Copy_of_Untitled0.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Untitled0.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ericzhu105/Green-AI/blob/master/Copy_of_Untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmxK_mdJ2AeO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\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 = 'Capture';\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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ciOBgS1Z8AVw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython.display import Image\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))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcbEV-SjSpsG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "8639fa24-5789-4cf8-ff6e-42acbcb6e9d8"
      },
      "source": [
        "import tensorflow.keras\n",
        "from PIL import Image, ImageOps\n",
        "import numpy as np\n",
        "import subprocess\n",
        "#import pygame\n",
        "import cv2\n",
        "\n",
        "# Disable scientific notation for clarity\n",
        "np.set_printoptions(suppress=True)\n",
        "\n",
        "# Load the model\n",
        "model = tensorflow.keras.models.load_model('/content/keras_model.h5')\n",
        "#/keras_model.h5')\n",
        "\n",
        "# Create the array of the right shape to feed into the keras model\n",
        "# The 'length' or number of images you can put into the array is\n",
        "# determined by the first position in the shape tuple, in this case 1.\n",
        "data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)\n",
        "\n",
        "# Replace this with the path to your image\n",
        "image = Image.open('/content/photo.jpg')\n",
        "\n",
        "#resize the image to a 224x224 with the same strategy as in TM2:\n",
        "#resizing the image to be at least 224x224 and then cropping from the center\n",
        "size = (224, 224)\n",
        "image = ImageOps.fit(image, size, Image.ANTIALIAS)\n",
        "\n",
        "#turn the image into a numpy array\n",
        "image_array = np.asarray(image)\n",
        "\n",
        "# display the resized image\n",
        "image.show()\n",
        "\n",
        "# Normalize the image\n",
        "normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1\n",
        "\n",
        "# Load the image into the array\n",
        "data[0] = normalized_image_array\n",
        "\n",
        "# run the inference\n",
        "prediction = model.predict(data)\n",
        "\n",
        "#print(prediction)  \n",
        "if(prediction[0][0] > prediction[0][1] ):\n",
        "  print(\"This can be recycled\")\n",
        "else:\n",
        "  print(\"This can't be recycled\") \n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}