def MyFCM(img, ImageType, numClust):
k = numClust
num_features = img.shape[2]
img_vector = np.zeros([img.shape[0] * img.shape[1], num_features])
img_pixel = np.zeros([img.shape[0] * img.shape[1], 2], int)
count = 0
for i in xrange(0, img.shape[0]):
for j in xrange(0, img.shape[1]):
img_vector[count] = img[i][j]
img_pixel[count] = np.array([i, j])
count += 1
num_samples = img_vector.shape[0]
num_features = img_vector.shape[1]
img_vector_T = img_vector.T
cntr, u, u0, d, jm, p, fpc = fuzz.cmeans(img_vector_T,
k,
2.,
error=0.05,
maxiter=20)
ClusterIm = np.zeros([img.shape[0], img.shape[1]], int)
uT = u.T
for i in xrange(0, num_samples):
row = img_pixel[i][0]
col = img_pixel[i][1]
max_val = 0.
max_cluster = 0
for j in xrange(0, k):
if uT[i][j] > max_val:
max_val = uT[i][j]
max_cluster = j + 1
ClusterIm[row][col] = max_cluster
ccImOneBase = getCCIM.getCCIM(ClusterIm, 4)
return ClusterIm, ccImOneBase
def MyKmeans05(img1,ImageType,numClust):
#normalizing image data
img2 = img1.astype(float)
img = img2 / 255
#diff constants for use
num_features = img.shape[2]
num_samples=img.shape[0]*img.shape[1]
img_height = img.shape[0]
img_width = img.shape[1]
#reshape img
img_vector = np.reshape(img,(num_samples,num_features))
#for hyper simply change the img_vector after pca with 3 dimensions
if(ImageType=='Hyper'):
pca = PCA(3)
principalComponents = pca.fit_transform(img_vector)
img_vector = principalComponents
kmeans= sklearn.cluster.KMeans(n_clusters=numClust,init="k-means++",max_iter=300).fit(img_vector)
ClusterIm = np.reshape(kmeans.labels_,(img_height,img_width))
ccImOneBase = getCCIM.getCCIM(ClusterIm, 4)
return ClusterIm, ccImOneBase
def MyKmeans5(img, ImageType, numClust):
num_features = img.shape[2]
k = numClust
img_vector = np.zeros([img.shape[0] * img.shape[1], num_features])
img_pixel = np.zeros([img.shape[0] * img.shape[1], 2], int)
count = 0
for i in xrange(0, img.shape[0]):
for j in xrange(0, img.shape[1]):
img_vector[count] = img[i][j]
img_pixel[count] = np.array([i, j])
count += 1
num_samples = img_vector.shape[0]
img_vector_T = img_vector.T
kmeans = sklearn.cluster.KMeans(n_clusters=k,
init="k-means++",
max_iter=300).fit(img_vector)
uT = kmeans.labels_
ClusterIm = np.zeros([img.shape[0], img.shape[1]], int)
for i in xrange(0, num_samples):
row = img_pixel[i][0]
col = img_pixel[i][1]
ClusterIm[row][col] = uT[i] + 1
ccImOneBase = getCCIM.getCCIM(ClusterIm, 4)
return ClusterIm, ccImOneBase
def MySOM05(img1, ImageType, numClust):
#normalizing image data
img2 = img1.astype(float)
img = img2 / 255
#diff constants for use
num_features = img.shape[2]
num_samples = img.shape[0] * img.shape[1]
img_height = img.shape[0]
img_width = img.shape[1]
#reshape img
img_vector = np.reshape(img, (num_samples, num_features))
#for hyper simply change the img_vector after pca with 3 dimensions
if (ImageType == 'Hyper'):
pca = PCA(3)
principalComponents = pca.fit_transform(img_vector)
img_vector = principalComponents
#map sizeis 25X25
N = 25
som = SOM((N, N), img_vector)
som.set_parameter(neighbor=0.1, learning_rate=0.2)
output_map = som.train(100000)
# print output_map.shape
somMap = output_map.reshape([output_map.shape[0] * output_map.shape[1], 3])
# kmeans to find new centroids
kmeans2 = sklearn.cluster.KMeans(n_clusters=numClust,
init="k-means++",
max_iter=10).fit(somMap)
uT = kmeans2.labels_
new_centroids = kmeans2.cluster_centers_
# use new_centroids for kmeans of 1 iteration only to find final output
kmeans3 = sklearn.cluster.KMeans(n_clusters=numClust,
init=new_centroids,
n_init=1,
max_iter=300).fit(img_vector)
ClusterIm = np.reshape(kmeans3.labels_, (img_height, img_width))
ccImOneBase = getCCIM.getCCIM(ClusterIm, 4)
return ClusterIm, ccImOneBase
def MyFCM05(img1, ImageType, numClust):
#normalizing image data
img2 = img1.astype(float)
img = img2 / 255
#diff constants for use
num_features = img.shape[2]
num_samples = img.shape[0] * img.shape[1]
img_height = img.shape[0]
img_width = img.shape[1]
#reshape img
img_vector = np.reshape(img, (num_samples, num_features))
img_vector_T = img_vector.T
#for hyper simply change the img_vector after pca with 3 dimensions
if (ImageType == 'Hyper'):
pca = PCA(3)
principalComponents = pca.fit_transform(img_vector)
img_vector = principalComponents
cntr, u, u0, d, jm, p, fpc = fuzz.cmeans(img_vector_T,
numClust,
2.,
error=0.05,
maxiter=20)
uT = u.T
output = np.zeros(num_samples)
for i in xrange(0, num_samples):
max_val = 0.
max_cluster = 0
for j in xrange(0, numClust):
if uT[i][j] > max_val:
max_val = uT[i][j]
max_cluster = j + 1
output[i] = max_cluster
ClusterIm = np.reshape(output, (img_height, img_width))
ClusterIm = ClusterIm.astype(int)
# imgplot = plt.imshow(img)
# plt.pause(2)
# plt.imshow(ClusterIm)
# plt.pause(5)
ccImOneBase = getCCIM.getCCIM(ClusterIm, 4)
return ClusterIm, ccImOneBase