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Gray Level Run Length Matrix.py
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Gray Level Run Length Matrix.py
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#GLRLM or Gray Level Run Length Matrix
#33 different features are extracted from this
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
from itertools import groupby
data = 0
def read_img(path=" "):
try:
img = Image.open(path)
img = img.convert('L')
self.data=np.array(img)
except:
img = None
def getGrayLevelRumatrix(array, theta):
#array: the numpy array of the image
#theta: Input, the angle used when calculating the gray scale run matrix, list type, can contain fields:['deg0', 'deg45', 'deg90', 'deg135']
#glrlm: output,the glrlm result
P = array
x, y = P.shape
min_pixels = np.min(P) # the min pixel
run_length = max(x, y) # Maximum parade length in pixels
num_level = np.max(P) - np.min(P) + 1 # Image gray level
deg0 = [val.tolist() for sublist in np.vsplit(P, x) for val in sublist] # 0deg
deg90 = [val.tolist() for sublist in np.split(np.transpose(P), y) for val in sublist] # 90deg
diags = [P[::-1, :].diagonal(i) for i in range(-P.shape[0]+1, P.shape[1])] #45deg
deg45 = [n.tolist() for n in diags]
Pt = np.rot90(P, 3) # 135deg
diags = [Pt[::-1, :].diagonal(i) for i in range(-Pt.shape[0]+1, Pt.shape[1])]
deg135 = [n.tolist() for n in diags]
def length(l):
if hasattr(l, '_len_'):
return np.size(l)
else:
i = 0
for _ in l:
i += 1
return i
glrlm = np.zeros((num_level, run_length, len(theta)))
for angle in theta:
for splitvec in range(0, len(eval(angle))):
flattened = eval(angle)[splitvec]
answer = []
for key, iter in groupby(flattened):
answer.append((key, length(iter)))
for ansIndex in range(0, len(answer)):
glrlm[int(answer[ansIndex][0]-min_pixels), int(answer[ansIndex][1]-1), theta.index(angle)] += 1
return glrlm
# The gray scale run matrix is only the measurement and statistics of the image pixel information. In the actual use process, the generated
# The gray scale run matrix is calculated to obtain image feature information based on the gray level co-occurrence matrix.
# First write a few common functions to complete the calculation of subscripts i and j (calcuteIJ ()), multiply and divide according to the specified dimension (apply_over_degree ())
# And calculate the sum of all pixels (calcuteS ())
def apply_over_degree(function, x1, x2):
rows, cols, nums = x1.shape
result = np.ndarray((rows, cols, nums))
for i in range(nums):
#print(x1[:, :, i])
result[:, :, i] = function(x1[:, :, i], x2)
# print(result[:, :, i])
result[result == np.inf] = 0
result[np.isnan(result)] = 0
return result
def calcuteIJ (rlmatrix):
gray_level, run_length, _ = rlmatrix.shape
I, J = np.ogrid[0:gray_level, 0:run_length]
return I, J+1
def calcuteS(rlmatrix):
return np.apply_over_axes(np.sum, rlmatrix, axes=(0, 1))[0, 0]
#The following code realizes the extraction of 11 gray runoff matrix features
#1.SRE
def getShortRunEmphasis(rlmatrix):
I, J = calcuteIJ(rlmatrix)
numerator = np.apply_over_axes(np.sum, apply_over_degree(np.divide, rlmatrix, (J*J)), axes=(0, 1))[0, 0]
S = calcuteS(rlmatrix)
return numerator / S
#2.LRE
def getLongRunEmphasis(rlmatrix):
I, J = calcuteIJ(rlmatrix)
numerator = np.apply_over_axes(np.sum, apply_over_degree(np.multiply, rlmatrix, (J*J)), axes=(0, 1))[0, 0]
S = calcuteS(rlmatrix)
return numerator / S
#3.GLN
def getGrayLevelNonUniformity(rlmatrix):
G = np.apply_over_axes(np.sum, rlmatrix, axes=1)
numerator = np.apply_over_axes(np.sum, (G*G), axes=(0, 1))[0, 0]
S = calcuteS(rlmatrix)
return numerator / S
# 4. RLN
def getRunLengthNonUniformity(rlmatrix):
R = np.apply_over_axes(np.sum, rlmatrix, axes=0)
numerator = np.apply_over_axes(np.sum, (R*R), axes=(0, 1))[0, 0]
S = calcuteS(rlmatrix)
return numerator / S
# 5. RP
def getRunPercentage(rlmatrix):
gray_level, run_length,_ = rlmatrix.shape
num_voxels = gray_level * run_length
return calcuteS(rlmatrix) / num_voxels
# 6. LGLRE
def getLowGrayLevelRunEmphasis(rlmatrix):
I, J = calcuteIJ(rlmatrix)
numerator = np.apply_over_axes(np.sum, apply_over_degree(np.divide, rlmatrix, (I*I)), axes=(0, 1))[0, 0]
S = calcuteS(rlmatrix)
return numerator / S
# 7. HGL
def getHighGrayLevelRunEmphais(rlmatrix):
I, J = calcuteIJ(rlmatrix)
numerator = np.apply_over_axes(np.sum, apply_over_degree(np.multiply, rlmatrix, (I*I)), axes=(0, 1))[0, 0]
S = calcuteS(rlmatrix)
return numerator / S
# 8. SRLGLE
def getShortRunLowGrayLevelEmphasis(rlmatrix):
I, J = calcuteIJ(rlmatrix)
numerator = np.apply_over_axes(np.sum, apply_over_degree(np.divide, rlmatrix, (I*I*J*J)), axes=(0, 1))[0, 0]
S = calcuteS(rlmatrix)
return numerator / S
# 9. SRHGLE
def getShortRunHighGrayLevelEmphasis(rlmatrix):
I, J = calcuteIJ(rlmatrix)
temp = apply_over_degree(np.multiply, rlmatrix, (I*I))
print('-----------------------')
numerator = np.apply_over_axes(np.sum, apply_over_degree(np.divide, temp, (J*J)), axes=(0, 1))[0, 0]
S = calcuteS(rlmatrix)
return numerator / S
# 10. LRLGLE
def getLongRunLow(rlmatrix):
I, J = calcuteIJ(rlmatrix)
temp = apply_over_degree(np.multiply, rlmatrix, (J*J))
numerator = np.apply_over_axes(np.sum, apply_over_degree(np.divide, temp, (J*J)), axes=(0, 1))[0, 0]
S = calcuteS(rlmatrix)
return numerator / S
# 11. LRHGLE
def getLongRunHighGrayLevelEmphais(rlmatrix):
I, J = calcuteIJ(rlmatrix)
numerator = np.apply_over_axes(np.sum,apply_over_degree(np.multiply, rlmatrix, (I*I*J*J)), axes=(0, 1))[0, 0]
S = calcuteS(rlmatrix)
return numerator / S
#import getGrayRumatrix
from PIL import Image
import numpy as np
from itertools import groupby
import csv
import warnings
warnings.filterwarnings("ignore")
import cv2
import os
import glob
import time
tic=time.time()
img_dir=".../..../..." #Enter the directory where all the images are stored
data_path=os.path.join(img_dir,'*g')
files=glob.glob(data_path)
i=0
for path in files:
data = 0
img = Image.open(path)
img = img.convert('L')
data=np.array(img)
DEG = [['deg0'], ['deg45'], ['deg90'], ['deg135']]
with open('GLRLM.csv','a+',newline='',encoding='utf-8') as f:
csv_writer = csv.writer(f)
if i==0:
csv_writer.writerow(['deg0_SRE','deg45_SRE','deg90_SRE','deg135_SRE','deg0_LRE','deg45_LRE','deg90_LRE','deg135_LRE',
'deg0_GLN','deg45_GLN','deg90_GLN','deg135_GLN','deg0_RLN','deg45_RLN','deg90_RLN','deg135_RLN',
'deg0_RP','deg45_RP','deg90_RP','deg135_RP','deg0_LGLRE','deg45_LGLRE','deg90_LGLRE','deg135_LGLRE',
'deg0_HGL','deg45_HGL','deg90_HGL','deg135_HGL','deg0_SRLGLE','deg45_SRLGLE','deg90_SRLGLE','deg135_SRLGLE',
'deg0_SRHGLE','deg45_SRHGLE','deg90_SRHGLE','deg135_SRHGLE','deg0_LRLGLE','deg45_LRLGLE','deg90_LRLGLE','deg135_LRLGLE',
'deg0_LRHGLE','deg45_LRHGLE','deg90_LRHGLE','deg135_LRHGLE'])
print("Processing Image",i+1)
i+=1
SRE_l=[]
LRE_l=[]
GLN_l=[]
RLN_l=[]
RP_l=[]
LGLRE_l=[]
HGL_l=[]
SRLGLE_l=[]
SRHGLE_l=[]
LRLGLE_l=[]
LRHGLE_l=[]
for deg in DEG:
now_deg = deg[0]
test_data = getGrayLevelRumatrix(data,deg)
#1
SRE = getShortRunEmphasis(test_data)
SRE = np.squeeze(SRE)
SRE_l.append(SRE)
#2
LRE = getLongRunEmphasis(test_data)
LRE = np.squeeze(LRE)
LRE_l.append(LRE)
#3
GLN = getGrayLevelNonUniformity(test_data)
GLN = np.squeeze(GLN)
GLN_l.append(GLN)
#4
RLN = getRunLengthNonUniformity(test_data)
RLN = np.squeeze(RLN)
RLN_l.append(RLN)
#5
RP = getRunPercentage(test_data)
RP = np.squeeze(RP)
RP_l.append(RP)
#6
LGLRE = getLowGrayLevelRunEmphasis(test_data)
LGLRE = np.squeeze(LGLRE)
LGLRE_l.append(LGLRE)
#7
HGL = getHighGrayLevelRunEmphais(test_data)
HGL = np.squeeze(HGL)
HGL_l.append(HGL)
#8
SRLGLE = getShortRunLowGrayLevelEmphasis(test_data)
SRLGLE = np.squeeze(SRLGLE)
SRLGLE_l.append(SRLGLE)
#9
SRHGLE = getShortRunHighGrayLevelEmphasis(test_data)
SRHGLE = np.squeeze(SRHGLE)
SRHGLE_l.append(SRHGLE)
#10
LRLGLE = getLongRunLow(test_data)
LRLGLE = np.squeeze(LRLGLE)
LRLGLE_l.append(LRLGLE)
#11
LRHGLE = getLongRunHighGrayLevelEmphais(test_data)
LRHGLE = np.squeeze(LRHGLE)
LRHGLE_l.append(LRHGLE)
csv_writer.writerow(SRE_l+LRE_l+GLN_l+RLN_l+RP_l+LGLRE_l+HGL_l+SRLGLE_l+SRHGLE_l+LRLGLE_l+LRHGLE_l)
toc=time.time()
print("Computation time is: {} minutes.".format(str((toc-tic)/60)))