def entropy_(block):
"""
Function to calculate the variance of the block
"""
a = entropy(block[:, :, 0])
b = entropy(block[:, :, 1])
c = entropy(block[:, :, 2])
return [a, b, c]
def test_v_measure_and_mutual_information(seed=36):
# Check relation between v_measure, entropy and mutual information
for i in np.logspace(1, 4, 4).astype(np.int):
random_state = np.random.RandomState(seed)
labels_a, labels_b = random_state.random_integers(0, 10, i),\
random_state.random_integers(0, 10, i)
assert_almost_equal(v_measure_score(labels_a, labels_b),
2.0 * mutual_info_score(labels_a, labels_b) /
(entropy(labels_a) + entropy(labels_b)), 0)
def test_v_measure_and_mutual_information(seed=36):
# Check relation between v_measure, entropy and mutual information
for i in np.logspace(1, 4, 4).astype(np.int):
random_state = np.random.RandomState(seed)
labels_a, labels_b = (random_state.randint(0, 10, i),
random_state.randint(0, 10, i))
assert_almost_equal(v_measure_score(labels_a, labels_b),
2.0 * mutual_info_score(labels_a, labels_b) /
(entropy(labels_a) + entropy(labels_b)), 0)
def test_v_measure_and_mutual_information(seed=36):
"""Check relation between v_measure, entropy and mutual information"""
for i in np.logspace(1, 4, 4):
random_state = np.random.RandomState(seed)
labels_a, labels_b = random_state.random_integers(0, 10, i),\
random_state.random_integers(0, 10, i)
assert_almost_equal(v_measure_score(labels_a, labels_b),
2.0 * mutual_info_score(labels_a, labels_b) /
(entropy(labels_a) + entropy(labels_b)), 0)
def test_entropy():
h = entropy([0, 0, 42.], log_base='e')
assert_almost_equal(h, 0.6365141, 5)
h = entropy([0, 0, 42.], log_base=2)
assert_almost_equal(h, 0.9182958, 5)
h = entropy([], log_base='e')
assert_almost_equal(h, 1)
h = entropy([], log_base=2)
assert_almost_equal(h, 1)
def test_v_measure_and_mutual_information(seed=36):
# Check relation between v_measure, entropy and mutual information
for i in np.logspace(1, 4, 4).astype(np.int):
random_state = np.random.RandomState(seed)
labels_a, labels_b = (random_state.randint(0, 10, i),
random_state.randint(0, 10, i))
v_m = v_measure_score(labels_a, labels_b)
mi = mutual_info_score(labels_a, labels_b, log_base='e')
h_a = entropy(labels_a, log_base='e')
h_b = entropy(labels_b, log_base='e')
assert_almost_equal(v_m, 2.0 * mi / (h_a + h_b), 0)
def _evaluate_VI(index_pair, top_partitions):
"""Worker for VI evaluations."""
MI = mutual_info_score(
top_partitions[index_pair[0]],
top_partitions[index_pair[1]],
)
Ex = entropy(top_partitions[index_pair[0]])
Ey = entropy(top_partitions[index_pair[1]])
JE = Ex + Ey - MI
if abs(JE) < 1e-8:
return 0.0
return (JE - MI) / JE
def getEntropy( KF ):
entropy_sum = 0
for i in range(1, TOTAL_KEY_FRAMES - 1):
while True:
try:
im1 = cv2.imread(READ_LOCATION + str(KF[i]) + ".jpg",0)
im2 = cv2.imread(READ_LOCATION + str(KF[i+1]) + ".jpg",0)
entropy_sum += abs(entropy(im1) - entropy(im2))
except:
print i, KF, KF[i], KF[i+1]
continue
break
return entropy_sum/(TOTAL_KEY_FRAMES - 1)
def nmi_score(y_true, y_pred):
"""NMI
https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html
this function is not the same with
sklearn.metrics.normalized_mutual_info_score
in that this function uses [H(y_true)+H(y_pred)/2] while
that of sklearn uses sqrt(H(y_true)H(y_pred))
"""
labels = align_labels(y_true)
mi = mutual_info_score(y_true, y_pred)
h1 = entropy(labels)
h2 = entropy(y_pred)
return 2 * mi / (h1 + h2)
def normalized_information_distance(c1, c2):
"""
Calculate Normalized Information Distance
Taken from Vinh, Epps, and Bailey, (2010). Information Theoretic Measures
for Clusterings Comparison: Variants, Properties, Normalization and
Correction for Chance, JMLR
<http://jmlr.csail.mit.edu/papers/volume11/vinh10a/vinh10a.pdf>
"""
denom = max(entropy(c1), entropy(c2))
# The clusterings are identical (so return 1.0) if both have zero entropy.
return (1.0 - (mutual_info_score(c1, c2) / denom)) if denom else 1.0
def test_v_measure_and_mutual_information(seed=36):
# Check relation between v_measure, entropy and mutual information
for i in np.logspace(1, 4, 4).astype(int):
random_state = np.random.RandomState(seed)
labels_a, labels_b = (random_state.randint(0, 10, i),
random_state.randint(0, 10, i))
assert_almost_equal(v_measure_score(labels_a, labels_b),
2.0 * mutual_info_score(labels_a, labels_b) /
(entropy(labels_a) + entropy(labels_b)), 0)
avg = 'arithmetic'
assert_almost_equal(v_measure_score(labels_a, labels_b),
normalized_mutual_info_score(labels_a, labels_b,
average_method=avg)
)
def sklearn_measures(U, V):
# http://scikit-learn.org/stable/modules/classes.html#clustering-metrics
import sklearn.metrics.cluster as sym
U_labels = np.nonzero(U)[1]
V_labels = np.nonzero(V)[1]
print U_labels, V_labels
# V2_labels = np.nonzero(V2)[1]
print 'entro(U)=',sym.entropy(U_labels),'entro(V)=',sym.entropy(V_labels), 'entro(U,V)=',sym.mutual_info_score(U_labels, V_labels)
res = [ ['ari', 'nmi', 'ami', 'vm' ], \
[ sym.adjusted_rand_score(U_labels, V_labels),\
sym.normalized_mutual_info_score(U_labels, V_labels),\
sym.adjusted_mutual_info_score(U_labels, V_labels),\
sym.v_measure_score(U_labels, V_labels)]]
print res
return res
def entropia_por_bloco(list_blocos_img):
# String de Retorno
return_lista_entropia_img = []
for i in range(len(list_blocos_img)):
return_lista_entropia_img.append(entropy(list_blocos_img[i]))
return return_lista_entropia_img
def extr_beauty_ftrs(imgFlNm):
img = os.path.basename(imgFlNm)
# print("Extracting beauty features for %s" % img)
try:
rgbImg = resize_img(io.imread(imgFlNm))
except Exception as e:
print("Invalid image!", e, 'image', imgFlNm)
return e
rgbImg = resize_img(io.imread(imgFlNm))
if len(rgbImg.shape) != 3 or rgbImg.shape[2] != 3:
print("Invalid image.. Continuing..")
final_ftr_obj_global[img] = None
return None
hsvImg = color.rgb2hsv(rgbImg)
grayImg = color.rgb2gray(rgbImg)
red, green, blue = get_arr(rgbImg)
hue, saturation, value = get_arr(hsvImg)
contrast = calc_contrast(red, green, blue)
ftrs = calc_color_ftrs(hue, saturation, value)
ftrs['contrast'] = contrast
ftrs['entropy'] = entropy(
grayImg
) # added to include entropy of the given image: more details: http://stackoverflow.com/a/42059758/5759063
ftrs.update(get_spat_arrng_ftrs(grayImg))
final_ftr_obj_global[img] = ftrs
return final_ftr_obj_global
def sklearn_measures(U, V):
# http://scikit-learn.org/stable/modules/classes.html#clustering-metrics
import sklearn.metrics.cluster as sym
U_labels = np.nonzero(U)[1]
V_labels = np.nonzero(V)[1]
print U_labels, V_labels
# V2_labels = np.nonzero(V2)[1]
print 'entro(U)=', sym.entropy(U_labels), 'entro(V)=', sym.entropy(
V_labels), 'entro(U,V)=', sym.mutual_info_score(U_labels, V_labels)
res = [ ['ari', 'nmi', 'ami', 'vm' ], \
[ sym.adjusted_rand_score(U_labels, V_labels),\
sym.normalized_mutual_info_score(U_labels, V_labels),\
sym.adjusted_mutual_info_score(U_labels, V_labels),\
sym.v_measure_score(U_labels, V_labels)]]
print res
return res
def extr_beauty_ftrs(imgFlNm):
img = os.path.basename(imgFlNm)
print("Extracting beauty features for %s" %imgFlNm)
try:
rgbImg = resize_img(io.imread(imgFlNm))
except Exception as e:
print("Invalid image")
return e
if len(rgbImg.shape) != 3 or rgbImg.shape[2] !=3:
print("Invalid image.. Continuing..")
final_ftr_obj_global[img] = None
return None
hsvImg = color.rgb2hsv(rgbImg)
grayImg = color.rgb2gray(rgbImg)
red, green, blue = get_arr(rgbImg)
hue, saturation, value = get_arr(hsvImg)
contrast = calc_contrast(red, green, blue)
ftrs = calc_color_ftrs(hue, saturation, value)
ftrs['contrast'] = contrast
ftrs['entropy'] = entropy(grayImg) # added to include entropy of the given image: more details: http://stackoverflow.com/a/42059758/5759063
ftrs.update(get_spat_arrng_ftrs(grayImg))
final_ftr_obj_global[img] = ftrs
return final_ftr_obj_global
def get_texture(output):
# texture Feature
grayImg = img_as_ubyte(color.rgb2gray(output))
#"distances" is a list of distances (in pixels) between the pixels being compared
distances = [1, 2, 3]
#"angles" is a list of angles (in radians) between pixels being compared
angles = [0, np.pi / 4, np.pi / 2, 3 * np.pi / 4]
# properties = ['energy', 'homogeneity']
glcm = greycomatrix(grayImg,
distances=distances,
angles=angles,
symmetric=True,
normed=True)
# feature1 for texture
contrast = greycoprops(glcm, prop='contrast')
# feature2 for texture
energy = greycoprops(glcm, prop='energy')
# feature3 for texture
correlation = greycoprops(glcm, prop='correlation')
# entropy=shannon_entropy(glcm)
# feature4 for texture
ent = entropy(glcm)
# entropy = shannon_entropy(glcm, base=np.e)
return contrast, energy, correlation, ent
def get_features(nodules):
features = []
i = 0
for nodule in nodules:
nodule_feature = Features(nodule)
nodule_feature.max_coord = np.max(nodule.pixels)
nodule_feature.conclusion = nodule.conclusion
with warnings.catch_warnings():
warnings.simplefilter("ignore")
grey_comatrix = skimg.greycomatrix(nodule.pixels, [1], [0, np.pi / 4, np.pi / 2, 3 * np.pi / 4],
nodule_feature.max_coord + 1)
nodule_feature.features['contrast'] = skimg.greycoprops(grey_comatrix, 'contrast').flatten().astype(float)
nodule_feature.features['dissimilarity'] = skimg.greycoprops(grey_comatrix, 'dissimilarity').flatten().astype(
float)
nodule_feature.features['homogeneity'] = skimg.greycoprops(grey_comatrix, 'homogeneity').flatten()
nodule_feature.features['energy'] = skimg.greycoprops(grey_comatrix, 'energy').flatten()
nodule_feature.features['correlation'] = skimg.greycoprops(grey_comatrix, 'correlation').flatten()
nodule_feature.features['ASM'] = skimg.greycoprops(grey_comatrix, 'ASM').flatten().astype(float)
nodule_feature.features['entropy'] = entropy(nodule.pixels)
features.append(nodule_feature)
i += 1
print 'progress =', i, '/', len(nodules), nodule.source_id
return np.array(features)
def detect_roughness(patch):
blur = cv2.GaussianBlur(patch, (5, 5), 5)
patch = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
if entropy(patch) > 4:
return True
else:
return False
def extract_entropy_full(path_img):
### FEATURE 1 ###
# Extraindo a entropia da imagem inteira
# Image.open(argv_img).convert('LA') converte em grayscale
rgbImg = io.imread(path_img)
grayImg = img_as_ubyte(color.rgb2gray(rgbImg))
return_entropia = entropy(grayImg)
return return_entropia
def extract_feature(arr):
radius = 1
n_points = radius * 8
arr = cv2.cvtColor(arr, cv2.COLOR_BGR2GRAY)
distances = [1, 5]
angles = [0, np.pi / 4, np.pi / 2, 3 * np.pi / 4]
glcm = greycomatrix(arr,
distances=distances,
angles=angles,
levels=256,
symmetric=False,
normed=False)
# properties = ['dissimilarity', 'homogeneity', 'contrast', 'ASM', 'energy', 'correlation']
# glcm_feats = np.hstack([greycoprops(glcm, prop=prop).ravel() for prop in properties])
glcm_feats = np.hstack(
[adadoc.greycoprops(glcm[:, :, i, :]) for i in range(0, 2)]).ravel()
hog_feats = hog(arr,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(1, 1),
block_norm='L2-Hys',
feature_vector=True)
ent = entropy(arr)
# # prepare filter bank kernels
# kernels = []
# for theta in range(4):
# theta = theta / 4. * np.pi
# for sigma in (1, 3):
# for frequency in (0.05, 0.25):
# kernel = np.real(gabor_kernel(frequency, theta=theta,
# sigma_x=sigma, sigma_y=sigma))
# kernels.append(kernel)
# gabor_feat = compute_feats(arr, kernels).ravel()
thresh_sauvola = threshold_sauvola(arr, window_size=31, k=0.2)
arr = arr > thresh_sauvola
arr = (255 - arr * 255).astype('uint8')
# arr = adadoc.adath(arr, method=adadoc.ADATH_SAUVOLA | adadoc.ADATH_INVTHRESH,
# xblock=21, yblock=21, k=0.2, dR=64, C=0)
lbp_code = local_binary_pattern(arr, n_points, radius, 'uniform')
# n_bins = int(lbp_code.max() + 1)
n_bins = 16
lbp_feats, _ = np.histogram(lbp_code,
normed=True,
bins=n_bins,
range=(0, n_bins))
data_feat = np.hstack([lbp_feats, ent, glcm_feats, hog_feats])
return data_feat
def _print_clusteringMetrics(_kMean, _X):
metrics = [['Clustering K-Means', 'Datos obtenidos'],
['Inercia', _kMean.inertia_],
['Entropy', entropy(_kMean.labels_)],
['Silhouette Score', silhouette_score(_X, _kMean.labels_, random_state = 0)],
['Calinski-Harabaz Score', calinski_harabaz_score(_X, _kMean.labels_)], ]
print('\nMinería de Datos - Clustering K-Means - <VORT>', '\n')
print(_kMean, '\n')
print(look(metrics))
def extract_feature(arr):
radius = 1
n_points = radius * 8
# arr = cv2.cvtColor(arr, cv2.COLOR_BGR2GRAY)
distances = [1, 5]
angles = [0, np.pi / 4, np.pi / 2, 3 * np.pi / 4]
glcm = greycomatrix(arr.copy(),
distances=distances,
angles=angles,
levels=256,
symmetric=False,
normed=False)
# properties = ['dissimilarity', 'homogeneity', 'contrast', 'ASM', 'energy', 'correlation']
# glcm_feats = np.hstack([greycoprops(glcm, prop=prop).ravel() for prop in properties])
glcm_feats = np.hstack(
[adadoc.greycoprops(glcm[:, :, i, :]) for i in range(0, 2)]).ravel()
hog_feats = hog(arr,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(1, 1),
block_norm='L2-Hys',
feature_vector=True)
ent = entropy(arr)
arr = adadoc.adath(arr,
method=adadoc.ADATH_SAUVOLA | adadoc.ADATH_INVTHRESH,
xblock=21,
yblock=21,
k=0.2,
dR=64,
C=0)
# thresh_sauvola = threshold_sauvola(arr, window_size=31, k=0.2)
# arr = arr > thresh_sauvola
# arr = (255 - arr * 255).astype('uint8')
lbp_code = local_binary_pattern(arr, n_points, radius, 'uniform')
# n_bins = int(lbp_code.max() + 1)
n_bins = 16
lbp_feats, _ = np.histogram(lbp_code,
normed=True,
bins=n_bins,
range=(0, n_bins))
data_feat = np.hstack([lbp_feats, ent, glcm_feats, hog_feats])
return data_feat
def normalized_max_mutual_info_score(labels_true, labels_pred):
"""
A variant version of NMI that is given as:
NMI_max = MI(U, V) / max{ H(U), H(V) }
based on 'adjusted mutual info score' in sklearn
Parameters
----------
:param labels_true: labels of clustering 1 (as a 1-dimensional ndarray)
:param labels_pred: labels of clustering 2 (as a 1-dimensional ndarray)
:return: diversity between these two clusterings as a float value
Returns
-------
:return: NMI-max between these two clusterings as a float value
"""
labels_true, labels_pred = check_clusterings(labels_true, labels_pred)
n_samples = labels_true.shape[0]
classes = np.unique(labels_true)
clusters = np.unique(labels_pred)
# Special limit cases: no clustering since the data is not split.
# This is a perfect match hence return 1.0.
if (classes.shape[0] == clusters.shape[0] == 1
or classes.shape[0] == clusters.shape[0] == 0):
return 1.0
contingency = contingency_matrix(labels_true, labels_pred)
contingency = np.array(contingency, dtype='float')
# Calculate the MI for the two clusterings
mi = metrics.mutual_info_score(labels_true,
labels_pred,
contingency=contingency)
# Calculate the expected value for the mutual information
# Calculate entropy for each labeling
h_true, h_pred = entropy(labels_true), entropy(labels_pred)
nmi_max = mi / max(h_true, h_pred)
return nmi_max
def ExtractFeature(fruit_images):
list_of_vectors = []
for img in fruit_images:
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
row, col = gray_img.shape
canvas = np.zeros((row, col, 1), np.uint8)
for i in range(row):
for j in range(col):
if gray_img[i][j] < 220:
canvas.itemset((i, j, 0), 255)
else:
canvas.itemset((i, j, 0), 0)
kernel = np.ones((3, 3), np.uint8)
canvas = cv2.morphologyEx(canvas, cv2.MORPH_CLOSE, kernel)
for i in range(row):
for j in range(col):
b, g, r = img[i][j]
if canvas[i][j] == 255:
img.itemset((i, j, 0), b)
img.itemset((i, j, 1), g)
img.itemset((i, j, 2), r)
else:
img.itemset((i, j, 0), 0)
img.itemset((i, j, 1), 0)
img.itemset((i, j, 2), 0)
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
rgb_means, rgb_std = cv2.meanStdDev(img)
hsv_means, hsv_std = cv2.meanStdDev(hsv_img)
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
coeff = pywt.dwt2(gray_img, "haar") # ---- Dekomposisi lv 1
LL, (LH, HL, HH) = coeff
Energy = (LH**2 + HL**2 + HH**2).sum() / img.size
Entropy = entropy(gray_img)
b, g, r = img[row / 2 - 1, col / 2 - 1]
# center_code = tools.CentreClass(b,g,r)
list_of_vectors.append([
rgb_means[2], rgb_means[1], rgb_means[0], rgb_std[2], rgb_std[1],
rgb_std[0], hsv_means[2], hsv_means[1], hsv_means[0], hsv_std[2],
hsv_std[1], hsv_std[0], Energy, Entropy
])
list_of_vectors = np.array(list_of_vectors)
return (list_of_vectors)
def glcm_f(segment_region):
glcm = greycomatrix(segment_region, [5], [0], 256)
stats = [
"dissimilarity", "correlation", "contrast", "homogeneity", "ASM",
"energy"
]
dissimilarity = greycoprops(glcm, stats[0])[0, 0]
correlation = greycoprops(glcm, stats[1])[0, 0]
contrast = greycoprops(glcm, stats[2])[0, 0]
homogeneity = greycoprops(glcm, stats[3])[0, 0]
ASM = greycoprops(glcm, stats[4])[0, 0]
energy = greycoprops(glcm, stats[5])[0, 0]
entropy_f = entropy(segment_region)
temp_features = [
dissimilarity, correlation, contrast, homogeneity, ASM, energy,
entropy_f
]
return temp_features
print("Entropy")
import numpy as np
from skimage import io, color, img_as_ubyte
from skimage.feature import greycomatrix, greycoprops
from sklearn.metrics.cluster import entropy
rgbImg = io.imread('D:/PAPERS/With_Pratap_2019/figs/airplane_cipher.png')
grayImg = img_as_ubyte(color.rgb2gray(rgbImg))
#grayImg = Image.open('Documents/figs/barbara.jpg')
print(entropy(grayImg))
def test_entropy():
ent = entropy([0, 0, 42.])
assert_almost_equal(ent, 0.6365141, 5)
def test_entropy():
ent = entropy([0, 0, 42.])
assert_almost_equal(ent, 0.6365141, 5)
assert_almost_equal(entropy([]), 1)
Vprocc2 = shatt.in_range(Vprocc2)
Vprocf2 = shatt.in_range(Vprocf2)
Vprocc3 = shatt.in_range(Vprocc3)
Vprocf3 = shatt.in_range(Vprocf3)
#save_without_retrieve_color(hsv, Vprocc2, Vprocf2, Vprocc3, Vprocf3)
# Retrieve true color to skin
muorig = V.mean()
Vnewc2 = shatt.retrieve_color(Vprocc2, muorig)
Vnewf2 = shatt.retrieve_color(Vprocf2, muorig)
Vnewc3 = shatt.retrieve_color(Vprocc3, muorig)
Vnewf3 = shatt.retrieve_color(Vprocf3, muorig)
# Convert Value into the range 0-1
Vnewc2 = shatt.in_range(Vnewc2)
Vnewf2 = shatt.in_range(Vnewf2)
Vnewc3 = shatt.in_range(Vnewc3)
Vnewf3 = shatt.in_range(Vnewf3)
save_with_retrieve_color(hsv, Vnewc2, Vnewf2, Vnewc3, Vnewf3)
# Select the image which have least entropy
Vlist = [V, Vnewc2, Vnewf2, Vnewc3, Vnewf3]
values = [img_as_ubyte(v) for v in Vlist]
entropy_vals = [entropy(v) for v in values]
print('entropy: ' + str(entropy_vals))
print('index: ' + str(entropy_vals.index(min(entropy_vals))))
img.itemset((i, j, 1), g)
img.itemset((i, j, 2), r)
else:
img.itemset((i, j, 0), 0)
img.itemset((i, j, 1), 0)
img.itemset((i, j, 2), 0)
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
rgb_means, rgb_std = cv2.meanStdDev(img)
hsv_means, hsv_std = cv2.meanStdDev(hsv_img)
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
coeff = pywt.dwt2(gray_img, "haar") # ---- Dekomposisi lv 1
LL, (LH, HL, HH) = coeff
Energy = (LH**2 + HL**2 + HH**2).sum() / img.size
Entropy = entropy(gray_img)
b, g, r = img[row / 2 - 1, col / 2 - 1]
center_code = tools.CentreClass(b, g, r)
list_of_vectors.append([
rgb_means[2], rgb_means[1], rgb_means[0], rgb_std[2], rgb_std[1],
rgb_std[0], hsv_means[2], hsv_means[1], hsv_means[0], hsv_std[2],
hsv_std[1], hsv_std[0], Energy, Entropy
])
list_of_vectors = np.array(list_of_vectors)
# X_train, X_test, y_train, y_test = train_test_split(list_of_vectors, label_ids, test_size=0.30, random_state=14)
X_tr = list_of_vectors
y_tr = label_ids
input = cv2.imread(path + i, cv2.IMREAD_GRAYSCALE)
glcm = skimage.feature.greycomatrix(
input, [1], [0, np.pi / 4, np.pi / 2, np.pi * 3 / 4],
256,
symmetric=True,
normed=True)
# 对比度，差异性，同质性，能量，相关，二阶矩
# for prop in {'contrast', 'dissimilarity','homogeneity', 'energy', 'correlation', 'ASM'}:
contrast = skimage.feature.greycoprops(glcm, "contrast")
dissimilarity = skimage.feature.greycoprops(glcm, "dissimilarity")
homogeneity = skimage.feature.greycoprops(glcm, "homogeneity")
energy = skimage.feature.greycoprops(glcm, "energy")
correlation = skimage.feature.greycoprops(glcm, "correlation")
ASM = skimage.feature.greycoprops(glcm, "ASM")
entropyImg = entropy(input) #得到熵
sheet1.write(flag + 1, 0, i)
sheet1.write(flag + 1, 1, contrast[0][0])
sheet1.write(flag + 1, 2, dissimilarity[0][0])
sheet1.write(flag + 1, 3, homogeneity[0][0])
sheet1.write(flag + 1, 4, energy[0][0])
sheet1.write(flag + 1, 5, correlation[0][0])
sheet1.write(flag + 1, 6, ASM[0][0])
sheet1.write(flag + 1, 7, entropyImg)
print(contrast[0][0], dissimilarity[0][0], homogeneity[0][0],
energy[0][0], correlation[0][0], ASM[0][0], entropyImg)
print("----------分界线-----------")
flag += 1
wb.save("data/glcm.xls")
def shading_attenuation_method(image, extract, margin):
"""
Apply the shading attenuation method to an image
Parameters
----------
image: 3D array
The image source
extract: scalar
Number of pixel to extract, extract x extract
margin: scalar
Margin from the borders
"""
hsv = rgb2hsv(image)
V = np.copy(hsv[:, :, 2])
shape = image.shape[0:2]
"""
Sampling pixels
---------------
"""
Yc, Xc = shatt.sampling_from_corners(margin=margin, extract=extract, shape=shape)
Yf, Xf = shatt.sampling_from_frames(margin=margin, extract=extract, shape=shape)
Zc = np.zeros((Xc.shape))
Zf = np.zeros((Xf.shape))
for j in range(0, Zc.shape[0]):
Zc[j] = np.copy(V[Yc[j], Xc[j]])
for j in range(0, Zf.shape[0]):
Zf[j] = np.copy(V[Yf[j], Xf[j]])
"""
Quadratic and cubic polynomial coefficients
-------------------------------------------
"""
Ac2 = shatt.quadratic_polynomial_function(Yc, Xc)
Af2 = shatt.quadratic_polynomial_function(Yf, Xf)
Ac3 = shatt.cubic_polynomial_function(Yc, Xc)
Af3 = shatt.cubic_polynomial_function(Yf, Xf)
"""
Fitting polynomial
------------------
"""
coeffc2 = np.linalg.lstsq(Ac2, Zc)[0]
coefff2 = np.linalg.lstsq(Af2, Zf)[0]
coeffc3 = np.linalg.lstsq(Ac3, Zc)[0]
coefff3 = np.linalg.lstsq(Af3, Zf)[0]
"""
Processed
---------
"""
Vprocc2 = shatt.apply_quadratic_function(V, coeffc2)
Vprocf2 = shatt.apply_quadratic_function(V, coefff2)
Vprocc3 = shatt.apply_cubic_function(V, coeffc3)
Vprocf3 = shatt.apply_cubic_function(V, coefff3)
# Convert Value into the range 0-1
Vprocc2 = shatt.in_range(Vprocc2)
Vprocf2 = shatt.in_range(Vprocf2)
Vprocc3 = shatt.in_range(Vprocc3)
Vprocf3 = shatt.in_range(Vprocf3)
# Retrieve true color to skin
muorig = V.mean()
Vnewc2 = shatt.retrieve_color(Vprocc2, muorig)
Vnewf2 = shatt.retrieve_color(Vprocf2, muorig)
Vnewc3 = shatt.retrieve_color(Vprocc3, muorig)
Vnewf3 = shatt.retrieve_color(Vprocf3, muorig)
# Convert Value into the range 0-1
Vnewc2 = shatt.in_range(Vnewc2)
Vnewf2 = shatt.in_range(Vnewf2)
Vnewc3 = shatt.in_range(Vnewc3)
Vnewf3 = shatt.in_range(Vnewf3)
# Select the image which have least entropy
Vlist = [V, Vnewc2, Vnewf2, Vnewc3, Vnewf3]
values = [img_as_ubyte(v) for v in Vlist]
entropy_vals = [entropy(v) for v in values]
print('\tentropy: '+str(entropy_vals))
index = entropy_vals.index(min(entropy_vals))
hsv[:, :, 2] = np.copy(Vlist[index])
attenuated = hsv2rgb(hsv)
return attenuated
def normalized_mutual_information(cl: np.ndarray, org: np.ndarray):
""" Clustering accuracy measure, which takes into account mutual information between two clusterings and entropy
of each cluster """
assert cl.shape == org.shape
return mutual_info_score(org, cl) / (abs(entropy(cl) + entropy(org)) / 2)
def upload_pic(request):
if request.POST:
file_doc = request.FILES['ahoy']
sv = fruit(document=file_doc)
sv.save()
oyo = fruit.objects.last()
oy = oyo.id
item = fruit.objects.get(id=oy)
#item = fruit.objects.get(id=oy)
img_file = item.document
ayay = img_file.url
print(ayay)
print(img_file)
image = cv2.imread(img_file.url)
image = cv2.medianBlur(image, 5)
fruit_images = []
fruit_images.append(image)
##### Feature Extraction
list_of_vectors = []
for img in fruit_images:
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
row, col = gray_img.shape
canvas = np.zeros((row, col, 1), np.uint8)
for i in range(row):
for j in range(col):
if gray_img[i][j] < 220:
canvas.itemset((i, j, 0), 255)
else:
canvas.itemset((i, j, 0), 0)
kernel = np.ones((3, 3), np.uint8)
canvas = cv2.morphologyEx(canvas, cv2.MORPH_CLOSE, kernel)
for i in range(row):
for j in range(col):
b, g, r = img[i][j]
if canvas[i][j] == 255:
img.itemset((i, j, 0), b)
img.itemset((i, j, 1), g)
img.itemset((i, j, 2), r)
else:
img.itemset((i, j, 0), 0)
img.itemset((i, j, 1), 0)
img.itemset((i, j, 2), 0)
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
rgb_means, rgb_std = cv2.meanStdDev(img)
hsv_means, hsv_std = cv2.meanStdDev(hsv_img)
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
coeff = pywt.dwt2(gray_img, "haar") # ---- Dekomposisi lv 1
LL, (LH, HL, HH) = coeff
Energy = (LH**2 + HL**2 + HH**2).sum() / img.size
Entropy = entropy(gray_img)
b, g, r = img[row / 2 - 1, col / 2 - 1]
list_of_vectors.append([
rgb_means[2], rgb_means[1], rgb_means[0], rgb_std[2],
rgb_std[1], rgb_std[0], hsv_means[2], hsv_means[1],
hsv_means[0], hsv_std[2], hsv_std[1], hsv_std[0], Energy,
Entropy
])
img_file = item.document
list_of_vectors = np.array(list_of_vectors)
X_tr = list_of_vectors
clf = joblib.load('clfRF70-12-g-wcc.pkl')
test_predict = clf.predict(X_tr)
fr = test_predict[0]
fra = int(fr)
item.Tipe = fra
item.save()
return HttpResponseRedirect(reverse('buah:hasil', args=[item.id]))
def info_var(z, zh):
"""Compute variation of information based on M. Meila (2007)."""
return entropy(z) + entropy(zh) - 2*mutual_info_score(z, zh)
def create_clusters(tweetsDF, my_token_pattern, min_dist_thres=0.6, min_max_diff_thres=0.4, max_dist_thres=0.8, iteration_no=1, min_clusters=1, printsize=True, nameprefix='', selection=True, strout=False, user_identifier='screen_name', cluster_list=None):
"""
Have modes:
mode1: get a certain number of clusters. Relax parameters for it. (This is the current Mode!)
mode2: get clusters that comply with certain conditions.
"min_max_diff_thres" should not be too small. Then You miss thresholds like: min 0.3 - min 0.7: The top is controlled by the maximum anyway. Do not fear from having it big: around 0.4
"""
if min_dist_thres > 0.85 and max_dist_thres>0.99:
logging.info("The parameter values are too high to allow a good selection. We just finish searching for clusters at that stage.")
logging.info("Threshold Parameters are: \nmin_dist_thres="+str(min_dist_thres)+"\tmin_max_diff_thres:="+str(min_max_diff_thres)+ "\tmax_dist_thres="+str(max_dist_thres))
return cluster_list
len_clust_list = 0
if cluster_list is None:
cluster_list = []
elif not selection and len(cluster_list)>0:
return cluster_list
else:
len_clust_list = len(cluster_list)
logging.info("Starting the iteration with:"+str(len_clust_list)+" clusters.")
clustered_tweet_ids = []
for clust_dict in cluster_list:
clustered_tweet_ids += clust_dict["twids"]
logging.info("Number of already clustered tweets are:"+str(len(clustered_tweet_ids)))
logging.info("Tweet set size to be clustered:"+str(len(tweetsDF)))
tweetsDF = tweetsDF[~tweetsDF.id_str.isin(clustered_tweet_ids)]
logging.info("Tweet set size to be clustered(after elimination of the already clustered tweets):"+str(len(tweetsDF)))
if len(tweetsDF)==0:
logging.info("Please check that the id_str has a unique value for each item.")
print("Please check that the id_str has a unique value for each item.")
return cluster_list
logging.info('Creating clusters was started!!')
logging.info("Threshold Parameters are: \nmin_dist_thres="+str(min_dist_thres)+"\tmin_max_diff_thres:="+str(min_max_diff_thres)+ "\tmax_dist_thres="+str(max_dist_thres))
cluster_bigram_cntr = Counter()
freqcutoff = int(m.log(len(tweetsDF))/2)
if freqcutoff == 0:
freqcutoff = 1 # make it at least 1.
#freqcutoff = int(m.log(len(tweetsDF))/2) # the bigger freq threshold is the quicker to find similar groups of tweets, although precision will decrease.
logging.info("Feature extraction parameters are:\tfrequencyCutoff:"+str(freqcutoff))
word_vectorizer = TfidfVectorizer(ngram_range=(1, 2), lowercase=False, norm='l2', min_df=freqcutoff, token_pattern=my_token_pattern)
text_vectors = word_vectorizer.fit_transform(tweetsDF[active_column])
# logging.info("Number of features:"+str(len(word_vectorizer.get_feature_names())))
logging.info("Features are:"+str(word_vectorizer.get_feature_names()))
n_clust = int(m.sqrt(len(tweetsDF))/2)+iteration_no*(min_clusters-len_clust_list) # The more clusters we need, the more clusters we will create.
n_initt = int(m.log10(len(tweetsDF)))+iteration_no # up to 1 million, in KMeans setting, having many iterations is not a problem. # more iterations higher chance of having candidate clusters.
logging.info("Clustering parameters are:\nnclusters="+str(n_clust)+"\tn_initt="+str(n_initt))
if len(tweetsDF) < 1000000:
km = KMeans(n_clusters=n_clust, init='k-means++', max_iter=500, n_init=n_initt) # , n_jobs=16
logging.info("The data set is small enough to use Kmeans")
else:
km = MiniBatchKMeans(n_clusters=n_clust, init='k-means++', max_iter=500, n_init=n_initt, batch_size=1000)
logging.info("The data set is BIG, MiniBatchKMeans is used.")
km.fit(text_vectors)
# Cluster = namedtuple('Cluster', ['cno', 'cstr','tw_ids'])
clustersizes = get_cluster_sizes(km, tweetsDF[active_column].values)
logging.info("Cluster sizes are:"+str(clustersizes))
for cn, csize in clustersizes.most_common(): # range(args.ksize):
cn = int(cn)
similar_indices = (km.labels_ == cn).nonzero()[0]
similar = []
similar_tuple_list = []
for i in similar_indices:
dist = sp.linalg.norm((km.cluster_centers_[cn] - text_vectors[i]))
similar_tuple_list.append((dist, tweetsDF['id_str'].values[i], tweetsDF[active_column].values[i], tweetsDF[user_identifier].values[i]))
if strout:
similar.append(str(dist) + "\t" + tweetsDF['id_str'].values[i] + "\t" + tweetsDF[active_column].values[i] + "\t" + tweetsDF[user_identifier].values[i])
similar_tuple_list = sorted(similar_tuple_list, key=itemgetter(0)) # sort based on the 0th, which is the distance from the center, element.
# test sortedness!
if strout:
similar = sorted(similar, reverse=False)
cluster_info_str = ''
user_list = [t[3] for t in similar_tuple_list] # t[3] means the third element in the similar_tuple_list.
if selection:
if (len(similar_tuple_list)>2) and (similar_tuple_list[0][0] < min_dist_thres) and (similar_tuple_list[-1][0] < max_dist_thres) and ((similar_tuple_list[0][0] + min_max_diff_thres) > similar_tuple_list[-1][0]): # the smallest and biggest distance to the centroid should not be very different, we allow 0.4 for now!
cluster_info_str += "cluster number and size are: " + str(cn) + ' ' + str(clustersizes[str(cn)]) + "\n"
for txt in tweetsDF[active_column].values[similar_indices]:
cluster_bigram_cntr.update(get_uni_bigrams(txt)) # regex.findall(r"\b\w+[-]?\w+\s\w+", txt, overlapped=True))
# cluster_bigram_cntr.update(txt.split()) # unigrams
frequency = reverse_index_frequency(cluster_bigram_cntr)
if strout:
topterms = [k+":" + str(v) for k, v in cluster_bigram_cntr.most_common() if k in word_vectorizer.get_feature_names()]
cluster_info_str += "Top terms are:" + ", ".join(topterms) + "\n"
if strout:
cluster_info_str += "distance_to_centroid" + "\t" + "tweet_id" + "\t" + "tweet_text\n"
if len(similar) > 20:
cluster_info_str += 'First 10 documents:\n'
cluster_info_str += "\n".join(similar[:10]) + "\n"
# print(*similar[:10], sep='\n', end='\n')
cluster_info_str += 'Last 10 documents:\n'
cluster_info_str += "\n".join(similar[-10:]) + "\n"
else:
cluster_info_str += "Tweets for this cluster are:\n"
cluster_info_str += "\n".join(similar) + "\n"
else:
logging.info("Cluster is not good. Smallest and largest distance to the cluster center are:"+str(similar_tuple_list[0][0])+"\t"+str(similar_tuple_list[-1][0]))
else:
cluster_info_str += "cluster number and size are: " + str(cn) + ' ' + str(clustersizes[str(cn)]) + "\n"
cluster_bigram_cntr = Counter()
for txt in tweetsDF[active_column].values[similar_indices]:
cluster_bigram_cntr.update(get_uni_bigrams(txt))
frequency = reverse_index_frequency(cluster_bigram_cntr)
if strout:
topterms = [k+":"+str(v) for k, v in cluster_bigram_cntr.most_common() if k in word_vectorizer.get_feature_names()]
cluster_info_str += "Top terms are:" + ", ".join(topterms) + "\n"
if strout:
cluster_info_str += "distance_to_centroid" + "\t" + "tweet_id" + "\t" + "tweet_text\n"
if len(similar) > 20:
cluster_info_str += 'First 10 documents:\n'
cluster_info_str += "\n".join(similar[:10]) + "\n"
# print(*similar[:10], sep='\n', end='\n')
cluster_info_str += 'Last 10 documents:\n'
cluster_info_str += "\n".join(similar[-10:]) + "\n"
else:
cluster_info_str += "Tweets for this cluster are:\n"
cluster_info_str += "\n".join(similar) + "\n"
if len(cluster_info_str) > 0: # that means there is some information in the cluster.
logging.info("\nCluster was appended. cluster_info_str:"+cluster_info_str+"\tmin_dist="+str(similar_tuple_list[0][0])+"\tmax_dist="+str(similar_tuple_list[-1][0]))
cluster_list.append({'cno': cn, 'cnoprefix': nameprefix+str(cn), 'user_entropy': entropy(user_list), 'rif': frequency, 'cstr': cluster_info_str, 'ctweettuplelist': similar_tuple_list, 'twids': list(tweetsDF[np.in1d(km.labels_, [cn])]["id_str"].values)}) # 'user_ent':entropy(user_list),
logging.info("length of cluster_list:"+str(len(cluster_list)))
len_clust_list = len(cluster_list) # use to adjust the threshold steps for the next iteration. If you are closer to the target step smaller.
if len_clust_list<min_clusters:
logging.info("There is not enough clusters, call the create_clusters again with relaxed threshold parameters (recursively). Iteration no:"+str(iteration_no))
factor = (min_clusters-len_clust_list)/1000 # if it needs more clusters, it will make a big step
min_dist_thres2, max_dist_thres2, min_max_diff_thres2 = relax_parameters(min_dist_thres, max_dist_thres, min_max_diff_thres, factor)
logging.info("Threshold step sizes are: \nmin_dist_thres="+str(min_dist_thres-min_dist_thres2)+"\tmax_dist_thres="+str(max_dist_thres-max_dist_thres2)+"\tmin_max_diff_thres="+str(min_max_diff_thres-min_max_diff_thres2))
return create_clusters(tweetsDF, my_token_pattern, min_dist_thres=min_dist_thres2, min_max_diff_thres=min_max_diff_thres2, max_dist_thres=max_dist_thres2, \
iteration_no=iteration_no+1, min_clusters=min_clusters, user_identifier=user_identifier, cluster_list=cluster_list)
return cluster_list