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‎best_model.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import libraries & model "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"from keras.models import model_from_json\n",
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"# from PIL import Image\n",
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"# import numpy as np\n",
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"# import pandas as pd\n",
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"# from sklearn.preprocessing import OneHotEncoder\n",
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"# from sklearn.metrics import f1_score, recall_score, precision_score\n",
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"import numpy as np\n",
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"import cv2\n",
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"from resizeimage import resizeimage\n",
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"import sys"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loaded model into notebook\n"
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]
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}
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],
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"source": [
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"# load json and create model\n",
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"json_file = open('model.json', 'r')\n",
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"loaded_model_json = json_file.read()\n",
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"json_file.close()\n",
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"loaded_model = model_from_json(loaded_model_json)\n",
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"\n",
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"# load weights into new model\n",
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"loaded_model.load_weights(\"model_weights.h5\")\n",
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"print(\"Loaded model into notebook\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"loaded_model.compile(loss='categorical_crossentropy',\n",
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" optimizer=\"sgd\",\n",
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" metrics=['acc'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"vgg19 (Model) (None, 1, 1, 512) 20024384 \n",
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"_________________________________________________________________\n",
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"flatten_1 (Flatten) (None, 512) 0 \n",
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"_________________________________________________________________\n",
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"dense_1 (Dense) (None, 128) 65664 \n",
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"_________________________________________________________________\n",
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"dense_2 (Dense) (None, 64) 8256 \n",
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"_________________________________________________________________\n",
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"dense_3 (Dense) (None, 7) 455 \n",
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"=================================================================\n",
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"Total params: 20,098,759\n",
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"Trainable params: 20,024,384\n",
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"Non-trainable params: 74,375\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"loaded_model.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Emotions dictionary\n",
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"emotions = {\"anger\" : 0,\n",
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"\"disgust\" : 1,\n",
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"\"fear\" : 2,\n",
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"\"happy\" : 3,\n",
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"\"sad\" : 4,\n",
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"\"surprise\" : 5,\n",
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"\"neutral\" : 6}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"https://github.com/opencv/opencv/tree/master/data/haarcascades"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Launch Video w/ Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"cap = cv2.VideoCapture(0)\n",
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"# Get user supplied values\n",
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"# imagePath = sys.argv[1]\n",
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"# cascPath = sys.argv[2]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load in the opencv file to detect face\n",
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"face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"while(True):\n",
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" # Capture frame-by-frame\n",
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" ret, frame = cap.read()\n",
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" \n",
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" # Conver to grayscale\n",
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" gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
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" \n",
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" # Face Detection\n",
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" faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n",
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" for (x,y,w,h) in faces:\n",
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" crop_img = gray[y:y+h, x:x+w]\n",
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"\n",
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" # Get width and height\n",
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" width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
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" height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
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"\n",
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" # Resize for our model (48x48x1)\n",
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" small = cv2.resize(crop_img, dsize = (48,48))\n",
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" # convert size from 48x48 to 1x48x48\n",
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" image3D = np.expand_dims(small,axis = 0)\n",
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" # convert to 1x48x48x1\n",
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" image4D = np.expand_dims(image3D, axis = 3)\n",
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" # convert to 1x48x48x3\n",
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" image4D3 = np.repeat(image4D, 3, axis=3)\n",
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"\n",
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" # Model each frame\n",
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" emotions_prob = loaded_model.predict(image4D3)[0]\n",
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" # Convert emotion probabilities into binary, where 1 is the emotion you're feeling\n",
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" listt = [1 if metric == emotions_prob.max() else 0 for metric in emotions_prob]\n",
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" # Get the index 1 in the binary list, listt \n",
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" emotion_index = listt.index(1)\n",
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" emotion = list(emotions.keys())[emotion_index]\n",
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"\n",
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" # Show Emotion on Video\n",
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" font = cv2.FONT_HERSHEY_SIMPLEX\n",
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" text_placement = (int(width/2 - 500),int(height/2 + 100))\n",
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" fontScale = 1\n",
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" fontColor = (255,255,255)\n",
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" lineType = 4\n",
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"\n",
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" cv2.putText(frame, \n",
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" '{}'.format(emotion), \n",
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" text_placement, \n",
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" font, \n",
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" fontScale,\n",
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" fontColor,\n",
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" lineType)\n",
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" \n",
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" # Display the resulting frame\n",
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" cv2.imshow('frame',frame)\n",
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" if cv2.waitKey(20) & 0xFF == ord('q'):\n",
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" break\n",
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"\n",
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"# When everything done, release the capture\n",
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"cap.release()\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows() "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎emotions.ipynb

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