CS114.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CS114.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwuTtMpAwGIv"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-E1uHS4wyIt"
      },
      "source": [
        "from sklearn import datasets, svm, metrics\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "oE_aF27aw4g3",
        "outputId": "dec7db2e-102f-4621-b5d1-8c948d0bb75d"
      },
      "source": [
        "digits = datasets.load_digits()\n",
        "\n",
        "_, axes = plt.subplots(nrows=1, ncols=9, figsize=(10, 3))\n",
        "for ax, image, label in zip(axes, digits.images, digits.target):\n",
        "    ax.set_axis_off()\n",
        "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
        "    ax.set_title('Training: %i' % label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x216 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuwV6FkuxLkj"
      },
      "source": [
        "# Làm phẳng hình ảnh\n",
        "n_samples = len(digits.images)\n",
        "data = digits.images.reshape((n_samples, -1))\n",
        "\n",
        "# Tạo bộ phân loại: bộ phân loại vector hỗ trợ\n",
        "clf = svm.SVC(gamma=0.001)\n",
        "\n",
        "# Chia dữ liệu thành 50% tập huấn luyện và 50% tập thử nghiệm\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    data, digits.target, test_size=0.5, shuffle=False)\n",
        "\n",
        "# Tìm hiểu các chữ số trên tập huấn luyện\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Dự đoán chữ số trên tập thử nghiệm\n",
        "predicted = clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "tzl4BkbUxNiX",
        "outputId": "d1811b2e-d675-430e-8097-40bb407328e3"
      },
      "source": [
        "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n",
        "for ax, image, prediction in zip(axes, X_test, predicted):\n",
        "    ax.set_axis_off()\n",
        "    image = image.reshape(8, 8)\n",
        "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
        "    ax.set_title(f'Prediction: {prediction}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAACXCAYAAAARS4GeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAL5klEQVR4nO3dX2xkZR3G8eeHZUFZ7SzGKLsuHcRgBANTRZEobsONCUI6gaAmanbGhLiJ/+qN4ULcSVj8d2MlCBiijNEEkEimxERjEDoRIYiyMxfGEIidyi7uBoFpXCHq4uvFOeiwtt3u+55h2l+/n6RJOzPPOe9Mfz3z9Mx010IIAgAA8OykUS8AAABg2Cg8AADAPQoPAABwj8IDAADco/AAAAD3KDwAAMA9t4XHzJpmti///BIzezxyO7ea2XXFrg4bBXOEIjBHSMUMpRtp4TGznpm9aGZHzOxw/g3dWvR+Qgi/DiG8Yw3rqZnZg8dk94QQri96Tcvs28xsn5kdNLMlM5s3s/OGvV8PmKNX7Js5isQcrbiOX5lZMLOxV3O/GxEz9Ip9n2Jm3zazp83seTO72cxOHvZ+V7MezvBcEULYKundki6U9JVjb7BJftCulvRpSZdIOl3Sw5J+NNIVbSzMUYY5SsMcDTCzT0ga6ZPUBsQMZa5Vdv/fJekcZY/H/z0Wr6b1UHgkSSGEg5J+ruzBUf4bxWfN7AlJT+SXXW5mHTPrm9lDZnb+y3kzmzSzx8zsb2Z2l6RTB66bMrMDA1/vNLN7zOwZM3vWzG4ys3dKulXSxXk77+e3/e9pxPzra8zsSTN7zszuNbPtA9cFM9tjZk/ka/yumdkaH4KzJD0YQvhTCOElST+WdO6JPo6bHXPEHBWBOZLMbFzSXklfPtHHD8yQpCsk3RhCeC6E8IykG5X9MjYy66bwmNlOSZdJ2j9wcVXSRZLONbNJST+Q9BlJb5T0PUn3WnbabIuklrLfZE+XdLekq1bYz2sk/UzSoqSypB2S7gwh/FHSHkkPhxC2hhBKy2QvlfR1SR+VdEa+jTuPudnlkt4r6fz8dh/Os2fmA3PmCg/BnZLONrNzLDvtt1vSL1a4LVbAHDFHRWCOJElfk3SLpEOr3AYrYIayXRzz+VvzIj0aIYSRfUjqSToiqa/sgb5Z0mvz64KkSwdue4uk64/JPy5pl6QPSXpakg1c95CkffnnU5IO5J9fLOkZSWPLrKem7LfjwcuaA9v5vqRvDVy3VdK/JJUH1vzBget/IunaNT4WWyR9J9/GUUkLks4a5fdno3wwR8wRc1T4HF0oqSNpTNmTaFhujXwwQ6s8Fvsk/UbSmyS9RdIj+fbOGNX3Zz28jlgNIdy3wnVPDXw+IWm3mX1+4LItkrYrexAPhvxRzi2usM2dkhZDCEcj1rpd0mMvfxFCOGJmzypr1L384sHfhl5QNkBr8VVlLXpnvo1PSrrfzM4LIbwQsdbNhjnKMEdpNv0cmdlJyp6ovxhCOHoCr4Ihs+lnKHeDpJKy4vwPSbdJmpR0OGKdhVg3L2mtYPCb/ZSkG0IIpYGP14UQ7pD0F0k7jnltcaXTbE9JOtOWf9PY8f7r+KeVDakkycxOU3Yq8uDx7sgaVCTdFUI4EEI4GkJoStom3n9RBOaIOSrCZpmjNyg7w3OXmR2S9Gh++QEzuyRx25vdZpkhhRBeDCF8LoSwI4TwNknPSvp9COHfqduOtd4Lz6DbJO0xs4ssc5qZfcTMXq/sL1GOSvqCmZ1sZldKet8K2/mtsmH6Rr6NU83sA/l1h5W9xrhlhewdkupmVjGzU5S9xv1ICKFXwP17VNLVZvZmMzvJzD6l7K8jnixg2/gf5ghF8DxHS8p+86/kH5fll79H2csSKIbnGZKZ7TCz7fl9e7+k65S9CX5kNkzhCSH8TtI1km6S9LyyA3gtv+6fkq7Mv35O0sck3bPCdl5S9u7xt0v6s6QD+e0l6X5Jf5B0yMz+ukz2PmXftJ8qG7CzJX18LevP3+B1ZJU3eH1TUlfZ6b++pC9JuiqE0F/L9rE2zBGK4HmOQubQyx/K3h8iSYfz+4YCeJ6h3NnK3nf0d0k/VPben1+uZdvDYq98iRAAAMCfDXOGBwAAIBaFBwAAuEfhAQAA7lF4AACAe8f7hwdH8o7mfj/tD0pqtVp0ttPpRGdT1j0/Px+dlaRKpZISH/a/LDaSOWo2m0n5RqMRnV1cXOnfCDu+VqsVnZ2eno7OFmCYc7Qh/7oi5ee6Wq1GZ2dnZ6OzKcfPArg8FqU+p6Uci1KOg1NTU9HZlDVLw3lO4wwPAABwj8IDAADco/AAAAD3KDwAAMA9Cg8AAHCPwgMAANyj8AAAAPcoPAAAwD0KDwAAcI/CAwAA3KPwAAAA9yg8AADAPQoPAABwj8IDAADcGxvWhvv9fnQ25b+kl6Rutxud3bVrV3S23W5HZ1utVnRWkiqVSlJ+ver1etHZer1e3EJeRQsLC6NeAgoyMzMTnS2Xy9HZarUanUXxUr8fKcf3lGNorVaLznY6neisNJznNM7wAAAA9yg8AADAPQoPAABwj8IDAADco/AAAAD3KDwAAMA9Cg8AAHCPwgMAANyj8AAAAPcoPAAAwD0KDwAAcI/CAwAA3KPwAAAA9yg8AADAvbFhbXh2djY62+12k/b9wAMPRGd7vV50tt1uR2cnJyejs1je+Ph4Un5paWkk+65Wq9FZFCvlOCalHcsWFhais6VSKTqL4vX7/aR8uVyOzrZarejs3NxcdLZSqURnh4UzPAAAwD0KDwAAcI/CAwAA3KPwAAAA9yg8AADAPQoPAABwj8IDAADco/AAAAD3KDwAAMA9Cg8AAHCPwgMAANyj8AAAAPcoPAAAwD0KDwAAcI/CAwAA3Bsb1oYnJyejs+Pj40n7np2djc72er3o7MTERHR2eno6OutZuVyOzqbMgSTV6/WkfKxWqxWdnZmZKXAlPszPz0dnG41G0r737t0bnU2Z/bm5uegsx6LipR6Lms1mdDblOS3luXhqaio6Oyyc4QEAAO5ReAAAgHsUHgAA4B6FBwAAuEfhAQAA7lF4AACAexQeAADgHoUHAAC4R+EBAADuUXgAAIB7FB4AAOAehQcAALhH4QEAAO5ReAAAgHsWQljt+lWvHJaU/85ekmq1WnS23W5HZy+44ILobKfTic4WwIa8/ZHMUblcTspPTU2NJFuv16Oz+/fvj85KUqVSSYkPc46iZ6harUbvNPXnMiXfarWisykzlLJfSZqenk6JuzwWbVQpx7GU5+EC8svOEWd4AACAexQeAADgHoUHAAC4R+EBAADuUXgAAIB7FB4AAOAehQcAALhH4QEAAO5ReAAAgHsUHgAA4B6FBwAAuEfhAQAA7lF4AACAexQeAADg3tioF7CccrmclO/3+8Us5AR1u93obLPZTNp3rVZLyq9XKd/LxcXFpH3PzMxEZyuVSnS2Xq9HZ+fn56OzUtq6hyllDubm5qKzExMT0VlJqlar0dl2u52071ip99mrRqMRnS2VSkn7TjkWpeh0OtHZbdu2FbiSYnCGBwAAuEfhAQAA7lF4AACAexQeAADgHoUHAAC4R+EBAADuUXgAAIB7FB4AAOAehQcAALhH4QEAAO5ReAAAgHsUHgAA4B6FBwAAuEfhAQAA7o2NegHD0O12R72EE9bv90e9hHWpVCpFZ3fv3p2070ajkZSPNT4+Hp2dmpoqbiHryKjmoNfrRWclqVwuR2fb7XZ0NuU+VyqV6KxnMzMz0dlqtZq0706nE52t1WrR2aWlpejsxMREdHZYOMMDAADco/AAAAD3KDwAAMA9Cg8AAHCPwgMAANyj8AAAAPcoPAAAwD0KDwAAcI/CAwAA3KPwAAAA9yg8AADAPQoPAABwj8IDAADco/AAAAD3KDwAAMA9CyGsdv2qV65X1Wo1Otvr9aKzpVIpOttqtaKzqfuWZEk7P76RzFGn00nKp8zR4uJidPb222+PztZqtehsAYY5RxvyWNRsNqOz9Xo9OruwsBCdLZfL0dkCuDwWpapUKtHZbrcbnd27d290ttFoRGcLsOwccYYHAAC4R+EBAADuUXgAAIB7FB4AAOAehQcAALhH4QEAAO5ReAAAgHsUHgAA4B6FBwAAuEfhAQAA7lF4AACAexQeAADgHoUHAAC4R+EBAADuWQhh1GsAAAAYKs7wAAAA9yg8AADAPQoPAABwj8IDAADco/AAAAD3KDwAAMC9/wC8aaBpmemiAAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x216 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZfBoQIKbnU5"
      },
      "source": [
        "**THỰC HIỆN PREDICT BẰNG WEBCAM LAPTOP**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRMM7n3fcERn"
      },
      "source": [
        "Chụp ảnh từ webcam laptop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYXlrgDbxvo0"
      },
      "source": [
        "#Code from Google Colab / Code snippets\n",
        "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "AtTuUk5Mxvo_",
        "outputId": "67d8ee0b-73ba-4204-ec3d-4b511b8b7a36"
      },
      "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": [
        {
          "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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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dy1vqSjsUTkZ",
        "outputId": "d1bb8a41-7d27-44b9-e143-d83b182810a2"
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "image = cv2.imread(\"/content/photo.jpg\")\n",
        "image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # chuyển sang ảnh xám\n",
        "image2 = cv2.resize(image, (8, 8), interpolation = cv2.INTER_AREA) # resize kích cỡ 8x8\n",
        "\n",
        "image3 = image2.reshape(64,)\n",
        "image3 = image2.reshape(1 , -1)\n",
        "print(image3)\n",
        "print(X_test[1])\n",
        "predicted = clf.predict(image3)\n",
        "print(predicted)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[134 137 134 137 141 140 137 132 138 138 132 132 145 145 142 138 145 147\n",
            "  146 152 146 151 148 144 153 157 160 161 157 161 157 153 161 156 165 164\n",
            "  172 169 162 161 162 165 165 167 175 169 174 169 172 171 179 185 183 186\n",
            "  183 176 176 183 189 191 192 191 188 181]]\n",
            "[ 0.  0.  6. 14. 16.  5.  0.  0.  0.  2. 16. 16. 16.  7.  0.  0.  0.  2.\n",
            " 15. 16. 15.  2.  0.  0.  0.  0.  6. 16. 15.  7.  0.  0.  0.  0. 14. 10.\n",
            "  6. 16.  3.  0.  0.  1. 16.  3.  0. 16.  7.  0.  0.  0. 10. 11. 11. 15.\n",
            "  3.  0.  0.  0.  3. 14. 16.  6.  0.  0.]\n",
            "[1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SjVUrnL7erzi"
      },
      "source": [
        "# **Trả lời câu hỏi bài tập colab số 1**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "POVdVRroe6sA"
      },
      "source": [
        "Kết quả chạy thực tế với webcam của laptop và giấy viết tay của các bạn có tốt không?\n",
        "\n",
        "*   Kết quả chạy thực tế với webcam của laptop và giấy viết tay cho kết quả không tốt.\n",
        "\n",
        "Nhìn vào đâu để kết luận là tốt hay không?\n",
        "\n",
        "*   Dựa vào dự đoán đầu ra cho kết quả không tốt.\n",
        "\n",
        "Nguyên nhân:\n",
        "\n",
        "*   Ảnh thực tế được chụp khác xa so với ảnh ví dụ được sử dụng bên trên ở nhiều mặt : chất lượng, độ tương phản, đọ nét của chữ, ...\n",
        "*   Chất lượng webcam .\n",
        "*   Việc tiền xử lý ảnh chưa tốt.\n",
        "\n",
        "Nếu tốt thì tiếp theo có thể dùng model digits recognition này vào các bài toán phức tạp hơn như nhận dạng biển số nhà không?\n",
        "\n",
        "*   Theo nhóm chúng em thì model này không thực sự tốt để có thể dùng vào các bài toán phực tạp hơn. Lý do là những vấn đề được đề cập trong phần \"Nguyên nhân\" ở trên."
      ]
    }
  ]
}