images[i])
            index += 1
            




def recognize_color(R,G,B):
    minimum = 10000
    for i in range(len(csv)):
        d = abs(R- int(csv.loc[i,"R"])) + abs(G- int(csv.loc[i,"G"]))+ abs(B- int(csv.loc[i,"B"]))
        if(d <= minimum):
            minimum = d
            cname = csv.loc[i,"color_name"]
    return cname
    

import pandas as pd
import cv2
#img = cv2.imread("ball1_green1.jpg")
#index=["color", "color_name", "hex", "R", "G", "B"]
#csv = pd.read_csv('colors.csv', names=index, header=None)
rgb_colors = {0: "RED", 1: "GREEN", 2: "BLUE"}

def get_image_color(image_file):
    img = cv2.imread(image_file)
    blue_color, green_color, red_color = (img[300, 300])    
    index = max_index(red_color, green_color, blue_color)
    return rgb_colors[index]


def max_index(a, b, c):
    largest = 0
    if (b >= c):
        largest = 1
    else:
        largest = 2     
    return largest



## Provide Image File Path
img_file1 = "ball1_green1.jpg"

## Check & Result
get_image_color(img_file1)