examples/face_dect.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8xHff6nf0WUr"
      },
      "source": [
        "# Face detection and recognition inference pipeline\n",
        "\n",
        "The following example illustrates how to use the `facenet_pytorch` python package to perform face detection and recogition on an image dataset using an Inception Resnet V1 pretrained on the VGGFace2 dataset.\n",
        "\n",
        "The following Pytorch methods are included:\n",
        "* Datasets\n",
        "* Dataloaders\n",
        "* GPU/CPU processing"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!apt-get install python3.12\n",
        "!pip install git+https://github.com/GitDataAI/jiaozifs_client_py.git\n",
        "!pip install torchvision\n",
        "!pip install Pillow\n",
        "!pip install numpy\n",
        "!pip install facenet_pytorch\n",
        "!pip install git+https://github.com/GitDataAI/jz_dataloader.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgsA56KD2wzv",
        "outputId": "85506cb4-9ca5-4cff-c601-02021deede8b"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Running command git clone --filter=blob:none --quiet https://github.com/GitDataAI/jiaozifs_client_py.git /tmp/pip-req-build-k4i293l2\n",
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            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch==2.2.1->torchvision->facenet_pytorch) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch==2.2.1->torchvision->facenet_pytorch) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch==2.2.1->torchvision->facenet_pytorch) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch==2.2.1->torchvision->facenet_pytorch) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch==2.2.1->torchvision->facenet_pytorch) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch==2.2.1->torchvision->facenet_pytorch) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.2.1->torchvision->facenet_pytorch) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.1->torchvision->facenet_pytorch) (12.4.99)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.2.1->torchvision->facenet_pytorch) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.2.1->torchvision->facenet_pytorch) (1.3.0)\n",
            "Installing collected packages: facenet_pytorch\n",
            "Successfully installed facenet_pytorch-2.5.3\n",
            "Collecting git+https://github.com/GitDataAI/jz_dataloader.git\n",
            "  Cloning https://github.com/GitDataAI/jz_dataloader.git to /tmp/pip-req-build-c1hx3mat\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/GitDataAI/jz_dataloader.git /tmp/pip-req-build-c1hx3mat\n",
            "  Resolved https://github.com/GitDataAI/jz_dataloader.git to commit be92f4a1604f954a623cba3a383b2cea95f6df73\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: jz-dataloader\n",
            "  Building wheel for jz-dataloader (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for jz-dataloader: filename=jz_dataloader-0.0.1-py3-none-any.whl size=9922 sha256=2042752f785714c45301b09762455d812259319157ce9cde7203f252eca726be\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-n9ixmfi8/wheels/76/85/48/7a6bb9b975ea0d8556f99cb0d2dbf04f3411d4cf40631522a1\n",
            "Successfully built jz-dataloader\n",
            "Installing collected packages: jz-dataloader\n",
            "Successfully installed jz-dataloader-0.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "4kIYz5w70WUs"
      },
      "outputs": [],
      "source": [
        "from facenet_pytorch import MTCNN, InceptionResnetV1\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "workers = 0 if os.name == 'nt' else 4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YpOroE8D0WUs"
      },
      "source": [
        "#### Determine if an nvidia GPU is available"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "TJwR6sMz0WUs",
        "outputId": "b90dfc71-b7c5-4c8e-8a34-d4c828b85314",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running on device: cpu\n"
          ]
        }
      ],
      "source": [
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "print('Running on device: {}'.format(device))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fiVYj6eO0WUt"
      },
      "source": [
        "#### Define MTCNN module\n",
        "\n",
        "Default params shown for illustration, but not needed. Note that, since MTCNN is a collection of neural nets and other code, the device must be passed in the following way to enable copying of objects when needed internally.\n",
        "\n",
        "See `help(MTCNN)` for more details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "xtHheWw-0WUt"
      },
      "outputs": [],
      "source": [
        "mtcnn = MTCNN(\n",
        "    image_size=160, margin=0, min_face_size=20,\n",
        "    thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,\n",
        "    device=device\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JdY9Zjo_0WUt"
      },
      "source": [
        "#### Define Inception Resnet V1 module\n",
        "\n",
        "Set classify=True for pretrained classifier. For this example, we will use the model to output embeddings/CNN features. Note that for inference, it is important to set the model to `eval` mode.\n",
        "\n",
        "See `help(InceptionResnetV1)` for more details."
      ]
    },
    {
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      "execution_count": 5,
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      "source": [
        "resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dsWsfSax0WUt"
      },
      "source": [
        "#### Define a dataset and data loader using Jiaozifs\n",
        "\n",
        "We add the `idx_to_class` attribute to the dataset to enable easy recoding of label indices to identity names later one."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "OOgkQoaF0WUt"
      },
      "outputs": [],
      "source": [
        "from jz_dataloader import ImageDataset, JiaozifsDataset\n",
        "import numpy as np\n",
        "\n",
        "def collate_fn(x):\n",
        "    return x[0]\n",
        "\n",
        "dataset = ImageDataset(\n",
        "    \"jimmy\",\n",
        "    \"facenet-pytorch-testdata\",\n",
        "    \"4761869ae66f6522ce8734f5b5bba9d0\",\n",
        "    \"123677af2bcb8b5155dab662db21e1fb\",\n",
        "    type=\"wip\",\n",
        "    path=\"test_images\",\n",
        "    refName=\"main\",\n",
        ")\n",
        "dataset.idx_to_class = {i:c for c, i in dataset.class_to_idx.items()}\n",
        "loader = DataLoader(dataset, collate_fn=collate_fn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v1kDwTuq0WUt"
      },
      "source": [
        "#### Perfom MTCNN facial detection\n",
        "\n",
        "Iterate through the DataLoader object and detect faces and associated detection probabilities for each. The `MTCNN` forward method returns images cropped to the detected face, if a face was detected. By default only a single detected face is returned - to have `MTCNN` return all detected faces, set `keep_all=True` when creating the MTCNN object above.\n",
        "\n",
        "To obtain bounding boxes rather than cropped face images, you can instead call the lower-level `mtcnn.detect()` function. See `help(mtcnn.detect)` for details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "iyIY0BLG0WUt",
        "outputId": "1b2c354a-1a00-45d4-8253-b7a15d9992b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "Face detected with probability: 0.999983\n",
            "1\n",
            "Face detected with probability: 0.999934\n",
            "2\n",
            "Face detected with probability: 0.999733\n",
            "3\n",
            "Face detected with probability: 0.999876\n",
            "4\n",
            "Face detected with probability: 0.999992\n"
          ]
        }
      ],
      "source": [
        "aligned = []\n",
        "names = []\n",
        "for x, y in loader:\n",
        "  print(y)\n",
        "  x_aligned, prob = mtcnn(x, return_prob=True)\n",
        "  if x_aligned is not None:\n",
        "      print('Face detected with probability: {:8f}'.format(prob))\n",
        "      aligned.append(x_aligned)\n",
        "      names.append(dataset.idx_to_class[y])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Z-CPUrD0WUt"
      },
      "source": [
        "#### Calculate image embeddings\n",
        "\n",
        "MTCNN will return images of faces all the same size, enabling easy batch processing with the Resnet recognition module. Here, since we only have a few images, we build a single batch and perform inference on it.\n",
        "\n",
        "For real datasets, code should be modified to control batch sizes being passed to the Resnet, particularly if being processed on a GPU. For repeated testing, it is best to separate face detection (using MTCNN) from embedding or classification (using InceptionResnetV1), as calculation of cropped faces or bounding boxes can then be performed a single time and detected faces saved for future use."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "IbxI2kv80WUu"
      },
      "outputs": [],
      "source": [
        "aligned = torch.stack(aligned).to(device)\n",
        "embeddings = resnet(aligned).detach().cpu()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7gnAiMi0WUu"
      },
      "source": [
        "#### Print distance matrix for classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "G1FuTLWX0WUu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "839441b9-de9f-4a8a-d680-41bc93a67e22"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                angelina_jolie  bradley_cooper  kate_siegel  paul_rudd  \\\n",
            "angelina_jolie        0.000000        1.447480     0.887728   1.434377   \n",
            "bradley_cooper        1.447480        0.000000     1.313749   1.011980   \n",
            "kate_siegel           0.887728        1.313749     0.000000   1.388993   \n",
            "paul_rudd             1.434377        1.011980     1.388993   0.000000   \n",
            "shea_whigham          1.399073        1.038684     1.379655   1.104095   \n",
            "\n",
            "                shea_whigham  \n",
            "angelina_jolie      1.399073  \n",
            "bradley_cooper      1.038684  \n",
            "kate_siegel         1.379655  \n",
            "paul_rudd           1.104095  \n",
            "shea_whigham        0.000000  \n"
          ]
        }
      ],
      "source": [
        "dists = [[(e1 - e2).norm().item() for e2 in embeddings] for e1 in embeddings]\n",
        "print(pd.DataFrame(dists, columns=names, index=names))"
      ]
    }
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