{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJBs_flRovLc"
      },
      "source": [
        "***Devision789***\n",
        "\n",
        "**https://github.com/Devision789**\n",
        "\n",
        "***ĐỌC SỐ THÙNG TRÊN CONTAINER ***\n",
        "\n",
        "lưu ý nếu các bạn chạy local thì xem xét trang github inference\n",
        "yêu cầu bản python và cài đúng nếu bản python không phù hợp thì hệ thống sẽ báo lỗi no module name distubils ( có thể package khác mình quên tên nó rồi )\n",
        "Mọi người cần hỗ trợ về vấn đề gì có thể liên hệ mình.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install inference"
      ],
      "metadata": {
        "id": "bN9p2OeRT6aP",
        "outputId": "217e8e8e-8598-4142-af3f-edbdedeeeeb5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "    Uninstalling rich-13.7.1:\n",
            "      Successfully uninstalled rich-13.7.1\n",
            "Successfully installed APScheduler-3.10.1 GPUtil-1.4.0 aioresponses-0.7.6 awscli-1.33.24 backoff-2.2.1 boto3-1.34.123 botocore-1.34.142 colorama-0.4.4 coloredlogs-15.0.1 cython-3.0.0 dataclasses-json-0.6.7 docker-6.1.3 docutils-0.16 fastapi-0.110.3 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 humanfriendly-10.0 inference-0.13.0 jmespath-1.0.1 marshmallow-3.21.3 mypy-extensions-1.0.0 onnxruntime-1.15.1 openai-1.35.12 pendulum-3.0.0 piexif-1.1.3 prometheus-fastapi-instrumentator-6.0.0 pulp-2.8.0 pybase64-1.3.2 pydot-2.0.0 pytest-asyncio-0.21.1 python-dotenv-1.0.1 redis-5.0.7 requests-toolbelt-1.0.0 rich-13.5.2 rsa-4.7.2 s3transfer-0.10.2 skypilot-0.5.0 starlette-0.37.2 structlog-24.2.0 supervision-0.21.0 time-machine-2.14.2 typer-0.9.0 typing-inspect-0.9.0 urllib3-1.26.19 zxing-cpp-2.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pandas"
      ],
      "metadata": {
        "id": "2hniGep2Wx_S",
        "outputId": "565f27fd-68f0-4a8a-9ec0-e97ee682e4d4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.0.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.25.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "from inference_sdk import InferenceHTTPClient\n",
        "\n",
        "# Khởi tạo khách hàng inference\n",
        "CLIENT = InferenceHTTPClient(\n",
        "    api_url=\"https://infer.roboflow.com\",\n",
        "    api_key=\"............\" # thay bằng api của bạn\n",
        ")\n",
        "\n",
        "# Danh sách các ảnh cần xử lý\n",
        "image_files = [\n",
        "    \"/content/tim-hieu-ve-ky-hieu-cac-loai-container-4.jpg\",\n",
        "    \"/content/vi-tri-seal-niem-phong-tren-container.jpg\",\n",
        "    \"/content/59ae0d3728579_1504578871.jpg\",\n",
        "    \"/content/60b9d63a33954_1622791738.jpeg\",\n",
        "    \"/content/cửa-container.jpg\"\n",
        "\n",
        "    # Thêm đường dẫn đến các ảnh khác ở đây\n",
        "]\n",
        "\n",
        "# Đường dẫn tới file Excel\n",
        "excel_file_path = \"/content/container_data3.xlsx\"\n",
        "\n",
        "# Hàm để phân tích kết quả OCR\n",
        "def parse_ocr_result(result, image_name):\n",
        "    return {\n",
        "        \"Image Name\": image_name,\n",
        "        \"Result\": result.get(\"result\", \"\"),\n",
        "        \"Time\": result.get(\"time\", \"\"),\n",
        "        \"Parent ID\": result.get(\"parent_id\", \"\")\n",
        "    }\n",
        "\n",
        "# Hàm để thêm dữ liệu vào file Excel\n",
        "def append_to_excel(df, file_path):\n",
        "    if os.path.exists(file_path):\n",
        "        # Nếu file tồn tại, thêm dữ liệu vào file hiện có\n",
        "        existing_df = pd.read_excel(file_path)\n",
        "        updated_df = pd.concat([existing_df, df], ignore_index=True)\n",
        "    else:\n",
        "        # Nếu file không tồn tại, tạo một DataFrame mới\n",
        "        updated_df = df\n",
        "\n",
        "    # Lưu DataFrame đã cập nhật vào file Excel\n",
        "    updated_df.to_excel(file_path, index=False)\n",
        "\n",
        "# Danh sách để lưu các kết quả đã phân tích\n",
        "all_data = []\n",
        "\n",
        "# Lặp qua các ảnh và thực hiện OCR\n",
        "for image_file in image_files:\n",
        "    # Thực hiện OCR trên ảnh\n",
        "    result = CLIENT.ocr_image(inference_input=image_file)\n",
        "\n",
        "    # In kết quả\n",
        "    print(result)\n",
        "\n",
        "    # Phân tích kết quả OCR\n",
        "    parsed_data = parse_ocr_result(result, os.path.basename(image_file))\n",
        "    all_data.append(parsed_data)\n",
        "\n",
        "# Tạo DataFrame từ tất cả các kết quả đã phân tích\n",
        "df = pd.DataFrame(all_data)\n",
        "\n",
        "# Thêm dữ liệu đã phân tích vào file Excel\n",
        "append_to_excel(df, excel_file_path)\n",
        "\n",
        "print(f\"Dữ liệu đã được lưu vào {excel_file_path}\")\n"
      ],
      "metadata": {
        "id": "mP56tRFJZRGW",
        "outputId": "87fd63e7-2151-47ab-d49d-063995e34940",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'result': 'BRINNBOX a OMAS - w BNDOX  - 606071 6 2261 MAX.GROSS TARE 30.480 KGS 67.200 LBS 2.185 KGS 4.820 LBS 62.380 LBS 33.2 CU.M. 1.173 CU.FT MAX.CARGO 28.295 KGS : CU.CAP.', 'time': 1.513302848999956, 'parent_id': None}\n",
            "{'result': 'TWCU 216664 6 2261 30 480 kg 67 200 Ib 2100 kg 4630 Ib 28 380 kg S 62570 Ib JJ MAX GROSS TARE WWW. jjsh pping.cn PAYLOAD B9 CU. CAP.  - 33.2 cu.m. 1172 cu.ft. SINGAMAS', 'time': 1.5103129280000758, 'parent_id': None}\n",
            "{'result': 'GLDU 053631 3 45G1 MGW. TARE NET CU.CAP 30.480 KGS 61.200 LES 3.929 8.640 E KES 3A5B LOS 76.2 CUM 2,690 CUFT 516161 7 42G1 32508 K5S 71650 LBS 3708 AES 8168 LBS 28M S 63482 LBS 878 CM 2394 CUFI. GOLD CNTNERODAF - 2 UTION HIGH TRAILER - -u', 'time': 1.6298546919997534, 'parent_id': None}\n",
            "{'result': 'KFS www.flo ens.com 32500  71.650 LB 3.720 5 8.200  22780 M 63.450 LB 67.7 CUM 2390 CUFT. MAX GROSS TARE - MAX CARGO a CAP. -', 'time': 1.4038100689999737, 'parent_id': None}\n",
            "{'result': 'NZLU 218146 2 22G1 30,480 KGS 67.200 LBS 2,120 KGS 4.670 LBS 28.360 KGS C. 62.530 LBS MAX.GROSS TARE cao EL-NET 33.2 CU.M. 1.170 CU.FT. CU CAP. INTE RNATIONAL LUXURY CONTAINER CIMC a', 'time': 1.4752892129999964, 'parent_id': None}\n",
            "Dữ liệu đã được lưu vào /content/container_data3.xlsx\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}