image_detection_demo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fabiansalamo/jupyter-lab-examples/blob/main/image_detection_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b739c0e2",
      "metadata": {
        "id": "b739c0e2"
      },
      "source": [
        "# Image Detection Demo with PytorchWildlife"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSRf06iAeHI-"
      },
      "source": [
        "This tutorial guides you on how to use PyTorchWildlife for image detection. We will go through the process of setting up the environment, defining the detection model, as well as performing inference and saving the results in different ways.\n",
        "\n",
        "## Prerequisites\n",
        "Install PytorchWildlife running the following commands\n",
        "\n",
        "Also, make sure you have a CUDA-capable GPU if you intend to run the model on a GPU. In Google Colab, click on Runtime in the menu bar, select Change runtime type, choose GPU under Hardware accelerator, and then click Save.\n",
        "\n",
        "This notebook can also run on CPU.\n",
        "\n"
      ],
      "id": "kSRf06iAeHI-"
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get update -y\n",
        "!sudo apt-get install python3.8 python3.8-dev python3.8-distutils libpython3.8-dev\n",
        "\n",
        "#change alternatives\n",
        "!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1\n",
        "!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 2\n",
        "\n",
        "#Check that it points at the right location\n",
        "!python3 --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q21pbO9z71ma",
        "outputId": "217f0462-c7a8-4558-8a0c-fd55e633613d"
      },
      "id": "Q21pbO9z71ma",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Get:3 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy/main amd64 libpython3.8-minimal amd64 3.8.18-1+jammy1 [794 kB]\n",
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            "debconf: unable to initialize frontend: Dialog\n",
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            "Python 3.8.18\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# install pip\n",
        "!curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py\n",
        "!python3 get-pip.py --force-reinstall\n",
        "\n",
        "#install colab's dependencies\n",
        "!python3 -m pip install ipython ipython_genutils ipykernel jupyter_console prompt_toolkit httplib2 astor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwuSn1x28Ser",
        "outputId": "ba8d3e19-dc83-47e7-add3-802dc188e2da"
      },
      "id": "JwuSn1x28Ser",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: wcwidth, pure-eval, ptyprocess, pickleshare, ipython_genutils, backcall, zipp, typing-extensions, traitlets, tornado, pyzmq, python-dateutil, pyparsing, pygments, psutil, prompt_toolkit, platformdirs, pexpect, parso, packaging, nest-asyncio, executing, decorator, debugpy, asttokens, astor, stack-data, matplotlib-inline, jupyter-core, jedi, importlib-metadata, httplib2, comm, jupyter-client, ipython, ipykernel, jupyter_console\n",
            "Successfully installed astor-0.8.1 asttokens-2.4.1 backcall-0.2.0 comm-0.2.1 debugpy-1.8.0 decorator-5.1.1 executing-2.0.1 httplib2-0.22.0 importlib-metadata-7.0.1 ipykernel-6.28.0 ipython-8.12.3 ipython_genutils-0.2.0 jedi-0.19.1 jupyter-client-8.6.0 jupyter-core-5.7.1 jupyter_console-6.6.3 matplotlib-inline-0.1.6 nest-asyncio-1.5.8 packaging-23.2 parso-0.8.3 pexpect-4.9.0 pickleshare-0.7.5 platformdirs-4.1.0 prompt_toolkit-3.0.43 psutil-5.9.7 ptyprocess-0.7.0 pure-eval-0.2.2 pygments-2.17.2 pyparsing-3.1.1 python-dateutil-2.8.2 pyzmq-25.1.2 stack-data-0.6.3 tornado-6.4 traitlets-5.14.1 typing-extensions-4.9.0 wcwidth-0.2.13 zipp-3.17.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# link to the old google package\n",
        "!ln -s /usr/local/lib/python3.10/dist-packages/google \\\n",
        "       /usr/local/lib/python3.8/dist-packages/google\n",
        "\n",
        "!sed -i \"s/from IPython.utils import traitlets as _traitlets/import traitlets as _traitlets/\" /usr/local/lib/python3.8/dist-packages/google/colab/*.py\n",
        "!sed -i \"s/from IPython.utils import traitlets/import traitlets/\" /usr/local/lib/python3.8/dist-packages/google/colab/*.py"
      ],
      "metadata": {
        "id": "7HZZCGqA8ZMe"
      },
      "id": "7HZZCGqA8ZMe",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Install PytorchWildlife\n",
        "!pip install pytorchwildlife"
      ],
      "metadata": {
        "id": "LdcriN_jp6B4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "73a544a9-9dd3-40cb-935f-d7203ec70031"
      },
      "id": "LdcriN_jp6B4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hCollecting torchvision==0.11.2 (from pytorchwildlife)\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from gradio==4.8.0->pytorchwildlife) (23.2)\n",
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            "\u001b[?25hBuilding wheels for collected packages: ultralytics-yolov5, ffmpy, fire\n",
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            "  Stored in directory: /root/.cache/pip/wheels/5b/eb/43/7295e71293b218ddfd627f935229bf54af9018add7fbb5aac6\n",
            "Successfully built ultralytics-yolov5 ffmpy fire\n",
            "Installing collected packages: pytz, pydub, ffmpy, websockets, urllib3, tzdata, tqdm, torch, toolz, tomlkit, termcolor, tensorboard-data-server, sniffio, shellingham, semantic-version, rpds-py, pyyaml, python-multipart, pydantic-core, pyasn1, protobuf, pkgutil-resolve-name, Pillow, orjson, oauthlib, numpy, mdurl, markupsafe, kiwisolver, importlib-resources, idna, h11, grpcio, fsspec, fonttools, filelock, exceptiongroup, cycler, colorama, click, charset-normalizer, certifi, cachetools, attrs, annotated-types, aiofiles, absl-py, werkzeug, uvicorn, typer, torchvision, torchaudio, thop, scipy, rsa, requests, referencing, pydantic, pyasn1-modules, pandas, opencv-python-headless, opencv-python, markdown-it-py, markdown, jinja2, httpcore, fire, contourpy, anyio, starlette, rich, requests-oauthlib, matplotlib, jsonschema-specifications, huggingface-hub, httpx, google-auth, supervision, seaborn, jsonschema, gradio-client, google-auth-oauthlib, fastapi, tensorboard, altair, ultralytics-yolov5, gradio, pytorchwildlife\n",
            "Successfully installed Pillow-10.1.0 absl-py-2.0.0 aiofiles-23.2.1 altair-5.2.0 annotated-types-0.6.0 anyio-4.2.0 attrs-23.2.0 cachetools-5.3.2 certifi-2023.11.17 charset-normalizer-3.3.2 click-8.1.7 colorama-0.4.6 contourpy-1.1.1 cycler-0.12.1 exceptiongroup-1.2.0 fastapi-0.109.0 ffmpy-0.3.1 filelock-3.13.1 fire-0.5.0 fonttools-4.47.2 fsspec-2023.12.2 google-auth-2.26.2 google-auth-oauthlib-1.0.0 gradio-4.8.0 gradio-client-0.7.1 grpcio-1.60.0 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 huggingface-hub-0.20.2 idna-3.6 importlib-resources-6.1.1 jinja2-3.1.3 jsonschema-4.20.0 jsonschema-specifications-2023.12.1 kiwisolver-1.4.5 markdown-3.5.2 markdown-it-py-3.0.0 markupsafe-2.1.3 matplotlib-3.7.4 mdurl-0.1.2 numpy-1.24.4 oauthlib-3.2.2 opencv-python-4.9.0.80 opencv-python-headless-4.9.0.80 orjson-3.9.10 pandas-2.0.3 pkgutil-resolve-name-1.3.10 protobuf-3.20.1 pyasn1-0.5.1 pyasn1-modules-0.3.0 pydantic-2.5.3 pydantic-core-2.14.6 pydub-0.25.1 python-multipart-0.0.6 pytorchwildlife-1.0.1.1 pytz-2023.3.post1 pyyaml-6.0.1 referencing-0.32.1 requests-2.31.0 requests-oauthlib-1.3.1 rich-13.7.0 rpds-py-0.16.2 rsa-4.9 scipy-1.10.1 seaborn-0.13.1 semantic-version-2.10.0 shellingham-1.5.4 sniffio-1.3.0 starlette-0.35.1 supervision-0.16.0 tensorboard-2.14.0 tensorboard-data-server-0.7.2 termcolor-2.4.0 thop-0.1.1.post2209072238 tomlkit-0.12.0 toolz-0.12.0 torch-1.10.1 torchaudio-0.10.1 torchvision-0.11.2 tqdm-4.66.1 typer-0.9.0 tzdata-2023.4 ultralytics-yolov5-0.1.1 urllib3-2.1.0 uvicorn-0.25.0 websockets-11.0.3 werkzeug-3.0.1\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "certifi",
                  "cycler",
                  "kiwisolver",
                  "tqdm"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1197e180",
      "metadata": {
        "id": "1197e180"
      },
      "source": [
        "Now, you must go to Runtime and restart your session to continue.\n",
        "\n",
        "## Importing libraries\n",
        "First, we'll start by importing the necessary libraries and modules."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c44e7713",
      "metadata": {
        "id": "c44e7713",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ace3ca7-2286-4463-c7f3-5a6b4a277230"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/tqdm/auto.py:21: 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 numpy as np\n",
        "from PIL import Image\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from PytorchWildlife.models import detection as pw_detection\n",
        "from PytorchWildlife.data import transforms as pw_trans\n",
        "from PytorchWildlife.data import datasets as pw_data\n",
        "from PytorchWildlife import utils as pw_utils"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a23982f",
      "metadata": {
        "id": "3a23982f"
      },
      "source": [
        "## Setting GPU\n",
        "If you are using a GPU for this exercise, please specify which GPU to use for the computations. By default, GPU number 0 is used. Adjust this as per your setup. You don't need to run this cell if you are using a CPU."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8f622040",
      "metadata": {
        "id": "8f622040"
      },
      "outputs": [],
      "source": [
        "torch.cuda.set_device(0) # Only use if you are running on GPU."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6abd07b5",
      "metadata": {
        "id": "6abd07b5"
      },
      "source": [
        "## Model Initialization\n",
        "We will initialize the MegaDetectorV5 model for image detection. This model is designed for detecting animals in images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eb25db43",
      "metadata": {
        "id": "eb25db43",
        "outputId": "da2e04b2-5ccf-4ef7-a93c-e30bc42756d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://zenodo.org/records/10023414/files/MegaDetector_v5b.0.0.pt?download=1\" to /root/.cache/torch/hub/checkpoints/MegaDetector_v5b.0.0.pt\n",
            "100%|██████████| 268M/268M [00:13<00:00, 21.4MB/s]\n",
            "INFO:yolov5:Fusing layers... \n",
            "Fusing layers... \n",
            "Fusing layers... \n",
            "INFO:yolov5:Model summary: 733 layers, 140054656 parameters, 0 gradients, 208.8 GFLOPs\n",
            "Model summary: 733 layers, 140054656 parameters, 0 gradients, 208.8 GFLOPs\n",
            "Model summary: 733 layers, 140054656 parameters, 0 gradients, 208.8 GFLOPs\n"
          ]
        }
      ],
      "source": [
        "DEVICE = \"cuda\"  # Use \"cuda\" if GPU is available \"cpu\" if no GPU is available\n",
        "detection_model = pw_detection.MegaDetectorV5(device=DEVICE, pretrained=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1e57dcca",
      "metadata": {
        "id": "1e57dcca"
      },
      "source": [
        "## Single Image Detection\n",
        "We will first perform detection on a single image. Make sure to verify that you have the image in the specified path."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -O repo.zip https://github.com/microsoft/CameraTraps/archive/refs/heads/main.zip\n",
        "!unzip -q repo.zip -d ./"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uuq_CVHmu9p",
        "outputId": "a091690e-b4c4-4f0a-ec49-4df21bd364da"
      },
      "id": "8uuq_CVHmu9p",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://codeload.github.com/microsoft/CameraTraps/zip/refs/heads/main [following]\n",
            "--2024-01-12 17:43:51--  https://codeload.github.com/microsoft/CameraTraps/zip/refs/heads/main\n",
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            "Length: 160497204 (153M) [application/zip]\n",
            "Saving to: ‘repo.zip’\n",
            "\n",
            "repo.zip            100%[===================>] 153.06M   101MB/s    in 1.5s    \n",
            "\n",
            "2024-01-12 17:43:53 (101 MB/s) - ‘repo.zip’ saved [160497204/160497204]\n",
            "\n",
            "replace ./CameraTraps-main/.gitignore? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0d730b20",
      "metadata": {
        "id": "0d730b20"
      },
      "outputs": [],
      "source": [
        "tgt_img_path = \"/content/CameraTraps-main/demo/demo_data/imgs/10050028_0.JPG\"\n",
        "img = np.array(Image.open(tgt_img_path).convert(\"RGB\"))\n",
        "transform = pw_trans.MegaDetector_v5_Transform(target_size=detection_model.IMAGE_SIZE,\n",
        "                                               stride=detection_model.STRIDE)\n",
        "results = detection_model.single_image_detection(transform(img), img.shape, temp_file_path)\n",
        "pw_utils.save_detection_images(results, \"./demo_output\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2e23329c",
      "metadata": {
        "id": "2e23329c"
      },
      "source": [
        "## Batch Image Detection\n",
        "Next, we'll demonstrate how to process multiple images in batches. This is useful when you have a large number of images and want to process them efficiently."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "561eff0c",
      "metadata": {
        "id": "561eff0c",
        "outputId": "c6f8f809-e0d1-4131-8330-66270593f239",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:478: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 1/1 [00:02<00:00,  2.95s/it]\n"
          ]
        }
      ],
      "source": [
        "tgt_folder_path = \"/content/CameraTraps-main/demo/demo_data/imgs\"\n",
        "dataset = pw_data.DetectionImageFolder(\n",
        "    tgt_folder_path,\n",
        "    transform=pw_trans.MegaDetector_v5_Transform(target_size=detection_model.IMAGE_SIZE,\n",
        "                                                 stride=detection_model.STRIDE)\n",
        ")\n",
        "loader = DataLoader(dataset, batch_size=32, shuffle=False,\n",
        "                    pin_memory=True, num_workers=8, drop_last=False)\n",
        "results = detection_model.batch_image_detection(loader)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bb41830b",
      "metadata": {
        "id": "bb41830b"
      },
      "source": [
        "## Output Results\n",
        "PytorchWildlife allows to output detection results in multiple formats. Here are the examples:\n",
        "\n",
        "### 1. Annotated Images:\n",
        "This will output the images with bounding boxes drawn around the detected animals. The images will be saved in the specified output directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f63310ab",
      "metadata": {
        "id": "f63310ab"
      },
      "outputs": [],
      "source": [
        "pw_utils.save_detection_images(results, \"batch_output\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bbd016ae",
      "metadata": {
        "id": "bbd016ae"
      },
      "source": [
        "### 2. Cropped Images:\n",
        "This will output the cropped images of the detected animals. The cropping is done around the detection bounding box, The images will be saved in the specified output directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "13653739",
      "metadata": {
        "id": "13653739"
      },
      "outputs": [],
      "source": [
        "pw_utils.save_crop_images(results, \"crop_output\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "49be4063",
      "metadata": {
        "id": "49be4063"
      },
      "source": [
        "### 3. JSON Format:\n",
        "This will output the detection results in JSON format. The JSON file will be saved in the specified output directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b627280b",
      "metadata": {
        "id": "b627280b"
      },
      "outputs": [],
      "source": [
        "pw_utils.save_detection_json(results, \"./batch_output.json\",\n",
        "                             categories=detection_model.CLASS_NAMES)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a4ee1d7b",
      "metadata": {
        "id": "a4ee1d7b"
      },
      "source": [
        "### Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "### Licensed under the MIT License."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
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        "version": 3
      },
      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.18"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}