{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nprint(os.listdir(\"../input\"))","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"from tqdm import tqdm_notebook\nfrom PIL import Image, ImageFilter, ImageChops, ImageOps","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!rm -r ../train\n!rm -r ../test","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!mkdir ../train\n!mkdir ../test\n!mkdir ../train/0\n!mkdir ../train/1\n\n\n!mkdir ../test/0\n!mkdir ../test/1","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/train_labels.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"one = data.query(\"label == 1\")['id'].values\none_train = one[:len(one) * 4 //5]\none_test = one[len(one) * 4 //5:]\n\nzero = data.query(\"label == 0\")['id'].values\nzero_train = zero[:len(zero) * 4 //5]\nzero_test = zero[len(zero) * 4 //5:]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ORIGINAL_SIZE = 96\nCROP_SIZE = 32\n\ndef process(path, mode, label):\n\n    \n    colorImage = Image.open(\"../input/train/%s.tif\" % (path))\n    \n    \n    start_crop = (ORIGINAL_SIZE - CROP_SIZE) // 2\n    end_crop = start_crop + CROP_SIZE\n    colorImage = colorImage.crop((start_crop, start_crop, end_crop, end_crop))\n\n    \n    for I in range(4):\n        colorImage = colorImage.transpose(Image.ROTATE_90)\n        colorImage.save(\"../%s/%d/%s_%d.png\" % (mode, label, path, I), \"PNG\")\n        \n    colorImage = colorImage.transpose(Image.FLIP_LEFT_RIGHT)\n\n    for I in range(4):\n        colorImage = colorImage.transpose(Image.ROTATE_90)\n        colorImage.save(\"../%s/%d/%s_%d.png\" % (mode, label, path, I+4), \"PNG\")\n    \n    colorImage = colorImage.transpose(Image.FLIP_TOP_BOTTOM)\n    \n    for I in range(4):\n        colorImage = colorImage.transpose(Image.ROTATE_90)\n        colorImage.save(\"../%s/%d/%s_%d.png\" % (mode, label, path, I+8), \"PNG\")  \n\n    colorImage = colorImage.transpose(Image.FLIP_LEFT_RIGHT)\n    \n    for I in range(4):\n        colorImage = colorImage.transpose(Image.ROTATE_90)\n        colorImage.save(\"../%s/%d/%s_%d.png\" % (mode, label, path, I+12), \"PNG\")  \n    \n    \n    '''    \n    colorImage = Image.open(\"../input/train/%s.tif\" % (path))\n    start_crop = (ORIGINAL_SIZE - CROP_SIZE) // 2\n    end_crop = start_crop + CROP_SIZE\n    colorImage = colorImage.crop((start_crop, start_crop, end_crop, end_crop))\n\n    colorImage.save(\"../%s/%d/%s.png\" % (mode, label, path), \"PNG\")\n    '''\n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def myFunc(image):\n    image = np.array(image)\n    return image / 255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for I in tqdm_notebook(one_train):\n    process(I, \"train\", 1)\nfor I in tqdm_notebook(one_test):\n    process(I, \"test\", 1)\nfor I in tqdm_notebook(zero_train):\n    process(I, \"train\", 0)\nfor I in tqdm_notebook(zero_test):\n    process(I, \"test\", 0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.models import Model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.applications.resnet50 import ResNet50\nfrom keras.layers import Input, Dense, GlobalAveragePooling2D\n\nmodel = ResNet50(include_top=False, weights='imagenet', input_tensor=None, input_shape=(32,32,3), pooling='avg', classes=2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x = model.output\nx = Dense(1024, activation='relu')(x)\npredictions = Dense(1, activation='sigmoid')(x)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"combined_model = Model(inputs=model.input, outputs=predictions)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"datagen = ImageDataGenerator()\ntrain_it = datagen.flow_from_directory('../train/', class_mode='binary', batch_size=128, target_size=(32,32))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_it = datagen.flow_from_directory('../test/', class_mode='binary', batch_size=128, target_size=(32,32))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"combined_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"combined_model.fit_generator(train_it, steps_per_epoch=128, epochs=5, validation_data=test_it, validation_steps=128)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}