Licence_Plate_Recognition_YOLO_V8_EasyOCR.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "soj7oqxKXLDW",
        "outputId": "723507b6-faf1-4087-e11c-e762ea5d0b48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR'...\n",
            "remote: Enumerating objects: 132, done.\u001b[K\n",
            "remote: Counting objects: 100% (38/38), done.\u001b[K\n",
            "remote: Compressing objects: 100% (35/35), done.\u001b[K\n",
            "remote: Total 132 (delta 6), reused 10 (delta 3), pack-reused 94\u001b[K\n",
            "Receiving objects: 100% (132/132), 14.73 MiB | 20.98 MiB/s, done.\n",
            "Resolving deltas: 100% (33/33), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/Arijit1080/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YI3lrkLBcV-o",
        "outputId": "fdf0951d-fa0c-4d31-ab46-ce8eeb77805d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SAy_5DdlcPaG",
        "outputId": "289873f9-87ad-4bc6-f1c8-20dd1fc1b0c4"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard>=2.4.1->-r requirements.txt (line 19)) (3.2.2)\n",
            "Building wheels for collected packages: antlr4-python3-runtime\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=ec060c2107e812b40b668960d48b90d12d79b446ca9d9a0ef33f0edd10ad0e5e\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
            "Successfully built antlr4-python3-runtime\n",
            "Installing collected packages: pyclipper, ninja, antlr4-python3-runtime, smmap, python-bidi, omegaconf, jedi, hydra-core, gitdb, GitPython, thop, ultralytics, Easyocr\n",
            "Successfully installed Easyocr-1.7.1 GitPython-3.1.35 antlr4-python3-runtime-4.9.3 gitdb-4.0.10 hydra-core-1.3.2 jedi-0.19.0 ninja-1.11.1 omegaconf-2.3.0 pyclipper-1.3.0.post5 python-bidi-0.4.2 smmap-5.0.0 thop-0.1.1.post2209072238 ultralytics-8.0.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pydevd_plugins"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python predictWithOCR.py model='/content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/best.pt' source='/content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jUhBdfQ-cbzV",
        "outputId": "2e0c445a-2079-4f2e-8b70-8535e54ab343"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-09-09 22:08:52.652688: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-09-09 22:08:53.665478: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Ultralytics YOLOv8.0.3 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "Fusing layers... \n",
            "Model summary: 168 layers, 3005843 parameters, 0 gradients, 8.1 GFLOPs\n",
            "video 1/1 (1/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 1 LicensePlate, 68.1ms\n",
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            "video 1/1 (610/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.1ms\n",
            "video 1/1 (611/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.2ms\n",
            "video 1/1 (612/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.3ms\n",
            "video 1/1 (613/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.1ms\n",
            "video 1/1 (614/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.2ms\n",
            "video 1/1 (615/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 7.0ms\n",
            "video 1/1 (616/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 5.8ms\n",
            "video 1/1 (617/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 5.7ms\n",
            "video 1/1 (618/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.8ms\n",
            "video 1/1 (619/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 5.8ms\n",
            "video 1/1 (620/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.2ms\n",
            "video 1/1 (621/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.4ms\n",
            "video 1/1 (622/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.5ms\n",
            "video 1/1 (623/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 9.2ms\n",
            "video 1/1 (624/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.2ms\n",
            "video 1/1 (625/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 5.9ms\n",
            "video 1/1 (626/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.1ms\n",
            "video 1/1 (627/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.4ms\n",
            "video 1/1 (628/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 6.6ms\n",
            "video 1/1 (629/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 1 LicensePlate, 10.7ms\n",
            "video 1/1 (630/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 8.2ms\n",
            "video 1/1 (631/631) /content/Licence-Plate-Detection-and-Recognition-using-YOLO-V8-EasyOCR/demo.mp4: 416x640 1 LicensePlate, 5.7ms\n",
            "Speed: 0.4ms pre-process, 6.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
            "Results saved to \u001b[1mruns/detect/train3\u001b[0m\n"
          ]
        }
      ]
    }
  ]
}