detecting-masks.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\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport cv2\nfrom scipy.spatial import distance\n\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","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"markdown","source":"# Using haar cascade to detect faces\nObject Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, \"Rapid Object Detection using a Boosted Cascade of Simple Features\" in 2001. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images.\nWe'll be using a Haar Cascade Model trained to detect faces in order to obtain the bounding box coordinates of faces in an image."},{"metadata":{"trusted":true},"cell_type":"code","source":"#loading haarcascade_frontalface_default.xml\nface_model = cv2.CascadeClassifier('../input/haar-cascades-for-face-detection/haarcascade_frontalface_default.xml')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\n#trying it out on a sample image\nimg = cv2.imread('../input/face-mask-detection/images/maksssksksss244.png')\n\nimg = cv2.cvtColor(img, cv2.IMREAD_GRAYSCALE)\n\nfaces = face_model.detectMultiScale(img) #returns a list of (x,y,w,h) tuples\n\nout_img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) #colored output image\n\n#plotting\nfor (x,y,w,h) in faces:\n    cv2.rectangle(out_img,(x,y),(x+w,y+h),(0,0,255),2)\nplt.figure(figsize=(10,10))\nplt.imshow(out_img)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Detecting social distancing violations\nThis can be done by iterating over the coordinates of faces and calculating the distance for each possible pair, if the distance for a particular pair is less than MIN_DISTANCE then the bounding boxes for those faces are colored red.\nMIN_DISTANCE must be manually initialized in such a way that it corresponds to the minimum allowable distance in real life (ex. 6ft in India)."},{"metadata":{"trusted":true},"cell_type":"code","source":"MIN_DISTANCE = 130","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"if len(faces)>=2:\n    label = [0 for i in range(len(faces))]\n    for i in range(len(faces)-1):\n        for j in range(i+1, len(faces)):\n            dist = distance.euclidean(faces[i][:2],faces[j][:2])\n            if dist<MIN_DISTANCE:\n                label[i] = 1\n                label[j] = 1\n    new_img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) #colored output image\n    for i in range(len(faces)):\n        (x,y,w,h) = faces[i]\n        if label[i]==1:\n            cv2.rectangle(new_img,(x,y),(x+w,y+h),(255,0,0),2)\n        else:\n            cv2.rectangle(new_img,(x,y),(x+w,y+h),(0,255,0),2)\n    plt.figure(figsize=(10,10))\n    plt.imshow(new_img)\n            \nelse:\n    print(\"No. of faces detected is less than 2\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Using mobilenet_v2 for mask detection\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.applications.mobilenet_v2 import MobileNetV2\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Flatten, Dense\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#augmentation\ntrain_dir = '../input/face-mask-12k-images-dataset/Face Mask Dataset/Train'\ntest_dir = '../input/face-mask-12k-images-dataset/Face Mask Dataset/Test'\nval_dir = '../input/face-mask-12k-images-dataset/Face Mask Dataset/Validation'\n\ntrain_datagen = ImageDataGenerator(rescale=1.0/255, horizontal_flip=True, zoom_range=0.2,shear_range=0.2)\ntrain_generator = train_datagen.flow_from_directory(directory=train_dir,target_size=(128,128),class_mode='categorical',batch_size=32)\n\nval_datagen = ImageDataGenerator(rescale=1.0/255)\nval_generator = train_datagen.flow_from_directory(directory=val_dir,target_size=(128,128),class_mode='categorical',batch_size=32)\n\ntest_datagen = ImageDataGenerator(rescale=1.0/255)\ntest_generator = train_datagen.flow_from_directory(directory=val_dir,target_size=(128,128),class_mode='categorical',batch_size=32)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mobilenet = MobileNetV2(weights='imagenet',include_top=False,input_shape=(128,128,3))\nfor layer in mobilenet.layers:\n    layer.trainable = False\nmodel = Sequential()\nmodel.add(mobilenet)\nmodel.add(Flatten())\nmodel.add(Dense(2,activation='sigmoid'))\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics =\"accuracy\")\nhistory = model.fit_generator(generator=train_generator,steps_per_epoch=len(train_generator)//32,epochs=20,validation_data=val_generator,validation_steps=len(val_generator)//32)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.evaluate_generator(test_generator)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_mask_img = cv2.imread('../input/face-mask-12k-images-dataset/Face Mask Dataset/Test/WithMask/1565.png')\nsample_mask_img = cv2.resize(sample_mask_img,(128,128))\nplt.imshow(sample_mask_img)\nsample_mask_img = np.reshape(sample_mask_img,[1,128,128,3])\nsample_mask_img = sample_mask_img/255.0","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.predict(sample_mask_img)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.save('masknet.h5')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Integrating with haar cascade\nWe now take crops of the faces detected in the image and use the model trained in the above section to determine whether the individual faces have a mask or not."},{"metadata":{"trusted":true},"cell_type":"code","source":"mask_label = {0:'MASK',1:'NO MASK'}\ndist_label = {0:(0,255,0),1:(255,0,0)}","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"if len(faces)>=2:\n    label = [0 for i in range(len(faces))]\n    for i in range(len(faces)-1):\n        for j in range(i+1, len(faces)):\n            dist = distance.euclidean(faces[i][:2],faces[j][:2])\n            if dist<MIN_DISTANCE:\n                label[i] = 1\n                label[j] = 1\n    new_img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) #colored output image\n    for i in range(len(faces)):\n        (x,y,w,h) = faces[i]\n        crop = new_img[y:y+h,x:x+w]\n        crop = cv2.resize(crop,(128,128))\n        crop = np.reshape(crop,[1,128,128,3])/255.0\n        mask_result = model.predict(crop)\n        cv2.putText(new_img,mask_label[mask_result.argmax()],(x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.5,dist_label[label[i]],2)\n        cv2.rectangle(new_img,(x,y),(x+w,y+h),dist_label[label[i]],2)\n    plt.figure(figsize=(10,10))\n    plt.imshow(new_img)\n            \nelse:\n    print(\"No. of faces detected is less than 2\")","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}