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Seg-Exct-Classif-Pipeline-Hybrid Method.py
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Seg-Exct-Classif-Pipeline-Hybrid Method.py
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# -*- KENAN MORANI - IZMIR DEMOCRACY UNIVERSITY -*-
#### COV19-CT DB Database #####
### part of IEEE ICASSP 2023: AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D)
### at https://mlearn.lincoln.ac.uk/icassp-2023-ai-mia/
#### B. 3rd COV19D Competition ---- I. Covid-19 Detection Challenge
#### kenan.morani@gmail.com
# Importing Libraries
import os, glob
import numpy as np
import matplotlib.pyplot as plt
import cv2
import nibabel as nib
import tensorflow as tf
from tensorflow import keras
from skimage.feature import canny
#from scipy import ndimage as ndi
from skimage import io
from skimage.exposure import histogram
from PIL import Image as im
import skimage
from skimage import data,morphology
from skimage.color import rgb2gray
#import scipy.ndimage as nd
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Conv2D, Dropout, BatchNormalization, Activation, MaxPool2D, Conv2DTranspose, Concatenate, Input
from tensorflow.keras.models import Model
#from tensorflow.keras.applications import VGG16
from keras.callbacks import ModelCheckpoint
from skimage import color, filters
from tensorflow.keras import backend as K
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import layers, models
from tensorflow.keras import activations
from tensorflow.keras.models import Sequential
from termcolor import colored
#import visualkeras
from collections import defaultdict
from PIL import ImageFont
from tensorflow.keras.preprocessing.image import load_img, img_to_array, array_to_img
import cv2
import csv
from sklearn.utils import class_weight
from collections import Counter
from skimage.morphology import ball, disk, dilation, binary_erosion, remove_small_objects, erosion, closing, reconstruction, binary_closing
from skimage.measure import label,regionprops, perimeter
from skimage.morphology import binary_dilation, binary_opening
from skimage.filters import roberts, sobel
from skimage import measure, feature
from skimage.segmentation import clear_border
#from skimage.util.montage import montage2d
from scipy import ndimage as ndi
#from mpl_toolkits.mplot3d.art3d import Poly3DCollection
#import dicom
import scipy.misc
#from tensorflow.keras.models import model_from_json
#import visualkeras
from tensorflow.python.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, ZeroPadding2D
#from collections import defaultdict
#from PIL import ImageFont
####################################################################################################
####################### Processing : Slicing and Saving Exctracted Slices from 3D Images [If needed] #########
###############################################################################
"""# Slicing and Saving"""
## The path here include the image volumes (308MB)
## and the lung masks (1MB) were extracted from the COVID-19 CT segmentation dataset
dataInputPath = '/home/idu/Desktop/COV19D/segmentation/volumes'
imagePathInput = os.path.join(dataInputPath, 'img/') ## Image volumes were exctracted to this subfolder
maskPathInput = os.path.join(dataInputPath, 'mask/') ## lung masks were exctracted to this subfolder
# Preparing the outputpath for slicing the CT volume from the above data++++
dataOutputPath = '/home/idu/Desktop/COV19D/segmentation/slices/'
imageSliceOutput = os.path.join(dataOutputPath, 'img/') ## Image volume slices will be placed here
maskSliceOutput = os.path.join(dataOutputPath, 'mask/') ## Annotated masks slices will be placed here
# Slicing only in Z direction
# Slices in Z direction shows the required lung area
SLICE_X = False
SLICE_Y = False
SLICE_Z = True
SLICE_DECIMATE_IDENTIFIER = 3
# Choosing normalization boundaries suitable from the chosen images
HOUNSFIELD_MIN = -1020
HOUNSFIELD_MAX = 2995
HOUNSFIELD_RANGE = HOUNSFIELD_MAX - HOUNSFIELD_MIN
# Normalizing the images
def normalizeImageIntensityRange (img):
img[img < HOUNSFIELD_MIN] = HOUNSFIELD_MIN
img[img > HOUNSFIELD_MAX] = HOUNSFIELD_MAX
return (img - HOUNSFIELD_MIN) / HOUNSFIELD_RANGE
#nImg = normalizeImageIntensityRange(img)
#np.min(nImg), np.max(nImg), nImg.shape, type(nImg)
# Reading image or mask volume
def readImageVolume(imgPath, normalize=True):
img = nib.load(imgPath).get_fdata()
if normalize:
return normalizeImageIntensityRange(img)
else:
return img
#readImageVolume(imgPath, normalize=False)
#readImageVolume(maskPath, normalize=False)
# Slicing image and saving
def sliceAndSaveVolumeImage(vol, fname, path):
(dimx, dimy, dimz) = vol.shape
print(dimx, dimy, dimz)
cnt = 0
if SLICE_X:
cnt += dimx
print('Slicing X: ')
for i in range(dimx):
saveSlice(vol[i,:,:], fname+f'-slice{str(i).zfill(SLICE_DECIMATE_IDENTIFIER)}_x', path)
if SLICE_Y:
cnt += dimy
print('Slicing Y: ')
for i in range(dimy):
saveSlice(vol[:,i,:], fname+f'-slice{str(i).zfill(SLICE_DECIMATE_IDENTIFIER)}_y', path)
if SLICE_Z:
cnt += dimz
print('Slicing Z: ')
for i in range(dimz):
saveSlice(vol[:,:,i], fname+f'-slice{str(i).zfill(SLICE_DECIMATE_IDENTIFIER)}_z', path)
return cnt
# Saving volume slices to file
def saveSlice (img, fname, path):
img = np.uint8(img * 255)
fout = os.path.join(path, f'{fname}.png')
cv2.imwrite(fout, img)
print(f'[+] Slice saved: {fout}', end='\r')
# Reading and processing image volumes for TEST images
for index, filename in enumerate(sorted(glob.iglob(imagePathInput+'*.nii.gz'))):
img = readImageVolume(filename, True)
print(filename, img.shape, np.sum(img.shape), np.min(img), np.max(img))
numOfSlices = sliceAndSaveVolumeImage(img, 't'+str(index), imageSliceOutput)
print(f'\n{filename}, {numOfSlices} slices created \n')
# Reading and processing image mask volumes for TEST masks
for index, filename in enumerate(sorted(glob.iglob(maskPathInput+'*.nii.gz'))):
img = readImageVolume(filename, False)
print(filename, img.shape, np.sum(img.shape), np.min(img), np.max(img))
numOfSlices = sliceAndSaveVolumeImage(img, 't'+str(index), maskSliceOutput)
print(f'\n{filename}, {numOfSlices} slices created \n')
# Exploring the data
imgPath = os.path.join(imagePathInput, '1.nii.gz')
img = nib.load(imgPath).get_fdata()
np.min(img), np.max(img), img.shape, type(img)
maskPath = os.path.join(maskPathInput, '1.nii.gz')
mask = nib.load(maskPath).get_fdata()
np.min(mask), np.max(mask), mask.shape, type(mask)
# Showing Mask slice
imgSlice = mask[:,:,20]
plt.imshow(imgSlice, cmap='gray')
plt.show()
# Showing Corresponding Image slice
imgSlice = img[:,:,20]
plt.imshow(imgSlice, cmap='gray')
plt.show()
"""# Training and testing Generator"""
# Define constants
#SEED = 42
### Setting the training and testing dataset for validation of the proposed VGG16-UNET model
### All t0&t1 Z-sliced slices (images and masks) were used for testing
#BATCH_SIZE_TRAIN = 32
#BATCH_SIZE_TEST = 32
IMAGE_HEIGHT = 224
IMAGE_WIDTH = 224
SIZE = IMAGE_HEIGHT = IMAGE_HEIGHT
IMG_SIZE = (IMAGE_HEIGHT, IMAGE_WIDTH)
#### Splitting the data into training and test sets happen manually
### t0 volumes and masks were chosen as test sets
data_dir = '/home/idu/Desktop/COV19D/segmentation/slices/'
data_dir_train = os.path.join(data_dir, 'training')
# The images should be stored under: "data/slices/training/img/img"
data_dir_train_image = os.path.join(data_dir_train, 'img')
# The images should be stored under: "data/slices/training/mask/img"
data_dir_train_mask = os.path.join(data_dir_train, 'mask')
data_dir_test = os.path.join(data_dir, 'test')
# The images should be stored under: "data/slices/test/img/img"
data_dir_test_image = os.path.join(data_dir_test, 'img')
# The images should be stored under: "data/slices/test/mask/img"
data_dir_test_mask = os.path.join(data_dir_test, 'mask')
BATCH_SIZE = 64
def orthogonal_rot(image):
return np.rot90(image, np.random.choice([-1, 0, 1]))
def create_segmentation_generator_train(img_path, msk_path, BATCH_SIZE):
data_gen_args = dict(rescale=1./255,
featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=90,
preprocessing_function=orthogonal_rot,
width_shift_range=0.2,
height_shift_range=0.2,
zoom_range=0.3
)
datagen = ImageDataGenerator(**data_gen_args)
img_generator = datagen.flow_from_directory(img_path, target_size=IMG_SIZE, class_mode=None, color_mode='grayscale', batch_size=BATCH_SIZE, seed=SEED)
msk_generator = datagen.flow_from_directory(msk_path, target_size=IMG_SIZE, class_mode=None, color_mode='grayscale', batch_size=BATCH_SIZE, seed=SEED)
return zip(img_generator, msk_generator)
# Remember not to perform any image augmentation in the test generator!
def create_segmentation_generator_test(img_path, msk_path, BATCH_SIZE):
data_gen_args = dict(rescale=1./255)
datagen = ImageDataGenerator(**data_gen_args)
img_generator = datagen.flow_from_directory(img_path, target_size=IMG_SIZE, class_mode=None, color_mode='grayscale', batch_size=BATCH_SIZE, seed=SEED)
msk_generator = datagen.flow_from_directory(msk_path, target_size=IMG_SIZE, class_mode=None, color_mode='grayscale', batch_size=BATCH_SIZE, seed=SEED)
return zip(img_generator, msk_generator)
BATCH_SIZE_TRAIN = BATCH_SIZE_TEST = BATCH_SIZE
SEED = 44
train_generator = create_segmentation_generator_train(data_dir_train_image, data_dir_train_mask, BATCH_SIZE_TRAIN)
test_generator = create_segmentation_generator_test(data_dir_test_image, data_dir_test_mask, BATCH_SIZE_TEST)
NUM_TRAIN = 2*745
NUM_TEST = 2*84
## Choosing number of training epoches
NUM_OF_EPOCHS = 20
def display(display_list):
plt.figure(figsize=(15,15))
title = ['Input Image', 'True Mask', 'Predicted Mask']
for i in range(len(display_list)):
plt.subplot(1, len(display_list), i+1)
plt.title(title[i])
plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]), cmap='gray')
plt.show()
def show_dataset(datagen, num=1):
for i in range(0,num):
image,mask = next(datagen)
display([image[0], mask[0]])
show_dataset(test_generator, 2)
#############################################################
######################## Method 1
##################################################################################
##################################################################################
#############################################################
######################## Image Processing and CNN modelling
##################################################################################
### Manual Cropping of the image; r
img = cv2.imread('/home/idu/Desktop/COV19D/val-seg1 (copy)/non-covid/ct_scan210/1.jpg')
img = skimage.color.rgb2gray(img)
r = cv2.selectROI(img)
# Plotting hostogram
# find frequency of pixels in range 0-255
img = Image.open('/home/idu/Desktop/COV19D/val-seg1 (copy)/non-covid/ct_scan210/166.jpg')
img = skimage.color.rgb2gray(img)
img = img < t
img = np.array(img)
#img = skimage.filters.gaussian(img, sigma=1.0)
# Flatten the image array to 1D
img = img.ravel()
# Plot the histogram
plt.hist(img)#, bins=256, range=(0, 256), color='red', alpha=0.4)
plt.xlabel("Pixel value")
#plt.xlim(0, 50)
plt.ylabel("Counts")
plt.title("Image histogram")
plt.show()
histr = cv2.calcHist([img],[0],None,[256],[0,256])
# show the plotting graph of an image
plt.plot(histr)
plt.show()
t = 0.45 #Histogram Threshold
#### Cropping right-lung as an ROI and removing upper and lowermost of the slices
count = []
folder_path = '/home/idu/Desktop/COV19D/val-seg1 (copy)/covid1'
#Change this directory to the directory where you need to do preprocessing for images
#Inside the directory must folder(s), which have the images inside them
for fldr in os.listdir(folder_path):
sub_folder_path = os.path.join(folder_path, fldr)
for filee in os.listdir(sub_folder_path):
file_path = os.path.join(sub_folder_path, filee)
img = cv2.imread(file_path)
#Grayscale images
img = skimage.color.rgb2gray(img)
# First cropping an image
#%r = cv2.selectROI(im)
#Select ROI from images before you start the code
#Reference: https://learnopencv.com/how-to-select-a-bounding-box-roi-in-opencv-cpp-python/
#{Last access 15th of Dec, 2021}
# Crop image using r
#img_cropped = img[int(r[1]):int(r[1]+r[3]), int(r[0]):int(r[0]+r[2])]
img_cropped=img
# Thresholding and binarizing images
# Reference: https://datacarpentry.org/image-processing/07-thresholding/
#{Last access 15th of Dec, 2021}
# Gussian Filtering
#img = skimage.filters.gaussian(img_cropped, sigma=1.0)
# Binarizing the image
#print (img)
img = img < t
count = np.count_nonzero(img)
#print(count)
if count > 260000: ## Threshold to be selected
#print(count)
img_cropped = np.expand_dims(img_cropped, axis=2)
img_cropped = array_to_img (img_cropped)
# Replace images with the image that includes ROI
img_cropped.save(str(file_path), 'JPEG')
#print('saved')
else:
#print(count)
# Remove non-representative slices
os.remove(str(file_path))
print('removed')
print(str(sub_folder_path))
# Check that there is at least one slice left
if not os.listdir(str(sub_folder_path)):
print(str(sub_folder_path), "Directory is empty")
count = []
#################### Building CNN Model
h = w = 224
#batch_size = 64
batch_size=128
#batch_size=256
train_datagen = ImageDataGenerator(rescale=1./255,
vertical_flip=True,
horizontal_flip=True)
train_generator = train_datagen.flow_from_directory(
'/home/idu/Desktop/COV19D/train-Preprocessed/', ## COV19-CT-DB Training set
target_size=(h, w),
batch_size=batch_size,
color_mode='grayscale',
classes = ['covid','non-covid'],
class_mode='binary')
val_datagen = ImageDataGenerator(rescale=1./255)
val_generator = val_datagen.flow_from_directory(
'/home/idu/Desktop/COV19D/validation-Preprocessed/', ## COV19-CT-DB Validation set
target_size=(h, w),
batch_size=batch_size,
color_mode='grayscale',
classes = ['covid','non-covid'],
class_mode='binary')
#### The CNN model
def make_model():
model = models.Sequential()
# Convulotional Layer 1
model.add(layers.Conv2D(16,(3,3),input_shape=(h,w,1), padding="same"))
model.add(layers.BatchNormalization())
model.add(layers.ReLU())
model.add(layers.MaxPooling2D((2,2)))
# Convulotional Layer 2
model.add(layers.Conv2D(32,(3,3), padding="same"))
model.add(layers.BatchNormalization())
model.add(layers.ReLU())
model.add(layers.MaxPooling2D((2,2)))
# Convulotional Layer 3
model.add(layers.Conv2D(64,(3,3), padding="same"))
model.add(layers.BatchNormalization())
model.add(layers.ReLU())
model.add(layers.MaxPooling2D((2,2)))
# Convulotional Layer 4
model.add(layers.Conv2D(128,(3,3), padding="same"))
model.add(layers.BatchNormalization())
model.add(layers.ReLU())
model.add(layers.MaxPooling2D((2,2)))
# Fully Connected Layer
model.add(layers.Flatten())
model.add(layers.Dense(256))
model.add(layers.BatchNormalization())
model.add(layers.ReLU())
model.add(layers.Dropout(0.1)) # Try 0.1 or 0.3
# Dense Layer
model.add(layers.Dense(1,activation='sigmoid'))
return model
model = make_model()
## Load model weights after saving
model.load_weights('/home/idu/Desktop/COV19D/ChatGPT-saved-models/imagepreprocesscnnclass.h5')
n_epochs= 100
# Compiling the model using SGD optimizer with a learning rate schedualer
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=[tf.keras.metrics.Precision(), tf.keras.metrics.Recall(),
'accuracy'])
early_stopping_cb = keras.callbacks.EarlyStopping(monitor="val_accuracy", patience=7)
checkpoint = ModelCheckpoint('/home/idu/Desktop/COV19D/ChatGPT-saved-models/imagepreprocesscnnclass.h5', save_best_only=True, save_weights_only=True)
###Learning Rate decay
initial_learning_rate = 0.1
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate,
decay_steps=100000,
decay_rate=0.96,
staircase=True)
def decayed_learning_rate(step):
return initial_learning_rate * decay_rate ^ (step / decay_steps)
## Class weight
counter = Counter(train_generator.classes)
max_val = float(max(counter.values()))
class_weights = {class_id : max_val/num_images for class_id, num_images in counter.items()}
class_weights
training_steps = 2*269309 // batch_size
training_steps = 434726 // batch_size ## Without Slice reduction
val_steps = 67285 // batch_size
val_steps = 106378 // batch_size ## Without Slice reduction
history=model.fit(train_generator,
steps_per_epoch=training_steps,
validation_data=val_generator,
validation_steps=val_steps,
verbose=2,
epochs=n_epochs,
callbacks=[early_stopping_cb, checkpoint],
class_weight=class_weights)
## saving the model
model.save('/home/idu/Desktop/COV19D/ChatGPT-saved-models/imagepreprocesscnnclass.h5')
model = keras.models.load_model('/home/idu/Desktop/COV19D/ChatGPT-saved-models/imagepreprocesscnnclass.h5')
# Evaluatin the model
model.evaluate(val_generator, batch_size=128)
#############################################################
######################## Method 2 & 3
##################################################################################
##################################################################################
#############################################################
######################## Stage 1 : SEGMENTAITON
##################################################################################
############ K-Means Clustering Based Segmetnation [Optional]
def extract_lungs(mask):
kernel = np.ones((5,5),np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
return mask
def kmeans_segmentation(image):
Z = image.reshape((-1,1))
Z = np.float32(Z)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 2
ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
center = np.uint8(center)
res = center[label.flatten()]
segmented_image = res.reshape((image.shape))
return segmented_image
def segment_and_extract_lungs(image):
segmented_image = kmeans_segmentation(image)
binary_mask = np.zeros(image.shape, dtype=np.uint8)
binary_mask[segmented_image == segmented_image.min()] = 1
binary_mask = extract_lungs(binary_mask)
lung_extracted_image = np.zeros(image.shape, dtype=np.uint8)
lung_extracted_image[binary_mask == 1] = image[binary_mask == 1]
return lung_extracted_image
# Modify here to run the code on all the required slices
input_folder = "/home/idu/Desktop/COV19D/test"
output_folder = "/home/idu/Desktop/COV19D/test-seg1"
if not os.path.exists(output_folder):
os.makedirs(output_folder)
for subdir, dirs, files in os.walk(input_folder):
for file in files:
if not file.endswith('.jpg'): # Check if the file is a JPEG image
continue
image_path = os.path.join(subdir, file)
try:
image = cv2.imread(image_path, 0) # Read the input image
lung_extracted_image = segment_and_extract_lungs(image)
except Exception as e:
print(f"Error processing {image_path}: {e}")
continue
subfolder_name = os.path.basename(subdir)
subfolder_path = os.path.join(output_folder, subfolder_name)
if not os.path.exists(subfolder_path):
os.makedirs(subfolder_path)
output_path = os.path.join(subfolder_path, file)
cv2.imwrite(output_path, lung_extracted_image)
################# Remove Non-representative Slices [optional]
def check_valid_image(image):
return cv2.countNonZero(image) > 1764 # Choose a threshold for removal
input_folder = "/home/idu/Desktop/COV19D/train-seg/non-covid"
output_folder = "/home/idu/Desktop/COV19D/train-seg-sliceremove/non-covid"
if not os.path.exists(output_folder):
os.makedirs(output_folder)
for subdir, dirs, files in os.walk(input_folder):
subfolder_name = subdir.split('/')[-1]
subfolder_path = os.path.join(output_folder, subfolder_name)
if not os.path.exists(subfolder_path):
os.makedirs(subfolder_path)
count = 0
for file in files:
image_path = os.path.join(subdir, file)
if '.jpg' in image_path:
image = cv2.imread(image_path, 0)
if check_valid_image(image):
count += 1
output_path = os.path.join(subfolder_path, file)
cv2.imwrite(output_path, image)
if count == 0:
print(f"No valid images were found in subfolder: {subfolder_name}")
# calculating average, min and max dice coeffecient on the test set
GT_path = '/home/idu/Desktop/COV19D/segmentation/slices/test/mask/img'
pred_path = '/home/idu/Desktop/COV19D/segmentation/slices/test/img/img'
### Mean IoU & Dice COeffecient Measures
# specify the img directory path
#path = "path/to/img/folder/"
# list files in img directory
files = os.listdir(GT_path)
files2 = os.listdir(pred_path)
print(files)
mean_iou = []
dicee = []
dim = (224,224)
num_classes = 2
for file in files:
# make sure file is an image
for filee in files2:
if str(filee) == str(file):
## Ground Truth Mask
p1 = os.path.join(GT_path, file)
print(p1)
img = cv2.imread(p1 , 0)
img = cv2.resize(img, dim)
edges = canny(img)
img = nd.binary_fill_holes(edges)
elevation_map = sobel(img)
# Since, the contrast difference is not much. Anyways we will perform it
markers = np.zeros_like(img)
markers[img < 0.1171875] = 1 # 30/255
markers[img > 0.5859375] = 2 # 150/255
segmentation = morphology.watershed(elevation_map, markers)
#img = img / 255.0
#imgg = img
#img = numpy.bool(img)
#img = np.asarray(img).astype(np.bool)
## Predicted mask
#p2 = os.path.join(pred_path, filee)
#img2 = cv2.imread(p2, 0)
#img2= cv2.resize(img2, dim)
#img2 = img2 / 255.0
#img2 = img2[None]
#img2 = np.expand_dims(img2, axis=-1)
#img2 = UNet_model.predict(img2) > 0.5
#imgg2 = UNet_model.predict(img2) #> 0.5
#imgg2 = imgg2.astype(numpy.float64)
#img2 = np.squeeze(img2)
#imgg2 = np.squeeze(imgg2)
#d = dtype(image)
#print(d)
#img2 = np.asarray(img2).astype(np.bool)
IOU_keras = MeanIoU(num_classes=num_classes)
IOU_keras.update_state(img, segmentation)
print("Mean IoU =", IOU_keras.result().numpy())
mean_iou.append(IOU_keras.result().numpy())
value = dice_coef(img, segmentation)
print("Dice coeffecient value is", value, "\n")
dicee.append(value)
############ UNET Based Segemtnation
# Building 2D-UNET model
def unet(n_levels, initial_features=64, n_blocks=2, kernel_size=5, pooling_size=2, in_channels=1, out_channels=1):
inputs = keras.layers.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, in_channels))
x = inputs
convpars = dict(kernel_size=kernel_size, activation='relu', padding='same')
#downstream
skips = {}
for level in range(n_levels):
for _ in range(n_blocks):
x = keras.layers.Conv2D(initial_features * 2 ** level, **convpars)(x)
x = BatchNormalization()(x)
if level < n_levels - 1:
skips[level] = x
x = keras.layers.MaxPool2D(pooling_size)(x)
# upstream
for level in reversed(range(n_levels-1)):
x = keras.layers.Conv2DTranspose(initial_features * 2 ** level, strides=pooling_size, **convpars)(x)
x = keras.layers.Concatenate()([x, skips[level]])
for _ in range(n_blocks):
x = keras.layers.Conv2D(initial_features * 2 ** level, **convpars)(x)
# output
activation = 'sigmoid' if out_channels == 1 else 'softmax'
x = BatchNormalization()(x)
x = keras.layers.Conv2D(out_channels, kernel_size=1, activation=activation, padding='same')(x)
return keras.Model(inputs=[inputs], outputs=[x], name=f'UNET-L{n_levels}-F{initial_features}')
## Choosing UNet depth
#UNet_model = unet(2) # 2-level depth UNet model
UNet_model = unet(3) # 3-level depth UNet model
#UNet_model = unet(4)# # 4-level depth UNet model
UNet_model.summary()
# Hyperparameters tuning
from tensorflow.keras.metrics import MeanIoU
import math
initial_learning_rate = 0.1
def lr_exp_decay(epoch, lr):
k = 1
return initial_learning_rate * math.exp(-k*epoch)
early_stopping = tf.keras.callbacks.EarlyStopping(
monitor="val_loss",
patience=3,
#verbose=1,
#mode="auto",
#baseline=None,
#restore_best_weights=False,
)
EPOCH_STEP_TRAIN = 2*12*NUM_TRAIN // BATCH_SIZE_TRAIN
EPOCH_STEP_TEST = 2*NUM_TEST // BATCH_SIZE_TEST
UNet_model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=[tf.keras.metrics.Precision(), tf.keras.metrics.Recall(),
#tf.keras.metrics.MeanIoU(num_classes = 2),
'accuracy'])
NUM_OF_EPOCHS = 20
UNet_model.fit_generator(generator=train_generator,
steps_per_epoch=EPOCH_STEP_TRAIN,
validation_data=test_generator,
validation_steps=EPOCH_STEP_TEST,
epochs=NUM_OF_EPOCHS,
callbacks=[early_stopping, tf.keras.callbacks.LearningRateScheduler(lr_exp_decay, verbose=1)]
)
#Evaluating the UNet models on the test partition
UNet_model.evaluate(test_generator, batch_size=128, steps=EPOCH_STEP_TEST)
#Saving the UNet model models with different depth levels and batch norm()
UNet_model.save('/home/idu/Desktop/COV19D/ChatGPT-saved-models/Segmentation models/UNet_model-3L-BatchNorm.h5')
#Loading saved models
UNet_model = keras.models.load_model('/home/idu/Desktop/COV19D/ChatGPT-saved-models/Segmentation models/UNet_model-3L-BatchNorm.h5')
# Displaying predicted slices against ground truth
def display(display_list):
title = ['Input Image', 'True Mask', 'Predicted Mask']
for i in range(len(display_list)):
plt.subplot(1, len(display_list), i+1)
plt.title(title[i])
plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]), cmap='gray')
plt.show()
def show_dataset(datagen, num=1):
for i in range(0, num):
image,mask = next(datagen)
display((image[0], mask[0]))
path = '/home/idu/Desktop/COV19D/segmentation/Segmentation Results/'
def show_prediction(datagen, num=1):
for i in range(0,num):
image,mask = next(datagen)
pred_mask = UNet_model.predict(image)[0] > 0.5
display([image[0], mask[0], pred_mask])
num_classes = 2
IOU_keras = MeanIoU(num_classes=num_classes)
IOU_keras.update_state(mask[0], pred_mask)
print("Mean IoU =", IOU_keras.result().numpy())
values = np.array(IOU_keras.get_weights()).reshape(num_classes, num_classes)
print(values)
show_prediction(test_generator, 12)
results = UNet_model.evaluate(test_generator, steps = EPOCH_STEP_TEST)
print("test loss, test acc:", results)
for name, value in zip(UNet_model.metrics_names, results):
print(name, ':', value)
import cv2 as cv
## Segmenting images based on k-means clustering and exctracting lung regions and saving them
#in the same directory as the original images (in .png format)
path = '/home/idu/Desktop/COV19D/segmentation/Segmentation Results/'
kernel = np.ones((5,5),np.float32)
def show_prediction(datagen, num=1):
for i in range(0,num):
image,mask = next(datagen)
pred_mask = UNet_model.predict(image)[0] > 0.5
print(pred_mask.dtype)
#pred_mask = pred_mask.astype(np.float32)
#pred_mask = pred_mask*255
print(pred_mask.dtype)
#pred_mask = pred_mask[None]
#pre_mask = np.squeeze(pred_mask, axis=0)
#pred_mask = np.expand_dims(pred_mask, axis=0)
print(pred_mask.dtype)
#pred_mask = cv.dilate(pred_mask, kernel, iterations = 1)
#pred_mask = Image.fromarray(pred_mask, 'L')
#pred_mask = pred_mask.convert('LA')
#pred_mask = np.expand_dims(pred_mask, axis=-1)
#pred_mask.show()
display([image[0], mask[0], pred_mask])
#num_classes = 2
#IOU_keras = MeanIoU(num_classes=num_classes)
#IOU_keras.update_state(mask[0], pred_mask)
#print("Mean IoU =", IOU_keras.result().numpy())
#mean_iou1.append(IOU_keras.result().numpy())
#values = np.array(IOU_keras.get_weights()).reshape(num_classes, num_classes)
#print(values)
show_prediction(test_generator, 2)
# calculating average, min and max dice coeffecient on the test set
GT_path = '/home/idu/Desktop/COV19D/segmentation/slices/test/mask/img'
pred_path = '/home/idu/Desktop/COV19D/segmentation/slices/test/img/img'
# Mean IoU & Dice COeffecient Measures
# specify the img directory path
#path = "path/to/img/folder/"
# list files in img directory
files = os.listdir(GT_path)
files2 = os.listdir(pred_path)
print(files)
from tensorflow.keras import backend as K
def dice_coef(img, img2):
if img.shape != img2.shape:
raise ValueError("Shape mismatch: img and img2 must have to be of the same shape.")
else:
lenIntersection=0
for i in range(img.shape[0]):
for j in range(img.shape[1]):
if ( np.array_equal(img[i][j],img2[i][j]) ):
lenIntersection+=1
lenimg=img.shape[0]*img.shape[1]
lenimg2=img2.shape[0]*img2.shape[1]
value = (2. * lenIntersection / (lenimg + lenimg2))
return value
mean_iou = []
dicee = []
dim = (224,224)
num_classes = 2
for file in files:
# make sure file is an image
for filee in files2:
if str(filee) == str(file):
## Ground Truth Mask
p1 = os.path.join(GT_path, file)
print(p1)
img = cv2.imread(p1 , 0)
img = cv2.resize(img, dim)
img = img / 255.0
#imgg = img
#img = img > 0.5
#img = numpy.bool(img)
#img = np.asarray(img).astype(np.bool)
## Predicted mask
p2 = os.path.join(pred_path, filee)
img2 = cv2.imread(p2, 0)
img2= cv2.resize(img2, dim)
img2 = img2 / 255.0
img2 = img2[None]
img2 = np.expand_dims(img2, axis=-1)
img2 = UNet_model.predict(img2) > 0.5
#imgg2 = UNet_model.predict(img2) #> 0.5
#imgg2 = imgg2.astype(numpy.float64)
img2 = np.squeeze(img2)
#imgg2 = np.squeeze(imgg2)
#d = dtype(image)
#print(d)
#img2 = np.asarray(img2).astype(np.bool)
IOU_keras = MeanIoU(num_classes=num_classes)
IOU_keras.update_state(img, img2)
print("Mean IoU =", IOU_keras.result().numpy())
mean_iou.append(IOU_keras.result().numpy())
value = dice_coef(img, img2)
print("Dice coeffecient value is", value, "\n")
dicee.append(value)
UNet_model.summary()
#print (img)
#print(img2)
dicee = np.array(dicee)
L = len(mean_iou)
print("Number of Values is", L)
# Taking average of dice values
av=np.mean(mean_iou)
avv=np.mean(dicee)
print ("average value is", av)
print ("average value is", avv)
# Taking maximuim and minimuim of dice values
mx=np.max(mean_iou)
mxx=np.max(dicee)
print ("maximuim value is", mx)
print ("maximuim value is", mxx)
mn=np.min(mean_iou)
mnn=np.min(dicee)
print ("minimuim value is", mn)
print ("minimuim value is", mnn)
md=np.median(mean_iou)
mdd=np.median(dicee)
print ("median value is", md)
print ("median value is", mdd)
#############################################################
######################## Stage 2 : LUNG EXCTRACTION
##################################################################################
UNet_model = tf.keras.models.load_model('/home/idu/Desktop/COV19D/segmentation/UNet_model-3L-BatchNorm.h5')
## Comparing the results of predicted masks between public dataset and COV19-CT database
## Ectracting with one example
file_path1 = '/home/idu/Desktop/COV19D/im/156.jpg'
file_path2 = '/home/idu/Desktop/COV19D/segmentation/slices/img/t2-slice140_z.png'
n1 = cv2.imread(file_path1, 0)
n2 = image2=cv2.imread(file_path2, 0)
image1=cv2.imread(file_path1, 0)
image2=cv2.imread(file_path2, 0)
#cv2.imwrite('/home/idu/Desktop/COV19D/im/156.png', image1)
#file_path11 = '/home/idu/Desktop/COV19D/im/156.png'
#image1=cv2.imread(file_path11, 0)
dim = (224, 224)
n1 = cv2.resize(n1, dim)
n2 = cv2.resize(n2, dim)
image1 = cv2.resize(image1, dim)
image2 = cv2.resize(image2, dim)
n1= n1 * 100.0 / 255.0
image1 = image1 * 100.0 / 255.0
hist,bins = np.histogram(image1.flatten(),256,[0,256])
plt.plot(hist, color = 'b')
hist,bins = np.histogram(image2.flatten(),256,[0,256])
plt.plot(hist, color = 'b')
#image2 = cv2.equalizeHist(image2) ### Histogram equalization
#image = image.expand_dims(segmented_data, axis=-1)
image1 = image1 / 255.0
image2 = image2 / 255.0
image1 = image1[None]