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Paddy_dis_model02/Paddy_De_Test.py

+# from tensorflow.keras.models import load_model
+# import os
+# import matplotlib.pyplot as plt
+# from tensorflow.keras.preprocessing import image
+# from tensorflow.keras.applications.vgg19 import preprocess_input
+# import numpy as np
+# import cv2
+#
+#
+# model=load_model("vgg-rps-final.h5")
+#
+# path = "Test_img"
+# i = 0
+#
+# img = image.load_img(path + "/ml27.jpg" , target_size=(224, 224))
+# # plt.imshow(img)
+# # plt.show()
+#
+# x = image.img_to_array(img)
+# x = np.expand_dims(x, axis=0)
+# images = np.vstack([x])
+# pred = model.predict(images, batch_size=1)
+# if pred[0][0] > 0.5:
+#     category = "Small"
+# elif pred[0][1] > 0.5:
+#     category = "Big"
+#
+# print(category)

Paddy_dis_model02/Paddy_De_Train.py

+# from tensorflow.keras.layers import Input, Lambda, Dense, Flatten,Dropout
+# from tensorflow.keras.models import Model
+# from tensorflow.keras.applications.vgg19 import VGG19
+# from tensorflow.keras.applications.vgg19 import preprocess_input
+# from tensorflow.keras.preprocessing import image
+# from tensorflow.keras.preprocessing.image import ImageDataGenerator
+# from tensorflow.keras.models import Sequential
+# import numpy as np
+# import pandas as pd
+# import os
+# import cv2
+# import matplotlib.pyplot as plt
+#
+# # re-size all the images to this
+# IMAGE_SIZE = [224, 224]
+#
+# train_path="ttv/train"
+# test_path="ttv/test"
+# val_path="ttv/val"
+#
+# x_train=[]
+#
+#
+# for folder in os.listdir(train_path):
+#     sub_path=train_path+"/"+folder
+#     for img in os.listdir(sub_path):
+#         image_path=sub_path+"/"+img
+#         img_arr=cv2.imread(image_path)
+#         img_arr=cv2.resize(img_arr,(224,224))
+#         x_train.append(img_arr)
+#
+# x_test = []
+#
+# for folder in os.listdir(test_path):
+#     sub_path = test_path + "/" + folder
+#     for img in os.listdir(sub_path):
+#         image_path = sub_path + "/" + img
+#         img_arr = cv2.imread(image_path)
+#         img_arr = cv2.resize(img_arr, (224, 224))
+#         x_test.append(img_arr)
+#
+# x_val = []
+#
+# for folder in os.listdir(val_path):
+#     sub_path = val_path + "/" + folder
+#     for img in os.listdir(sub_path):
+#         image_path = sub_path + "/" + img
+#         img_arr = cv2.imread(image_path)
+#         img_arr = cv2.resize(img_arr, (224, 224))
+#         x_val.append(img_arr)
+#
+#
+# train_x=np.array(x_train)
+# test_x=np.array(x_test)
+# val_x=np.array(x_val)
+#
+# train_x.shape,test_x.shape,val_x.shape
+#
+# train_x=train_x/255.0
+# test_x=test_x/255.0
+# val_x=val_x/255.0
+#
+#
+#
+# from tensorflow.keras.preprocessing.image import ImageDataGenerator
+#
+# # train_datagen = ImageDataGenerator(rescale = 1./255,
+# #                                    shear_range = 0.2,
+# #                                    zoom_range = 0.2,
+# #                                    horizontal_flip = True)
+#
+# train_datagen = ImageDataGenerator(rescale = 1./255)
+# test_datagen = ImageDataGenerator(rescale = 1./255)
+# val_datagen = ImageDataGenerator(rescale = 1./255)
+#
+#
+# training_set = train_datagen.flow_from_directory(train_path,
+#                                                  target_size = (224, 224),
+#                                                  batch_size = 32,
+#                                                  class_mode = 'sparse')
+#
+# test_set = test_datagen.flow_from_directory(test_path,
+#                                             target_size = (224, 224),
+#                                             batch_size = 32,
+#                                             class_mode = 'sparse')
+#
+# val_set = val_datagen.flow_from_directory(val_path,
+#                                             target_size = (224, 224),
+#                                             batch_size = 32,
+#                                             class_mode = 'sparse')
+#
+# training_set.class_indices
+# train_y=training_set.classes
+# test_y=test_set.classes
+# val_y=val_set.classes
+# train_y.shape,test_y.shape,val_y.shape
+#
+#
+# # add preprocessing layer to the front of VGG
+# vgg = VGG19(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
+#
+# # don't train existing weights
+# for layer in vgg.layers:
+#     layer.trainable = False
+#
+# # our layers - you can add more if you want
+# x = Flatten()(vgg.output)
+#
+# prediction = Dense(3, activation='softmax')(x)
+#
+# # create a model object
+# model = Model(inputs=vgg.input, outputs=prediction)
+#
+# # view the structure of the model
+# model.summary()
+#
+#
+# # tell the model what cost and optimization method to use
+# model.compile(
+#   loss='sparse_categorical_crossentropy',
+#   optimizer="adam",
+#   metrics=['accuracy']
+# )
+#
+# from tensorflow.keras.callbacks import EarlyStopping
+# early_stop=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=5)
+#
+# #Early stopping to avoid overfitting of model
+#
+#
+# # fit the model
+# history = model.fit(
+#   train_x,
+#   train_y,
+#   validation_data=(val_x,val_y),
+#   epochs=10,
+#   callbacks=[early_stop],
+#   batch_size=32,shuffle=True)
+#
+# # loss
+# plt.plot(history.history['loss'], label='train loss')
+# plt.plot(history.history['val_loss'], label='val loss')
+# plt.legend()
+#
+# plt.savefig('vgg-loss-rps-1.png')
+# plt.show()
+#
+#
+# # accuracies
+# plt.plot(history.history['accuracy'], label='train acc')
+# plt.plot(history.history['val_accuracy'], label='val acc')
+# plt.legend()
+#
+# plt.savefig('vgg-acc-rps-1.png')
+# plt.show()
+#
+#
+# model.evaluate(test_x,test_y,batch_size=32)
+#
+#
+#
+# from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
+# import numpy as np
+#
+# y_pred=model.predict(test_x)
+# y_pred=np.argmax(y_pred,axis=1)
+#
+# accuracy_score(y_pred,test_y)
+#
+# print(classification_report(y_pred,test_y))
+#
+#
+# confusion_matrix(y_pred,test_y)
+#
+#
+# model.save("vgg-rps-final.h5")
+#
+#
+#