Create Model

skin_disease/.gitignore

+.idea
+data
+model_saved.h5
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skin_disease/convolutional_neural_network.py

+# importing libraries
+import keras, tensorflow
+import numpy as np
+
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers import Activation, Dropout, Flatten, Dense
+from keras import backend as K
+
+
+img_width, img_height = 350, 350
+
+train_data_dir = 'data/train'
+validation_data_dir = 'data/test'
+nb_train_samples = 168
+nb_validation_samples = 20
+epochs = 20
+batch_size = 20
+
+if K.image_data_format() == 'channels_first':
+    input_shape = (3, img_width, img_height)
+else:
+    input_shape = (img_width, img_height, 3)
+
+model = Sequential()
+model.add(Conv2D(32, (2, 2), input_shape = input_shape))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size =(2, 2)))
+
+model.add(Conv2D(32, (2, 2)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size =(2, 2)))
+
+model.add(Conv2D(64, (2, 2)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size =(2, 2)))
+
+model.add(Flatten())
+model.add(Dense(64))
+model.add(Activation('relu'))
+model.add(Dropout(0.5))
+model.add(Dense(2))
+model.add(Activation('softmax'))
+
+model.compile(loss ='sparse_categorical_crossentropy',
+              optimizer ='adam',
+              metrics =['accuracy'])
+
+train_datagen = ImageDataGenerator(
+    rescale = 1. / 255,
+    shear_range = 0.2,
+    zoom_range = 0.2,
+    horizontal_flip = True)
+
+test_datagen = ImageDataGenerator(rescale = 1. / 255)
+
+train_generator = train_datagen.flow_from_directory(train_data_dir,
+                                                    target_size =(img_width, img_height),
+                                                    batch_size = batch_size, class_mode ='sparse')
+
+validation_generator = test_datagen.flow_from_directory(
+    validation_data_dir,
+    target_size =(img_width, img_height),
+    batch_size = batch_size, class_mode ='sparse')
+
+model.fit_generator(train_generator,
+                    steps_per_epoch = nb_train_samples // batch_size,
+                    epochs = epochs, validation_data = validation_generator,
+                    validation_steps = nb_validation_samples // batch_size)
+
+model.save_weights('model_saved.h5')

skin_disease/predict_image_class.py

+from keras.layers import Activation
+import keras
+import numpy as np
+import tensorflow
+from keras import backend as K
+from keras.layers import Activation
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers import Dense, Dropout, Flatten
+from keras.models import Sequential
+print(tensorflow.__version__)
+print(keras.__version__)
+def predictImageClass(image):
+    K.clear_session()
+    tensorflow.reset_default_graph()
+
+    nb_train_samples = 112
+    nb_validation_samples = 20
+    epochs = 10
+    batch_size = 2
+    img_width, img_height = 350, 350
+
+    if K.image_data_format() == 'channels_first':
+        input_shape = (3, img_width, img_height)
+    else:
+        input_shape = (img_width, img_height, 3)
+
+    modelI = Sequential()
+    modelI.add(Conv2D(32, (2, 2), input_shape=input_shape))
+    modelI.add(Activation('relu'))
+    modelI.add(MaxPooling2D(pool_size=(2, 2)))
+
+    modelI.add(Conv2D(32, (2, 2)))
+    modelI.add(Activation('relu'))
+    modelI.add(MaxPooling2D(pool_size=(2, 2)))
+
+    modelI.add(Conv2D(64, (2, 2)))
+    modelI.add(Activation('relu'))
+    modelI.add(MaxPooling2D(pool_size=(2, 2)))
+
+    modelI.add(Flatten())
+    modelI.add(Dense(64))
+    modelI.add(Activation('relu'))
+    modelI.add(Dropout(0.5))
+    modelI.add(Dense(2))
+    modelI.add(Activation('softmax'))
+
+    modelI.compile(loss='sparse_categorical_crossentropy',
+                   optimizer='adam',
+                   metrics=['accuracy'])
+
+    modelI.load_weights('model_saved.h5')
+
+    sess = keras.backend.get_session()
+    # image = request.args.get('image', default = 1, type = str)
+    print("------------------")
+    print(image)
+    print("------------------")
+    img = tensorflow.read_file(image)
+    img = tensorflow.image.decode_jpeg(img, channels=3)
+    img.set_shape([None, None, 3])
+    img = tensorflow.image.resize_images(img, (350, 350))
+    img = img.eval(session=sess)  # convert to numpy array
+    img = np.expand_dims(img, 0)  # make 'batch' of 1
+
+    pred = modelI.predict(img)
+    # pred = labels["label_names"][np.argmax(pred)]
+    print(pred)
+    y = ["Ring worms", "Sarcoptic & demodectic mange"]
+    print(np.argmax(pred))
+    print(y[np.argmax(pred)])
+    return y[np.argmax(pred)]
+
+predictImageClass("data/test/Ringworm/Ringworm1.jpg")
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