for filename in glob.glob('C:/Users/oezkan/HasanCan/RawImages/*.tif'):
img = cv2.imread(filename, 0)
image_list.append(img)
start_time1 = time.time()
for im in range(len(image_list)):
# labeled signal
_, labeled_crack = cv2.threshold(label_list[im], 245, 255,
cv2.THRESH_BINARY)
kernel_label = np.ones((3, 3), np.uint8)
labeled_crack = cv2.dilate(labeled_crack, kernel_label, iterations=1)
# apply fft for grid fingers
img_fft = fft(image_list[im], mask_fft)
# apply histogram equalization
img_Eq = cv2.equalizeHist(img_fft)
img_roi = createROI(img_Eq)
#list of parameters for filters
param_bl = [[9, 11], [100, 150]] #changed
param_gf = [[2, 4], [0.2, 0.4]]
param_ad = [[10, 15], [(0.5, 0.5), (1., 1.)]] #changed
bl_class_result = ""
ad_class_result = ""
gf_class_result = ""
wave_class_result = ""
'''
from fftFunction import fft
from adaptedFrangi import frangi
from skimage import img_as_ubyte
import threading
import time
import numpy as np
import cv2
from matplotlib import pyplot as plt
from Vesselness2D import calculateVesselness2D
img_filtered_list = []
mask_fft = cv2.imread('C:/Users/oezkan/eclipse-workspace/thesis/filters/ModQ_EL_Poly-Bereket3.tif',0)
img_raw = cv2.imread('C:/Users/oezkan/HasanCan/RawImages/0000006214_bad_.tif',0)
img_fft = fft(img_raw, mask_fft)
img_Eq = cv2.equalizeHist(img_fft)
class myThread (threading.Thread):
def __init__(self, name, img_Eq, param1,param2,sigma_x,sigma_y):
threading.Thread.__init__(self)
self.name = name
self.img_Eq = img_Eq
self.param_1 = param1
self.param_2 = param2
self.sigmas = (sigma_x,sigma_y)
def run(self):
bilteralFiltering(self.name,self.img_Eq, self.param_1,self.param_2,sigmas)
# Define a function for the thread
def bilteralFiltering(threadName,img_Eq,param1,param2,sigma):
def perform_eCS_algorithm(img_in,
sigma_x=1.0,
sigma_y=1.0,
angle=0.0,
mask_fft=np.zeros((0))):
"""
Perform eCS algorithm described in:
Enhanced Crack Segmentation (eCS): A Reference Algorithm for Segmenting
Cracks in Multicrystalline Silicon Solar Cells
Parameters
----------
img_in : ndarray
Input image.
angle : float
Rotation angle in degrees in counter-clockwise direction.
sigma_x : float
Gaussian filter sigma in x-direction
sigma_y: float
Gaussian filter sigma in y-direction
mask_fft : ndarray
mask for fft filtering to get rid of grid fingers
Returns
-------
img_result : ndarray
processed image
"""
if (mask_fft.size != 0):
img_in = fft(img_in, mask_fft)
img_equalized = cv2.equalizeHist(img_in)
img_roi = createROI(img_equalized)
plt.figure()
#plt.imshow(img_roi) - Good estimation of ROI. Implement in dataArtist
img_smoothed = cv2.bilateralFilter(img_equalized, 3, 25, 25)
if angle != 0.0:
img_smoothed = rotate(img_smoothed,
angle,
resize=True,
preserve_range=True)
img_result = img_as_ubyte(
frangi(img_smoothed,
sigma_x=sigma_x,
sigma_y=sigma_y,
beta1=0.5,
beta2=0.05,
black_ridges=True))
if (sigma_x != sigma_y):
img_y = img_as_ubyte(
frangi(img_smoothed,
sigma_x=sigma_y,
sigma_y=sigma_x,
beta1=0.5,
beta2=0.05,
black_ridges=True))
img_result = np.maximum(img_result, img_y)
if angle != 0.0: #Modified to handel rect img of diff sizes with np.shape
w = img_result.shape[0]
w_org = img_in.shape[0] #changed to higt and width
offset = (w - w_org) // 2
h_org = img_in.shape[1]
#img is rotated back, and scaled to right size
img_result = rotate(img_result,
-angle,
resize=True,
preserve_range=True)[offset:offset + w_org,
offset:offset + h_org]
img_result = img_result * img_roi
return img_result.astype(
'float32') #does this need to be float32? can unit8?
#wavelet = 'haar'
#decompose to 2nd level coefficients
[cA, (cH, cV, cD)] = pywt.dwt2(img, wavelet=wavelet)
maxval = cA.max()
cA = cA * (img.max() / maxval)
return cA
imagePath = 'C:/Users/oezkan/HasanCan/RawImages/'
imagePathSave = 'C:/Users/Desktop/results/newResults/'
img = cv2.imread(imagePath + "0000007542_bad_.tif", 0)
mask_fft = cv2.imread(
'C:/Users/oezkan/eclipse-workspace/thesis/filters/ModQ_EL_Poly-Bereket3.tif',
0)
img = fft(img, mask_fft)
# apply histogram equalization
img_Eq = cv2.equalizeHist(img)
# mask is the ROI
mask = createROI(img_Eq)
#img = cv2.pyrDown( img, (512,512))
#mask = cv2.pyrDown(mask, (512,512))
meanVal = img.mean()
img_maskIn = cv2.convertScaleAbs(numpy.where(mask == 0, meanVal, img))
mask_inv = cv2.convertScaleAbs(myInvertarr(mask))
# ----------------------------------------------------------------------------
0)
mask_fft = cv2.imread(
'C:/Users/oezkan/eclipse-workspace/thesis/filters/ModQ_EL_Poly-Bereket3.tif',
0)
labeled_crack = cv2.imread(
'C:/Users/oezkan/eclipse-workspace/thesis/filters/originalImages/0000331736_CrackLabel.tif'
)
maskRoi = cv2.imread(
'C:/Users/oezkan/eclipse-workspace/thesis/filters/originalImages/0000331736_BusLabel.tif',
0)
img_filtered = cv2.bilateralFilter(img_raw, 9, 75,
75) # I can also apply different filters
# apply fft for grid fingers
img_fft = fft(img_filtered, mask_fft)
MORPH = True
# labeled signal
_, labeled_crack = cv2.threshold(labeled_crack[:, :, 2], 127, 255,
cv2.THRESH_BINARY)
kernel_label = np.ones((2, 2), np.uint8)
labeled_crack = cv2.dilate(labeled_crack, kernel_label, iterations=1)
#histogram equalization
img_fft = cv2.equalizeHist(img_fft) # if not at the end I got a black image
img_fft = cv2.bilateralFilter(img_fft, 9, 75,
75) # I can also apply different filters
#creating ROI for frangi