animal_model_with_interface.ipynb

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      "gpuType": "T4",
      "authorship_tag": "ABX9TyO5+vSDBV9dS8a5IbYLiM9Z",
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      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SahilCarterr/Dash-Cam-Video-Analyzer/blob/New-Code-Updates/animal_model_with_interface.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://dl.fbaipublicfiles.com/pyslowfast/dataset/class_names/kinetics_classnames.json\n",
        "!pip install pytorchvideo\n",
        "from typing import Dict\n",
        "import torch\n",
        "import json\n",
        "from torchvision.transforms import Compose, Lambda\n",
        "from torchvision.transforms._transforms_video import (\n",
        "    CenterCropVideo,\n",
        "    NormalizeVideo,\n",
        ")\n",
        "from pytorchvideo.data.encoded_video import EncodedVideo\n",
        "from pytorchvideo.transforms import (\n",
        "    ApplyTransformToKey,\n",
        "    ShortSideScale,\n",
        "    UniformTemporalSubsample,\n",
        "    UniformCropVideo\n",
        ")\n",
        "\n",
        "class VideoClassifier:\n",
        "    def __init__(self, device=\"cuda\"):\n",
        "        self.device = device\n",
        "        self.model_name = \"slowfast_r50\"\n",
        "        self.model = torch.hub.load(\"facebookresearch/pytorchvideo\", model=self.model_name, pretrained=True)\n",
        "        self.model = self.model.to(self.device)\n",
        "        self.model = self.model.eval()\n",
        "\n",
        "        with open(\"kinetics_classnames.json\", \"r\") as f:\n",
        "            self.kinetics_classnames = json.load(f)\n",
        "\n",
        "        self.kinetics_id_to_classname = {}\n",
        "        for k, v in self.kinetics_classnames.items():\n",
        "            self.kinetics_id_to_classname[v] = str(k).replace('\"', \"\")\n",
        "\n",
        "        self.transform = self._create_transform()\n",
        "\n",
        "    def _create_transform(self):\n",
        "        side_size = 256\n",
        "        mean = [0.45, 0.45, 0.45]\n",
        "        std = [0.225, 0.225, 0.225]\n",
        "        crop_size = 256\n",
        "        num_frames = 32\n",
        "        sampling_rate = 2\n",
        "        frames_per_second = 30\n",
        "        alpha = 4\n",
        "\n",
        "        class PackPathway(torch.nn.Module):\n",
        "            def __init__(self):\n",
        "                super().__init__()\n",
        "\n",
        "            def forward(self, frames: torch.Tensor):\n",
        "                fast_pathway = frames\n",
        "                slow_pathway = torch.index_select(\n",
        "                    frames,\n",
        "                    1,\n",
        "                    torch.linspace(\n",
        "                        0, frames.shape[1] - 1, frames.shape[1] // alpha\n",
        "                    ).long(),\n",
        "                )\n",
        "                frame_list = [slow_pathway, fast_pathway]\n",
        "                return frame_list\n",
        "\n",
        "        transform = ApplyTransformToKey(\n",
        "            key=\"video\",\n",
        "            transform=Compose(\n",
        "                [\n",
        "                    UniformTemporalSubsample(num_frames),\n",
        "                    Lambda(lambda x: x/255.0),\n",
        "                    NormalizeVideo(mean, std),\n",
        "                    ShortSideScale(size=side_size),\n",
        "                    CenterCropVideo(crop_size),\n",
        "                    PackPathway()\n",
        "                ]\n",
        "            ),\n",
        "        )\n",
        "        return transform\n",
        "\n",
        "    def classify_video(self, video_path: str) -> Dict[str, str]:\n",
        "        clip_duration = (32 * 2) / 30  # Assuming fixed values for duration\n",
        "\n",
        "        start_sec = 0\n",
        "        end_sec = start_sec + clip_duration\n",
        "\n",
        "        video = EncodedVideo.from_path(video_path)\n",
        "        video_data = video.get_clip(start_sec=start_sec, end_sec=end_sec)\n",
        "        video_data = self.transform(video_data)\n",
        "\n",
        "        inputs = video_data[\"video\"]\n",
        "        inputs = [i.to(self.device)[None, ...] for i in inputs]\n",
        "\n",
        "        preds = self.model(inputs)\n",
        "        post_act = torch.nn.Softmax(dim=1)\n",
        "        preds = post_act(preds)\n",
        "        pred_classes = preds.topk(k=100).indices\n",
        "\n",
        "        pred_class_names = [self.kinetics_id_to_classname[int(i)] for i in pred_classes[0]]\n",
        "\n",
        "        single_words_list = [word for sublist in [phrase.split() for phrase in pred_class_names] for word in sublist]\n",
        "\n",
        "        animals = [\n",
        "            \"antelope\", \"badger\", \"bear\", \"bison\", \"boar\", \"cat\", \"chimpanzee\", \"cow\",\n",
        "            \"coyote\", \"deer\", \"dog\", \"donkey\", \"elephant\", \"fox\", \"goat\", \"goose\",\n",
        "            \"gorilla\", \"hare\", \"hedgehog\", \"hippopotamus\", \"horse\", \"hyena\", \"kangaroo\",\n",
        "            \"koala\", \"leopard\", \"lion\", \"okapi\", \"orangutan\", \"otter\", \"ox\", \"panda\",\n",
        "            \"pig\", \"porcupine\", \"possum\", \"raccoon\", \"reindeer\", \"rhinoceros\", \"sheep\",\n",
        "            \"snake\", \"tiger\", \"wolf\", \"zebra\"\n",
        "        ]\n",
        "\n",
        "        found = any(word in animals for word in single_words_list)\n",
        "\n",
        "        return { \"Animal Found\" if found else \"Not found\"}\n",
        "\n",
        "# Example usage:\n",
        "classifier = VideoClassifier()\n",
        "#result = classifier.classify_video(\"car_racing1.mp4\")\n",
        "# print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "mj-1U-AESwzm",
        "outputId": "f0eeb674-812e-4455-cddd-f13bd325a773"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-02-25 17:09:10--  https://dl.fbaipublicfiles.com/pyslowfast/dataset/class_names/kinetics_classnames.json\n",
            "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 13.226.210.25, 13.226.210.78, 13.226.210.15, ...\n",
            "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|13.226.210.25|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10326 (10K) [text/plain]\n",
            "Saving to: ‘kinetics_classnames.json.1’\n",
            "\n",
            "\r          kinetics_   0%[                    ]       0  --.-KB/s               \rkinetics_classnames 100%[===================>]  10.08K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-02-25 17:09:10 (236 MB/s) - ‘kinetics_classnames.json.1’ saved [10326/10326]\n",
            "\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_main\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "!pip install gradio\n",
        "import gradio as gr\n",
        "\n",
        "\n",
        "# Instantiate your VideoClassifier\n",
        "classifier = VideoClassifier()\n",
        "\n",
        "# Define a function to classify the video\n",
        "def classify_video(video):\n",
        "    # Since video is a file object, you need to pass the video directly to the classifier\n",
        "    result = classifier.classify_video(video)\n",
        "    return result\n",
        "\n",
        "# Create Gradio interface\n",
        "iface = gr.Interface(\n",
        "    fn=classify_video,\n",
        "    inputs=\"video\",\n",
        "    outputs=\"text\",\n",
        "    title=\"Animal Detection Model\",\n",
        "    description=\"Upload a video here.\",\n",
        "    allow_flagging=False\n",
        ")\n",
        "\n",
        "# Launch the interface\n",
        "iface.launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RXi7uo2stWn8",
        "outputId": "5d2df0c9-c801-4deb-9c67-4640e9b5558f"
      },
      "execution_count": 4,
      "outputs": [
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        },
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            "Using cache found in /root/.cache/torch/hub/facebookresearch_pytorchvideo_main\n",
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            "  warnings.warn(\n"
          ]
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            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://444a865b35403d5ed2.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
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      "outputs": []
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      "metadata": {
        "id": "XogeETzZEUO5"
      },
      "execution_count": 4,
      "outputs": []
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}