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z&-KDGJ2_n0#dK)Hv7Hz|Y0`5Z(YU^7R?lW8)!U5kMiBYP7$MS^5)>^|6LXy=3S#r>+rX5B+|wf(x0 zlV5VMjit=QLD_evlf}Br7-7699HCsiShK9$rP{bEW--#^N!T(E0deYcX-IDY%O}Dh zv;#4MY!x$MJ(1Y&L)t{Cb`zcpSa40+ymy;1bo*By;preN18<2X11t?S@h-i^G^*-4 zuNT|Fx8rktc~k=lyY_i2Hsb}D@e{2i5o*2Iing-82fzMt9E6%Ii@P;}=)(V2whR z-9n0~FTRxv6o?Bix2f%y{s@C(uI)TRj`-Xpu;#ve**Hqf`290e}dDf{{{YqZvWcCue|Zk7Qkr#)xzIc z.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 +} From 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z;VmDtKT07C@*0mXv}1c^-9C1nMKk|uW!D8KdGJx4j(!y6gO-65O+&+!IVB3wX=$*T zBhcf*NgmI@^T{`g8Q+@^hnxy|HvCAKWA4+hDt_Gz|5F7C=D$PXe<7;B!hhZD`V$_2`!D$4w!VJF{~CM$ z6VFWe@8T@(Edxq-{|A7_`frUKhXdH3j+Y~ qUp(Sh_}_>0zrvT9{sRBk052~E_HJGP0POn*=-onpyq^&P0RA6vMkA2` literal 0 HcmV?d00001 diff --git a/mask detection final (1).ipynb b/mask detection final (1).ipynb new file mode 100644 index 0000000..6fb93d3 --- /dev/null +++ b/mask detection final (1).ipynb @@ -0,0 +1,370 @@ +{ + "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..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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