create_image_embedding_csv.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2022-11-06 13:46:57.311792: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "/Users/jaganlalthoppe/workspace/data-science/image-embeddings-using-clip/env/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import random\n",
        "from datetime import datetime, timedelta\n",
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "from skimage import transform\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "from imgbeddings import imgbeddings\n",
        "import umap\n",
        "import umap.plot\n",
        "\n",
        "import csv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "files_to_remove = ['.DS_Store']\n",
        "batch_size = 64\n",
        "\n",
        "root_folder = os.path.abspath('')\n",
        "data_folder = 'data/beanleaf_dataset'\n",
        "beandataset_folder = os.path.join(root_folder, data_folder)\n",
        "beanleaf_data_partition_folders = os.listdir(beandataset_folder)\n",
        "beanleaf_data_partition_folders = [i for i in beanleaf_data_partition_folders if i not in files_to_remove]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Get files from each folder (test, train & validation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "def run_scandir_by_extn(dir, ext):\n",
        "    subfolders, files = [], []\n",
        "\n",
        "    for f in os.scandir(dir):\n",
        "        if f.is_dir():\n",
        "            subfolders.append(f.path)\n",
        "        if f.is_file():\n",
        "            if os.path.splitext(f.name)[1].lower() in ext:\n",
        "                files.append(f.path)\n",
        "\n",
        "    for dir in list(subfolders):\n",
        "        sf, f = run_scandir_by_extn(dir, ext)\n",
        "        subfolders.extend(sf)\n",
        "        files.extend(f)\n",
        "    return subfolders, files\n",
        "\n",
        "def run_scandir_with_exclusion(dir, exclude):\n",
        "    subfolders, files = [], []\n",
        "\n",
        "    for f in os.scandir(dir):\n",
        "        if f.is_dir():\n",
        "            subfolders.append(f.path)\n",
        "        if f.is_file():\n",
        "            if f.name not in exclude:\n",
        "                files.append(f.path)\n",
        "\n",
        "    for dir in list(subfolders):\n",
        "        sf, f = run_scandir_with_exclusion(dir, exclude)\n",
        "        subfolders.extend(sf)\n",
        "        files.extend(f)\n",
        "    return subfolders, files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/jaganlalthoppe/workspace/data-science/image-embeddings-using-clip/env/lib/python3.9/site-packages/huggingface_hub/file_download.py:588: FutureWarning: `cached_download` is the legacy way to download files from the HF hub, please consider upgrading to `hf_hub_download`\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "ibed = imgbeddings()\n",
        "\n",
        "#column headers for the csv\n",
        "header = ['model_name', 'model_version', 'name', 'url', 'actual_label', 'predicted_label', 'score', 'prediction_ts', 'vector']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Load the model to log the predictions score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "classes = ['angular_leaf_spot', 'bean_rust', 'healthy']\n",
        "model_loaded = tf.keras.models.load_model('./models/')\n",
        "\n",
        "def process(filename):\n",
        "   np_image = Image.open(filename)\n",
        "   np_image = np.array(np_image).astype('float32')\n",
        "   np_image = transform.resize(np_image, (224, 224, 3))\n",
        "   np_image = np.expand_dims(np_image, axis=0)\n",
        "   return np_image\n",
        "\n",
        "def get_predicted_class_and_score(url):\n",
        "   predicted = model_loaded.predict(process(url))\n",
        "   predicted_index = np.argmax(predicted)\n",
        "\n",
        "   return [classes[predicted_index], predicted[0][predicted_index]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create embeddings for test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [],
      "source": [
        "embeddings_folder = 'data/embeddings'\n",
        "embeddings_folder_path = os.path.join(root_folder, embeddings_folder)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "import yaml\n",
        "\n",
        "# Please note you have to create a config file similar to `arize_config_sample.yaml` and provide arize key details and model details\n",
        "with open('arize_config.yaml', 'r') as file:\n",
        "    config = yaml.safe_load(file)\n",
        "\n",
        "arize_model_config = config['model-details']\n",
        "\n",
        "BASE_URL = config['image-base-url']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [],
      "source": [
        "class_dict = {\n",
        "    'angular_leaf_spot': 0,\n",
        "    'bean_rust': 1,\n",
        "    'healthy': 2\n",
        "}\n",
        "\n",
        "embeddings_for_all_images = []\n",
        "labels_for_all_images = []\n",
        "\n",
        "# header format - ['model_name', 'model_version', 'name', 'url', 'actual_label', 'predicted_label', 'score', 'prediction_ts', 'vector']\n",
        "\n",
        "def write_to_csv(model_name, model_version, folder_name, files, writer, actual_label, predicted_label, prediction_ts, random_prediction=False):\n",
        "    file_count = len(files)\n",
        "    chunks = (file_count - 1)\n",
        "    # actual_label = class_dict[actual_label]\n",
        "    # predicted_label = class_dict[predicted_label]\n",
        "    classes = ['angular_leaf_spot', 'bean_rust', 'healthy']\n",
        "    for i in range(chunks):\n",
        "        embeddings = []\n",
        "        batch_files = files[i*batch_size:(i+1)*batch_size]\n",
        "        if len(batch_files):\n",
        "            embeddings = ibed.to_embeddings(batch_files)\n",
        "            # Iterate directory\n",
        "            for index in range(len(batch_files)):\n",
        "                data = []\n",
        "                data.append(model_name)\n",
        "                data.append(model_version)\n",
        "                data.append(os.path.basename(batch_files[index]))\n",
        "                data.append(BASE_URL + folder_name + '/' + actual_label + '/' + os.path.basename(batch_files[index]) + '?raw=1')\n",
        "                data.append(actual_label)\n",
        "                [predicted_class, score] = get_predicted_class_and_score(batch_files[index])\n",
        "                predicted_label = predicted_class\n",
        "\n",
        "                if random_prediction:\n",
        "                    predicted_label = random.choice(classes)\n",
        "\n",
        "                data.append(predicted_label)\n",
        "                data.append(score)\n",
        "                data.append(prediction_ts)\n",
        "                data.append(embeddings[index])\n",
        "\n",
        "                embeddings_for_all_images.append(embeddings[index])\n",
        "                labels_for_all_images.append(predicted_label)\n",
        "\n",
        "                # write the data\n",
        "                writer.writerow(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create embeddings for train dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2022-10-23 14:21:45.703949\n"
          ]
        }
      ],
      "source": [
        "# now_ts = datetime.timestamp(datetime.now())\n",
        "two_weeks_ago = datetime.now() + timedelta(days=-14)\n",
        "print(two_weeks_ago)\n",
        "two_weeks_ago_ts = datetime.timestamp(two_weeks_ago)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "train_file_path = os.path.join(embeddings_folder_path, 'beanleaf_train.csv')\n",
        "\n",
        "train_dataset_folder = os.path.join(beandataset_folder, beanleaf_data_partition_folders[1])\n",
        "train_dataset_classes_folder = os.listdir(train_dataset_folder)\n",
        "train_dataset_classes_folder = [i for i in train_dataset_classes_folder if i not in files_to_remove]\n",
        "\n",
        "for train_class in train_dataset_classes_folder:\n",
        "    fullpath_with_class = os.path.join(train_dataset_folder, train_class)\n",
        "    train_subfolders, train_files = run_scandir_with_exclusion(fullpath_with_class, files_to_remove)\n",
        "\n",
        "    with open(train_file_path, 'w', encoding='UTF8', newline='') as f:\n",
        "        writer = csv.writer(f)\n",
        "        # write the header\n",
        "        writer.writerow(header)\n",
        "        write_to_csv(arize_model_config['MODEL_ID'], arize_model_config['MODEL_VERSION'], 'beanleaf_dataset/' + beanleaf_data_partition_folders[1], train_files, writer, train_class, train_class, two_weeks_ago_ts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create embeddings for validation dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "validation_file_path = os.path.join(embeddings_folder_path, 'beanleaf_validation.csv')\n",
        "\n",
        "validation_dataset_folder = os.path.join(beandataset_folder, beanleaf_data_partition_folders[2])\n",
        "validation_dataset_classes_folder = os.listdir(validation_dataset_folder)\n",
        "validation_dataset_classes_folder = [i for i in validation_dataset_classes_folder if i not in files_to_remove]\n",
        "\n",
        "for validation_class in validation_dataset_classes_folder:\n",
        "    fullpath_with_class = os.path.join(validation_dataset_folder, validation_class)\n",
        "    validation_subfolders, validation_files = run_scandir_with_exclusion(fullpath_with_class, files_to_remove)\n",
        "\n",
        "    with open(validation_file_path, 'w', encoding='UTF8', newline='') as f:\n",
        "        writer = csv.writer(f)\n",
        "        # write the header\n",
        "        writer.writerow(header)\n",
        "        write_to_csv(arize_model_config['MODEL_ID'], arize_model_config['MODEL_VERSION'], 'beanleaf_dataset/' + beanleaf_data_partition_folders[2], validation_files, writer, validation_class, validation_class, two_weeks_ago_ts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create embeddings for test dataset\n",
        "Use test csv file (beanleaf_production.csv) as production"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "one_day_ago = datetime.now() + timedelta(days=-1)\n",
        "one_day_ago_ts = datetime.timestamp(one_day_ago)\n",
        "\n",
        "leaf_production_file_path = os.path.join(embeddings_folder_path, 'beanleaf_production.csv')\n",
        "\n",
        "test_dataset_folder = os.path.join(beandataset_folder, beanleaf_data_partition_folders[0])\n",
        "test_dataset_classes_folder = os.listdir(test_dataset_folder)\n",
        "test_dataset_classes_folder = [i for i in test_dataset_classes_folder if i not in files_to_remove]\n",
        "\n",
        "for test_class in test_dataset_classes_folder:\n",
        "    fullpath_with_class = os.path.join(test_dataset_folder, test_class)\n",
        "    test_subfolders, test_files = run_scandir_with_exclusion(fullpath_with_class, files_to_remove)\n",
        "    \n",
        "    with open(leaf_production_file_path, 'w', encoding='UTF8', newline='') as f:\n",
        "        writer = csv.writer(f)\n",
        "        # write the header\n",
        "        writer.writerow(header)\n",
        "        write_to_csv(arize_model_config['MODEL_ID'], arize_model_config['MODEL_VERSION'], 'beanleaf_dataset/' + beanleaf_data_partition_folders[0], test_files, writer, test_class, test_class, one_day_ago_ts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create embedding for cat & dog"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 192ms/step\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n"
          ]
        }
      ],
      "source": [
        "now = datetime.now()\n",
        "now_ts = datetime.timestamp(now)\n",
        "\n",
        "catdog_production_file_path = os.path.join(embeddings_folder_path, 'catdog_production.csv')\n",
        "catdog_folder = os.path.join(root_folder, 'data', 'catsanddogs')\n",
        "\n",
        "catdog_dataset_classes_folder = os.listdir(catdog_folder)\n",
        "catdog_dataset_classes_folder = [i for i in catdog_dataset_classes_folder if i not in files_to_remove]\n",
        "\n",
        "for animal_class in catdog_dataset_classes_folder:\n",
        "    fullpath_with_class = os.path.join(catdog_folder, animal_class)\n",
        "    animals_subfolders, animals_files = run_scandir_with_exclusion(fullpath_with_class, files_to_remove)\n",
        "\n",
        "    with open(catdog_production_file_path, 'w', encoding='UTF8', newline='') as f:\n",
        "        writer = csv.writer(f)\n",
        "        # write the header\n",
        "        writer.writerow(header)\n",
        "        write_to_csv(arize_model_config['MODEL_ID'], '1.1', 'catsanddogs', animals_files, writer, animal_class, animal_class, now_ts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### UMAP unsupervised clustering\n",
        "umap is a tool that can cluster embeddings (better/more efficient than the popular tSNE) and then visualize the results.\n",
        "Using cosine similarity works best here."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/Users/jaganlalthoppe/workspace/mlops/azure/beanleaf-disease-classifier/0_data/catsanddogs\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 64/64 [00:02<00:00, 26.38it/s]\n"
          ]
        },
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mnotebook controller is DISPOSED. \n",
            "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
          ]
        },
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mnotebook controller is DISPOSED. \n",
            "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
          ]
        }
      ],
      "source": [
        "catdog_folder = os.path.join(root_folder, 'data', 'catsanddogs')\n",
        "print(catdog_folder)\n",
        "\n",
        "catdog_dataset_classes_folder = os.listdir(catdog_folder)\n",
        "catdog_dataset_classes_folder = [i for i in catdog_dataset_classes_folder if i not in files_to_remove]\n",
        "\n",
        "for animal_class in catdog_dataset_classes_folder:\n",
        "    fullpath_with_class = os.path.join(catdog_folder, animal_class)\n",
        "    train_subfolders, animals_files = run_scandir_with_exclusion(fullpath_with_class, files_to_remove)\n",
        "\n",
        "    file_count = len(animals_files)\n",
        "    chunks = (file_count - 1)\n",
        "    for i in range(chunks):\n",
        "        embeddings = []\n",
        "        batch_files = animals_files[i*batch_size:(i+1)*batch_size]\n",
        "        if len(batch_files):\n",
        "            embeddings = ibed.to_embeddings(batch_files)\n",
        "            # Iterate directory\n",
        "            for index in range(len(batch_files)):\n",
        "                embeddings_for_all_images.append(embeddings[index])\n",
        "                labels_for_all_images.append('animal')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mnotebook controller is DISPOSED. \n",
            "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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        },
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mnotebook controller is DISPOSED. \n",
            "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
          ]
        }
      ],
      "source": [
        "umap_beanleaf = umap.UMAP(\n",
        "    n_neighbors=3, n_components=2, metric=\"cosine\", random_state=42\n",
        ").fit(embeddings_for_all_images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mnotebook controller is DISPOSED. \n",
            "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
          ]
        },
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mnotebook controller is DISPOSED. \n",
            "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
          ]
        }
      ],
      "source": [
        "umap.plot.points(umap_beanleaf, labels=np.array(labels_for_all_images))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "authorship_tag": "ABX9TyN3DDB2KsCqw+9BU+ATcfCD",
      "include_colab_link": true,
      "name": "Leaf Disease Classification - Computer Vision.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.9.7 ('env': venv)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "vscode": {
      "interpreter": {
        "hash": "1a4d301a8b84987ac195ee0e9454eae9b3b9e25737823c7cd48634e53209c667"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}