{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vLYqOipDn7J",
        "outputId": "d0995580-9b7a-40cd-8147-7fdf58f148fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Smart-Traffic'...\n",
            "remote: Enumerating objects: 12, done.\u001b[K\n",
            "remote: Counting objects: 100% (9/9), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 12 (delta 2), reused 0 (delta 0), pack-reused 3\u001b[K\n",
            "Unpacking objects: 100% (12/12), 199.01 KiB | 939.00 KiB/s, done.\n",
            "Filtering content: 100% (2/2), 57.18 MiB | 19.07 MiB/s, done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://huggingface.co/ottoykh/Smart-Traffic"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ultralytics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ku7viwceDrF-",
        "outputId": "b6246bc3-2849-4c1e-f6bc-b3bc7860bf78"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.1.18-py3-none-any.whl (716 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m716.0/716.0 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1)\n",
            "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.8.0.76)\n",
            "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.4.0)\n",
            "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.1)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.31.0)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.11.4)\n",
            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.1.0+cu121)\n",
            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.16.0+cu121)\n",
            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.66.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)\n",
            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)\n",
            "Collecting thop>=0.1.1 (from ultralytics)\n",
            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.5.3)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.13.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.49.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.5)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (23.2)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2023.4)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2024.2.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (4.9.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n",
            "Installing collected packages: thop, ultralytics\n",
            "Successfully installed thop-0.1.1.post2209072238 ultralytics-8.1.18\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ultralytics import YOLO\n",
        "\n",
        "from IPython.display import display, Image\n",
        "import requests\n",
        "from PIL import Image\n",
        "import time\n",
        "import datetime\n",
        "import os"
      ],
      "metadata": {
        "id": "tjNmyigvEPut"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image_urls = [\n",
        "    \"https://tdcctv.data.one.gov.hk/AID01217.JPG\",\n",
        "    \"https://tdcctv.data.one.gov.hk/AID01216.JPG\",\n",
        "    \"https://tdcctv.data.one.gov.hk/AID01215.JPG\",\n",
        "    \"https://tdcctv.data.one.gov.hk/AID01214.JPG\",\n",
        "    \"https://tdcctv.data.one.gov.hk/AID01213.JPG\",\n",
        "    \"https://tdcctv.data.one.gov.hk/AID01212.JPG\",\n",
        "    \"https://tdcctv.data.one.gov.hk/AID01211.JPG\",\n",
        "    \"https://tdcctv.data.one.gov.hk/AID01210.JPG\",\n",
        "    \"https://tdcctv.data.one.gov.hk/AID01209.JPG\"\n",
        "]\n"
      ],
      "metadata": {
        "id": "NxP8UKN4EUh3"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pytz\n",
        "from urllib.parse import urlparse\n",
        "import json\n",
        "\n",
        "hong_kong_timezone = pytz.timezone('Asia/Hong_Kong')\n",
        "\n",
        "while True:\n",
        "    current_time = datetime.datetime.now(tz=hong_kong_timezone).strftime(\"%Y%m%d%H%M%S\")\n",
        "    folder_name = f\"/content/{current_time}\"\n",
        "    print(folder_name)\n",
        "    os.makedirs(folder_name, exist_ok=True)\n",
        "\n",
        "    for image_url in image_urls:\n",
        "        response = requests.get(image_url)\n",
        "        image_data = response.content\n",
        "        parsed_url = urlparse(image_url)\n",
        "        image_name = os.path.basename(parsed_url.path)\n",
        "        file_name = os.path.join(folder_name, image_name)\n",
        "        with open(file_name, \"wb\") as file:\n",
        "            file.write(image_data)\n",
        "        print(file_name)\n",
        "    folder_name_formatted = f\"'{folder_name}'\"\n",
        "\n",
        "    !yolo task=segment mode=predict model='/content/Smart-Traffic/best.pt' conf=0.45 source={folder_name_formatted} save=true save_txt=true\n",
        "\n",
        "    time.sleep(120)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iNxB2wbrEa5q",
        "outputId": "7854ac0b-c652-4660-bd03-356bc0cbff0c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/20240223165431\n",
            "/content/20240223165431/AID01217.JPG\n",
            "/content/20240223165431/AID01216.JPG\n",
            "/content/20240223165431/AID01215.JPG\n",
            "/content/20240223165431/AID01214.JPG\n",
            "/content/20240223165431/AID01213.JPG\n",
            "/content/20240223165431/AID01212.JPG\n",
            "/content/20240223165431/AID01211.JPG\n",
            "/content/20240223165431/AID01210.JPG\n",
            "/content/20240223165431/AID01209.JPG\n",
            "Ultralytics YOLOv8.1.18 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "YOLOv8s-seg summary (fused): 195 layers, 11782309 parameters, 0 gradients, 42.5 GFLOPs\n",
            "\n",
            "image 1/9 /content/20240223165431/AID01209.JPG: 480x640 (no detections), 750.7ms\n",
            "image 2/9 /content/20240223165431/AID01210.JPG: 480x640 2 Private-cars, 813.9ms\n",
            "image 3/9 /content/20240223165431/AID01211.JPG: 480x640 1 Minibus, 3 Private-cars, 1039.4ms\n",
            "image 4/9 /content/20240223165431/AID01212.JPG: 480x640 (no detections), 996.6ms\n",
            "image 5/9 /content/20240223165431/AID01213.JPG: 480x640 1 Bus, 2 Private-cars, 1 Taxi, 652.4ms\n",
            "image 6/9 /content/20240223165431/AID01214.JPG: 480x640 2 Private-cars, 2 Taxis, 1 Truck, 661.9ms\n",
            "image 7/9 /content/20240223165431/AID01215.JPG: 480x640 2 Private-cars, 1 Taxi, 626.7ms\n",
            "image 8/9 /content/20240223165431/AID01216.JPG: 480x640 1 Minibus, 5 Private-cars, 639.9ms\n",
            "image 9/9 /content/20240223165431/AID01217.JPG: 480x640 3 Private-cars, 619.7ms\n",
            "Speed: 3.2ms preprocess, 755.7ms inference, 13.1ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Results saved to \u001b[1mruns/segment/predict4\u001b[0m\n",
            "7 labels saved to runs/segment/predict4/labels\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/predict\n",
            "/content/20240223165647\n",
            "/content/20240223165647/AID01217.JPG\n",
            "/content/20240223165647/AID01216.JPG\n",
            "/content/20240223165647/AID01215.JPG\n",
            "/content/20240223165647/AID01214.JPG\n",
            "/content/20240223165647/AID01213.JPG\n",
            "/content/20240223165647/AID01212.JPG\n",
            "/content/20240223165647/AID01211.JPG\n",
            "/content/20240223165647/AID01210.JPG\n",
            "/content/20240223165647/AID01209.JPG\n",
            "Ultralytics YOLOv8.1.18 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "YOLOv8s-seg summary (fused): 195 layers, 11782309 parameters, 0 gradients, 42.5 GFLOPs\n",
            "\n",
            "image 1/9 /content/20240223165647/AID01209.JPG: 480x640 2 Private-cars, 1 Taxi, 733.2ms\n",
            "image 2/9 /content/20240223165647/AID01210.JPG: 480x640 2 Private-cars, 628.8ms\n",
            "image 3/9 /content/20240223165647/AID01211.JPG: 480x640 (no detections), 648.8ms\n",
            "image 4/9 /content/20240223165647/AID01212.JPG: 480x640 2 Private-cars, 1 Taxi, 650.8ms\n",
            "image 5/9 /content/20240223165647/AID01213.JPG: 480x640 4 Private-cars, 1 Truck, 642.1ms\n",
            "image 6/9 /content/20240223165647/AID01214.JPG: 480x640 1 Bus, 3 Private-cars, 625.7ms\n",
            "image 7/9 /content/20240223165647/AID01215.JPG: 480x640 4 Private-cars, 1 Truck, 839.4ms\n",
            "image 8/9 /content/20240223165647/AID01216.JPG: 480x640 2 Private-cars, 995.4ms\n",
            "image 9/9 /content/20240223165647/AID01217.JPG: 480x640 4 Private-cars, 970.9ms\n",
            "Speed: 3.2ms preprocess, 748.4ms inference, 12.3ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Results saved to \u001b[1mruns/segment/predict5\u001b[0m\n",
            "8 labels saved to runs/segment/predict5/labels\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/predict\n",
            "/content/20240223165903\n",
            "/content/20240223165903/AID01217.JPG\n",
            "/content/20240223165903/AID01216.JPG\n",
            "/content/20240223165903/AID01215.JPG\n",
            "/content/20240223165903/AID01214.JPG\n",
            "/content/20240223165903/AID01213.JPG\n",
            "/content/20240223165903/AID01212.JPG\n",
            "/content/20240223165903/AID01211.JPG\n",
            "/content/20240223165903/AID01210.JPG\n",
            "/content/20240223165903/AID01209.JPG\n",
            "Ultralytics YOLOv8.1.18 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "YOLOv8s-seg summary (fused): 195 layers, 11782309 parameters, 0 gradients, 42.5 GFLOPs\n",
            "\n",
            "image 1/9 /content/20240223165903/AID01209.JPG: 480x640 2 Private-cars, 1 Taxi, 755.6ms\n",
            "image 2/9 /content/20240223165903/AID01210.JPG: 480x640 1 Bus, 3 Private-cars, 649.8ms\n",
            "image 3/9 /content/20240223165903/AID01211.JPG: 480x640 (no detections), 627.9ms\n",
            "image 4/9 /content/20240223165903/AID01212.JPG: 480x640 2 Private-cars, 1 Taxi, 639.2ms\n",
            "image 5/9 /content/20240223165903/AID01213.JPG: 480x640 4 Private-cars, 1 Truck, 662.7ms\n",
            "image 6/9 /content/20240223165903/AID01214.JPG: 480x640 1 Bus, 3 Private-cars, 632.2ms\n",
            "image 7/9 /content/20240223165903/AID01215.JPG: 480x640 4 Private-cars, 1 Truck, 612.9ms\n",
            "image 8/9 /content/20240223165903/AID01216.JPG: 480x640 2 Private-cars, 638.8ms\n",
            "image 9/9 /content/20240223165903/AID01217.JPG: 480x640 4 Private-cars, 623.8ms\n",
            "Speed: 3.0ms preprocess, 649.2ms inference, 11.9ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Results saved to \u001b[1mruns/segment/predict6\u001b[0m\n",
            "8 labels saved to runs/segment/predict6/labels\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/predict\n",
            "/content/20240223170118\n",
            "/content/20240223170118/AID01217.JPG\n",
            "/content/20240223170118/AID01216.JPG\n",
            "/content/20240223170118/AID01215.JPG\n",
            "/content/20240223170118/AID01214.JPG\n",
            "/content/20240223170118/AID01213.JPG\n",
            "/content/20240223170118/AID01212.JPG\n",
            "/content/20240223170118/AID01211.JPG\n",
            "/content/20240223170118/AID01210.JPG\n",
            "/content/20240223170118/AID01209.JPG\n",
            "Ultralytics YOLOv8.1.18 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "YOLOv8s-seg summary (fused): 195 layers, 11782309 parameters, 0 gradients, 42.5 GFLOPs\n",
            "\n",
            "image 1/9 /content/20240223170118/AID01209.JPG: 480x640 1 Bus, 1 Taxi, 807.7ms\n",
            "image 2/9 /content/20240223170118/AID01210.JPG: 480x640 3 Private-cars, 668.4ms\n",
            "image 3/9 /content/20240223170118/AID01211.JPG: 480x640 (no detections), 654.9ms\n",
            "image 4/9 /content/20240223170118/AID01212.JPG: 480x640 2 Private-cars, 1 Taxi, 660.6ms\n",
            "image 5/9 /content/20240223170118/AID01213.JPG: 480x640 1 Bus, 2 Private-cars, 659.2ms\n",
            "image 6/9 /content/20240223170118/AID01214.JPG: 480x640 1 Minibus, 6 Private-cars, 1 Taxi, 642.2ms\n",
            "image 7/9 /content/20240223170118/AID01215.JPG: 480x640 3 Private-cars, 620.5ms\n",
            "image 8/9 /content/20240223170118/AID01216.JPG: 480x640 4 Private-cars, 1 Taxi, 634.2ms\n",
            "image 9/9 /content/20240223170118/AID01217.JPG: 480x640 2 Private-cars, 1 Taxi, 607.3ms\n",
            "Speed: 3.9ms preprocess, 661.7ms inference, 14.4ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Results saved to \u001b[1mruns/segment/predict7\u001b[0m\n",
            "8 labels saved to runs/segment/predict7/labels\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/predict\n",
            "/content/20240223170334\n",
            "/content/20240223170334/AID01217.JPG\n",
            "/content/20240223170334/AID01216.JPG\n",
            "/content/20240223170334/AID01215.JPG\n",
            "/content/20240223170334/AID01214.JPG\n",
            "/content/20240223170334/AID01213.JPG\n",
            "/content/20240223170334/AID01212.JPG\n",
            "/content/20240223170334/AID01211.JPG\n",
            "/content/20240223170334/AID01210.JPG\n",
            "/content/20240223170334/AID01209.JPG\n",
            "Ultralytics YOLOv8.1.18 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "YOLOv8s-seg summary (fused): 195 layers, 11782309 parameters, 0 gradients, 42.5 GFLOPs\n",
            "\n",
            "image 1/9 /content/20240223170334/AID01209.JPG: 480x640 7 Private-cars, 1 Taxi, 1209.1ms\n",
            "image 2/9 /content/20240223170334/AID01210.JPG: 480x640 (no detections), 643.8ms\n",
            "image 3/9 /content/20240223170334/AID01211.JPG: 480x640 1 Private-car, 615.6ms\n",
            "image 4/9 /content/20240223170334/AID01212.JPG: 480x640 2 Private-cars, 1 Taxi, 625.5ms\n",
            "image 5/9 /content/20240223170334/AID01213.JPG: 480x640 1 Taxi, 628.4ms\n",
            "image 6/9 /content/20240223170334/AID01214.JPG: 480x640 1 Private-car, 1 Taxi, 616.1ms\n",
            "image 7/9 /content/20240223170334/AID01215.JPG: 480x640 2 Private-cars, 623.7ms\n",
            "image 8/9 /content/20240223170334/AID01216.JPG: 480x640 1 Bus, 611.1ms\n",
            "image 9/9 /content/20240223170334/AID01217.JPG: 480x640 1 Private-car, 630.6ms\n",
            "Speed: 3.1ms preprocess, 689.3ms inference, 9.7ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Results saved to \u001b[1mruns/segment/predict8\u001b[0m\n",
            "8 labels saved to runs/segment/predict8/labels\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/predict\n",
            "/content/20240223170552\n",
            "/content/20240223170552/AID01217.JPG\n",
            "/content/20240223170552/AID01216.JPG\n",
            "/content/20240223170552/AID01215.JPG\n",
            "/content/20240223170552/AID01214.JPG\n",
            "/content/20240223170552/AID01213.JPG\n",
            "/content/20240223170552/AID01212.JPG\n",
            "/content/20240223170552/AID01211.JPG\n",
            "/content/20240223170552/AID01210.JPG\n",
            "/content/20240223170552/AID01209.JPG\n",
            "Ultralytics YOLOv8.1.18 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "YOLOv8s-seg summary (fused): 195 layers, 11782309 parameters, 0 gradients, 42.5 GFLOPs\n",
            "\n",
            "image 1/9 /content/20240223170552/AID01209.JPG: 480x640 7 Private-cars, 1 Taxi, 892.9ms\n",
            "image 2/9 /content/20240223170552/AID01210.JPG: 480x640 2 Private-cars, 974.9ms\n",
            "image 3/9 /content/20240223170552/AID01211.JPG: 480x640 4 Private-cars, 976.1ms\n",
            "image 4/9 /content/20240223170552/AID01212.JPG: 480x640 4 Private-cars, 1 Taxi, 612.6ms\n",
            "image 5/9 /content/20240223170552/AID01213.JPG: 480x640 2 Private-cars, 1 Taxi, 614.1ms\n",
            "image 6/9 /content/20240223170552/AID01214.JPG: 480x640 1 Minibus, 6 Private-cars, 1 Taxi, 609.9ms\n",
            "image 7/9 /content/20240223170552/AID01215.JPG: 480x640 2 Private-cars, 621.7ms\n",
            "image 8/9 /content/20240223170552/AID01216.JPG: 480x640 (no detections), 624.3ms\n",
            "image 9/9 /content/20240223170552/AID01217.JPG: 480x640 2 Private-cars, 605.0ms\n",
            "Speed: 3.4ms preprocess, 725.7ms inference, 15.1ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Results saved to \u001b[1mruns/segment/predict9\u001b[0m\n",
            "8 labels saved to runs/segment/predict9/labels\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/predict\n",
            "/content/20240223170810\n",
            "/content/20240223170810/AID01217.JPG\n",
            "/content/20240223170810/AID01216.JPG\n",
            "/content/20240223170810/AID01215.JPG\n",
            "/content/20240223170810/AID01214.JPG\n",
            "/content/20240223170810/AID01213.JPG\n",
            "/content/20240223170810/AID01212.JPG\n",
            "/content/20240223170810/AID01211.JPG\n",
            "/content/20240223170810/AID01210.JPG\n",
            "/content/20240223170810/AID01209.JPG\n",
            "Ultralytics YOLOv8.1.18 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "YOLOv8s-seg summary (fused): 195 layers, 11782309 parameters, 0 gradients, 42.5 GFLOPs\n",
            "\n",
            "image 1/9 /content/20240223170810/AID01209.JPG: 480x640 1 Minibus, 4 Private-cars, 1 Taxi, 746.6ms\n",
            "image 2/9 /content/20240223170810/AID01210.JPG: 480x640 2 Private-cars, 624.7ms\n",
            "image 3/9 /content/20240223170810/AID01211.JPG: 480x640 4 Private-cars, 639.6ms\n",
            "image 4/9 /content/20240223170810/AID01212.JPG: 480x640 4 Private-cars, 1 Taxi, 828.6ms\n",
            "image 5/9 /content/20240223170810/AID01213.JPG: 480x640 2 Private-cars, 1 Taxi, 987.7ms\n",
            "image 6/9 /content/20240223170810/AID01214.JPG: 480x640 2 Private-cars, 1 Taxi, 975.8ms\n",
            "image 7/9 /content/20240223170810/AID01215.JPG: 480x640 1 Minibus, 2 Private-cars, 1 Taxi, 629.0ms\n",
            "image 8/9 /content/20240223170810/AID01216.JPG: 480x640 (no detections), 618.1ms\n",
            "image 9/9 /content/20240223170810/AID01217.JPG: 480x640 2 Private-cars, 639.6ms\n",
            "Speed: 3.1ms preprocess, 743.3ms inference, 13.4ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Results saved to \u001b[1mruns/segment/predict10\u001b[0m\n",
            "8 labels saved to runs/segment/predict10/labels\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-eb142f4ed618>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"yolo task=segment mode=predict model='/content/Smart-Traffic/best.pt' conf=0.45 source={folder_name_formatted} save=true save_txt=true\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    }
  ]
}