{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":9068223,"sourceType":"datasetVersion","datasetId":5469469}],"dockerImageVersionId":30746,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"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\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import preprocessing\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.preprocessing.image import load_img\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D,BatchNormalization\nimport os\nimport pandas as pd\nimport numpy as np\n# Input data files are available in the read-only \"../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# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"f60dd78e-cc8c-467d-98a0-1152a82ea15a","_cell_guid":"6063904b-fcbc-4c9c-94c7-4f58fbfc9721","execution":{"iopub.status.busy":"2024-08-01T08:15:22.859286Z","iopub.execute_input":"2024-08-01T08:15:22.859849Z"},"editable":false,"trusted":true},"execution_count":null,"outputs":[{"name":"stdout","text":"/kaggle/input/mood-detection/Dataset/labels.txt\n/kaggle/input/mood-detection/Dataset/test/surprise/265708769.png\n/kaggle/input/mood-detection/Dataset/test/surprise/853493510.png\n/kaggle/input/mood-detection/Dataset/test/surprise/180748518.png\n/kaggle/input/mood-detection/Dataset/test/surprise/121055828.png\n/kaggle/input/mood-detection/Dataset/test/surprise/722443276.png\n/kaggle/input/mood-detection/Dataset/test/surprise/726536869.png\n/kaggle/input/mood-detection/Dataset/test/surprise/257586962.png\n/kaggle/input/mood-detection/Dataset/test/surprise/928373399.png\n/kaggle/input/mood-detection/Dataset/test/surprise/611883075.png\n/kaggle/input/mood-detection/Dataset/test/surprise/829658531.png\n/kaggle/input/mood-detection/Dataset/test/surprise/231572859.png\n/kaggle/input/mood-detection/Dataset/test/surprise/857463060.png\n/kaggle/input/mood-detection/Dataset/test/surprise/677491196.png\n/kaggle/input/mood-detection/Dataset/test/surprise/898997797.png\n/kaggle/input/mood-detection/Dataset/test/surprise/419610753.png\n/kaggle/input/mood-detection/Dataset/test/surprise/835430600.png\n/kaggle/input/mood-detection/Dataset/test/surprise/324475678.png\n/kaggle/input/mood-detection/Dataset/test/surprise/34321190.png\n/kaggle/input/mood-detection/Dataset/test/surprise/878009756.png\n/kaggle/input/mood-detection/Dataset/test/surprise/869866879.png\n/kaggle/input/mood-detection/Dataset/test/surprise/563404466.png\n/kaggle/input/mood-detection/Dataset/test/surprise/743363428.png\n/kaggle/input/mood-detection/Dataset/test/surprise/546699343.png\n/kaggle/input/mood-detection/Dataset/test/surprise/135909327.png\n/kaggle/input/mood-detection/Dataset/test/surprise/73384319.png\n/kaggle/input/mood-detection/Dataset/test/surprise/330827907.png\n/kaggle/input/mood-detection/Dataset/test/surprise/373198359.png\n/kaggle/input/mood-detection/Dataset/test/surprise/510887392.png\n/kaggle/input/mood-detection/Dataset/test/surprise/321512894.png\n/kaggle/input/mood-detection/Dataset/test/surprise/52384797.png\n/kaggle/input/mood-detection/Dataset/test/surprise/655820977.png\n/kaggle/input/mood-detection/Dataset/test/surprise/270336962.png\n/kaggle/input/mood-detection/Dataset/test/surprise/106553961.png\n/kaggle/input/mood-detection/Dataset/test/surprise/711521728.png\n/kaggle/input/mood-detection/Dataset/test/surprise/270549526.png\n/kaggle/input/mood-detection/Dataset/test/surprise/31068238.png\n/kaggle/input/mood-detection/Dataset/test/surprise/837546987.png\n/kaggle/input/mood-detection/Dataset/test/surprise/539237947.png\n/kaggle/input/mood-detection/Dataset/test/surprise/852776462.png\n/kaggle/input/mood-detection/Dataset/test/surprise/282168683.png\n/kaggle/input/mood-detection/Dataset/test/surprise/509909254.png\n/kaggle/input/mood-detection/Dataset/test/surprise/295316018.png\n/kaggle/input/mood-detection/Dataset/test/surprise/623279590.png\n/kaggle/input/mood-detection/Dataset/test/surprise/360780584.png\n/kaggle/input/mood-detection/Dataset/test/surprise/275732146.png\n/kaggle/input/mood-detection/Dataset/test/surprise/995836945.png\n/kaggle/input/mood-detection/Dataset/test/surprise/293785215.png\n/kaggle/input/mood-detection/Dataset/test/surprise/20811323.png\n/kaggle/input/mood-detection/Dataset/test/surprise/792230911.png\n/kaggle/input/mood-detection/Dataset/test/surprise/14568738.png\n/kaggle/input/mood-detection/Dataset/test/surprise/162340275.png\n/kaggle/input/mood-detection/Dataset/test/surprise/539134389.png\n/kaggle/input/mood-detection/Dataset/test/surprise/484732954.png\n/kaggle/input/mood-detection/Dataset/test/surprise/23545584.png\n/kaggle/input/mood-detection/Dataset/test/surprise/378483548.png\n/kaggle/input/mood-detection/Dataset/test/surprise/688639949.png\n/kaggle/input/mood-detection/Dataset/test/surprise/627189912.png\n/kaggle/input/mood-detection/Dataset/test/surprise/407783151.png\n/kaggle/input/mood-detection/Dataset/test/surprise/632139633.png\n/kaggle/input/mood-detection/Dataset/test/surprise/513212860.png\n/kaggle/input/mood-detection/Dataset/test/surprise/302779881.png\n/kaggle/input/mood-detection/Dataset/test/surprise/945947325.png\n/kaggle/input/mood-detection/Dataset/test/surprise/164988070.png\n/kaggle/input/mood-detection/Dataset/test/surprise/338828701.png\n/kaggle/input/mood-detection/Dataset/test/surprise/635369336.png\n/kaggle/input/mood-detection/Dataset/test/surprise/953113260.png\n/kaggle/input/mood-detection/Dataset/test/surprise/810274893.png\n/kaggle/input/mood-detection/Dataset/test/surprise/829275028.png\n/kaggle/input/mood-detection/Dataset/test/surprise/406635427.png\n/kaggle/input/mood-detection/Dataset/test/surprise/792574004.png\n/kaggle/in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= '/kaggle/input/mood-detection/Dataset/train'\n\nTEST_DIR = '/kaggle/input/mood-detection/Dataset/test'\n\ndef createdataframe(dir):\n    image_paths = []\n    labels = []\n    for label in os.listdir(dir):\n        for imagename in os.listdir(os.path.join(dir, label)):\n            image_paths.append(os.path.join(dir, label, imagename))\n            labels.append(label)\n        print(label, \"completed\")\n    return image_paths, labels\ntrain = pd.DataFrame()\ntrain['image'], train['label'] = createdataframe(TRAIN_DIR)\nprint(train)\ntest = pd.DataFrame()\ntest['image'], test['label'] = createdataframe(TEST_DIR)\nprint(test)\nfrom tqdm.notebook import tqdm\n","metadata":{"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from keras.preprocessing.image import save_img\nfrom keras.preprocessing.image import img_to_array\n\ndef extract_features(images):\n    features = []\n    for image in tqdm(images):\n         \n        img = load_img(image, color_mode='grayscale')\n        \n        img = np.array(img)\n        features.append(img)\n    features = np.array(features)\n    features = features.reshape(len(features), 48, 48, 1)\n    return features","metadata":{"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_features = extract_features(train['image'])\ntest_features = extract_features(test['image'])","metadata":{"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_train = train_features / 255.0\nx_test = test_features / 255.0","metadata":{"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\nle.fit(train['label'])\ny_train = le.transform(train['label'])\ny_test = le.transform(test['label'])\ny_train = to_categorical(y_train, num_classes=7)\ny_test = to_categorical(y_test, num_classes=7)\n","metadata":{"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nmodel = Sequential()\n\nmodel.add(Conv2D(32, (5, 5), strides = (1, 1), name = 'conv0', input_shape = (32, 32, 1)))\n\nmodel.add(BatchNormalization(axis = 3, name = 'bn0'))\nmodel.add(Activation('relu'))\n\nmodel.add(MaxPooling2D((2, 2), name='max_pool'))\nmodel.add(Conv2D(64, (3, 3), strides = (1,1), name=\"conv1\"))\nmodel.add(Activation('relu'))\nmodel.add(AveragePooling2D((3, 3), name='avg_pool'))\n\nmodel.add(GlobalAveragePooling2D())\nmodel.add(Dense(300, activation=\"relu\", name='rl'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(1,activation='sigmoid', name='sm'))","metadata":{"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"conv_model = convolutional_model((48, 48, 1))\nconv_model.compile(optimizer='adam',\n                  loss='categorical_crossentropy',\n                  metrics=['accuracy'])\nconv_model.summary()\ntrain_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(48)\ntest_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(48)\nhistory = conv_model.fit(train_dataset, epochs=100, validation_data=test_dataset)    \n","metadata":{"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_json = model.to_json()\nwith open(\"mooddetector.json\", 'w') as json_file:\n    json_file.write(model_json)\nmodel.save(\"mooddetector.h5\")","metadata":{"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"label = ['anger', 'disgust', 'fear', 'happiness', 'neutral', 'sadness', 'surprise']\ndef ef(image):\n    img = load_img(image, color_mode='grayscale')\n    feature = np.array(img)\n    feature = feature.reshape(1, 48, 48, 1)\n    return feature / 255.0","metadata":{"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image = '/kaggle/input/mood-detection/Dataset/train/anger/44151112.png'\nprint(\"original image is of anger\")\nimg = ef(image)\npred = model.predict(img)\npred_label = label[pred.argmax()]\nprint(\"model prediction is \", pred_label)","metadata":{"editable":false,"trusted":true},"execution_count":null,"outputs":[]}]}