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CNN Model.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Dec 27 22:27:57 2018
+
+@author: Rutvij Lingras
+"""
+
+
+import sys
+import os
+import pandas as pd
+import numpy as np
+import pickle
+import cv2
+from os import listdir
+from tqdm import tqdm
+#from __future__ import unicode_literals
+from sklearn.preprocessing import LabelBinarizer
+from keras.models import Sequential
+from keras.layers.normalization import BatchNormalization
+from keras.layers.convolutional import Conv2D
+from keras.layers.convolutional import MaxPooling2D
+from keras.layers.core import Activation, Flatten, Dropout, Dense
+from keras import backend as K
+from keras.preprocessing.image import ImageDataGenerator
+from keras.optimizers import Adam
+from keras.preprocessing import image
+from 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
+from random import shuffle
+import random
+from keras.models import model_from_json
+
+Pepper__bell___Bacterial_spot = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Pepper__bell___Bacterial_spot"
+Pepper__bell___healthy = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Pepper__bell___Bacterial_spot"
+Potato___Early_blight = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Potato___Early_blight"
+Potato___healthy = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Potato___healthy"
+Potato___Late_blight = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Potato___Late_blight"
+Tomato__Target_Spot = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Tomato__Target_Spot"
+Tomato__Tomato_mosaic_virus = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Tomato__Tomato_mosaic_virus"
+Tomato__Tomato_YellowLeaf__Curl_Virus = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Tomato__Tomato_YellowLeaf__Curl_Virus"
+Tomato_Bacterial_spot = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Tomato_Bacterial_spot"
+Tomato_Early_blight = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Tomato_Early_blight"
+Tomato_healthy = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Tomato_healthy"
+Tomato_Late_blight = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Tomato_Late_blight"
+Tomato_Leaf_Mold = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Tomato_Leaf_Mold"
+Tomato_Septoria_leaf_spot = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Tomato_Septoria_leaf_spot"
+Tomato_Spider_mites_Two_spotted_spider_mite = "/Users/m0rem0rem0re/Desktop/BennetCNN/PlantVillage/Tomato_Spider_mites_Two_spotted_spider_mite"
+
+img_width = 256
+img_height = 256
+depth=3
+EPOCHS = 25
+INIT_LR = 1e-3
+BS = 32
+X = []
+X1 = []
+Y = []
+Y1 = []
+training_data = []
+temp = []
+label_binarizer = LabelBinarizer()
+
+'''def labelizer(label_list):
+    image_labels = label_binarizer.fit_transform(label_list)
+    return image_labels
+'''
+#temp = temp.append(labelizer(Y[:10]))
+for img in tqdm(os.listdir(Pepper__bell___Bacterial_spot)[:200]):
+	label = 'Pepper__bell___Bacterial_spot'
+	Y.append(label)
+	path = os.path.join(Pepper__bell___Bacterial_spot,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+
+
+for img in tqdm(os.listdir(Pepper__bell___healthy)[:200]):
+	label = 'Pepper__bell___healthy'
+	Y.append(label)
+	path = os.path.join(Pepper__bell___healthy,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+for img in tqdm(os.listdir(Potato___Early_blight)[:200]):
+	label = 'Potato___Early_blight'
+	Y.append(label)
+	path = os.path.join(Potato___Early_blight,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+for img in tqdm(os.listdir(Potato___healthy)[:200]):
+	label = 'Potato___healthy'
+	Y.append(label)
+	path = os.path.join(Potato___healthy,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+for img in tqdm(os.listdir(Tomato__Target_Spot)[:200]):
+	label = 'Tomato__Target_Spot'
+	path = os.path.join(Tomato__Target_Spot,img)
+	img = cv2.imread(path)
+	Y.append(label)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+for img in tqdm(os.listdir(Tomato__Tomato_mosaic_virus)[:200]):
+	label = 'Tomato__Tomato_mosaic_virus'
+	Y.append(label)
+	path = os.path.join(Tomato__Tomato_mosaic_virus,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+for img in tqdm(os.listdir(Tomato__Tomato_YellowLeaf__Curl_Virus)[:200]):
+	label = 'Tomato__Tomato_YellowLeaf__Curl_Virus'
+	Y.append(label)
+	path = os.path.join(Tomato__Tomato_YellowLeaf__Curl_Virus,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+for img in tqdm(os.listdir(Tomato_Bacterial_spot)[:200]):
+	label = 'Tomato_Bacterial_spot'
+	Y.append(label)
+	path = os.path.join(Tomato_Bacterial_spot,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+for img in tqdm(os.listdir(Tomato_Early_blight)[:200]):
+	label = 'Tomato_Early_blight'
+	Y.append(label)
+	path = os.path.join(Tomato_Early_blight,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+for img in tqdm(os.listdir(Tomato_healthy)[:200]):
+	label = 'Tomato_healthy'
+	Y.append(label)
+	path = os.path.join(Tomato_healthy,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+for img in tqdm(os.listdir(Tomato_Late_blight)[:200]):
+	label = 'Tomato_Late_blight'
+	Y.append(label)
+	path = os.path.join(Tomato_Late_blight,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+for img in tqdm(os.listdir(Tomato_Leaf_Mold)[:200]):
+	label = 'Tomato_Leaf_Mold'
+	Y.append(label)
+	path = os.path.join(Tomato_Leaf_Mold,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+for img in tqdm(os.listdir(Tomato_Septoria_leaf_spot)[:200]):
+	label = 'Tomato_Septoria_leaf_spot'
+	Y.append(label)
+	path = os.path.join(Tomato_Septoria_leaf_spot,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+
+for img in tqdm(os.listdir(Tomato_Spider_mites_Two_spotted_spider_mite)[:200]):
+	label = 'Tomato_Spider_mites_Two_spotted_spider_mite'
+	Y.append(label)
+	path = os.path.join(Tomato_Spider_mites_Two_spotted_spider_mite,img)
+	img = cv2.imread(path)
+	img = cv2.resize(img, (256,256))
+	X.append([np.asarray(img)])
+print(np.array(X).shape)
+
+
+train_data = []
+train_data = list(zip(X,Y))
+random.shuffle(train_data)
+
+X1 = np.array([i[0] for i in train_data])
+Y1 = np.array([i[1] for i in train_data])
+
+X=np.array(X)
+Y=np.array(Y)
+
+
+'''train_data = pd.DataFrame({'A':X, 'B':Y})
+train_data = train_data.sample(frac=1).reset_index(drop=True)
+
+X1 = train_data["A"].tolist()
+Y1 = train_data["B"].tolist()
+'''
+label_binarizer = LabelBinarizer()
+image_labels = label_binarizer.fit_transform(Y1)
+pickle.dump(label_binarizer,open('label_transform.pkl', 'wb'))
+n_classes = len(label_binarizer.classes_)
+
+np_image_list = np.array(X1, dtype=np.float16) / 225.0
+
+print("[INFO] Spliting data to train, test")
+x_train, x_test, y_train, y_test = train_test_split(X1, 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 = (img_height, img_width, depth)
+chanDim = -1
+if K.image_data_format() == "channels_first":
+    inputShape = (depth, img_height, img_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"))
+
+opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
+
+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}")
+
+print("[INFO] Saving model...")
+pickle.dump(model,open('cnn_model.pkl', 'wb'))
+
+
+model_json = model.to_json()
+with open("model.json","w") as json_file:
+    json_file.write(model_json)
+
+model.save_weights("model.h5")
+
+#Loading the saved model
+
+json_file = open('model.json', 'r')
+loaded_model_json = json_file.read()
+json_file.close()
+loaded_model = model_from_json(loaded_model_json)
+# load weights into new model
+loaded_model.load_weights("model.h5")
+lb = pickle.load(open(f"label_transform.pkl","rb"))
+
+INIT_LR = 1e-3
+opt=Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
+loaded_model.compile(loss='binary_crossentropy',
+              optimizer='rmsprop',
+              metrics=['accuracy'])
+
+img = cv2.imread('img4.jpg')
+img = cv2.resize(img,(256,256))
+img = np.reshape(img,[1,256,256,3])
+
+classes = loaded_model.predict_classes(img)
+
+from scipy import sparse
+
+sA = sparse.csr_matrix(classes)
+lb = pickle.load(open(f"label_transform.pkl","rb"))
+disease = f"{lb.inverse_transform(sA)[0]}"
+print("The Disease detected is :",disease)
+
+img2 = cv2.imread('img4.jpg')
+plt.imshow(img2,)

_config.yml

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+theme: jekyll-theme-cayman