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