def testImage(image_list):
global arr1
global flag
arr1.clear()
for index in range(len(image_list)):
img = io.imread(image_list[index], as_grey=True)
infile = cv2.imread(image_list[index])
infile = infile[:, :, 0]
hues = (np.array(infile) / 255.) * 179
outimageHSV = np.array([[[b, 255, 255] for b in a]
for a in hues]).astype(int)
outimageHSV = np.uint8(outimageHSV)
outimageBGR = cv2.cvtColor(outimageHSV, cv2.COLOR_HSV2BGR)
rgb = io.imread(image_list[index])
lab = color.rgb2lab(rgb)
outimageBGR = lab
for i in range(outimageBGR.shape[0]):
for j in range(outimageBGR.shape[1]):
sum = 0
for k in range(outimageBGR.shape[2]):
sum = sum + outimageBGR[i][j][k]
sum = sum / (3 * 255)
if (i < img.shape[0] and j < img.shape[1]):
img[i][j] = sum
S = preprocessing.MinMaxScaler((0, 19)).fit_transform(img).astype(int)
Grauwertmatrix = feature.greycomatrix(
S, [1, 2, 3], [0, np.pi / 4, np.pi / 2, 3 * np.pi / 4],
levels=20,
symmetric=False,
normed=True)
arr1.append(feature.greycoprops(Grauwertmatrix, 'contrast'))
arr1.append(feature.greycoprops(Grauwertmatrix, 'correlation'))
arr1.append(feature.greycoprops(Grauwertmatrix, 'homogeneity'))
arr1.append(feature.greycoprops(Grauwertmatrix, 'ASM'))
arr1.append(feature.greycoprops(Grauwertmatrix, 'energy'))
arr1.append(feature.greycoprops(Grauwertmatrix, 'dissimilarity'))
arr1.append(Features.sumOfSquares(Grauwertmatrix))
arr1.append(Features.sumAverage(Grauwertmatrix))
arr1.append(Features.sumVariance(Grauwertmatrix))
arr1.append(Features.Entropy(Grauwertmatrix))
arr1.append(Features.sumEntropy(Grauwertmatrix))
arr1.append(Features.differenceVariance(Grauwertmatrix))
arr1.append(Features.differenceEntropy(Grauwertmatrix))
arr1.append(Features.informationMeasureOfCorelation1(Grauwertmatrix))
arr1.append(Features.informationMeasureOfCorelation2(Grauwertmatrix))
flag = 1
def pr():
print('\n')
print("Contrast : %f" % (np.mean(ContrastStats)))
print("Energy : %f" % (np.mean(Energy)))
print("Dissimilarity : %f" % (np.mean(Dissimilarity)))
print("Correlation : %f" % (np.mean(CorrelationtStats)))
print("Homogeneity : %f" % (np.mean(ASMStats)))
print("Sum of Squares : %f" % (np.mean(Features.sumOfSquares(Grauwertmatrix))))
print("Sum Average : %f" % (np.mean(Features.sumAverage(Grauwertmatrix))))
print("Sum Variance : %f" % (np.mean(Features.sumVariance(Grauwertmatrix))))
print("Sum Entropy : %f" % (np.mean(Features.sumEntropy(Grauwertmatrix))))
print("Entropy : %f" % (np.mean(Features.Entropy(Grauwertmatrix))))
print("Difference Variance : %f" % (np.mean(Features.differenceVariance(Grauwertmatrix))))
print("Difference Entropy : %f" % (np.mean(Features.differenceEntropy(Grauwertmatrix))))
print("Information measure of corelation1: %f" % (np.mean(Features.informationMeasureOfCorelation1(Grauwertmatrix))))
print("Information measure of corelation2: %f" % (np.mean(Features.informationMeasureOfCorelation2(Grauwertmatrix))))