from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import os

#başlangıç ​​öğrenme oranını, eğitilecek dönem sayısını belirleyen parametreler.
INIT_LR = 1e-4
EPOCHS = 1#yapay sinir ağı eğitimimizin süresini etkiler ancak kesinliğinide arttırır ben 10-15 arası öneriyorum 1-2 saat eğitim sürüyor
BS = 32

DIRECTORY = r"C:\Users\gkhnmlym\Desktop\GO\python\mask project\Face-Mask-Detection-master\dataset"
CATEGORIES = ["with_mask", "without_mask"]

#veri kümesi dizinimizdeki görüntülerin listesini alın, ardından veri listesini (yani görüntüler) ve sınıf görüntülerini başlatın

print("[INFO] loading images...")

data = []
labels = []

for category in CATEGORIES:
    path = os.path.join(DIRECTORY, category)
    for img in os.listdir(path):
    	img_path = os.path.join(path, img)
    	image = load_img(img_path, target_size=(224, 224))
    	image = img_to_array(image)
    	image = preprocess_input(image)

    	data.append(image)
    	labels.append(category)

# etiketlerde tek sıcak kodlama gerçekleştirin
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
labels = to_categorical(labels)

data = np.array(data, dtype="float32")
labels = np.array(labels)

(trainX, testX, trainY, testY) = train_test_split(data, labels,
	test_size=0.20, stratify=labels, random_state=42)

# veri büyütme için eğitim görüntü oluşturucusunu oluşturun
aug = ImageDataGenerator(
	rotation_range=20,
	zoom_range=0.15,
	width_shift_range=0.2,
	height_shift_range=0.2,
	shear_range=0.15,
	horizontal_flip=True,
	fill_mode="nearest")

# MobileNetV2 ağını yükleyin, baş FC katman setlerinin kapalı kalmasını sağlayın
baseModel = MobileNetV2(weights="imagenet", include_top=False,
	input_tensor=Input(shape=(224, 224, 3)))

# temel modelin üstüne yerleştirilecek modelin başını oluşturun
headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(7, 7))(headModel)
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(128, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(2, activation="softmax")(headModel)

#kafa FC modelini temel modelin üstüne yerleştirin (bu, eğiteceğimiz gerçek model olacaktır)
model = Model(inputs=baseModel.input, outputs=headModel)

# temel modeldeki tüm katmanlar üzerinde döngü yapın ve ilk eğitim sürecinde güncellenmemeleri için onları dondurun

for layer in baseModel.layers:
	layer.trainable = False

# modelimizi derleyin

print("[INFO] compiling model...")
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="binary_crossentropy", optimizer=opt,
	metrics=["accuracy"])

# ağın başını eğitmek

print("[INFO] training head...")
H = model.fit(
	aug.flow(trainX, trainY, batch_size=BS),
	steps_per_epoch=len(trainX) // BS,
	validation_data=(testX, testY),
	validation_steps=len(testX) // BS,
	epochs=EPOCHS)

# test setiyle ilgili tahminlerde bulunun
print("[INFO] evaluating network...")
predIdxs = model.predict(testX, batch_size=BS)

# test setindeki her bir görüntü için, karşılık gelen en büyük tahmin edilen olasılığa sahip etiketin dizinini bulmamız gerekir

predIdxs = np.argmax(predIdxs, axis=1)

# güzel biçimlendirilmiş bir sınıflandırma raporu gösterin

print(classification_report(testY.argmax(axis=1), predIdxs,
	target_names=lb.classes_))

print("[INFO] saving mask detector model...")
model.save("mask_detector.model")

# eğitim grafiği çıkarır
N = EPOCHS
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, N), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="lower left")
plt.savefig("plot.png")
85965f932d68415c4b102efae60bb19dc0fba89e
<mask token>

<mask token>
print('[INFO] loading images...')
<mask token>
for category in CATEGORIES:
    path = os.path.join(DIRECTORY, category)
    for img in os.listdir(path):
        img_path = os.path.join(path, img)
        image = load_img(img_path, target_size=(224, 224))
        image = img_to_array(image)
        image = preprocess_input(image)
        data.append(image)
        labels.append(category)
<mask token>
for layer in baseModel.layers:
    layer.trainable = False
print('[INFO] compiling model...')
<mask token>
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
print('[INFO] training head...')
<mask token>
print('[INFO] evaluating network...')
<mask token>
print(classification_report(testY.argmax(axis=1), predIdxs, target_names=lb
    .classes_))
print('[INFO] saving mask detector model...')
model.save('mask_detector.model')
<mask token>
plt.style.use('ggplot')
plt.figure()
plt.plot(np.arange(0, N), H.history['loss'], label='train_loss')
plt.plot(np.arange(0, N), H.history['val_loss'], label='val_loss')
plt.plot(np.arange(0, N), H.history['acc'], label='train_acc')
plt.plot(np.arange(0, N), H.history['val_acc'], label='val_acc')
plt.title('Training Loss and Accuracy')
plt.xlabel('Epoch #')
plt.ylabel('Loss/Accuracy')
plt.legend(loc='lower left')
plt.savefig('plot.png')

<mask token>
INIT_LR = 0.0001
EPOCHS = 1
BS = 32
DIRECTORY = (
    'C:\\Users\\gkhnmlym\\Desktop\\GO\\python\\mask project\\Face-Mask-Detection-master\\dataset'
    )
CATEGORIES = ['with_mask', 'without_mask']
print('[INFO] loading images...')
data = []
labels = []
for category in CATEGORIES:
    path = os.path.join(DIRECTORY, category)
    for img in os.listdir(path):
        img_path = os.path.join(path, img)
        image = load_img(img_path, target_size=(224, 224))
        image = img_to_array(image)
        image = preprocess_input(image)
        data.append(image)
        labels.append(category)
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
labels = to_categorical(labels)
data = np.array(data, dtype='float32')
labels = np.array(labels)
trainX, testX, trainY, testY = train_test_split(data, labels, test_size=0.2,
    stratify=labels, random_state=42)
aug = ImageDataGenerator(rotation_range=20, zoom_range=0.15,
    width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15,
    horizontal_flip=True, fill_mode='nearest')
baseModel = MobileNetV2(weights='imagenet', include_top=False, input_tensor
    =Input(shape=(224, 224, 3)))
headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(7, 7))(headModel)
headModel = Flatten(name='flatten')(headModel)
headModel = Dense(128, activation='relu')(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(2, activation='softmax')(headModel)
model = Model(inputs=baseModel.input, outputs=headModel)
for layer in baseModel.layers:
    layer.trainable = False
print('[INFO] compiling model...')
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
print('[INFO] training head...')
H = model.fit(aug.flow(trainX, trainY, batch_size=BS), steps_per_epoch=len(
    trainX) // BS, validation_data=(testX, testY), validation_steps=len(
    testX) // BS, epochs=EPOCHS)
print('[INFO] evaluating network...')
predIdxs = model.predict(testX, batch_size=BS)
predIdxs = np.argmax(predIdxs, axis=1)
print(classification_report(testY.argmax(axis=1), predIdxs, target_names=lb
    .classes_))
print('[INFO] saving mask detector model...')
model.save('mask_detector.model')
N = EPOCHS
plt.style.use('ggplot')
plt.figure()
plt.plot(np.arange(0, N), H.history['loss'], label='train_loss')
plt.plot(np.arange(0, N), H.history['val_loss'], label='val_loss')
plt.plot(np.arange(0, N), H.history['acc'], label='train_acc')
plt.plot(np.arange(0, N), H.history['val_acc'], label='val_acc')
plt.title('Training Loss and Accuracy')
plt.xlabel('Epoch #')
plt.ylabel('Loss/Accuracy')
plt.legend(loc='lower left')
plt.savefig('plot.png')

from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import os
INIT_LR = 0.0001
EPOCHS = 1
BS = 32
DIRECTORY = (
    'C:\\Users\\gkhnmlym\\Desktop\\GO\\python\\mask project\\Face-Mask-Detection-master\\dataset'
    )
CATEGORIES = ['with_mask', 'without_mask']
print('[INFO] loading images...')
data = []
labels = []
for category in CATEGORIES:
    path = os.path.join(DIRECTORY, category)
    for img in os.listdir(path):
        img_path = os.path.join(path, img)
        image = load_img(img_path, target_size=(224, 224))
        image = img_to_array(image)
        image = preprocess_input(image)
        data.append(image)
        labels.append(category)
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
labels = to_categorical(labels)
data = np.array(data, dtype='float32')
labels = np.array(labels)
trainX, testX, trainY, testY = train_test_split(data, labels, test_size=0.2,
    stratify=labels, random_state=42)
aug = ImageDataGenerator(rotation_range=20, zoom_range=0.15,
    width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15,
    horizontal_flip=True, fill_mode='nearest')
baseModel = MobileNetV2(weights='imagenet', include_top=False, input_tensor
    =Input(shape=(224, 224, 3)))
headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(7, 7))(headModel)
headModel = Flatten(name='flatten')(headModel)
headModel = Dense(128, activation='relu')(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(2, activation='softmax')(headModel)
model = Model(inputs=baseModel.input, outputs=headModel)
for layer in baseModel.layers:
    layer.trainable = False
print('[INFO] compiling model...')
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
print('[INFO] training head...')
H = model.fit(aug.flow(trainX, trainY, batch_size=BS), steps_per_epoch=len(
    trainX) // BS, validation_data=(testX, testY), validation_steps=len(
    testX) // BS, epochs=EPOCHS)
print('[INFO] evaluating network...')
predIdxs = model.predict(testX, batch_size=BS)
predIdxs = np.argmax(predIdxs, axis=1)
print(classification_report(testY.argmax(axis=1), predIdxs, target_names=lb
    .classes_))
print('[INFO] saving mask detector model...')
model.save('mask_detector.model')
N = EPOCHS
plt.style.use('ggplot')
plt.figure()
plt.plot(np.arange(0, N), H.history['loss'], label='train_loss')
plt.plot(np.arange(0, N), H.history['val_loss'], label='val_loss')
plt.plot(np.arange(0, N), H.history['acc'], label='train_acc')
plt.plot(np.arange(0, N), H.history['val_acc'], label='val_acc')
plt.title('Training Loss and Accuracy')
plt.xlabel('Epoch #')
plt.ylabel('Loss/Accuracy')
plt.legend(loc='lower left')
plt.savefig('plot.png')

from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import os

#başlangıç ​​öğrenme oranını, eğitilecek dönem sayısını belirleyen parametreler.
INIT_LR = 1e-4
EPOCHS = 1#yapay sinir ağı eğitimimizin süresini etkiler ancak kesinliğinide arttırır ben 10-15 arası öneriyorum 1-2 saat eğitim sürüyor
BS = 32

DIRECTORY = r"C:\Users\gkhnmlym\Desktop\GO\python\mask project\Face-Mask-Detection-master\dataset"
CATEGORIES = ["with_mask", "without_mask"]

#veri kümesi dizinimizdeki görüntülerin listesini alın, ardından veri listesini (yani görüntüler) ve sınıf görüntülerini başlatın

print("[INFO] loading images...")

data = []
labels = []

for category in CATEGORIES:
    path = os.path.join(DIRECTORY, category)
    for img in os.listdir(path):
    	img_path = os.path.join(path, img)
    	image = load_img(img_path, target_size=(224, 224))
    	image = img_to_array(image)
    	image = preprocess_input(image)

    	data.append(image)
    	labels.append(category)

# etiketlerde tek sıcak kodlama gerçekleştirin
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
labels = to_categorical(labels)

data = np.array(data, dtype="float32")
labels = np.array(labels)

(trainX, testX, trainY, testY) = train_test_split(data, labels,
	test_size=0.20, stratify=labels, random_state=42)

# veri büyütme için eğitim görüntü oluşturucusunu oluşturun
aug = ImageDataGenerator(
	rotation_range=20,
	zoom_range=0.15,
	width_shift_range=0.2,
	height_shift_range=0.2,
	shear_range=0.15,
	horizontal_flip=True,
	fill_mode="nearest")

# MobileNetV2 ağını yükleyin, baş FC katman setlerinin kapalı kalmasını sağlayın
baseModel = MobileNetV2(weights="imagenet", include_top=False,
	input_tensor=Input(shape=(224, 224, 3)))

# temel modelin üstüne yerleştirilecek modelin başını oluşturun
headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(7, 7))(headModel)
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(128, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(2, activation="softmax")(headModel)

#kafa FC modelini temel modelin üstüne yerleştirin (bu, eğiteceğimiz gerçek model olacaktır)
model = Model(inputs=baseModel.input, outputs=headModel)

# temel modeldeki tüm katmanlar üzerinde döngü yapın ve ilk eğitim sürecinde güncellenmemeleri için onları dondurun

for layer in baseModel.layers:
	layer.trainable = False

# modelimizi derleyin

print("[INFO] compiling model...")
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="binary_crossentropy", optimizer=opt,
	metrics=["accuracy"])

# ağın başını eğitmek

print("[INFO] training head...")
H = model.fit(
	aug.flow(trainX, trainY, batch_size=BS),
	steps_per_epoch=len(trainX) // BS,
	validation_data=(testX, testY),
	validation_steps=len(testX) // BS,
	epochs=EPOCHS)

# test setiyle ilgili tahminlerde bulunun
print("[INFO] evaluating network...")
predIdxs = model.predict(testX, batch_size=BS)

# test setindeki her bir görüntü için, karşılık gelen en büyük tahmin edilen olasılığa sahip etiketin dizinini bulmamız gerekir

predIdxs = np.argmax(predIdxs, axis=1)

# güzel biçimlendirilmiş bir sınıflandırma raporu gösterin

print(classification_report(testY.argmax(axis=1), predIdxs,
	target_names=lb.classes_))

print("[INFO] saving mask detector model...")
model.save("mask_detector.model")

# eğitim grafiği çıkarır
N = EPOCHS
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, N), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="lower left")
plt.savefig("plot.png")