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ZPointsBased.py
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ZPointsBased.py
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""" © 2019-2020 Kent Barter All Rights Reserved """
import os
import cv2
import xlwt
import xlsxwriter
import statistics
import numpy as np
import matplotlib.image as mpimg
from time import gmtime, strftime
from xlwt import Workbook
from matplotlib import pyplot as plt
from matplotlib.pyplot import figure
figure(num=None, figsize=(10, 10.24), dpi=96, facecolor='w', edgecolor='k')
from Project import Directory # file imports
from HeatMap import HeatMap
from HeatMapH import HeatMapH
from Settings import Settings
class Image:
def __init__(self, img_List):
self.img_List = img_List
# retreving the setting from the settings file
f = "settings.txt"
s = Settings(f)
self.s = s.retriveSettings()
self.name = ""
#layers
self.outer_distance_list = []
self.white_top_list = []
self.mid_top_list = []
self.mid_bot_list = []
self.white_bot_list = []
self.inner_distance_list = []
# new heatmap
self.outer_distance_gaps = []
for x in range(0, 100):
l = []
for y in range(0, 950):
l.append("B")
self.outer_distance_gaps.append(l)
# getting the number of points measured for each frame
self.outer_distance_measurement_number = []
self.white_top_measurement_number = []
self.mid_top_measurement_number = []
self.mid_bot_measurement_number = []
self.white_bot_measurement_number = []
self.inner_distance_measurement_number = []
#thinkness
self.retinal_thickness = []
#getting the image points for heatmap2 (move heatmap points to set methods / each point should be unique)
self.top_points = []
self.top_white = []
self.mid_points = []
self.bot_white = []
self.bot_points = []
#start and stop / frame information
self.start = int(self.s[0])
self.stop = int(self.s[1])
self.number_of_images = self.stop - self.start
self.frame_list = []
#BOUNDS SETTINGS
self.end_bound_list = [] # list that contains the end bouds (4) // if length = 0 no bounds set
self.white_value_threshold_list = [102, 99] # first value -> main | second value -> secondary[end boundaries]
#SETTINGS // THRESHOLDING VARIBLES
self.image_set_number = int(self.s[2])
self.white_value_threshold = int(self.s[3]) #Standard Value -> 110 ==> [IF THE OTHER VALUE IS NOT USED]
self.minimum_gap_value = int(self.s[4]) #Standard Value -> -35
self.maximum_gap_value = int(self.s[5]) #Standard Value -> -150
self.min_gap_value = int(self.s[6]) #Standard Value -> 4
# ALL MEASUREMENT ARE IN MICROMETERS (1 pixel * 1.62) // set to one for measurment in pixels
self.newton_meter_conversion = 1.62
self.white_value_threshold_string = ""
for x in range(0, len(self.white_value_threshold_list)):
self.white_value_threshold_string = self.white_value_threshold_string + str(self.white_value_threshold_list[x]) + " "
self.end_bound_string = ""
for x in range(0, len(self.end_bound_list)):
self.end_bound_string = self.end_bound_string + str(self.end_bound_list[x]) + " "
# Set by the program on its first run // AUTOMATIC SETTING
self.last_top_value = 0
self.white_top_per = 0
self.mid_top_per = 0
self.mid_bot_per = 0
self.white_bot_per = 0
self.inner_distance_per = 0
#Novel characteristics of the cultured Lumpfish Cyclopterus lumpus eye during post-hatch larval and juvenile developmental stages
#Retinal Thickness - nfl to post
#top white - nfl to glc
#bot white - onl to post
#inner distance - glc to only
#Focusing on the center of the eye, Images 30-70
#Two Types of errors can be detected
def getNumberOfImages(self):
return self.number_of_images
def getImageList(self):
return self.img_List
def getRetinalThickness(self):
return self.retinal_thickness
#RETURING IMAGE DATA
def getTopPoints(self):
return self.top_points
def getTopWhite(self):
return self.top_white
def getMidPoints(self):
return self.mid_points
def getRetinalThicknessGaps(self):
return self.outer_distance_gaps
def getBotWhite(self):
return self.bot_white
def getBotPoints(self):
return self.bot_points
def getName(self):
return self.name
def getFrameList(self):
return self.frame_list
def getHeat(self):
return self.s[8]
def getimagelist(self):
return self.img_List
def Scheduler(self):
for x in range(self.start, self.stop):
currentImage = self.img_List[self.image_set_number][x]
self.animal_number = currentImage[5:-10]
print(currentImage)
img = Image.prepareImage(self, currentImage)
if Image.TestImage(self, img) == True:
self.frame_list.append(currentImage[-8:-5])
# setting the end bound
if len(self.end_bound_list) > 0:
if (x >= self.end_bound_list[0] and x <= self.end_bound_list[1]) or (x >= self.end_bound_list[2] and x <= self.end_bound_list[3]):
self.white_value_threshold = self.white_value_threshold_list[1] # won't return to orginal value
else:
self.white_value_threshold = self.white_value_threshold_list[0]
Image.medianDerterminant(self, img, x)
else:
print("This Image has an error if type: ")
#print(len(self.retinal_thickness))
#print(len(self.retinal_thickness[0]))
#print(self.retinal_thickness)
"""Data Storage"""
if int(self.s[7]) == 1: Image.StoreDataClassic(self)
if int(self.s[7]) == 2:
Image.StoreDataModern(self)
Image.StoreDataClassic(self)
Image.StoreCommaSeperatedValues(self)
if int(self.s[7]) == 3: Image.StoreCommaSeperatedValues(self)
cv2.destroyAllWindows()
def TestImage(self, mat_img):
histr = cv2.calcHist([mat_img],[0],None,[256],[0,256])
hist_sum_Pixel = sum(histr[50:100])
hist_sum_Blank = sum(histr[100:125])
error = True
if hist_sum_Blank < 9000:
print("Blank Error")
error = False
return error
def prepareImage(self, currentImg):
# denoise folled by smoothing filters
mat_img = mpimg.imread(currentImg, 0) #loading as greyscale image
mat_img = cv2.fastNlMeansDenoising(mat_img, None, 12, 13, 21) #((mat_img, None, 9, 13, 21))
mat_img = cv2.GaussianBlur(mat_img,(5,5),0)
mat_img = cv2.medianBlur(mat_img,5) # stsandard value - 5
ret,mat_img = cv2.threshold(mat_img,127,255,cv2.THRESH_TRUNC)
return mat_img
def intDisplay(self, image):
#Display matplot can when (Pillow is a requirement)
plt.subplot(121),plt.imshow(image,cmap = 'gray')
plt.title('Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.hist(image.ravel(),256,[0,256])
plt.title('Histogram'), plt.xticks([]), plt.yticks([])
plt.show(), plt.close(fig = None)
def pointDetector(self, img):
"""finding critical points, to be mapped"""
#finding as mant points as possible
feature_points = []
edges = cv2.Canny(img,12,200, 20) #image,min,max,apeture
plt.imshow(edges,cmap = 'gray'), plt.show()
#indeces and points
indices = np.where(edges != [0])
coordinates = zip(indices[0], indices[1])
for x in coordinates:
feature_points.append(x)
print("Number of Points: ", len(feature_points))
return feature_points
#band detection to save on computing time
def medianDerterminant(self, image, currentImage):
outer_distance = []
white_top_distance = []
mid_top_distance = []
mid_bot_distance = []
white_bot = []
white_top = []
inner_distance = []
#setting the points for the heatmap image
image_bot = []
image_white_top = []
image_mid = []
image_white_bot = []
image_top = []
for pointx in range(0, 950):
midpath = []
midwhite = []
top = []
bot = []
for y in range(0,288): #RESTRICTING THE BOTTOM RANGE 1024 total
color = image[y, pointx]
midpath.append([y, color[0]])
#setting this value based on a function (histogram) (setting based on number of pixals of each value
#print(np.percentile(midpath, 55))
for x in midpath:
if midpath[midpath.index(x)][1] >= self.white_value_threshold: #110 is the standerd value // lower the value to allow more to be considred apart of the line // increase to tighten
midwhite.append(midpath.index(x))
#should be getting the largest gap // manipluatio of the gap value can change which part of the retina will be detected by adjusting the minimum and maximum diatances
value_to_print = 0
for x in range(1, len(midwhite) - 1):
if midwhite[x] - midwhite[x + 1] <= self.minimum_gap_value and midwhite[x] - midwhite[x + 1] >= self.maximum_gap_value: #-35 and -150 are the standard values
image[midwhite[x + 1]][pointx] = (0,255,0,-1) #green bot green
image[midwhite[x]][pointx] = (0,255,0,-1) #green top green
image[midwhite[0]][pointx] = (255,0,0,-1) #blue top blue
image[midwhite[-1]][pointx] = (255,0,0,-1) #blue bot blue
image_top.append(midwhite[0])
image_white_top.append(midwhite[x])
image_white_bot.append(midwhite[x + 1])
image_bot.append(midwhite[-1])
#total width of the retina
total_width = midwhite[-1] - midwhite[0]
if total_width >= self.min_gap_value and midwhite[0] != 0:
outer_distance.append(total_width)
self.outer_distance_gaps[currentImage][pointx] = total_width
#top white area of the retina
top_white_width = midwhite[x] - midwhite[0]
if top_white_width >= self.min_gap_value and midwhite[0] != 0:
white_top.append(top_white_width)
#bottom white area of the retina
bot_white_width = midwhite[-1] - midwhite[x + 1]
if bot_white_width >= self.min_gap_value and midwhite[0] != 0:
white_bot.append(bot_white_width)
#inner area of the retina (between both white sections)
inner_point = midwhite[x + 1] - midwhite[x]
if inner_point >= self.min_gap_value and midwhite[0] != 0:
inner_distance.append(inner_point)
# Testing settings
# 21 30 2 105 -15 -215 1 1 155
# 0 1 0 100 -35 -500 1 1 155
#saving the image to the image (priting whne the image has been complete)
image_scale = cv2.cvtColor(image,cv2.COLOR_RGB2BGR) # moving from cv2's BRG mode
image_crop = image_scale[0:750, 0:1000]
#path = "C:\\Users\\krbar\\Desktop\\Project\\Images"
path = "Images"
#name = time_current = strftime("%Y-%m-%d %H-%M-%S", gmtime()) + ".tiff"
self.name = self.animal_number.split(os.sep)[-1]
name_start = self.animal_number.split(os.sep)[-1]
name = name_start + " " + str(self.frame_list[-1]) + " " + strftime("%Y-%m-%d %H-%M-%S", gmtime()) + ".tiff"
mpimg.imsave(os.path.join(path , name), image_crop)
#getting the heatmap points for each image
self.top_points.append(image_top)
self.top_white.append(image_white_top)
self.mid_points.append(image_mid)
self.bot_white.append(image_white_bot)
self.bot_points.append(image_bot)
#the avarge values of thickness of each layery
if len(outer_distance) > 1: outer_distance_avg = statistics.mean(outer_distance)
else: outer_distance_avg = 0
if len(white_top) > 1: white_top_avg = statistics.mean(white_top)
else: white_top_avg = 0
if len(mid_top_distance) > 1 : mid_top_avg = statistics.mean(mid_top_distance)
else: mid_top_avg = 0
if len(mid_bot_distance) > 1: mid_bot_avg = statistics.mean(mid_bot_distance)
else: mid_bot_avg = 0
if len(white_bot) > 1: white_bot_avg = statistics.mean(white_bot)
else: white_bot_avg = 0
if len(inner_distance) > 1: inner_distance_avg = statistics.mean(inner_distance)
else: inner_distance_avg = 0
#getting the volume of each layer (to get the (percentage of each layer)
outer_distance_volume = sum(outer_distance)
white_top_volume = sum(white_top)
mid_top_volume = sum(mid_top_distance)
mid_bot_volume = sum(mid_bot_distance)
white_bot_volume = sum(white_bot)
inner_distance_volume = sum(inner_distance)
# getting the number of meaurements of each average
self.outer_distance_measurement_number.append(len(outer_distance))
self.white_top_measurement_number.append(len(white_top))
self.mid_top_measurement_number.append(len(mid_top_distance))
self.mid_bot_measurement_number.append(len(mid_bot_distance))
self.white_bot_measurement_number.append(len(white_bot))
self.inner_distance_measurement_number.append(len(inner_distance))
#collecting the avarage of each image
self.outer_distance_list.append(outer_distance_avg)
self.white_top_list.append(white_top_avg)
self.mid_top_list.append(mid_top_avg)
self.mid_bot_list.append(mid_bot_avg)
self.white_bot_list.append(white_bot_avg)
self.inner_distance_list.append(inner_distance_avg)
#collecting the outer distance
self.retinal_thickness.append(outer_distance)
#volume percentage (of each layer compared to the total volume of the retina)
if outer_distance_volume != 0:
self.white_top_per = (white_top_volume / outer_distance_volume) *100
self.mid_top_per = (mid_top_volume / outer_distance_volume) *100
self.mid_bot_per = (mid_bot_volume / outer_distance_volume) *100
self.white_bot_per = (white_bot_volume / outer_distance_volume) *100
self.inner_distance_per = (inner_distance_volume / outer_distance_volume) *100
#formating printout
print("Number of points: ", len(outer_distance))
print("Volume and Percentage")
print("Total Volume: ", outer_distance_volume), print()
print("NFL to GLC Volume: ", white_top_volume), print("NFL/GLC Volume Percentage: ", self.white_top_per), print()
print("ONL to RPE Volume: ", white_bot_volume), print("ONL to RPE Volume Percentage: ", self.white_bot_per), print()
print("GLC to ONL Volume: ", inner_distance_volume), print("GLC to ONL Volume Percentage: ", self.inner_distance_per), print()
#print("Animal Number: ", self.animal_number), print(self.frame_list)
# at the end of the computation send the value of self.outer_distance_list to the heatmap
cv2.waitKey(1)
"""Data Storage methods"""
def StoreDataClassic(self): #xls format
wb = xlwt.Workbook(encoding="utf-8")
sheet = wb.add_sheet('SD-OST')
style = xlwt.easyxf('font: bold 1')
sheet.write(0, 0, "Frame Number", style)
sheet.write(0, 2, "Retinal Thickness (um)" , style)
sheet.write(0, 3, "Number of Readings", style)
sheet.write(0, 6, "NFL/GLC (um)" , style)
sheet.write(0, 7, "Number of Readings", style)
sheet.write(0, 10, "IPL/INL (um)" , style)
sheet.write(0, 11, "Number of Readings", style)
sheet.write(0, 14, "OPL/ONL/IS/OS/RPE (um)" , style)
sheet.write(0, 15, "Number of Readings", style)
#volumes percentages
sheet.write(0, 20, "Specimen", style)
sheet.write(3, 20, "NFL/GLC Volume Percentage", style)
sheet.write(6, 20, "IPL/INL Volume Percentage", style)
sheet.write(9, 20, "OPL/ONL/IS/OS/RPE Volume Percentage", style)
sheet.write(12, 20, "White Value Threshold", style)
sheet.write(15, 20, "Minimum Gap Threshold", style)
sheet.write(18, 20, "Maximum Gap Threshold", style)
sheet.write(21, 20, "Mimimum Thickness Value", style)
listEnd = len(self.outer_distance_list)
for x in range(0, listEnd):
outdist = round(self.outer_distance_list[x] * self.newton_meter_conversion, 1)
wtop = round(self.white_top_list[x]* self.newton_meter_conversion, 1)
indist = round(self.inner_distance_list[x]* self.newton_meter_conversion, 1)
wbot = round(self.white_bot_list[x]* self.newton_meter_conversion, 1)
sheet.write(x + 1, 0, self.frame_list[x]) # Frame Number Messurments:
sheet.write(x + 1, 2, outdist) # Retinal Thicness
sheet.write(x + 1, 3, self.outer_distance_measurement_number[x]) # Number of Measurements
sheet.write(x + 1, 6, wtop) # NFL to GLC
sheet.write(x + 1, 7, self.white_top_measurement_number[x]) # Number of Measurements
sheet.write(x + 1, 10, indist) # GLC to Coroid
sheet.write(x + 1, 11, self.inner_distance_measurement_number[x]) # Number of Measurements
sheet.write(x + 1, 14, wbot) # ONL to Post
sheet.write(x + 1, 15, self.white_bot_measurement_number[x])
#end values
sheet.write(1, 20, self.animal_number)
sheet.write(4, 20, round(self.white_top_per, 1))
sheet.write(7, 20, round(self.inner_distance_per, 1))
sheet.write(10, 20, round(self.white_bot_per, 1))
#settings varibles
sheet.write(13, 20, self.white_value_threshold)
sheet.write(16, 20, self.minimum_gap_value)
sheet.write(19, 20, self.maximum_gap_value)
sheet.write(22, 20, self.min_gap_value)
time_current = strftime("%Y-%m-%d %H-%M-%S", gmtime())
name_start = self.animal_number.split(os.sep)[-1]
wb.save(name_start + " " + time_current + ".xls")
def StoreDataModern(self): #xlsx format
time_current = strftime("%Y-%m-%d %H-%M-%S", gmtime())
name_start = self.animal_number.split(os.sep)[-1]
workbook = xlsxwriter.Workbook((name_start + " " + time_current + ".xlsx"))
sheet = workbook.add_worksheet()
style = workbook.add_format({'bold': True})
sheet.write(0, 0, "Frame Number", style)
sheet.write(0, 2, "Retinal Thickness (um)" , style)
sheet.write(0, 3, "Number of Readings", style)
sheet.write(0, 6, "NFL/GLC (um)" , style)
sheet.write(0, 7, "Number of Readings", style)
sheet.write(0, 10, "IPL/INL (um)" , style)
sheet.write(0, 11, "Number of Readings", style)
sheet.write(0, 14, "OPL/ONL/IS/OS/RPE (um)" , style)
sheet.write(0, 15, "Number of Readings", style)
#volumes percentages
sheet.write(0, 20, "Specimen", style)
sheet.write(3, 20, "NFL/GLC Volume Percentage", style)
sheet.write(6, 20, "IPL/INL Volume Percentage", style)
sheet.write(9, 20, "OPL/ONL/IS/OS/RPE Volume Percentage", style)
sheet.write(12, 20, "White Value Threshold", style)
sheet.write(15, 20, "Minimum Gap Threshold", style)
sheet.write(18, 20, "Maximum Gap Threshold", style)
sheet.write(21, 20, "Mimimum Thickness Value", style)
listEnd = len(self.outer_distance_list)
for x in range(0, listEnd):
outdist = round(self.outer_distance_list[x] * self.newton_meter_conversion, 1)
wtop = round(self.white_top_list[x]* self.newton_meter_conversion, 1)
indist = round(self.inner_distance_list[x]* self.newton_meter_conversion, 1)
wbot = round(self.white_bot_list[x]* self.newton_meter_conversion, 1)
sheet.write(x + 1, 0, self.frame_list[x]) # Frame Number Messurments:
sheet.write(x + 1, 2, outdist) # Retinal Thicness
sheet.write(x + 1, 3, self.outer_distance_measurement_number[x]) # Number of Measurements
sheet.write(x + 1, 6, wtop) # NFL to GLC
sheet.write(x + 1, 7, self.white_top_measurement_number[x]) # Number of Measurements
sheet.write(x + 1, 10, indist) # GLC to Coroid
sheet.write(x + 1, 11, self.inner_distance_measurement_number[x]) # Number of Measurements
sheet.write(x + 1, 14, wbot) # ONL to Post
sheet.write(x + 1, 15, self.white_bot_measurement_number[x])
#end values
sheet.write(1, 20, self.animal_number)
sheet.write(4, 20, round(self.white_top_per, 1))
sheet.write(7, 20, round(self.inner_distance_per, 1))
sheet.write(10, 20, round(self.white_bot_per, 1))
#settings varibles
sheet.write(13, 20, self.white_value_threshold)
sheet.write(16, 20, self.minimum_gap_value)
sheet.write(19, 20, self.maximum_gap_value)
sheet.write(22, 20, self.min_gap_value)
workbook.close()
def StoreCommaSeperatedValues(self): #csv format
time_current = strftime("%Y-%m-%d %H-%M-%S", gmtime())
listEnd = len(self.outer_distance_list)
name_start = self.animal_number.split(os.sep)[-1]
with open(name_start + "" + time_current + ".csv", "w") as file:
file.write("Frame Number, ,Retinal Thickness,Number of Readings, , NFL/GLC (um),Number of Readings, , IPL/INL (um), Number of Readings, , OPL/ONL/IS/OS/RPE (um), Number of Readings,\n")
for x in range(0, listEnd, 1):
outdist = round(self.outer_distance_list[x] * self.newton_meter_conversion, 1)
wtop = round(self.white_top_list[x]* self.newton_meter_conversion, 1)
indist = round(self.inner_distance_list[x]* self.newton_meter_conversion, 1)
wbot = round(self.white_bot_list[x]* self.newton_meter_conversion, 1)
frame_num = str(self.frame_list[x]) # Frame Number Measurements:
out_dist = str(outdist) # Retinal Thicness
out_dist_number = str(self.outer_distance_measurement_number[x]) # Number of Measurements
wt_top = str(wtop) # NFL to GLC
wt_top_number = str(self.white_top_measurement_number[x]) # Number of Measurements
wt_bot = str(wbot) # ONL to Post
wt_bot_number = str(self.white_bot_measurement_number[x]) # Number of Measurements
in_dist = str(indist) # GLC to ONL
in_dist_number = str(self.inner_distance_measurement_number[x]) # Number of Measurements
seperator = ","
blank = " "
tojoin = [frame_num, blank, out_dist, out_dist_number, blank, wt_top, wt_top_number, blank, in_dist, in_dist_number, blank, wt_bot, wt_bot_number]
x = seperator.join(tojoin)
file.write(x + "\n")
file.write("\n")
file.write("Specimen, ,NFL/GLC Volume Percentage, ,IPL/INL/OPL/ONL/IS Volume Percentage, ,OS/RPE Volume Percentage\n")
line = self.animal_number + ", ," + str(round(self.white_top_per,1)) + ", ," + str(round(self.inner_distance_per,1)) + ", ," + str(round(self.white_bot_per,1)) + "\n"
file.write(line)
file.write("\n")
file.write("White Value Threshold, , Minimum Gap Threshold, , Maximum Gap Threshold, , Mimimum Thickness Value\n")
line2 = str(self.white_value_threshold) + ", ," + str(self.minimum_gap_value) + ", ," + str(self.maximum_gap_value) + ", ," + str(self.min_gap_value) + "\n"
file.write(line2)
file.close()
# directory.openDirectory() = img_list
def __main__():
#directory_name = str(input("Enter the name of the working directory: "))
directory = Directory("Data")
image = Image(directory.openDirectory())
image.Scheduler()
#creating the retinal heatmap (and the location list)
retinal_thickness = image.getRetinalThickness()
name = image.getName()
frame = image.getFrameList()
heat = image.getHeat()
gaps = image.getRetinalThicknessGaps()
retinalMap = HeatMap(retinal_thickness, name, frame, heat, gaps)
retinalMap.sceduler()
#creating retinal heatmap2
#top_points = image.getTopPoints()
#top_white = image.getTopWhite()
#mid_points = image.getMidPoints()
#bot_white = image.getBotWhite()
#bot_points = image.getBotPoints()
#heatmap = HeatMapH(top_points, top_white, mid_points, bot_white, bot_points)
#heatmap.sceduler()
#showing the heatmap image (Not using the image display section to complete the analysis)
#if (retinalMap.getIsSaved() == True):
# image_location = ImageDisplay(directory.openDirectory(), retinalMap.getPixelWidth(), image.getNumberOfImages(), retinalMap.getfile_name())
# image_location.pixel_location()
# image_location.showImg()
__main__()