import numpy as np
import pickle
import cv2
import tensorflow.keras
import tensorflow.core
import tensorflow.python
from os import listdir
from sklearn.preprocessing import LabelBinarizer
from tensorflow.keras.models import Sequential
from tensorflow.python.keras.layers.normalization import BatchNormalization
from tensorflow.python.keras.layers.convolutional import Conv2D
from tensorflow.python.keras.layers.convolutional import MaxPooling2D
from tensorflow.python.keras.layers.core import Activation, Flatten, Dropout, Dense
from tensorflow.keras import backend as K
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import img_to_array
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
EPOCHS = 25
INIT_LR = 1e-3
BS = 32
default_image_size = tuple((256, 256))
image_size = 0
directory_root = 'C:/Users/Btl/Desktop/PlantVillage'
width=256
height=256
depth=3
def convert_image_to_array(image_dir):
try:
image = cv2.imread(image_dir)
if image is not None :
image = cv2.resize(image, default_image_size)
return img_to_array(image)
else :
return np.array([])
except Exception as e:
print(f"Error : {e}")
return None
image_list, label_list = [], []
try:
print("[INFO] Loading images ...")
root_dir = listdir(directory_root)
for directory in root_dir:
# remove .DS_Store from list
if directory == ".DS_Store":
root_dir.remove(directory)
for plant_folder in root_dir:
plant_disease_folder_list = listdir(f"{directory_root}/{plant_folder}")
for disease_folder in plant_disease_folder_list:
# remove .DS_Store from list
if disease_folder == ".DS_Store":
plant_disease_folder_list.remove(disease_folder)
for plant_disease_folder in plant_disease_folder_list:
print(f"[INFO] Processing {plant_disease_folder} ...")
plant_disease_image_list = listdir(f"{directory_root}/{plant_folder}/{plant_disease_folder}/")
for single_plant_disease_image in plant_disease_image_list:
if single_plant_disease_image == ".DS_Store":
plant_disease_image_list.remove(single_plant_disease_image)
for image in plant_disease_image_list[:200]:
image_directory = f"{directory_root}/{plant_folder}/{plant_disease_folder}/{image}"
if image_directory.endswith(".jpg") == True or image_directory.endswith(".JPG") == True:
image_list.append(convert_image_to_array(image_directory))
label_list.append(plant_disease_folder)
print("[INFO] Image loading completed")
except Exception as e:
print(f"Error : {e}")
image_size = len(image_list)
label_binarizer = LabelBinarizer()
image_labels = label_binarizer.fit_transform(label_list)
pickle.dump(label_binarizer,open('label_transform.pkl', 'wb'))
n_classes = len(label_binarizer.classes_)
print(label_binarizer.classes_)
np_image_list = np.array(image_list, dtype=np.float16) / 225.0
print("[INFO] Spliting data to train, test")
x_train, x_test, y_train, y_test = train_test_split(np_image_list, image_labels, test_size=0.2, random_state = 42)
aug = ImageDataGenerator(
rotation_range=25, width_shift_range=0.1,
height_shift_range=0.1, shear_range=0.2,
zoom_range=0.2,horizontal_flip=True,
fill_mode="nearest")
model = Sequential()
inputShape = (height, width, depth)
chanDim = -1
if K.image_data_format() == "channels_first":
inputShape = (depth, height, width)
chanDim = 1
model.add(Conv2D(32, (3, 3), padding="same",input_shape=inputShape))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(Conv2D(64, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(Conv2D(128, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation("relu"))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(n_classes))
model.add(Activation("softmax"))
model.summary()
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
# distribution
model.compile(loss="binary_crossentropy", optimizer=opt,metrics=["accuracy"])
# train the network
print("[INFO] training network...")
history = model.fit_generator(
aug.flow(x_train, y_train, batch_size=BS),
validation_data=(x_test, y_test),
steps_per_epoch=len(x_train) // BS,
epochs=EPOCHS, verbose=1
)
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
#Train and validation accuracy
plt.plot(epochs, acc, 'b', label='Training accurarcy')
plt.plot(epochs, val_acc, 'r', label='Validation accurarcy')
plt.title('Training and Validation accurarcy')
plt.legend()
plt.figure()
#Train and validation loss
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title('Training and Validation loss')
plt.legend()
plt.show()
print("[INFO] Calculating model accuracy")
scores = model.evaluate(x_test, y_test)
print(f"Test Accuracy: {scores[1]*100}")
filename = 'label_transform.pkl'
image_labels = pickle.load(open(filename, 'rb'))
def predict_disease(image_path):
image_array = convert_image_to_array(image_path)
np_image = np.array(image_array, dtype=np.float16) / 225.0
np_image = np.expand_dims(np_image,0)
plt.imshow(plt.imread(image_path))
result = np.argmax(model.predict_classes(np_image))
print((image_labels.classes_[result][0]))
predict_disease('C:/Users/Betul/Desktop/PlantVillage/Pepper__bell___/Pepper__bell___healthy/0a3f2927-4410-46a3-bfda-5f4769a5aaf8___JR_HL 8275.JPG')
問題は私が何をしても、出力結果は同じ値、同じクラスです。私は初心者なので、このモデルのどこが悪いのかわかりません。私を助けてください。
最後のレイヤー、損失関数（バイナリ、カテゴリ、スパース）、エポックカウントを変更しようとしましたが、いいえ。
1 エポックでも 50 エポックでも同じ分類になります。同じ出力。
私はこのデータセットを使用し、画像のように変更しました。
