CarColor_Min_Distance.py

import numpy as np
from matplotlib import pyplot as plt
import cv2 


def ValorMaxHistogram(img):
    counts, pixels = np.histogram(img, np.array(range(0, 258)))
    #print(pixels)
    #print(counts)
    OcurrenciasMax=0
    ValorMax=0
    for i in range(len(counts)-1):
            if i==0: continue
            if counts[i] > OcurrenciasMax:
                OcurrenciasMax=counts[i]
                ValorMax=pixels[i]
    return counts, pixels, ValorMax
        
def CarColorImg_Min_Distance(img, model, TabNames, License):        

         #cv2.imshow("img", img)
         #cv2.waitKey()
         
         img0=img[:, :, 0]
         counts, pixels, Valor0=ValorMaxHistogram(img0)
         img[:, :, 0]=np.where(img[:, :, 0]==Valor0, 255, img[:, :, 0])
         #https://stackoverflow.com/questions/57398643/how-to-extract-individual-channels-from-an-rgb-image
         # valor 0 is b, valor 1 is g , valor 2 is r
         # hay que invertir el orden para que sea r g b

         
         # web  to see the result of  r g b composition colors
         # https://www.rapidtables.com/web/color/RGB_Color.html

         """
         print("Valor 0 blue = " + str(Valor0))        
         pixels = pixels[:-1]
         plt.bar(pixels, counts, align='center')
         plt.savefig('histogram0.png')
         plt.xlim(0, 256)
         plt.show()
         """
         img1=img[:, :, 1]
         counts, pixels, Valor1=ValorMaxHistogram(img1)
         img[:, :, 1]=np.where(img[:, :, 1]==Valor1, 255, img[:, :, 1])
         """
         print("Valor 1 Green = " + str(Valor1))        
         pixels = pixels[:-1]
         plt.bar(pixels, counts, align='center')
         plt.savefig('histogram1.png')
         plt.xlim(0, 256)
         plt.show()
         """
         img2=img[:, :, 2]
         counts, pixels, Valor2=ValorMaxHistogram(img2)
         img[:, :, 2]=np.where(img[:, :, 2]==Valor2, 255, img[:, :, 2])
         """
         print("Valor 2 red= " + str(Valor2))        
         pixels = pixels[:-1]
         plt.bar(pixels, counts, align='center')
         plt.savefig('histogram2.png')
         plt.xlim(0, 256)
         plt.show()
         """
         cv2.imshow("ROI", img)
         cv2.waitKey()
         
         x_test=[]
         x_test.append(Valor2)
         x_test.append(Valor1)
         x_test.append(Valor0)
         X_test=[]
         X_test.append(np.array(x_test))
         

         Y_predict_test=model.predict(X_test)

         #print(X_test)
         #print(Y_predict)

         
         
         return Valor2, Valor1, Valor0, Y_predict_test