plants_disease_detection-jinbaosite.ipynb

{"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 the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.layers import Activation, Dropout, Flatten, Dense\nfrom keras import backend\nfrom keras.optimizers import SGD, Adam\nfrom __future__ import print_function\nfrom __future__ import absolute_import\nfrom keras.preprocessing import image\nimport numpy as np\nimport os\nimport json\nimport warnings\nimport keras\nfrom keras import layers\nfrom keras.datasets import cifar10\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.layers import Input\nfrom keras.layers import Dense\nfrom keras.layers import Activation\nfrom keras.layers import Flatten\nfrom keras.layers import Conv2D, PReLU, Dropout\nfrom keras.layers import MaxPooling2D\nfrom keras.layers import AveragePooling2D\nfrom keras.layers import GlobalAveragePooling2D\nfrom keras.layers import GlobalMaxPooling2D\nfrom keras.layers import BatchNormalization\nfrom keras.models import Model\nfrom keras.callbacks import LearningRateScheduler, ModelCheckpoint, TensorBoard, EarlyStopping, ReduceLROnPlateau\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n%matplotlib inline","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"7b61d9eec9a5a79ee029c4c5409fa30a00fae541"},"cell_type":"code","source":"img_width, img_height = 150, 150\ntrain_data_dir = '../input/train/train'\nvalidation_data_dir = '../input/val/val'\nnb_train_samples = 32739\nnb_validation_samples = 4982\nepochs = 30\nbatch_size = 64\nclasses = 61\nmodel_name = 'DenseNet-61.h5'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"305db16ef713c7890036495863b4155d25ffc0ea"},"cell_type":"code","source":"def dense_block(x, blocks, name):\n    for i in range(blocks):\n        x = conv_block(x, 32, name=name + '_block' + str(i + 1))\n    return x","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"675f5651c0bdbd0ff4bd475a8c41df2c5bc0d3ee"},"cell_type":"code","source":"def transition_block(x, reduction, name):\n    bn_axis = 3\n    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,\n                                  name=name + '_bn')(x)\n    x = layers.Activation('relu', name=name + '_relu')(x)\n    x = layers.Conv2D(int(backend.int_shape(x)[bn_axis] * reduction), 1,\n                      use_bias=False,\n                      name=name + '_conv')(x)\n    x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x)\n    return x","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2c0145be7452d0736971f8770099fbaba6ae831e"},"cell_type":"code","source":"def conv_block(x, growth_rate, name):\n    bn_axis = 3\n    x1 = layers.BatchNormalization(axis=bn_axis,\n                                   epsilon=1.001e-5,\n                                   name=name + '_0_bn')(x)\n    x1 = layers.Activation('relu', name=name + '_0_relu')(x1)\n    x1 = layers.Conv2D(4 * growth_rate, 1,\n                       use_bias=False,\n                       name=name + '_1_conv')(x1)\n    x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,\n                                   name=name + '_1_bn')(x1)\n    x1 = layers.Activation('relu', name=name + '_1_relu')(x1)\n    x1 = layers.Conv2D(growth_rate, 3,\n                       padding='same',\n                       use_bias=False,\n                       name=name + '_2_conv')(x1)\n    x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1])\n    return x","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"d7fb6aa3054efe296259b68c8ffdf0cb86517de0"},"cell_type":"code","source":"def DenseNet(blocks, input_shape=(150,150,3), classes=61):\n\n    img_input = Input(shape=input_shape)\n\n    bn_axis = 3\n\n    x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)\n    x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)\n    x = layers.BatchNormalization(\n        axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x)\n    x = layers.Activation('relu', name='conv1/relu')(x)\n    x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)\n    x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)\n\n    x = dense_block(x, blocks[0], name='conv2')\n    x = transition_block(x, 0.5, name='pool2')\n    x = dense_block(x, blocks[1], name='conv3')\n    x = transition_block(x, 0.5, name='pool3')\n    x = dense_block(x, blocks[2], name='conv4')\n    x = transition_block(x, 0.5, name='pool4')\n    x = dense_block(x, blocks[3], name='conv5')\n\n    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)\n    \n    x = layers.GlobalAveragePooling2D(name='avg_pool')(x)\n    \n    x = Dense(512)(x)\n    x = BatchNormalization()(x)\n    x = PReLU()(x)\n    x = Dropout(0.5)(x)\n    x = Dense(classes, activation='softmax', name='fc61')(x)\n\n    inputs = img_input\n\n    model = Model(inputs, x, name='densenet')\n    \n    return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"adc8277d336c36434b447cd41dcc63b7b358d622"},"cell_type":"code","source":"model = DenseNet(blocks=[6, 12, 24, 16], input_shape=(150,150,3),classes=61)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"dcf0881c67a7e6bec6795121583b8417bd1fe644"},"cell_type":"code","source":"print(os.listdir('../input'))\nprint(os.listdir('../input/densenet-jinbao'))\nprint(os.listdir('../input/ai-challenger-pdr2018'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"cd4637ab2376e5a4b56556d578d337f3b5b21064"},"cell_type":"code","source":"from keras.preprocessing import image\nimport numpy as np\nmodel.load_weights('../input/densenet-jinbao/DenseNet-61.h5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"722eba58c70bf9bb8ab3c99caafe7a0f3056817c"},"cell_type":"code","source":"rr = [0,\n      1,10,11,12,13,14,15,16,17,18,19,\n      2,20,21,22,23,24,25,26,27,28,29,\n      3,30,31,32,33,34,35,36,37,38,39,\n      4,40,41,42,43,44,45,46,47,48,49,\n      5,50,51,52,53,54,55,56,57,58,59,\n      6,60,\n      7,\n      8,\n      9]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"43bb7a9a770b8d12a0969d55d706422d2bc7e851"},"cell_type":"code","source":"image_path = '../input/ai-challenger-pdr2018/val/val/10/0ecbf075894bda7b5993de6209b7147c.jpg'\nimg = image.load_img(image_path, target_size=(150, 150))\nx = image.img_to_array(img)/255.0\nx = np.expand_dims(x, axis=0)\n# x = preprocess_input(x)\n# 对图像进行分类\npreds = model.predict(x)\n\nprint(rr[np.argmax(preds)])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"722193dff037f927796db41adf5018cff74b62b8"},"cell_type":"code","source":"images = os.listdir('../input/ai-challenger-pdr2018/testa/testA')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"1896e667e5b1f416b4b71aa8acbab6dcc171ebfa"},"cell_type":"code","source":"result = []\nfor img1 in images:\n    image_path = '../input/ai-challenger-pdr2018/testa/testA/' + img1\n    img = image.load_img(image_path, target_size=(150, 150))\n    x = image.img_to_array(img)/255.0\n    x = np.expand_dims(x, axis=0)\n    preds = model.predict(x)\n    tmp = dict()\n    tmp['image_id'] = img1\n    tmp['disease_class']=rr[int(np.argmax(preds))]\n    result.append(tmp)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"95e58101a52add1fea0216a03766965dab11760a"},"cell_type":"code","source":"import json\njson2 = json.dumps(result)\nf = open('result3.json','w',encoding='utf-8')\nf.write(json2)\nf.close()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}