Image processing/Bag_of_visual_words.ipynb

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      "source": [
        "# Bag of Viual Words\n"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "Here we'll be doing:\n",
        "\n",
        "\n",
        "\n",
        "*   Make dataset(Horses or humans dataset).\n",
        "*   Make list of descriptors( to extract local features using ORB).\n",
        "*   Use K-means to make clusters of these extracted features.\n",
        "*   Use cluster centeres to represent particular cluster.\n",
        "*   Build histogram of train images( these contain feature count of same cluster in a image).\n",
        "*   Fit a KNN on these histograms to compare an unseen image.\n",
        "*  Plot a histogram showing found train features in sample image vs thier frequency. \n",
        "\n"
      ]
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      "source": [
        "import tensorflow as tf\n",
        "import cv2\n",
        "import numpy as np\n",
        "import tensorflow_datasets as tfds\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.neighbors import NearestNeighbors"
      ],
      "execution_count": 0,
      "outputs": []
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        "train_ds = tfds.load('horses_or_humans', split='train',as_supervised=True, shuffle_files=True)\n",
        "train_ds = train_ds.take(500)\n",
        "\n",
        "test_ds = tfds.load('horses_or_humans', split='test', shuffle_files=True, as_supervised=True)\n",
        "test_ds = test_ds.take(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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            "\u001b[1mDownloading and preparing dataset horses_or_humans/3.0.0 (download: 153.59 MiB, generated: Unknown size, total: 153.59 MiB) to /root/tensorflow_datasets/horses_or_humans/3.0.0...\u001b[0m\n"
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          "text": [
            "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
            "  InsecureRequestWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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            "\n",
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          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/horses_or_humans/3.0.0.incomplete2UEPK6/horses_or_humans-train.tfrecord\n"
          ],
          "name": "stdout"
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            "\r"
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            "\rShuffling and writing examples to /root/tensorflow_datasets/horses_or_humans/3.0.0.incomplete2UEPK6/horses_or_humans-test.tfrecord\n"
          ],
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          "text": [
            "\u001b[1mDataset horses_or_humans downloaded and prepared to /root/tensorflow_datasets/horses_or_humans/3.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTJHEuEIW0d0",
        "colab_type": "code",
        "outputId": "11eb4c62-2ec4-41f6-a441-e70f306e63ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "isinstance(train_ds, object)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3h3K9gZmvIdS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# function builds historam of local features with thier counts\n",
        "\n",
        "def build_histogram(descriptor_list, kmeans):\n",
        "    histogram = np.zeros(len(kmneans_centers))\n",
        "    cluster_result =  kmeans.predict(descriptor_list)\n",
        "    for i in cluster_result:\n",
        "        histogram[i] += 1.0\n",
        "    return histogram"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLI4_KiHJSwh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# make list of descriptors\n",
        "\n",
        "descriptors = [] # all images descriptors\n",
        "\n",
        "orb = cv2.ORB_create(200)\n",
        "\n",
        "for i, (image, label) in enumerate(tfds.as_numpy(train_ds)):\n",
        "\n",
        "  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "  _, descriptor = orb.detectAndCompute(gray, None)\n",
        "  \n",
        "  descriptors.extend(descriptor)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9h4jk26yJARI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# fit kmeans on descriptors and kmeans centers\n",
        "\n",
        "kmeans = KMeans(50, n_jobs=-1)\n",
        "\n",
        "kmeans.fit(descriptors)\n",
        "kmneans_centers = kmeans.cluster_centers_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kz7SFPeW0jW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# build histogram of train data\n",
        "\n",
        "# contains number of features per image(features frqeuncy in each image)\n",
        "local_train_features = [] \n",
        "\n",
        "for i, desc in enumerate(descriptors):\n",
        "\n",
        "  # descriptors_dict[i] = descriptor\n",
        "  hist = build_histogram(descriptor, kmeans)\n",
        "  local_train_features.append(hist)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVQP3vpK4JjZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# use kmeans to compare histograms with train data\n",
        "\n",
        "neighbor = NearestNeighbors(n_neighbors = 20) # 20 nearest features\n",
        "neighbor.fit(local_train_features)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CuOhL9ajKsIo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test for one sample\n",
        "\n",
        "test_sample_ds = test_ds.take(1)\n",
        "eg = [image for image, label in tfds.as_numpy(test_sample_ds)]\n",
        "\n",
        "data = cv2.cvtColor(eg[0], cv2.COLOR_BGR2GRAY)\n",
        "_, descriptor = orb.detectAndCompute(data, None)\n",
        "histogram = build_histogram(descriptor, kmeans)\n",
        "\n",
        "dist, result = neighbor.kneighbors([histogram])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MID6Pzf6LsIl",
        "colab_type": "code",
        "outputId": "c31c4448-f726-4cf7-d77f-d5a89e0b47ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "# 20 nearest features indexes\n",
        "\n",
        "result"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11932,     8,     0,     7,  5963, 95490,     3,     6,   364,\n",
              "         2978, 23869, 47743,     1,     2,     4,     5,    82,   738,\n",
              "         1485,   176]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYfXwA8rLsFz",
        "colab_type": "code",
        "outputId": "b4fd8b0d-048d-4529-b11a-1a32ea42e6c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "# plot the histogram of features and thier frequency\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams[\"figure.figsize\"] = (10,5)\n",
        "\n",
        "plt.hist(histogram, bins=50)\n",
        "plt.xlim(0,50)\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jPOrqiDr-zxe",
        "colab_type": "text"
      },
      "source": [
        "Summary:</br>\n",
        "With the histogram computed of test data, it can be compared with training features to find the match of features. If the computed train local features labeled they can be matched to find class of image. </br>Like 30 features from class1, 50 features matched from class2, So image is classified as class2."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLt_a6ZOBY3a",
        "colab_type": "text"
      },
      "source": [
        "References: http://www.laurencemoroney.com/horses-or-humans-dataset/ </br>\n",
        "https://towardsdatascience.com/bag-of-visual-words-in-a-nutshell-9ceea97ce0fb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZlzWkGIDDVN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}