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models.py
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models.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import pickle
import cv2
from tensorflow.keras.models import Sequential
from os import listdir
from sklearn.preprocessing import LabelBinarizer
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Activation, Flatten, Dropout, Dense
from 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
print('ok')
# In[2]:
EPOCHS = 7
INIT_LR = 1e-3
BS = 32
default_image_size = tuple((256, 256))
image_size = 0
directory_root = 'plantvillage/'
width=256
height=256
depth=3
# In[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
# In[4]:
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}")
# In[5]:
image_size = len(image_list)
print(image_size)
# print(label_list)
# In[6]:
label_binarizer = LabelBinarizer()
image_labels = label_binarizer.fit_transform(label_list)
pickle.dump(label_binarizer,open('label_transform2.pkl', 'wb'))
n_classes = len(label_binarizer.classes_)
# In[8]:
print(label_binarizer.classes_)
# In[9]:
np_image_list = np.array(image_list, dtype=np.float16) / 225.0
# In[10]:
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)
# In[11]:
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")
# In[13]:
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"))
# In[ ]:
# In[14]:
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...")
# In[ ]:
history = model.fit(
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
)
# In[ ]:
print("[INFO] Calculating model accuracy")
scores = model.evaluate(x_test, y_test)
print(f"Test Accuracy: {scores[1]*100}")
# In[ ]:
print("[INFO] Saving model...")
model.save('cnn_model')
# pickle.dump(model,open('cnn_model.pkl', 'wb'))
# ## Predication the decices
# In[ ]:
import tensorflow
import numpy as np
import pickle
import cv2
# In[ ]:
model = tensorflow.keras.models.load_model('cnn_model')
img = 'imgs/p_b_s.JPG'
arr_img = convert_image_to_array(img)
# In[ ]:
labelencoder= open('label_transform2.pkl', 'rb')
labeltransformer = pickle.load(labelencoder)
npimagelist = np.array([arr_img], dtype=np.float16) / 225.0
REDICTEDCLASSES2 = model.predict_classes(npimagelist)
print(REDICTEDCLASSES2)
# pred = model.predict(npimagelist)
print(label_binarizer.classes_[REDICTEDCLASSES2])
# In[ ]:
import tensorflow
# In[ ]:
tensorflow.keras.layers.BatchNormalization