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TEAM AVENGERS #3

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prahars2466789 Jan 25, 2021
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prahars2466789 Jan 25, 2021
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Added MASK DETECTION AND UPDATED
prahars2466789 Feb 12, 2021
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370 changes: 370 additions & 0 deletions TEAM AVENGERS/mask detection final (1).ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'with mask': 0, 'without mask': 1}\n",
"['with mask', 'without mask']\n",
"[0, 1]\n"
]
}
],
"source": [
"import cv2,os\n",
"\n",
"data_path='D:/face mask detection/dataset'\n",
"categories=os.listdir(data_path)\n",
"labels=[i for i in range(len(categories))]\n",
"\n",
"label_dict=dict(zip(categories,labels))\n",
"\n",
"print(label_dict)\n",
"print(categories)\n",
"print(labels)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"img_size=100\n",
"data=[]\n",
"target=[]\n",
"\n",
"\n",
"for category in categories:\n",
" folder_path=os.path.join(data_path,category)\n",
" img_names=os.listdir(folder_path)\n",
" \n",
" for img_name in img_names:\n",
" img_path=os.path.join(folder_path,img_name)\n",
" img=cv2.imread(img_path)\n",
"\n",
" try:\n",
" gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) \n",
" #Coverting the image into gray scale\n",
" resized=cv2.resize(gray,(img_size,img_size))\n",
" #resizing the gray scale into 100x100, since we need a fixed common size for all the images in the dataset\n",
" data.append(resized)\n",
" target.append(label_dict[category])\n",
" #appending the image and the label(categorized) into the list (dataset)\n",
"\n",
" except Exception as e:\n",
" print('Exception:',e)\n",
" #if any exception rasied, the exception will be printed here. And pass to the next image"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"data=np.array(data)/255.0\n",
"data=np.reshape(data,(data.shape[0],img_size,img_size,1))\n",
"target=np.array(target)\n",
"\n",
"from keras.utils import np_utils\n",
"\n",
"new_target=np_utils.to_categorical(target)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"np.save('data',data)\n",
"np.save('target',new_target)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"data=np.load('data.npy')\n",
"target=np.load('target.npy')\n",
"\n",
"#loading the save numpy arrays in the previous code"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
]
}
],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense,Activation,Flatten,Dropout\n",
"from keras.layers import Conv2D,MaxPooling2D\n",
"from keras.callbacks import ModelCheckpoint\n",
"\n",
"model=Sequential()\n",
"\n",
"model.add(Conv2D(100,(3,3),input_shape=data.shape[1:]))\n",
"model.add(Activation('relu'))\n",
"model.add(MaxPooling2D(pool_size=(2,2)))\n",
"#The first CNN layer followed by Relu and MaxPooling layers\n",
"\n",
"model.add(Conv2D(100,(3,3)))\n",
"model.add(Activation('relu'))\n",
"model.add(MaxPooling2D(pool_size=(2,2)))\n",
"#The second convolution layer followed by Relu and MaxPooling layers\n",
"\n",
"model.add(Flatten())\n",
"model.add(Dropout(0.5))\n",
"#Flatten layer to stack the output convolutions from second convolution layer\n",
"model.add(Dense(50,activation='relu'))\n",
"#Dense layer of 64 neurons\n",
"model.add(Dense(2,activation='softmax'))\n",
"#The Final layer with two outputs for two categories\n",
"\n",
"model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"train_data,test_data,train_target,test_target=train_test_split(data,target,test_size=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 990 samples, validate on 248 samples\n",
"Epoch 1/20\n",
"990/990 [==============================] - 50s 50ms/step - loss: 0.7056 - acc: 0.5030 - val_loss: 0.6675 - val_acc: 0.5403\n",
"Epoch 2/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.5983 - acc: 0.6707 - val_loss: 0.6071 - val_acc: 0.6290\n",
"Epoch 3/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.4896 - acc: 0.7505 - val_loss: 0.3372 - val_acc: 0.8750\n",
"Epoch 4/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.2950 - acc: 0.8899 - val_loss: 0.2297 - val_acc: 0.9073\n",
"Epoch 5/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.2045 - acc: 0.9232 - val_loss: 0.1656 - val_acc: 0.9395\n",
"Epoch 6/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.1493 - acc: 0.9485 - val_loss: 0.1352 - val_acc: 0.9637\n",
"Epoch 7/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.1162 - acc: 0.9657 - val_loss: 0.1308 - val_acc: 0.9637\n",
"Epoch 8/20\n",
"990/990 [==============================] - 50s 50ms/step - loss: 0.0882 - acc: 0.9687 - val_loss: 0.1061 - val_acc: 0.9677\n",
"Epoch 9/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.0564 - acc: 0.9879 - val_loss: 0.1027 - val_acc: 0.9677\n",
"Epoch 10/20\n",
"990/990 [==============================] - 50s 50ms/step - loss: 0.0464 - acc: 0.9848 - val_loss: 0.0861 - val_acc: 0.9718\n",
"Epoch 11/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.0453 - acc: 0.9818 - val_loss: 0.1713 - val_acc: 0.9395\n",
"Epoch 12/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.0391 - acc: 0.9838 - val_loss: 0.0742 - val_acc: 0.9798\n",
"Epoch 13/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.0388 - acc: 0.9889 - val_loss: 0.0797 - val_acc: 0.9758\n",
"Epoch 14/20\n",
"990/990 [==============================] - 50s 50ms/step - loss: 0.0303 - acc: 0.9909 - val_loss: 0.0828 - val_acc: 0.9798\n",
"Epoch 15/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.0173 - acc: 0.9960 - val_loss: 0.0945 - val_acc: 0.9637\n",
"Epoch 16/20\n",
"990/990 [==============================] - 49s 49ms/step - loss: 0.0353 - acc: 0.9909 - val_loss: 0.0663 - val_acc: 0.9798\n",
"Epoch 17/20\n",
"990/990 [==============================] - 49s 49ms/step - loss: 0.0376 - acc: 0.9869 - val_loss: 0.1402 - val_acc: 0.9355\n",
"Epoch 18/20\n",
"990/990 [==============================] - 49s 49ms/step - loss: 0.0452 - acc: 0.9808 - val_loss: 0.1066 - val_acc: 0.9597\n",
"Epoch 19/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.0123 - acc: 0.9980 - val_loss: 0.0814 - val_acc: 0.9718\n",
"Epoch 20/20\n",
"990/990 [==============================] - 49s 50ms/step - loss: 0.0277 - acc: 0.9889 - val_loss: 0.0722 - val_acc: 0.9758\n"
]
}
],
"source": [
"checkpoint = ModelCheckpoint('model-{epoch:03d}.model',monitor='val_loss',verbose=0,save_best_only=True,mode='auto')\n",
"history=model.fit(train_data,train_target,epochs=20,callbacks=[checkpoint],validation_split=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import keras\n",
"from keras.models import load_model\n",
"import cv2\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:131: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
"\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
"WARNING:tensorflow:From C:\\Users\\User\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
"\n"
]
}
],
"source": [
" model = load_model('model-017.model', compile=False)\n",
"\n",
"\n",
"face_clsfr=cv2.CascadeClassifier('D:/PyPower/Project1/PyPower_face-detection/haarcascade_frontalface_default.xml')\n",
"\n",
"source=cv2.VideoCapture(0)\n",
"\n",
"labels_dict={0:'with_mask',1:'without_mask'}\n",
"color_dict={0:(0,255,0),1:(0,0,255)}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"while(True):\n",
"\n",
" ret,img=source.read()\n",
" gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
" faces=face_clsfr.detectMultiScale(gray,1.3,5) \n",
"\n",
" for x,y,w,h in faces:\n",
" \n",
" face_img=gray[y:y+w,x:x+w]\n",
" resized=cv2.resize(face_img,(100,100))\n",
" normalized=resized/255.0\n",
" reshaped=np.reshape(normalized,(1,100,100,1))\n",
" result=model.predict(reshaped)\n",
"\n",
" label=np.argmax(result,axis=1)[0]\n",
" \n",
" cv2.rectangle(img,(x,y),(x+w,y+h),color_dict[label],2)\n",
" cv2.rectangle(img,(x,y-40),(x+w,y),color_dict[label],-1)\n",
" cv2.putText(img, labels_dict[label], (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)\n",
" \n",
" \n",
" cv2.imshow('LIVE',img)\n",
" key=cv2.waitKey(1)\n",
" \n",
" if(key==27):\n",
" break\n",
" \n",
"cv2.destroyAllWindows()\n",
"source.release()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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