from PIL import Image
from scipy.ndimage import filters
import matplotlib.pyplot as plt
import funciones as fun
import cv2
from sklearn.cluster import KMeans
image = Image.open('img2.jpg').convert('RGB')
plt.figure()
plt.gray()
plt.imshow(image)
plt.axis('off')
plt.title('Imagen 2 Original')
Viewimagedouble = np.double(image)
returnImage3 = fun.rgb2ycbcr(Viewimagedouble)
Y = returnImage3[:, :, 0]
Yi = np.uint8(255 * Y / Y.max())
h = fun.my_hist(Yi)
Yeq = fun.my_equal(Yi, h)
returnImage3[:, :, 0] = Yeq
sendImage = np.double(returnImage3)
previousImage = fun.ycbcr2rgb(sendImage)
finalImage = np.uint8(previousImage)
plt.figure()
plt.gray()
plt.imshow(finalImage)
plt.axis('off')
plt.title('Imagen 2 Ecualizada YcbCr')
Im_ga = np.array(finalImage)
plt.imshow(imageList)
#para llamar las funciones RGB2YCBCR y YCBCR2RGB
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import funciones as fun
image = Image.open('img2.jpg').convert('RGB')
plt.figure()
plt.gray()
plt.imshow(image)
doubleImage = np.double(image)
returnImage = fun.rgb2ycbcr(doubleImage)
plt.figure()
plt.gray()
plt.imshow(returnImage)
returnImage2 = fun.ycbcr2rgb(returnImage)
Viewimage = np.uint8(returnImage2)
plt.figure()
plt.gray()
plt.imshow(Viewimage)
#para llamar las funciones del histograma y la equalizacion
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import funciones as fun