{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"code","source":"import matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom IPython.display import display\n%matplotlib inline\n\nimport pandas as pd\nimport numpy as np\nimport cv2\nimport os\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom glob import glob\nfrom skimage.feature import hog\nfrom skimage.color import rgb2grey\n\nfrom sklearn import svm, datasets\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.metrics import roc_curve, auc\n\nfrom sklearn.preprocessing import RobustScaler","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = list()\nlabel = list()\n\ndataset_train = \"/kaggle/input/2019-fall-pr-project/train/train/\"\ntrain_data_list = glob(dataset_train+'*.*.jpg')\n\nfor i in range(len(train_data_list)):\n    if train_data_list[i][47] == 'c':\n        label.append(0)\n    else :\n        label.append(1)\n    image = Image.open(train_data_list[i])\n    image = image.resize((32,32), Image.ANTIALIAS)\n    image = np.array(image)/255\n    image = image.reshape(-1)\n    train.append(image)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"scaler = RobustScaler()\n\ntrain = np.array(train)\nscaler.fit(train)\ntrain = scaler.transform(train)\nlabel = np.array(label)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(train, label, test_size=0.25, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"grid_para= {'C': [1, 5, 10, 50], \n            'gamma': [ 0.01,0.05,0.001] ,'class_weight' : ['balanced']}\nsvc = svm.SVC(kernel = 'rbf',gamma=\"scale\")\nclf = GridSearchCV(svc, grid_para, cv=2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"clf.fit(X_train, y_train)\ny_predict = clf.predict(X_test)\nprint(confusion_matrix(y_test, y_predict))\nprint(classification_report(y_test, y_predict))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test = list()\n\ndataset_test = \"/kaggle/input/2019-fall-pr-project/test1/test1/\"\ntest_data_list = glob(dataset_test+'*.jpg')\n\nfor i in range(len(test_data_list)):\n  image = Image.open(test_data_list[i])\n  image = image.resize((32,32), Image.ANTIALIAS)\n  image = np.array(image)/255\n  image = image.reshape(-1)\n  test.append(image)\n\ntest = np.array(test)\nscaler.fit(test)\ntest = scaler.transform(test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"result = clf.predict(test)\ntest = result.reshape(-1,1)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}