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import numpy as np # module pour la manipulation de matrice

import pylab as plt # module pour affichage des données

# from matplotlib import pyplot as plt # Module image propre à python

# from scipy.ndimage import label, generate_binary_structure

import cv2 # module pour la manimpulation d'image via OpenCV

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# Lecture d'une image & information sur l'image

# fname = "voilier_oies_blanches.jpg"

# fname = "img_ds.jpg"

# fname = "Maya-and-Mantra.jpg"

fname = "Data/baboon.jpg"

# fname = "house.jpg"

img_C = cv2.imread(fname) # Lecture image en couleurs BGR

b, g, r = cv2.split(img_C) # Recuperation des plan de couleurs BGR ou b=img_C[:,:,0] ...

img_C2 = cv2.merge([r, g, b]) # Reconstruction image format RGB ou np.concatenate((b,g,r),axis=1)

imgRGB = cv2.cvtColor(img_C, cv2.COLOR_BGR2RGB) # Passage BGR --> RGB via cv2.cvtColor (non utilisé par la suite)

# 2.1.1 Affichage image via opencv --> affichage format BGR

cv2.namedWindow("mon image BGR", cv2.WINDOW_NORMAL) # Pour dimensionner la fenetre d'affichage

cv2.imshow("mon image BGR", img_C)

cv2.namedWindow("mon image RGB", cv2.WINDOW_NORMAL)

cv2.imshow("mon image RGB", img_C2)

# Affichage image via plt --> affichage format RGB

plt.figure()

plt.subplot(121)

plt.imshow(img_C)

plt.xticks([]), plt.yticks([])

plt.title(" Mon image BGR")

plt.subplot(122)

plt.imshow(img_C2)

plt.xticks([]), plt.yticks([])

plt.title(" Mon image RGB")

plt.show()

# 2.1.2 Affichage des trois canaux séparéments

plt.figure()

plt.subplot(131)

plt.imshow(b, cmap='gray')

plt.xticks([]), plt.yticks([])

plt.title(" Mon image B")

plt.subplot(132)

plt.imshow(g, cmap='gray')

plt.xticks([]), plt.yticks([])

plt.title(" Mon image G")

plt.subplot(133)

plt.imshow(r, cmap='gray')

plt.xticks([]), plt.yticks([])

plt.title(" Mon image R")

plt.show()

rMean = np.mean(r)

gMean = np.mean(g)

bMean = np.mean(b)

a = np.array((rMean,gMean,bMean)) #couleur moyenne de l'image

def distEuclid(a,z):

return np.linalg.norm(z-a)

px = np.shape(img_C2)[0]

py = np.shape(img_C2)[1]

D = np.zeros((px,py))

D0 = 84 #seuil

for i in range(px):

for j in range(py):

if distEuclid(img_C2[i][j],a) < D0:

D[i][j] = 1

else:

D[i][j] = 0

plt.figure()

plt.imshow(D, cmap='gray')

plt.xticks([]), plt.yticks([])

plt.title(" Mon image Binarisée - Euclidienne")

plt.show()

sigmaR = np.std(r) #ecart type couleur rouge

sigmaG = np.std(g) #ecart type couleur vert

sigmaB = np.std(b) #ecart type couleur bleu

alpha = 1.25

Rmin = rMean - alpha*sigmaR

Gmin = gMean - alpha*sigmaG

Bmin = bMean - alpha*sigmaB

Rmax = rMean + alpha*sigmaR

Gmax = gMean + alpha*sigmaG

Bmax = bMean + alpha*sigmaB

D2 = np.zeros((px,py))

for i in range(px):

for j in range(py):

if (Rmin<=img_C2[i][j][0] and Rmax>=img_C2[i][j][0] and Gmin<=img_C2[i][j][1] and Gmax>=img_C2[i][j][1] and Bmin<=img_C2[i][j][2] and Bmax>=img_C2[i][j][2]):

D2[i][j] = 1

else:

D2[i][j] = 0

plt.figure()

plt.imshow(D2, cmap='gray')

plt.xticks([]), plt.yticks([])

plt.title(" Mon image Binarisée - Mahalanobis")

plt.show()

fname1 = "Data/crayons.jpg"

img_C = cv2.imread(fname1) # Lecture image en couleurs BGR

cv2.imshow("Mon image", img_C)

b, g, r = cv2.split(img_C) # Recuperation des plan de couleurs BGR ou b=img_C[:,:,0] ...

imgRGB = cv2.cvtColor(img_C, cv2.COLOR_BGR2RGB) # Passage BGR --> RGB via cv2.cvtColor (non utilisé par la suite)

Rmin = 0

Rmax = 255

Gmin = 0

Gmax = 255

Bmin = 0

Bmax = 255

def binariseRGB(pos):

cv2.imshow("mon Resultat", img_Calque)

# Test

binariseRGB(0)

# Creation des barres de défilement

cv2.createTrackbar("Rmin", "mon Mask", Rmin, 255, binariseRGB)

cv2.createTrackbar("Rmax", "mon Mask", Rmax, 255, binariseRGB)

cv2.createTrackbar("Gmin", "mon Mask", Gmin, 255, binariseRGB)

cv2.createTrackbar("Gmax", "mon Mask", Gmax, 255, binariseRGB)

cv2.createTrackbar("Bmin", "mon Mask", Bmin, 255, binariseRGB)

cv2.createTrackbar("Bmax", "mon Mask", Bmax, 255, binariseRGB)

##### FIN

cv2.waitKey(0)

cv2.destroyAllWindows()