test_predict.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DLbFxQTJrojY",
        "outputId": "bc9526ec-3d37-43e5-9a3b-59bde6067ae9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2024-07-07 05:33:50--  https://lodmedia.hb.bizmrg.com/case_files/1112984/test_dataset_test_data_Minprirodi.zip\n",
            "Resolving lodmedia.hb.bizmrg.com (lodmedia.hb.bizmrg.com)... 95.163.53.117\n",
            "Connecting to lodmedia.hb.bizmrg.com (lodmedia.hb.bizmrg.com)|95.163.53.117|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4442528628 (4.1G) [application/zip]\n",
            "Saving to: ‘test_dataset_test_data_Minprirodi.zip’\n",
            "\n",
            "test_dataset_test_d 100%[===================>]   4.14G  13.9MB/s    in 5m 27s  \n",
            "\n",
            "2024-07-07 05:39:20 (12.9 MB/s) - ‘test_dataset_test_data_Minprirodi.zip’ saved [4442528628/4442528628]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://lodmedia.hb.bizmrg.com/case_files/1112984/test_dataset_test_data_Minprirodi.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "edO170I2r-bL"
      },
      "outputs": [],
      "source": [
        "from IPython.display import clear_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rjmmf6O5sFGR"
      },
      "outputs": [],
      "source": [
        "!unzip -P FnmZz4qRHDfxE9GV25rXU8 test_dataset_test_data_Minprirodi.zip\n",
        "clear_output()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_lcIKtB6r8KJ",
        "outputId": "31b25275-3168-4a8e-f9e8-3ace0c5f7c23"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.2.50-py3-none-any.whl (799 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/799.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m409.6/799.4 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m799.4/799.4 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
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            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->ultralytics)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, ultralytics-thop, ultralytics\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 ultralytics-8.2.50 ultralytics-thop-2.0.0\n"
          ]
        }
      ],
      "source": [
        "!pip install ultralytics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jpBoNZXbryQ4"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import torch\n",
        "import pickle\n",
        "\n",
        "from torch import nn\n",
        "from torchvision import models\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from ultralytics import YOLO\n",
        "from torchvision.transforms.functional import normalize\n",
        "from itertools import repeat\n",
        "from pathlib import Path\n",
        "\n",
        "\n",
        "class Prediction(object):\n",
        "    def __init__(self, name, cls, prob, bbox):\n",
        "        super().__init__()\n",
        "        self.name = name\n",
        "        self.cls = cls\n",
        "        self.prob = prob\n",
        "        self.bbox = bbox\n",
        "\n",
        "    def __repr__(self):\n",
        "        return f\"Prediction({self.name}, {self.cls})\"\n",
        "\n",
        "\n",
        "class Model(object):\n",
        "    CLASSES = [\"Badger\", \"Bear\", \"Bison\", \"Cat\", \"Dog\",\n",
        "               \"Empty\", \"Fox\", \"Goral\", \"Hare\", \"Lynx\",\n",
        "               \"Marten\", \"Moose\", \"Mountain_Goat\",\n",
        "               \"Musk_Deer\", \"Racoon_Dog\", \"Red_Deer\",\n",
        "               \"Roe_Deer\", \"Snow_Leopard\", \"Squirrel\",\n",
        "               \"Tiger\", \"Wolf\", \"Wolverine\"]\n",
        "\n",
        "    def __init__(self, detector_path: str, classifier_path: str, le_path: str):\n",
        "        self.detector = YOLO(detector_path)\n",
        "        self.classifier = models.vgg19(pretrained=False)\n",
        "        # for param in self.classifier.parameters():\n",
        "        #     param.requires_grad = False\n",
        "        self.classifier.classifier = nn.Sequential(\n",
        "            nn.Linear(25088, 4096),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Dropout(0.5),\n",
        "            nn.Linear(4096, 4096),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Dropout(0.5),\n",
        "            nn.Linear(4096, 19)\n",
        "        )\n",
        "        self.classifier.load_state_dict(torch.load(classifier_path, map_location=torch.device('cpu')))\n",
        "        self.classifier.cuda()\n",
        "        self.classifier.eval()\n",
        "\n",
        "        with open(le_path, \"rb\") as file:\n",
        "            self.le: LabelEncoder = pickle.load(file)\n",
        "\n",
        "    def predict(self, frame):\n",
        "        if not isinstance(frame, (np.ndarray, str)):\n",
        "            frame = frame.filename\n",
        "        # print(frame)\n",
        "        detections = self.detector.predict(frame, verbose=False)\n",
        "        classes = []\n",
        "        croped_frames = self.extract_crops(detections)\n",
        "        for (img_name, batch_images_cls) in croped_frames.items():\n",
        "            logits = self.classifier(batch_images_cls.to(\"cuda\"))\n",
        "            probabilities = torch.nn.functional.softmax(logits, dim=1)\n",
        "            top_p, top_class_idx = probabilities.topk(1, dim=1)\n",
        "\n",
        "            top_p = top_p.cpu().detach().numpy().ravel()\n",
        "            top_class_idx = top_class_idx.cpu().numpy().ravel()\n",
        "            # print(top_class_idx)\n",
        "            class_names = self.le.inverse_transform(top_class_idx)\n",
        "\n",
        "            classes.extend(\n",
        "                [\n",
        "                    Prediction(name, cls, prob, bbox.xyxy)\n",
        "                    for name, cls, prob, bbox in\n",
        "                    zip(repeat(img_name, len(class_names)), class_names, top_p, detections[0].boxes)\n",
        "                ]\n",
        "            )\n",
        "\n",
        "        return classes\n",
        "\n",
        "    @staticmethod\n",
        "    def extract_crops(results: list) -> dict[str, torch.Tensor]:\n",
        "        dict_crops = {}\n",
        "        for res_per_img in results:\n",
        "            if len(res_per_img) > 0:\n",
        "                crops_per_img = []\n",
        "                for box in res_per_img.boxes:\n",
        "                    x0, y0, x1, y1 = box.xyxy.cpu().numpy().ravel().astype(np.int32)\n",
        "                    crop = res_per_img.orig_img[y0: y1, x0: x1]\n",
        "\n",
        "                    # Do squared crop\n",
        "                    # crop = letterbox(img=crop, new_shape=config.imgsz, color=(0, 0, 0))\n",
        "                    crop = cv2.resize(crop, (360, 360), interpolation=cv2.INTER_LINEAR)\n",
        "                    crop = cv2.cvtColor(crop, cv2.COLOR_RGB2BGR)\n",
        "                    # cv2.imshow('crop', crop)\n",
        "                    # cv2.waitKey(111111110)\n",
        "                    # Convert Array crop to Torch tensor with [batch, channels, height, width] dimensions\n",
        "                    crop = torch.from_numpy(crop.transpose(2, 0, 1)) / 255\n",
        "                    crop = crop.unsqueeze(0)\n",
        "                    crop = normalize(crop.float(), mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "                    crops_per_img.append(crop)\n",
        "\n",
        "                dict_crops[Path(res_per_img.path).name] = torch.cat(crops_per_img)  # if len(crops_per_img) else None\n",
        "        return dict_crops\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "joeMz_xBtrxr"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bsPO-2ohr289"
      },
      "outputs": [],
      "source": [
        "from collections import defaultdict\n",
        "from copy import deepcopy\n",
        "from datetime import datetime\n",
        "from PIL import Image\n",
        "from PIL.ExifTags import TAGS\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "\n",
        "class AnimalImage(object):\n",
        "    def __init__(self, filename: str, *args, **kwargs):\n",
        "        super().__init__(*args, **kwargs)\n",
        "        self.filename = filename\n",
        "        self.datetime = \"\"\n",
        "\n",
        "        image = Image.open(self.filename)\n",
        "        exif_data = image._getexif()\n",
        "        if exif_data:\n",
        "            for tag_id, value in exif_data.items():\n",
        "                tag = TAGS.get(tag_id, tag_id)\n",
        "                if tag == \"DateTime\":\n",
        "                    self.datetime = value\n",
        "                    break\n",
        "        if self.datetime != \"\":\n",
        "            self.datetime = datetime.strptime(self.datetime, \"%Y:%m:%d %H:%M:%S\")\n",
        "        self.objects = []\n",
        "\n",
        "    def __repr__(self):\n",
        "        return f\"AnimalImage({self.datetime})\"\n",
        "\n",
        "\n",
        "class FramesTracker(Model):\n",
        "    def __init__(self, path_to_folder, detector_path: str, classifier_path: str, le_path: str, files):\n",
        "        super().__init__(detector_path, classifier_path, le_path)\n",
        "        self.path_to_folder = path_to_folder\n",
        "        self.files = list(map(AnimalImage, files))\n",
        "        self.current_animals = {}\n",
        "        self.all_animals = []\n",
        "\n",
        "    def sort(self, *, reverse=False):\n",
        "        self.files.sort(key=lambda x: x.datetime, reverse=reverse)\n",
        "\n",
        "\n",
        "def predict(folder_path, files):\n",
        "    tracker = FramesTracker(\n",
        "        folder_path + \"/*.JPG\",\n",
        "        \"./drive/MyDrive/weights/yolov8n-2.pt\",\n",
        "        \"./drive/MyDrive/weights/last_animal_model-6.pt\",\n",
        "        \"./drive/MyDrive/weights/le.bf\",\n",
        "        files\n",
        "    )\n",
        "    tracker.sort()\n",
        "\n",
        "    for i in tqdm(tracker.files):\n",
        "        i.objects = tracker.predict(i)\n",
        "\n",
        "    for i in range(len(tracker.files)):\n",
        "        time_has_passed = defaultdict(lambda: 0)\n",
        "        if i != 0:\n",
        "            for j in tracker.files[i].objects:\n",
        "                if j.cls in tracker.current_animals:\n",
        "                    time_has_passed[j.cls] = (\n",
        "                            tracker.files[i].datetime - tracker.current_animals[j.cls][\"last_seen\"]\n",
        "                    ).seconds\n",
        "        for j in tracker.files[i].objects:\n",
        "            if time_has_passed[j.cls] > 30 * 60:\n",
        "                tracker.all_animals.append(deepcopy(tracker.current_animals[j.cls]))\n",
        "                del tracker.current_animals[j.cls]\n",
        "            if j.cls != \"Empty\":\n",
        "                if j.cls not in tracker.current_animals:\n",
        "                    tracker.current_animals[j.cls] = {\n",
        "                        \"first_seen\": tracker.files[i].datetime,\n",
        "                        \"last_seen\": tracker.files[i].datetime,\n",
        "                        \"count\": 0,\n",
        "                        \"cls\": j.cls,\n",
        "                        \"filename\": tracker.files[i].filename\n",
        "                    }\n",
        "                tracker.current_animals[j.cls][\"count\"] = max(\n",
        "                    tracker.current_animals[j.cls][\"count\"],\n",
        "                    len([1 for animal in tracker.files[i].objects if animal.cls == j.cls])\n",
        "                )\n",
        "                tracker.current_animals[j.cls][\"last_seen\"] = tracker.files[i].datetime\n",
        "\n",
        "    for i in tracker.current_animals:\n",
        "        tracker.all_animals.append(deepcopy(tracker.current_animals[i]))\n",
        "    # print(tracker.all_animals)\n",
        "    return tracker.all_animals\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N3QG8IZss-T_"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from glob import glob"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G6xPnV7RuFkd"
      },
      "outputs": [],
      "source": [
        "full_dataset = pd.DataFrame(columns=[\"name_folder\", \"class\", \"date_registration_start\", \"date_registration_end\", \"count\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTEd8CJfuF87",
        "outputId": "f80e5a39-0752-4c61-f5c8-eee28734295e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['test_data_Minprirodi/traps/51',\n",
              " 'test_data_Minprirodi/traps/52',\n",
              " 'test_data_Minprirodi/traps/57',\n",
              " 'test_data_Minprirodi/traps/55',\n",
              " 'test_data_Minprirodi/traps/59',\n",
              " 'test_data_Minprirodi/traps/58',\n",
              " 'test_data_Minprirodi/traps/60',\n",
              " 'test_data_Minprirodi/traps/53',\n",
              " 'test_data_Minprirodi/traps/54',\n",
              " 'test_data_Minprirodi/traps/56']"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "glob(\"test_data_Minprirodi/traps/*\")"
      ]
    },
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        "id": "b_u_0ETQt8wI",
        "outputId": "2d72c194-c9f3-4376-cf1e-feada8e9115c"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1c25242b0d8045e29be15ff7095be1d5",
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
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              "model_id": "4071135bc1394b8c9b0356b642b6db1e",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
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              "model_id": "6745b665930e47b5b1951788230204ca",
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        },
        {
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          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
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              "model_id": "e9e56f7e689b42a5b9596c171f546544",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
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        },
        {
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          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
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              "model_id": "19649a0beba443d6900ad64ac06a8714",
              "version_major": 2,
              "version_minor": 0
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            ]
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
        },
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              "model_id": "9c7edf0d861543ab99a5735e95ee9aed",
              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a95651e813684646adb0e2aa338a7d65",
              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e660d45da205400c84b8af9f82a871f5",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "93cd1886f625445bb56a9ea6c90b6095",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f02d931fca61484da658ddff04be7a61",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for folder in tqdm(glob(\"test_data_Minprirodi/traps/*\")):\n",
        "    all_animals = predict(\"\", glob(folder + \"/*\"))\n",
        "    name_folders = []\n",
        "    classes = []\n",
        "    date_registration_starts = []\n",
        "    date_registration_ends = []\n",
        "    counts = []\n",
        "\n",
        "    for row in all_animals:\n",
        "        name_folders.append(int(row['filename'].split('/')[-2]))\n",
        "        classes.append(row['cls'])\n",
        "        date_registration_starts.append(str(row['first_seen']))\n",
        "        date_registration_ends.append(str(row['last_seen']))\n",
        "        counts.append(row['count'])\n",
        "\n",
        "    dataframe_data = {\n",
        "        'name_folder': name_folders,\n",
        "        'class': classes,\n",
        "        'date_registration_start': date_registration_starts,\n",
        "        'date_registration_end': date_registration_ends,\n",
        "        'count': counts\n",
        "    }\n",
        "    df = pd.DataFrame(dataframe_data)\n",
        "    full_dataset = pd.concat([full_dataset, df], ignore_index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "tZXW6-udsVcH",
        "colab": {
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              "1          51       Bear     2018-11-29 12:03:56   2018-11-29 12:04:00     1\n",
              "2          51  Musk_Deer     2019-01-04 10:09:45   2019-01-04 10:09:46     1\n",
              "3          51   Roe_Deer     2019-01-04 10:09:40   2019-01-04 10:10:26     1\n",
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