{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"dffb9c22-1fb6-4fea-2d6c-cf509a4fc36f"},"source":"print 1"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"cb0cfc79-e40f-33a9-3e3d-85c4e59c8a73"},"outputs":[],"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 the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"f37826ab-62b2-4131-ab38-71f0326ce05f"},"outputs":[],"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 the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ffbdd1d0-8ded-e1d1-6ebe-375c0b98921b"},"outputs":[],"source":"pd.read_csv('../input/KaggleNOAASeaLions.7z')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"8a61ea9d-115a-b0c6-4fb0-b0a895a395cd"},"outputs":[],"source":"import pandas as pd\nimport numpy as np\nimport glob\n\ntrain = pd.read_csv('../input/Train/train.csv')\ntrain1 = glob.glob('../input/Train/*.jpg')\ntrain2 = glob.glob('../input/TrainDotted/*.jpg')\nsubmission = pd.read_csv('../input/sample_submission.csv')\nprint(len(train),len(train1), len(train2), len(submission))"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ece555d5-c3c4-d142-d294-d80ce2f0ece2"},"outputs":[],"source":"train.shape, len(train1),len(train2)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"6b811f02-6b2c-5554-04ca-52d9cdbe3a69"},"outputs":[],"source":"train1[1]"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"70a09659-dcc0-5f72-1da3-d6fa2f24bc83"},"outputs":[],"source":"from PIL import Image, ImageDraw, ImageFilter\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nplt.rcParams['figure.figsize'] = (12.0, 8.0)\nim1 = Image.open(train1[1])\nim2 = Image.open(train1[1].replace('Train','TrainDotted'))\nimx = np.concatenate((im1.resize((450, 400), Image.ANTIALIAS), im2.resize((450, 400), Image.ANTIALIAS)), axis=1)\nplt.imshow(imx); plt.axis('off')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"b67e6f3a-2886-9f81-cd83-f968cdd9c3ab"},"outputs":[],"source":"plt.imshow(im2)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"858764be-89d3-8a47-0517-9da280727a51"},"outputs":[],"source":"train[train.train_id == int(train1[1].split('/')[3].split('.')[0])].T"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"acc5c432-ebbd-9ceb-4669-0a43b81f889c"},"outputs":[],"source":"plt.rcParams['figure.figsize'] = (10.0, 10.0)\nim3 = im2.crop((2400,100,2900,600))\nplt.imshow(im3); plt.axis('off')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ba089ab0-8dde-4f96-7b52-dd53acce068a"},"outputs":[],"source":"from PIL import ImageChops\nim_diff = ImageChops.difference(im1, im2)\nplt.imshow(im_diff); plt.axis('off')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ae6eb773-3bc3-680c-ea7c-5837bbec5b58"},"outputs":[],"source":"plt.imshow(im1); plt.axis('off')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"550bc09b-2edd-4908-0fd0-86a57d7e85e3"},"outputs":[],"source":"plt.imshow(im2); plt.axis('off')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"9b7df231-634f-8c63-775b-850e1b0bdbcd"},"outputs":[],"source":"from PIL import ImageChops\nim_diff = ImageChops.difference(im1, im2)\n\nplt.imshow(im2); plt.axis('off')\nplt.imshow(im_diff); plt.axis('off')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"2fc8eca2-93ba-733c-7d68-c703f2d5d85a"},"outputs":[],"source":"import pandas as pd\nimport numpy as np\nimport glob\nimport os\nimport cv2\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\ntrain_data = pd.read_csv('../input/Train/train.csv')\ntrain_imgs = sorted(glob.glob('../input/Train/*.jpg'), key=lambda name: int(os.path.basename(name)[:-4]))\ntrain_dot_imgs = sorted(glob.glob('../input/TrainDotted/*.jpg'), key=lambda name: int(os.path.basename(name)[:-4]))\n\nsubmission = pd.read_csv('../input/sample_submission.csv')\n\n\nprint(train_data.shape)\nprint('Number of Train Images: {:d}'.format(len(train_imgs)))\nprint('Number of Dotted-Train Images: {:d}'.format(len(train_dot_imgs)))\n\n\n\nprint(train_data.head(6))\n\n#test_imgs = glob.glob('../input/Test/*.jpg')\n#print('Number of Test Images: {:d}'.format(len(test_imgs)))\n#from subprocess import check_output\n#print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ab8b74ff-517a-790b-aee7-ead53bc8c32d"},"outputs":[],"source":"test_imgs = glob.glob('../input/Test/*.jpg')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"37762af9-c00b-fcd6-4c43-05e9af5f9a95"},"outputs":[],"source":"len(test_imgs)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"01731940-88e0-30ec-e432-c50213bde03c"},"outputs":[],"source":""}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.0"}},"nbformat":4,"nbformat_minor":0}