reconhecimento.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "from tensorflow.keras.models import model_from_json\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from skimage import io\n",
        "from tensorflow.keras.utils import load_img, img_to_array \n",
        "from google.colab.patches import cv2_imshow\n",
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "from IPython.display import Image\n",
        "\n",
        "\n",
        "def take_photo(filename='photo.jpg', quality=0.8):\n",
        "  js = Javascript('''\n",
        "    async function takePhoto(quality) {\n",
        "      const div = document.createElement('div');\n",
        "      const capture = document.createElement('button');\n",
        "      capture.textContent = 'Tirar foto';\n",
        "      div.appendChild(capture);\n",
        "\n",
        "      const video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "      document.body.appendChild(div);\n",
        "      div.appendChild(video);\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      // Resize the output to fit the video element.\n",
        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "      // Wait for Capture to be clicked.\n",
        "      await new Promise((resolve) => capture.onclick = resolve);\n",
        "\n",
        "      const canvas = document.createElement('canvas');\n",
        "      canvas.width = video.videoWidth;\n",
        "      canvas.height = video.videoHeight;\n",
        "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
        "      stream.getVideoTracks()[0].stop();\n",
        "      div.remove();\n",
        "      return canvas.toDataURL('image/jpeg', quality);\n",
        "    }\n",
        "    ''')\n",
        "  display(js)\n",
        "  data = eval_js('takePhoto({})'.format(quality))\n",
        "  binary = b64decode(data.split(',')[1])\n",
        "  with open(filename, 'wb') as f:\n",
        "    f.write(binary)\n",
        "  return filename\n",
        "\n",
        "\n",
        "try:\n",
        "  filename = take_photo()\n",
        "  # print('Saved to {}'.format(filename))\n",
        "  \n",
        "  # Show the image which was just taken.\n",
        "  # display(Image(filename))\n",
        "except Exception as err:\n",
        "  # Errors will be thrown if the user does not have a webcam or if they do not\n",
        "  # grant the page permission to access it.\n",
        "  print(str(err))\n",
        "\n",
        "\n",
        "\n",
        "#load model\n",
        "model = model_from_json(open(\"fer.json\", \"r\").read())\n",
        "#load weights\n",
        "model.load_weights('fer.h5')\n",
        "\n",
        "\n",
        "face_haar_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
        "\n",
        "\n",
        "\n",
        "# ret,test_img=cap.read()\n",
        "test_img = io.imread('photo.jpg')\n",
        "\n",
        "# captures frame and returns boolean value and captured image\n",
        "\n",
        "gray_img= cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "faces_detected = face_haar_cascade.detectMultiScale(gray_img, 1.32, 5)\n",
        "\n",
        "\n",
        "for (x,y,w,h) in faces_detected:\n",
        "    cv2.rectangle(test_img,(x,y),(x+w,y+h),(255,0,0),thickness=7)\n",
        "    roi_gray=gray_img[y:y+w,x:x+h]#cropping region of interest i.e. face area from  image\n",
        "    roi_gray=cv2.resize(roi_gray,(48,48))\n",
        "    img_pixels = image.img_to_array(roi_gray)\n",
        "    img_pixels = np.expand_dims(img_pixels, axis = 0)\n",
        "    img_pixels /= 255\n",
        "\n",
        "    predictions = model.predict(img_pixels)\n",
        "\n",
        "    #find max indexed array\n",
        "    max_index = np.argmax(predictions[0])\n",
        "\n",
        "    emotions = ('NERVOSO', 'NOJO', 'MEDO', 'FELIZ', 'TRISTE', 'SURPRESO', 'NEUTRO')\n",
        "    predicted_emotion = emotions[max_index]\n",
        "\n",
        "    ##1 2 5\n",
        "\n",
        "    cv2.putText(test_img, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (237,214,7), 2)\n",
        "\n",
        "resized_img = cv2.resize(test_img, (1000, 700))\n",
        "cv2_imshow(cv2.resize(cv2.cvtColor(test_img, cv2.COLOR_RGB2BGRA), (1000, 700)))\n",
        "# cv2_imshow(resized_img)"
      ],
      "metadata": {
        "id": "P0PPzbj4io-q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "outputId": "40b83616-f718-4422-ce97-1da480cd15e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function takePhoto(quality) {\n",
              "      const div = document.createElement('div');\n",
              "      const capture = document.createElement('button');\n",
              "      capture.textContent = 'Tirar foto';\n",
              "      div.appendChild(capture);\n",
              "\n",
              "      const video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
              "\n",
              "      document.body.appendChild(div);\n",
              "      div.appendChild(video);\n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "\n",
              "      // Resize the output to fit the video element.\n",
              "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
              "\n",
              "      // Wait for Capture to be clicked.\n",
              "      await new Promise((resolve) => capture.onclick = resolve);\n",
              "\n",
              "      const canvas = document.createElement('canvas');\n",
              "      canvas.width = video.videoWidth;\n",
              "      canvas.height = video.videoHeight;\n",
              "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
              "      stream.getVideoTracks()[0].stop();\n",
              "      div.remove();\n",
              "      return canvas.toDataURL('image/jpeg', quality);\n",
              "    }\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 323ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=1000x700 at 0x7F64410D9090>"
            ]
            
          },
          "metadata": {}
        }
      ]
    }
  ]
}