FaceMaskDetectiononcolab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FaceMaskDetectiononcolab.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP7/44LdswDx9sigku/rItg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/electrolabopen/Facedetectorcolab/blob/master/FaceMaskDetectiononcolab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzGb-DS3eP32",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "02f212a8-7518-4dfe-c161-2c0de5968626"
      },
      "source": [
        "!git clone https://github.com/aieml/face-mask-detection-keras.git\n",
        "!1s"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'face-mask-detection-keras' already exists and is not an empty directory.\n",
            "/bin/bash: 1s: command not found\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSm9Irc3ee2S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "826084f2-9629-4a5d-fb66-117b03a8025f"
      },
      "source": [
        "import cv2,os\n",
        "\n",
        "data_path='/content/observations/experiements/data'\n",
        "categories=os.listdir(data_path)\n",
        "labels=[i for i in range(len(categories))]\n",
        "\n",
        "label_dict=dict(zip(categories,labels)) #empty dictionary\n",
        "\n",
        "print(label_dict)\n",
        "print(categories)\n",
        "print(labels)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'with_mask': 0, 'without_mask': 1}\n",
            "['with_mask', 'without_mask']\n",
            "[0, 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npO1Lz_qe8gu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/prajnasb/observations.git\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZOMqSDQf30k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=[]\n",
        "target=[]\n",
        "img_size=100\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) #folder_path\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 50x50, 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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JhzedMB_f93g",
        "colab_type": "code",
        "colab": {}
      },
      "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDp1wvN1nPW_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save('data',data)\n",
        "np.save('target',new_target)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8FaYen1nclu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "data=np.load('data.npy')\n",
        "target=np.load('target.npy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFbrs3K6neBH",
        "colab_type": "code",
        "colab": {}
      },
      "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(200,(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='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTTjfpNqnkmP",
        "colab_type": "code",
        "colab": {}
      },
      "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8NCeS18np52",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "outputId": "e600484e-9310-4900-e174-ce1c30429d9a"
      },
      "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)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 990 samples, validate on 248 samples\n",
            "Epoch 1/20\n",
            "990/990 [==============================] - 7s 8ms/step - loss: 0.6894 - accuracy: 0.5737 - val_loss: 0.6730 - val_accuracy: 0.5121\n",
            "Epoch 2/20\n",
            "990/990 [==============================] - 1s 803us/step - loss: 0.5976 - accuracy: 0.6808 - val_loss: 0.4663 - val_accuracy: 0.8589\n",
            "Epoch 3/20\n",
            "990/990 [==============================] - 1s 818us/step - loss: 0.4242 - accuracy: 0.8212 - val_loss: 0.3275 - val_accuracy: 0.9153\n",
            "Epoch 4/20\n",
            "990/990 [==============================] - 1s 806us/step - loss: 0.2888 - accuracy: 0.8879 - val_loss: 0.1958 - val_accuracy: 0.9435\n",
            "Epoch 5/20\n",
            "990/990 [==============================] - 1s 836us/step - loss: 0.2032 - accuracy: 0.9273 - val_loss: 0.1779 - val_accuracy: 0.9395\n",
            "Epoch 6/20\n",
            "990/990 [==============================] - 1s 875us/step - loss: 0.1600 - accuracy: 0.9404 - val_loss: 0.1532 - val_accuracy: 0.9516\n",
            "Epoch 7/20\n",
            "990/990 [==============================] - 1s 818us/step - loss: 0.1430 - accuracy: 0.9434 - val_loss: 0.1412 - val_accuracy: 0.9556\n",
            "Epoch 8/20\n",
            "990/990 [==============================] - 1s 802us/step - loss: 0.0941 - accuracy: 0.9667 - val_loss: 0.0882 - val_accuracy: 0.9677\n",
            "Epoch 9/20\n",
            "990/990 [==============================] - 1s 824us/step - loss: 0.0733 - accuracy: 0.9768 - val_loss: 0.1480 - val_accuracy: 0.9315\n",
            "Epoch 10/20\n",
            "990/990 [==============================] - 1s 808us/step - loss: 0.0731 - accuracy: 0.9717 - val_loss: 0.0783 - val_accuracy: 0.9758\n",
            "Epoch 11/20\n",
            "990/990 [==============================] - 1s 834us/step - loss: 0.0440 - accuracy: 0.9859 - val_loss: 0.1140 - val_accuracy: 0.9476\n",
            "Epoch 12/20\n",
            "990/990 [==============================] - 1s 810us/step - loss: 0.0553 - accuracy: 0.9778 - val_loss: 0.1662 - val_accuracy: 0.9435\n",
            "Epoch 13/20\n",
            "990/990 [==============================] - 1s 808us/step - loss: 0.0419 - accuracy: 0.9869 - val_loss: 0.1042 - val_accuracy: 0.9718\n",
            "Epoch 14/20\n",
            "990/990 [==============================] - 1s 802us/step - loss: 0.0265 - accuracy: 0.9929 - val_loss: 0.0691 - val_accuracy: 0.9718\n",
            "Epoch 15/20\n",
            "990/990 [==============================] - 1s 831us/step - loss: 0.0310 - accuracy: 0.9879 - val_loss: 0.0887 - val_accuracy: 0.9637\n",
            "Epoch 16/20\n",
            "990/990 [==============================] - 1s 813us/step - loss: 0.0224 - accuracy: 0.9919 - val_loss: 0.0799 - val_accuracy: 0.9718\n",
            "Epoch 17/20\n",
            "990/990 [==============================] - 1s 820us/step - loss: 0.0219 - accuracy: 0.9939 - val_loss: 0.0623 - val_accuracy: 0.9718\n",
            "Epoch 18/20\n",
            "990/990 [==============================] - 1s 821us/step - loss: 0.0206 - accuracy: 0.9929 - val_loss: 0.0753 - val_accuracy: 0.9637\n",
            "Epoch 19/20\n",
            "990/990 [==============================] - 1s 839us/step - loss: 0.0166 - accuracy: 0.9960 - val_loss: 0.0596 - val_accuracy: 0.9758\n",
            "Epoch 20/20\n",
            "990/990 [==============================] - 1s 803us/step - loss: 0.0140 - accuracy: 0.9960 - val_loss: 0.1529 - val_accuracy: 0.9435\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWbVIAa0sp_x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "3220a207-341d-490b-d96a-352914f3f65a"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "plt.plot(history.history['loss'],'r',label='training loss')\n",
        "plt.plot(history.history['val_loss'],label='validation loss')\n",
        "plt.xlabel('# epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dyZbFgcsywP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "a3a408c8-1acd-4361-d34d-5f08cf3e90bd"
      },
      "source": [
        "plt.plot(history.history['accuracy'],'r',label='training accuracy')\n",
        "plt.plot(history.history['val_accuracy'],label='validation accuracy')\n",
        "plt.xlabel('# epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u370igDes56O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a0837620-2bf8-418c-db84-dd505d6bbb86"
      },
      "source": [
        "print(model.evaluate(test_data,test_target))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "138/138 [==============================] - 0s 613us/step\n",
            "[0.30515542043292004, 0.9420289993286133]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dqat0IsRtQ3e",
        "colab_type": "text"
      },
      "source": [
        "# **Detectar mascarilla en imagenes.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubzbhpRKtboq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import load_model\n",
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrr_wDgktfzP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = load_model('model-017.model')\n",
        "\n",
        "face_clsfr=cv2.CascadeClassifier('/content/face-mask-detection-keras/haarcascade_frontalface_default.xml')\n",
        "\n",
        "img=cv2.imread(\"/content/sample_data/fe2a0ab0-0238-4d64-bf12-1d1008efb9fb.jpg\")\n",
        "\n",
        "labels_dict={0:'MASK',1:'NO MASK'}\n",
        "color_dict={0:(0,255,0),1:(0,0,255)}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-o39PWTtkvQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "img_cvt = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "face_clsfr=cv2.CascadeClassifier('/content/face-mask-detection-keras/haarcascade_frontalface_default.xml')\n",
        "\n",
        "faces=face_clsfr.detectMultiScale(gray,1.3,5)\n",
        "    \n",
        "  \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiOjOz7oBu5T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for (x,y,w,h) in faces:\n",
        "\n",
        "\n",
        "    \n",
        "  face_img=img_cvt[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",
        "plt.grid(None)   \n",
        "plt.xticks([])\n",
        "plt.yticks([])\n",
        "imgplot = plt.imshow(img)  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECLtB_FqEQsb",
        "colab_type": "text"
      },
      "source": [
        "Para Dectar mascarillas en un video."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3W1osWvEZcB",
        "colab_type": "code",
        "colab": {}
      },
      "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()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eiu6BLz0Ei2r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THj3opqCEnPe",
        "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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsVTdiK6EnOa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "cbf11c38-c28a-4da6-d6fe-a83ea71fe32e"
      },
      "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": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/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",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saved to photo.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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Yril03KyLdmOGpXkujmTM7hVSxddDi1ZBTTYxaprJSTGpbFVbpkkx6PSxStj5KVyS7vYmyDhcHG5UmySYBZYEW6QBZsG0jFq/0tJ7muTqJmdKEpT3ZWM12Vrz2T9b/JZgim7b2I6ivuy7SHc0qR2dxn8jJ+t1wRACkgaViLccbdCp4zdKMLBwa8G6GnfbuiH233UR7tfSMbg14F2urNsoP2ISx7BMyuDGBbKDXgrply7Z3GwgABkAAAAAAAAAAAAAAAAAAA19Kyyw6/DKL/zK/wAGQlB9s4y9nYUPsEH3JSSpMsrbajL0rO9T07TZm/1wTNaPPy1KuB8EUtydWwojZ6pJWtxxVIEqYSbYaICpPkHdDStLfcNFvRxWwJDS2YQ3SaHqfY2kqe7u/wABsuR1tyLb9xywgt2CVMbVbcCtXVqxWb6MSj5QU0qYW1zwLYNETVAtvJKl+5F835HyQbSGn8C/USiqq3t8C19ls937jS23G3vabE05PbcrjQ7EU1wQd3bRYpftQOvyLFHKCdbkbuycnXjYVWK/hUeBmvc5XVtsDt+TrSs4/WJfy1H5OrDezcgAGdBu1oo3p4mTI/8AimdDTKtLH8HPru1Ur4OXG810ScK5S7XsXZZemJmyf4jT9y7O2oQtUjSzo5oRb7h4JVOSkV23OKHDbO0xaPcXZbrbgrine7Ls1dloz4922E6GosunaBTcuRLd0Ri6k0LR6WLsk6exLs29Eyhq5OiFuM6bH6s7hGyMskfkmtS+GjLDJJXuWPJcfUibC9LGzHlUvJamc/HTez3Loxy3aexGWI5bAsoUprlFiyJkanyFqbJxfuU9y9yVuibr4Pa5vcEyrwSXuLdCOqyqGNuznT1MXp3G90V9WySnkUI3tvsc7ufudfjw42zyok7kyIxG6AADSsAEa9JG2iiEbZ1dBo3kaaRnnlI2wwXxXdFbbFcsab2R03peyBneOmY7jT1YuytmSjgUk20XzhZZihtuVINOTqMNMx5MbTO7nxpvY52pgk9jTFGWLnNUIvnjvwVOLRe2Vx0iMQ7GkgGkOgPSIEqE0A0QAAEAAAAAAAPov0Vnebo8I239mTg/jyj0MXbPC/QWqcNTqNK26yR71FeWj3UOF4ODzSzJWNWRa8jpELLIryzHTTSLVv5Dfgn8kZb7isTyJR2EqSocXswSHJRoXTHblyJqiSW3yP8A0tQJfILZ7kk/dhQ56/Y4R8bgtvwTpJbi/A/YtIt3XsJ0+CUuNyKTSC0g9vyC5HFe/Anzsxy0D9L4Hd8i/uNLbcLySSp8bji+20luQWydMlG62Deuwail4B/gN1YrsPaJ4FWtyE/S9ticqRRlzRxpubSXyGx/jxMntwcHrT9cTvTXuef61K80V8HXj2pzQADYPQaf/lot+xhxb6yRv0yrSw/BhwRrVuXNHLj8umThTnjWoaZbqot4Isjq1/xF+5bl3wpNbF76PSiX+JB14FJVqUmTz3eNvjghn9OeLfgIVaZ08dIowcTvwXP9V1szPxNrdCnKksduZWtpTfuXYV/M+KK8tePcc7GjTqaZXJt5GXyaSi1VlMVfc34HDvCKdKiaT7EmQir/AAT/APUSTHSlC9MlRqx6qWKVNJmRb50mPM/5qFrZcV08erjLlGnHHHm4SObijHtvyPFlljyelmV5Fw06c9NT2Yljl7WSx5u+K7uTZpUpGVlQwyhKPKF3Kt+DrTxJqmji9WrDHYrGckx6uccU55FTlJUccszZHNlZ2Y46jLKgAGUQW7NWLB6bZHQ4Hmzxijt5tLGChCO7fLMs8tcRt48N8ufpNM5T4PWdJ00YQTdEeldPXanNHWWjSjUW0YZZ/bpxw05utSUvTwc+cd+DuajR9sGzE9JJrgNq05M1T+BLg2ywU2mjNlj2MqWIs0p5szZsafJdN0til3vubSyMqw5Y0UOJuyxbfwUTgh+0Rqs0oquCDhsaGmRcR7FxZ3GgSLJEXsG06IixiaGVIQ63BjSQAAEAAADv/Q+X7XX8b94SX9Ue51OtjjdRp0fPPpnKsPV8c3x2yT/dNHTeszT1ssbtQXDObzTdVjp6n/eM1ukvwdbTZFlxKa4Z5TBilk7XKTSPS9Oi1p/g5rJF/wCNadPZ0OvLFGkvkbbrcncGgvIk6tVYNOrTGvdlWleBz4Q+PIm0TVUL2hbCS/YKS8kqXuFeyde4r+B/hNCobbXAk65Ya/BFIjdknLf4Eq5XA5BsqFXaOTSXJXLJF+xRbTTGnRmlnim1Um17IplqkppSnHG3x3MchNyavcJ5YwXqlGP5fJkl/Nkm5ZKX+iVJ/nyENJHu7oQSk/Ldv+oXEqteqUdlFv8ABVLU5ZPZRhFeW7/8Fy0+z7iUcWONJL+u4SyEyzWTKk3lk1/07X/QrenUpLuxX2u1KST390dHt8IVb8Fe8DxGVVG2eb6zLu1EVd0vY9Jlbr4PL9Vd6yVHThNVTGAAah6DTV/Bwr2KcEO7JJ8Fmi30WP8ABLDds5bPp2Y3hh1qcc0S7L/ger2I9Ri/uQaJZ1em35oeuINM8/VHHvsg1UU3BpEXL+XEu1DThAfVha+0p7drfkzZl69jVl9WCL9jLlfdTDGHx8LsS5/BXlTpfLLIPZt+wku6UU7D5PlHKvRFr2FGP8pss1acUiHcvtRiE6CEVSr3IwX86l4LaTnS/qR0/wDiS+CtixD/ANwl8hlVZR7rOPUbNMe+U6aIp9tk8MBYcndiWxdj38GOVGVdHSaeOTEmzoYcX26S3KdDC8CRsiqjsZbjKm+ODy/1JayR9j1FtHC+pcN4fuLwX47rKJseXGIDtZmC3EdDRaHJnku2Nk5ZTGbq8MbldNXScTW8V6mev6F0n+Izx+7+nyUdH6R2dvctz3PRNDGC2aTdc8HFnnu8PR8fj1OVul6DCMO6MbS/Yy5dGoSf8uSp0evhhePArkjk66UY5P1Rf48EyZVpeHBzafEsVy59jmZey3SR0OpZ4yvte/ucDPnSb33KmLPKoaqEbdI52bDGSbNGXO293sZp5U7NMcWVrn6jHXBmlGka9Q7dmWXNLc1krLKyqJplUl7o1Ou0qotLPGFsUoPzwae2mSlG0GtjhzpRIOBryQ3tFUo1uxdFdMzW9EWjRLt8FUmhyp0roVEn+RclJRYibRAabNAAACbejxc+oYoRbUpvtVe562Gi7H61v5PMfTST65pLdeq/7H0yekjkld1+xzefiqjjxjJQrHHul4R6HT4Vjwxxp21yyvBo8WJ3FXL3fJpXPsc1u1kkN7rZjq3YpL2JKjlpeAY1TFL4HDgtew4qitu3uRyajFjjeTJCC/6pJD1KVaO5X5FLZbcGVauMo3jTkv8AuCy5Mi/T2/CdhqRLVJ2iqU4Jvi/hFTxTnVyb+La/7Fi09Pf/ALjl0SDzpcEPuyfBoWGNEo4opvbcrY5Y6yze90TWGVGvsrgjJNJU0TuhRHDXJZGCjwTaS55FGnaK19lotr4GnTCthRTZHA0nF82CX9RqDqySVBboiS/H7iSpDp870NVfGxGU2NPB5q7WeU6l/wA3M9XqKrY8jrv+ayfk7/HrYigAA2N3+nu9FBFuKPJn6Vb0qXizdDaRy5XVduHTn9RVSh+R6hfyNvYs6lG4r8kNSq03PKHKdYOcSfybMqUtNGl4Mm32vdm1SUtKkh0QYF36ftfJz8vLi/DN+l9La8GPUx7cr+Qx7KrMDuDLFGpR92U6dWmalGpJ0LK6pxTreIplbj6Yk9a7aE3x+BzqCdox9MZy+CWgjFqbbIzajpWl/mZbo41ibC3gd1RH/mf3HqluEVeo/clq1sP5hWJaWSUKZsxKzFpF6La2NuJ0Z5d8IrudOjeLY2pNIx9L/wAM3oyyn2zu0Hvt5OR9Qr/+PyKT2O20jh/U1/wE/a0GH9oTx4AB6DJbp4PJmjFeWfQ/p/QRhgUpx3PJ/Smj/iuoxtXGO7PpePHDFiSgtjk8+W7p6H6Xx6ntVmmxQTXg7sdRj0cIuTg/Ozs81PVRxHP6h1HujUXz5MMZa6M8tcPTa76ncE446/8Ag4Gt+oPuN7JX7Hn8mpvazldQzy8G+OP248s67Ot6yqe5yn1LvlaZws+WUuWyqGRo2mEY3OvQZNdtyZ/4y5cnK+43yEZPuLmhu11fv9z5HHd2c1SakjbibpD4GlrTdka2Lo8blU3vtwMkbohPIq5RVnm1dMxyySsCu1+TKip5U+WZpNvlkRaRa0ylErtFQDG02IiAtDad7EWCBgLdkAAMmrpeSWLqOmnHlZI/0vc+xJqcnJLtTeyR8VxtxyRa5TTPr+m1WKPT9Pkc4xi8cZNt+Wjm/UTiKja9txqUe12vV+THLWRcU4Rc0+Ghd+ab/Skjkkq2xtVyUzywjyyr7M5td0mv3LI6WDdPehyT5G6qnrEnUYTk35S2X9yP3c02uykvNrc1rBCPCJdqj4Dc+iY/tZJ/qbV/JOGlikvg0JKyaSKgQhjivBakq/HCFEm0K0kaQ9gfsEk0LYKtxpNsfdtVEYUm2+A0Wtk002Rl7k3XKu/bwRm9gmhrXarl2TSGlsNbKkObJFR23HtXGxNJdu5FLfgdJOLfbsgQot2Tk755EQdXtwL8CabezaLJQUYrwxatD55qXtueR1T7tRkfyev1dfal42PG5HeSTXud3jnJogAGodzoz/k/B0O3c53Q3eGavdM6l0jmz1K7PHf4sXUEnjSfuV6nfAm/Yu1i9Db4RTmV6Zb+AlXXOf8Ag3satLFz07Xgz3Wm+bNfTXeJplXpM7GNdroza5VkTRsyKp7FGtV41L2JxvJ3WmfTSq3/AGN74TObgfrR0ZJ9qHnOSx5jFrH3ZEkSx0lL3rYrz75di3HFOr8j+Dk5Q1NdsVE1YPTga9zLm9eZJcLY2KFYicupBplwK9SPWqiOC1nZLV3KSH/9F8crdPFRwQ93uasaVlGONQSL8S3MsuWeWnb6Yn28HRox9OjWNNm3cysJGSOR9SQ7um5Umr2e/wAHZktjn9WwvL0/URit3Bv/AMlYcVNeBABxVs9BjOXuPoPTtafJma2lLtT/AO563UNwjscj6SwrD0nGuG/UdTU12/J52d3la9jDxyYyOJrs0lJpnP8AVmlSujfrcfdINNCOONsct+GNjI9J27tGDV4Yu9jq6zWwgnbSOBrOpQt07NcN1llpjz6VdzozPB2jya5yexRLUykdEljntkTeOmNR32KPvSJrKFhzKNWONtGrGnaMOHJbOzoMEszSXkm5eva5NklsUZFuzu6jpeXFjTceVZxdXB47sJnL0MsbHNz87GeSotyydsqclRUZ1W4t8EeyXsTeReBLI14KjOo9kvYTi0XLMqpobnCTHRpRTCjQ4Rl+krlCiZkeoqGW9qaI9o9j1VgNqmIaUscXKaiuWz6v0jSJaDTd6fd2K7PmPScTz9S02NK+7JFf3PsWKPZBR8RVHN+ot40rGI/bS2SSLIR7UNIlRyWtEUkvALkbQLgWy7HgVPySryJ3QxqoVvsSivcEnQ4vZphyWgmCYckaqSoqb+S0sbXsDlaB8CqkKwdG/TFfIlTJL9PwR/A5b8FbsnEVdzJXtuJeKDV+xo6pcb/kaSfA+3YlVKg3pGyaS5Iqr32RJJXd2T9KfGwqO1TrwOMW5DjV7jlCvKoXJkuaobltXkKvj+rIyVchNlx8Pnusklhm2vTR458s9Z1FtaXJ+DyR6HjMAAGgdfoa2yWzsJHE6G/5mRfB3IrY5PJr2u3Z4terLrv8GX4Mr30qr2N2qV42Ykq04f4uscor+Gbrc19Mr7e5ne2mf5NGgaUUO9FO2jPFVZh1O+J2dPKvRbOfP1xkkhY7l5OudjdZEdWUl9tP4ORK45Pg6Tl/JT+DTOIwvwxOpZXZrxJKNeEY47zNtduJtvwGS8VWnj3ZpSfBsyyrGVaeNQVLkuz/AOHRnl2bFhV5Gwn6s6RPFy34Kou9TfsV8oybFfg04U20UR4Nek3nHYyrGu7o41iRqjbRVhXoSSLoKjOcEjTs6PT+iZeo45p+mEscmm/hGbFG5b+DldV+o9Vp59mln2KKq63LntenR4vFM5cq+fSi4ycXynRbpYd+aMWuXQtQ+/NKXluzX0pqOpi3xZ2ZX+O3Ljj/ACfUekYVHQ4VF/5F/WiWqxtp2XdKg1o8T+DXPTPInXk87l686eU1Hpn6nsjl6/qEcSahuz1Ov6POS9Ldv4OVH6T1OolJyxuMF5dbmmOUnbDKV4fW6qeWTtujH9uc9z6HqPorJCPdHHKaXK8nJ6l0eWiaTw5MUqupeTox8s6jmz8Vrx8ouLppoR1ddj748U0cyWNx5Npltz5YWCEe6xVuNWlQ4QbYykWQ2ao+g/Q+ijnnHJlvtR4vQ6SWfPjhCLbbPsX0x02Oh0+HHSuO8pI5/JnHb4PFu7db640WHFj08sKSWXEpdqXF8HyLrmDtcj7X9WfzOl6ZqO8E05Pe0fHvqONTZljk6PJhNPHZ7TZnbNOo/U7Mx2Y9PMz7CBqgTrgcpNpWUzRABqvIA4ycXsWwyd2zKBrkVmzl01KKrYrcWmGOXgsaI6aRnmiBdlWxSXEZdu39IPFHrOOWeXbGKtX5fg+pY5KUE9qfsfGtHLty2fTPpfUy1Wg3drG+0w80+Txd3l7Da2ItW/Yn4OSrRW40qu6YVQfgUI0tnvuJ2vFoE/cfd4oDJ2/wKleyr9wb3HdIIQSpifI07Yx7pIuXsPfgQ4/AoSV0hVT34JRi/JY0vHI6SpxtWv6Cjs9+CxtV5sjLwPY2ld/gGnSsa2VcDcajdhsuC4vbYFuuBKVO1yFiI+7tXCr5RCq/ce3bdbCSsdmyF1wEm62r9x0m9uQk320w0b5t1j0aTI7rY8kes69JfwUzyZ3eO7gAABoHQ6NPt1DXiSPQHmemyS1cLdXseljx8GHk3t1eDpDUf4bMOOvsSivB0Mv6Gc/Gl25UvDI39tLpllf8M17M0aKNQTK6UtLKldPks0kk8UQt4E02ZP8ADZi09NzVGyf+GYdLL+fkT4Jk2q2MGpTjkZqU/wDhb/Yp1y/mtkVL/h6vb2NtbkY9WjTpOZtyP0KNbMyaSHdI3uK7o/BGfbTHpZiXakq2FqX6S3ngozPeiOdqtUQXbBsq08e7PJk8rag1YaGNW6LnEtZZVsjE6GggvuJmKN+x1emQuVmNyZ2uviWxakiMFsSogJqax45yatUeK6i3kyzfye0yRUtNkrwjzWl0UtdrP4fCu7K7aRpjK7PHu4aeU1EHGb2NXTKeaCfuS6rD7eaUGqadMz6KXZqIP5Om84uS46zfYOkRlLS4mrqjvaOCbpLc4nQttDgb2bitjv6JpTvZfJw5WyvUxnDsaXTw+3VRfvsanj0sYNZVCEX6XJ0uQ6di+9wvU/Yo63oJyhPDPu+zKLU4NefD/YntNwcbrPTMugTmnKUOU272PF9W1mPNBw1NyUdo14Pb5tbPJBwzdzUVXqPK9WwafLlclFO+TTGsri8FrseLdpHJz44vhHrOoaXErpHFy6dJ+lHXhY588a432dzZoenZNRkjGK5O10voubV5Y1B9rfJ73pX00tI4TaTa/qLyeXHEePw+15cv6X+nPtTx5MkfVLY99p9P9tKMatbENJp1ijdJL3LZSk91de5x5Ze3O3pYeOYzUVdffboqk7XafJPqRbSo+m9UyynicZ+OD511/E3KS2fyVhOdp8s4eEy07M7VM26vG4TZlkrO3G8PK8mPKvkkotiqi6KTW3Jow0q7GSWNlzXbESlS4JtpxV9sTiXJoGkyfZSlbMui20CivJNRXgLVTaGaK7DKbcsfQzEPDpGTTpIpy3Pof0XKMtFlS9MozqvfY+d6a07R7f6FzyjqtVilJSWTEml/pp8/3MvNPyMZXsvySukJK4ojve5ycqNtjT2DahC2ZrgaYorZjiqsfCQxNOtlyP58DtfkBtFL+gpunSBuuCDtteBwbST9i1Kl8Far8lqfpvlD3rotxJSb54CSpq1VkFKlyNZFzKW/yyRdFJq9+CpZ0p9rdP2Hm1GH7nZGak/LT2X7lGpzYVFKHbKf+pLj9x++PyPW3qNkWnvJjcr4ex5xdcwaHNKHUMrjFv0y7ePhhm+rel4324pZsr/6YUv6lTD25ibNdvQrl1wNOlZ4zL9bYU2sOky93+qckkdr6d6tDq2knlUXGUJdso+zKvis+E8O23G6jvHm68he/IY3T3TS/ANxT2RHroEmr33CdqVrbYG9k4qmJttPcXfQfMvqCVaGaS5a3/c8sen+qUlpoJc91s8wd3i/qYAANAt00u3PB/J6mLtbHk8f64/k9Vp5N44/gx8tkdHg+U5q4HNg2smSL48HVauJzZpLUvflGLoquCbw5EqSvhEdHVb3aZbirtyJbFWldt/koTTdL9PBzsDX8ZLc6T/wzl4Y1rmwmhVfUF678GXu9FGzqW8tjBua49MMrZXQ6elTv9jYo3k34M2ijaib4R9Rll3tvjODSpGTLtJ0a8rSgYJy3oNjJTqLlSRr08e2CVGfHFyyX4N8FsqJzy+GFWQSZ2OmKldbnJguDt9PXo+TPf0lvSJIiiSFdpWQSaaflHO+msUsf1fjjHlRlJv4pm5Nppi0OP7XXMGo4i/TJ+Ffkcru/TZfDy31/gjHruTNjiorL6pJcWed02OU8ke207R7H64hLLqp5WnTk6fuea6bS1uKNbyklR0Y5fx0z8uH8+H17pMV/C40lukjsaWCUrb5OboI9sVH2R1IVFXVs48u+3fjrTpR170kYOG/ynui3U9flnXbnydyqlfKPP6vUeyRydTqJuToILI6HVtRD9UGm37nnNZnbTaLM88mV722JdNzTipSVRfFmskjPKVwdQp551FcnQ6V0P7k1POriv8AKdjTdJpqTaOrghHEklT97Hcr8I9Pt0uk6DT6RQcYxUGt1wbMkoW+3c5eXqEcGOtvwLp+oesy+m6+DPLG3trhjp1sUJ5XSdG7LovtaZSfIaKCglJtKvHsdqGPT6npWTN/E4lOK/wm0pP5JmP4a+2nz7rD7YT4Z4XrEk4uj1/1Jq8ccrUHZ4bquoUmzbGVl5MuHmNck5s58407OnqkpPYxZYG+PDz89bZWhK48FnaPtL2xuiWS+SezXBX2UyzGt9x7KQ1G0SUCaS8E4pck1cUuI9qJzjb2CKFDV5F6GYXydLIqT2OfNeplYM8+V+BVGz2P0HhU+rqb2jHFJNvZcWv+x5TS4u5pJH0j6Z6d/C6COSf68vqS9vyZeTISu1HcGlY0qQnzv/U5Nq4DVrbga4CLajTdijyIj/Ya8qgFLgNEKa2Y/TwiPdYJ7Dk0C2tghLkbH2DTRg+oOo/7q6Rk1kcccjU4wUG65Ogla8V8s4X1thWX6a1CX6lOLXtyPGTclTXl831l1CS7ccNPji/Ha2/6mDVfUvU88eyWpcYeYwikcZ2qLMOOeWfbFHf6Y4zpG3qvpPqGXI56eSlNSfd9yT/T8Hejqcc8soQmpSjyc1x0fQvpbSy0WSOTrepkm4bSUY+bXKdnE6hDVdMnizyyJvvTjJbbrfezi8vhx8t3jw1w8txh/WGnnp+qyUu68kVNLxTXgy/T/Sn1fNPEtRjwzitlkdd34Pe9N6p0X6r6XKH1Pl1H+84enAsPan8W/a/g8T1Dpur6L1PEpSUJQyrtnHj+pt48/Wft/Kcv5XbP1vpOfpOqWHPTbVpx4aPT/wCzf/ltWq370/2o5/171TT6rW4ceiyRyrFFqWRRpW62Xvwaf9m0k8+rhJ+lxV/D3Kxyzy8e8u2T3sZJ/CD0ySpK/YhGl8k1xdcnNbBoJbbxF4uNNea8A5SrZkZN1vZlaeny36sbUNOv9Tf9q/8Ak84d/wCrHefCr2Sdf2OAeph0YAAKAPT9Oblo8bvlHB02knn3VJfJ3un43hwKEndGPksvDbw3Vajm6lNamMvHsdGzBrk1OLXuQ6rpHEn3S9mQwRqUq33J45/zmvDQ8EayyvyKwo0Nv7dHNi+zXX8HRm0o7HPjX8XY5JDqGud/kwpbo3a5UzCv1o0w6Y5Tl1tHH0qvBuhHyY9Ekkjb+lWjC9uicKNVJJUYJu5bGjVSbb3MifqRURlWvDGkaIbFWO+00RVGV1a57ktxL1I7uihWNbcnFwU5o9Bp1UERfxCW+BxGuBUEmwk1aFTbTXgfgcWPS/HdVi+ocX39C8kf8q3TPJdJxN6/A3Buskf+57PXwn9qcV+mapoyaHp+OGnU1Ks6mqXwXMvWOy43LnT3Dn25pNU03yjUslxOXp5d0Iq96N+NUt+TJ0S8K8y7k72Miwd8tlZtmlLZPclgglt5YC7PQ9Oh392VK/Ce50smPFDH2pU0U42sa4ba4IZMkskqnKvwOavaaz5IRU+571yYdZrceOV2v6GjW5YYotKSk69zzHUtRbdvc1kLelWu11d77vk919HaeOj6Biz6h1m1TeWSfhcJHzGGL+JzRjO3CT9X4PbdW6woYIxxPtgopJWLKW9HM3b6l1hYpOMJJL4PP6jrc9+2bX4Z4nrHXZq33NnExdbk8iUrpvkeHjvaMv1GM4es6jrXkm25XZw9XNStleTUqcbTuzPPJa3OmTTmz8ly6VyhduyjJFeRZ88cfLKseojOW4MLVeWFPYrjyb5dtN8oyKnJ0Mtl22wosSBRJ1S2grRPukwa2EgVtLubGl7CVotjyhW6NXTfJQ8fdk4N8UvJCaSlaImVibp0/p3QfxfUMWOrjH1y/CPosUovZbeDz30jo/taeWd33T2T+D0VUY53ZcLHul3PgTXkjVoe6MjD33Cthq6ZHcV18gR34Brcd/FAvUG58JRewmSYqHLaSCdX7gnb2Bc7jS32HocpL5OZ9Uw7vpvqDSvtUH//AHR01wYOvxv6c6puu77ScU/LUkVJyT5JNXKj0XQtLDN0bV6iMG8mJ7O+aXHH/k83b2PZfRsn/uHVQS2+/wCrb/pR1+XcxTJuvM5f4iUvuSUkbI5dd1vU49LKTnJK1F8JLyetz9Ox5IK0lZmxaXFoNTCaik5bdxj/ANidSctPSOHrui5en6SWqhl9WPf2f7Ha+qZLXfRHTddJVnh2LLK/1t3uXfUrhm6VlwQmu6VPZ2jN1Gv/AMb4YxdqOSKu/Pcx+K5Za9u0Zeu+Hh7fduex/wBnCbz69RpNQi38qzxx7H/Zy712sSf/AKar+p05WyVna99BSpVXyWV82JOkq2XuNNM4LN3ZwTTXFMhL5W5KUr5FOVpVYpKenyL6onethH/TG/8A/f0OMdX6lv8A3pKL5UV/8/8Ak5R6GH9YANciAoOhpNT2zjHwd3FLuhaPK4+bs7nTs1pJmGeElVhdV0L2MnUIOWJyXKNjtkcke7G0zO2Su1ysTS7H5ZqgmpmGNRyNPdxZ0oLhjODJtCznw/5lM36iVQObi9WpSGR9Qav5MWHfIkzX1Ck0ZdOryL8l4/1Z3+0juaWPoRbkbSZHTpfbSI5pbM52/TFqZJmByTzRXszTqppWYtP6tRG/c3wnG3N5Mvh3sVVSLIr3KcSdF8E2zm0jht0MO7IrO5BUqOX02Hq7jrRDQpxsdbgnuMORyKG9nsK6Hd8i1Ruuh03TLqGaGBtK3VvwR+psC0WbT6bDLvnCe8q27fJLoOX7HUllS7qi12vj8mbrOoln1ty3dk2PQ8OVyx5dHRv0L3Ohjm63ZyNJkpJHSxP5FGy7uTkTg6dqyttJbLf3L8F9rbWwxtZ9z07ypGLU6mm+2l8luokoxe9HJz5E73LxlorNq80mt22zjaqffLt3s6md+ltmbp2mep1yT4NOmeXLd0TpP3MX3HH+pH6h6NqVpHlwzuK/ytW3/Q9jo8Kw6aKXC4RHPjhODvjyZ3K74pzF8K1mKU5u0/3Mb00U7qj6x1zpehztueNKcV+qLas8Z1Dp2KLax8L3N8M3J5MJ24MJ9q7UyUsjoepwvC7M823HY21GFqjPHvlbFCMYbsWTuRVbb3DlO41vOu3tRHG1e5VFInENm0pIls/yQx7rckrT4GBJEXGkWg3aqjO7PSu6RJbpWJRROOy2I0acNkaun6Wet1uLDjVuclEzRO39Jvt6r5/QwvRV7XR4I6XBDDH9ONdqNEZbkYVXA7pnLe0ppb2yMqsG2mDarj9xdHA1sJNr8DbAJQinbHfwJ8gm/cd0Nm6f4It70mDE0/AhsPcI7BukNN+w07D/ALGTrcO7oXUWmvTgk69/H/k2MzdUi5dJ1+P/AFYZK/2HLyV5fHYbwier+kJNdM1zcqSyRuP7Pc8qmlFVweh+mJtxzYsSub3aO7ybuF0jenosuSf24NTfa91uU6xSlijKb38GePfj0+LDJv8AlrtKM0/WvLOfLD+M0Xk8+sbEM7c4uKttrajf0vsn/s61lvu7W+y/fv8A/s5ua4wck6fwdH6X7c/0f1jBH09ik4t/6v1Kv6URjuY3/wAcvhu3hWqbvk9b/s3pdQ1Tlddm55JXNJ+T1X+z9tdVyxeyjhcv7o7MtyOm/l9FW/n9iX5FCux3X/kjP4OG36OJSa7diDarYLSXmyLXmkTdm+O9dafVc7V1tz+Ec819UbfUM9/6qMh6WPUAAAGDjzub9Bl7ciOeWYZ9kycpuCccvWYpd0UwezZm6fl+5iW/Bpbs5678bLHE1CePWyvzudDDK8Zn6nFx7Zr3JaSXdj3Hd6Gz1EnRiwy/4lGzPXaznY3/AMRH8ixl52Vq7qfNmfQxcsq8l/VHuhdKjc2zTrFG/wCbsQ9MDJnyLdIt1OVY8bMUItweSWyMZtrbwxayXCKtN/jRFml3ZGxYpds0zpk/jpxZZby27+F3E04o9xj0su6KZ0dNG5I47edK07GggowTNiKMEe3Gky66JHCRJS2I2Ac0z4GmR/Im0gmwu0+X7eSzs4NNo5aeefKpSyNenfZfsefsctTljicYSaXsKyujw+T14rXF1kdPg6enyrt25POaDNJzccj39zqY8vbImzXbrmcvMdeE3Rox5uyNLb33OX95NL3JwlJxdscVuLNZmc2znzlW1F2SdX5Mkp3u+DSFuKNXN9tHQ6D24V9yVNs5GqypySLYal40knX4Y7plcpt6vN1GMYbOS+PBmydVXZ6bT+TzefXOqu/czS1VqromY/gvdr6jr33OpNr2s4mfN9xtsebL3NlKinZrjwxy3XJ6kzHhTbo3dRg5S9JRpsTim5o2mTC41j1MXFmV87G/V7t1wY+2mV2jkk9qJohKFb+SPdQtH7NMJNP3NPdatnPhNqRqhLuHo9rud0NqxL4HTfkiwbDqOzCImhq74IsNZFWeo+ktK3knnkq7fSrXJ5nErkke+6FBx0GNcLwZ5daK11fagtt7guBcbmNLhPkKrkXcwbtck7MmvYPAcu0JrfZjGkthPkXA+7yh6LRXuDfzuGz/ACJKudx8DQpt8jWwWCI2XCSdqmv3KNfNw6brK84ZKv2L968fuRyQUoSjLhqmK38luPjv8N2wS3fzR0+gax9J1U88cam5QcKfB7R/TXTu1L7WTb2m6FD6d6fDjC79nN0ds8/DPV+3lP8AeLyyk6/oVfxUu/jY9xDouiSf/Dxa9nuaIdN0sY1HBj//AFQr5ZZqxnl4fbuvn2bNPKu2C3KsWDWJNY8eZQfPanTPp0NLjhSUIRS9opFrhaVrZcbUTuT+sLHwYx8yx9E12SpR0mXtflqv+53vpjo+s0fVFmzY1DD2OLuSbtnsXHam9vYFCKVkfvWrmEiSVR34Iyae62JEZelJPz5Fcl8Fu0RX6iyqV2mV/Ith8U1k/uavNP8A1Tb/ALlJKbucn8kT0IAAAMAAAA6vR8zjJwfDOxd8Hl9PkePLFr3PRYcndBP3MPJNV0+G8aLVwU8Ek14MPT57OD5Xg6E90/JztNFw1c79iPhqu1DVOjnQaWdfk26i964MGP8Ax1+SsOqWVWdSbc0n7GjpdRxSkzHqW8morl8GvKlptLGKe73ZWX9dIn9rUMuR5s3auLJa7L9vCscQ0aUISyz/AGMGpyPJkbDHGW6+j8mWsVQABs5HZ6an9tWdzQxvIjj9Oi1iVne6dHe6OHPnKtpXXithpVyLHwSexG7Cp38BW9gLjgXtSgciMgatAvwVu1QveiMkmNsjT3JuzUKX2syZvxzUqdmHVYlPC15FosjWPtk90Nt4sviuwp+pNcGtTuBzcLuJd9xpUgdMsW5Wq5Mk5Nrbgk3JvcMlRgXJ9i5ON1DM1JfDKXql2XZHqVObo4+fM42i5JWGeTbl1b7nTIR1Tfk42XUsr/iZ8KzSYRn716KWphGNye5z9R1LnsZx8moyStNsq75PguYJ/djoPWSlLceTVNxpHN72Dm2V6xP7jTLI2JSMykx97DRe8bOVuUzj7FaytFkMkXyLWi3KgotbmjE9iDW2xLFyK0NOPkk2rqyEF7En7sVpibaexKPyK15RJRtkW7Nt6XieXV441s5JM+h6aH28cYR4XB5L6Z0ndJ5HwnR7DHXCWxhmSy7HRAlwvkz6MNvhAm/YFuDVBsaCbWzDYXjkSewknfwHgjfgcXvwEtoiUUhrkipVvQ2yhYJVfIUm+RMEItGPnyKh8DsLgUuAqlu9rD8oTa8hNfJ7Nr2ew0vArRLhC3ykLZ8k075dkFV3ZL5J5LRuKSbbdkfy9h3w1z5I3f5L7I1s74HzfqS/IkndOVjaV7Uw6FqFX52ItUT7t6SHKoxa3sXtCl2+FvkQAemYAAAAAAADs9Ny9+Lt8xOMaNHl+1mT8EZzcX48tZO6mrMuWNaqM1x5NHplHvXkpyVLdeDndnajUSdtWZcEU8jb8Ky/JG2ylLswzfl7GmPSKjpV3ai343LNRJ6jUqK4sjpn9vBkm/1cInhX2Mcss362th287Zzoa7KoRWGDtI55KcnOTlJ22RNMZqMcst0GjRw78v4M5v6WrnIM7rEp27OlivSkjv6TF2QRzem4E2pM7CW2xw3caaXQpId2RjwG3kJkNGmDEmSk/AdhB8Ct0EthXY5KAF0DfyK7D/Tmw1a3OdqLwZk1wzoPkhmxRyQpoU1Ozl0Wm1ST3ZrxZ/uS9jhZIvT5Um9jpaGcXvY7I6cMtulvZn1DtNWXqSlEx6yXbF0TI0ri9TydqdHn5uWSVcnX17lkk0iXTen9805I2l9Y58pbeEOk9G/iJKeWNw+T0K6No8TXZCvhnW6bhx4sai1wh6iEHe+5N8uXxW+HjkYsWn0eOFTwwdeHFbmHX4tLk7nHFCK9kuC/VNwtWcTU6pRkyscrflp/Bh12jxW6ikcjUaXs/B1c+qjJcnPzZPuLY1m3P5PW8MLgiLii5wbewPE0abc9xnwo7RqLL1jQnH4F7J9IcHXJdFIpjFtmjGkuSbRIsg6QOXd8Eqi0RVJ7i2oQfuadNhlmyqMU3fsZ3Fv9J6r6U0VT+9lW1bbE5XSa7XSNG9LplF1bOjBtcckVaVDjado5rd009/cabIJt8jj+SaE7Dli8g7SFJBuHvYJKiNsbdclDRPikKDdkX8C8i0FiXcyT4oSVL3Di72Y9lo4utgrcUWnyCe4t6Kmtluw/LYVYV4D22SSd+RUHlIOGvYNbPsKhoT5tKkwH6p0f77jVoikk9iXcHM+S0cfI00vyRVjUWn7i2WzafNoUWl8jv+w1VXQ9y/IQe7sVJum6/JLw72ZCSS3Fb+CfJZfT+ujfpg69pFb6Hru6vtJ/KZ9DWJPwP7a8UdX72WzfOZ9G10ecLr3TIPpWtX/t5v8AB9FeJXuQlhTvYr9zIPnMun6uPOCZBaTUP/0cn7qj6M8EWt0VPTxp+kc8l+j0+dvDlXOOf/6sj2yX+Vr9j3ssEe5+lEJaSH+hD/cv0NPN9NzPLgeOXK4LFFwtM7k9LFLaNfg5mtwyh4Mu/h0YZ3Wq5WpyeqkU5pOoxLopfcbkuCMcf3JOT4RpNRWVCSaSb9Ed2Z9Rl+5PbhGifqj2rgq/hm+GPGyc1l5MreIzAalpW/I/4KXhl++LLTIdfoWNzlLbyZ8XTcmR0mej6Pof4XC1Ldt3Zl5M5cdQTh0tLi7EjWkVYl4RcnSOayq2d+5FtDbdEX+Q0cqafsF+5CL3JyqhaPhF7kbG3sQfJUG09mLyR7nexGWRQXqYrjtS7lEXNLlo5uo6gltA5mo1sndyNMfFai5R1tdkwyj6mjLodVWWk9jkfcc7k2dD6ewrVarKm9oxuzS4aivHlbk9Jjmmrvkz6x2mkVtvFLsvgjkuS5MJXY58sL+5dHY0GNKCaMlK0jdplUSrdiNqy9qoo1GdxjdgzHreKsXrGm2LXaptPc4Oqyd9s6Or4dnLy+djbGa7c/kyrDOTughzyLLakJbcmskc9q7gLTZXC2/gsewUQqtg0NMTZmeionB+5GTVFXdQ+ya1IXLKFk2J48sVJKToXJ7dbpeknq88YqO3l+x73SYoYMUIY0lGKo53QdJDT6OE4tSU1vJbpnVXwznzy2nW07/oOiLrxyD4M9Gluh3sQtj8bgFm/I9q3Id1qkS4K3BCdWNpNURb3It7ki8m7e17A1QkNsf+lrRwk1wPe9+QjSG+Q3L0NhV8WSe+xAl3JchYWxfaqadj4W5H7ncuGv2oHv8AISQj7r4RLwQt0mO34DUJL9XwJXfhglttyLce5CS29goFdJNgvS6ZOwlY7YlJLYE1+/sLkUJB5v2Fuw8js1yjRt/BFjb9iL2VsLs9OW47bEY2mTVg1bNNmjW+/JF/Ja/7lUtlTL3ArlyRnstic+CuVpBDintTdikrJvcEhjlQ4WZtRihN9sjfSuzn55d2V9q4HtccrU9LqTcH6X4ObkwSxzcX+k9LOVx+Tl9QjST8h7U/aucsStFnavYaj7FkY3yTaSvtVl2LE5uki3Hhc3UUdPR6RQSbW4uSLR6Ptjb5NsMbWxNLt5LFVWK2wbKK7SxU0RtAmuQ9qDfwRZnz67Bgt5JpV48nKzdYWSX8lf1NJjci276pEXJe55zNrczhcJtS8GTFq9c5vunz5ZX7NG3rpNJcnK6j1KOnT5b+DJDV5e2pu2YNbJSi3LcrHx67G3R03V55Vbg0veyGo1kpt77HP02WMobKiGfN6u1GkwkLa3LlpcmDPkfcle7DPlr8maWS3dblE6Hf24kmzufRHqlqnt6WlX5v/wCDzKm5QPS/Qrr+MT/zuNftf/yZ+X+ta+H+8dvqmF/rit0cuOd32tnoNSk4NHmupYZRblj5Rx4u7KfLbD3NeFutji6XU7JSZ1tNlTqmOpxrfBOT4M+txGzE0lZXncWtwlaOHm0/czBqNKkmehnGNXscfqORK0jTG29MspHC1GFRbt7mWUTZllcnZnkbTfy5qhHbZEnxyK14D9x8iIrbcblYJKwlXgWxtFyKpy3HkdFTdsqRGSbnsVt2xsiVIm1t6d1LVdPy92nzTir3je0vyj2nR/qrR6hLHrH/AA+b/U/0P9/B8+AnPx459lLY+yY8sckVLHJSi+GuGTTvk+T9N6rq+nzvT5X2+YPeL/Y9v0T6k02vcceX+VqH/llxL8HL5PB69LmW3o0F+CtSJKSMLNGne3O4+7bciuR3sM9JRryKSsirB2Gwaqgb9iN0FlQku4klZDdsn45AtJWgdeSC2ZNqx28EGr4ewU0LdPaxp26I0Eu1pJ1yJug2QvmxzEjW/IJeG7RFO2TQ+hTWy2Ipu90N7CtIW7SSVfgdWR25HGXger9lpK6ewu6nugfwRcvcLhaWjcuaQqvkdPtUqdP4oUnW6D1kOObe2wCqwNtQG9iuaTJp2VzTsnj4CJXk4JyZBoer9nKqpvca2CSaEuStH2WS1GTOZHIu6SXLZ0NZLtxNnG0/fPUX/lJ4VpsyQapUcvqSqSTOrkk1c5NHE12RzyBpKlKjTpknJKRlV3yXYpU0F2ene02GEapGxRr4MOhzJwSbs291rYUtBvdbgtkJST5IZprHBuyvkkc2dY4ts891PrPbcMfP5F1nXtRaTPOSbk227ZrhhNJW59RkzSucmzdocbWPuldsx6PH9zKr4R05SUIm2gdtMmpP8GRZu6dWW5pVDZ7gFmXPGK5OfqM7mt2Vzk3KiDxye7EEseocFS2Iyy72uSDjXkTVL5GA25y+RMcXVv4EAXYZemmem+hZKWu1OOT3+33RX7//AGeXxVTs7f0dqP4f6j0zcFOOS8bi3za2/vRn5JvGtPFdZR7jOu45epgne1nUy/qafuZc8E0cM4elZw81rNPKE+6OxdodUotdzpo3ajFaZxtVglG5Y+TTGezDKevMejxauL3ciGp1Kddp5nFqpY3WRtF89cmtmV+3YU8jp6jU1F7nH1WotP3KM2qcr33MU8jvk0xxTlntOTadpg5Wt+Slzcgvbc0ZBvfYO5tC2E3Qy2ffsR764ISkR3Y5im5fRyfcCQ0qBjLXzQ+CBKREcTQAABAadO1yIAD1v079SvEoafXybgto5G/0/n4PZYdThzpfay48l8dkkz5AXYNTmwSUsOWcGuO10YZ+GZXcOV9giyVniei/Vf6MXUnb4WWv+57HDlhnxRyY5KeOX6ZRdpnNlhce172tY/hkWOqW7I0EZ7fgFbXuwbtAvgqXQSi9tyXdRW7BJryHZrLJx5TrZfBWmS5WwkrG/bci1a3Iok9lQtDkm/DDjhjSfkH8FSQglb2JNe5W7oE35H60lgkrZHu35HvewspQlw6QO6ryKLd7ob5smY0g9gtPYUZW9x734KJLuaVeCD+aobRF+xOpA51sFdjfApG0v4Mrp+Qkm6GqaHuO6CuSdlbLJvZq6KpBNDaMlfBG1wOSpckNuR2nEdTj78bijmYYfZtNG3JllDJ6t0yjLOMp8BJFMurUpxpOkcrUtKVLwdTVqUoNYm4v3OXq4vFjcsnJX+DSmLsti1ZzZZ6exPHknJ7WxXCnI9F02W51XljGPqZ57Q5ZQpyRZn1blzLYWHi32m11J6yC+Tm67Xdyas52XVJWos5+fO3LmzfHxyJQ1mR5Mm7M423J2xGgaNJNxm0i7NKTXsYoycXaJvI2LQaMDUN5O2PLnt7cmVTfCItO97GG/DjX2u57yb3IZpUizDf2kZ8ibb3AtqG/YQPkQGkv0v3EOvQn8kQCcGWYMssOpxZYunCSkUoGwOXT6pDJHNghOP8AmSZTmdKjm/TGrWfpUIuu7F6Xv/Q26h7HBljq16eOW5tnyukYs1NfJoySvyZMr9wl0VczXYFNPtXqOZOE8b+Tt5XvuY8yTd1ZrjnflhnjO45cpSUtyLm29zRmxXuZ3B2byysbKTlQOVoJQoj2sfCLs+4i3Y+1j7R8Fq1Ea2JUkFBs5joWJsGhMDtJiABswAAAAAAAAAAAdTo3WtT0uf8AKanifOOXH/0zlgKyXih9E6b9UaPUr+denl/1O1/U70ZrJBThJOL4adpnx43dN6rq+nTvBkfa+YSdxf7GGX6efCpk+qqh8P4OB0L6g0/UWsU2sOof+WT2l+Gd2UrVXuYZYXG6XuVLusN6IX8ko78sWk7SU0l8jct7I/0GkxesC6DtBuVRb4JN0hXj5JO7XIJIipbbjbHLRs0knuFb8CbdBTofrftJxq+CXcV24rcN2TboLW73E7rYrTfHgkpbDLaXK5Er8CXJJWxf6A/AqVDVpNe5CnxQ7lDjnRbJNEEmhdz8GnwSfAlL3Ip+4qaFb+VSW8QTXkqb8MlNNr2Rml3t1exPtHVj+jzqyxcopzQyY491mWGuUZNSY9p8n6bLBtyxUo8bmLIop20a4ZFOCm3s1aMOryR3SNMeemHSM8kF7HH6jL78nFcF2S5tep7PwyLia4+O/Jezn4tCrubtexthCGONJIjkyKCdswZ9U3dbI00W2zNn7VSMGfO35M0s035CDVXIoLIRlk+ES+zGL5sX3FGOxVPJbAl0qqqRnyRUXsOMiMxaNEthjTSbKiyOSo0MLX2QXCKHNtibbe4gDo4N8CM+aVWW6W/slOoAM4AABNv0JEB+BAAMQwDq/T+sen1EoX6Z1/U9W8vdHdngMcnCakuUeo0Or+/hTvc5/Ljd7jq8GfGq35ZpGPN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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
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        }
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    }
  ]
}