ML/youtube/LicensePlateReader/License_Plate_Detector.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Mochoye/Python/blob/master/ML/youtube/LicensePlateReader/License_Plate_Detector.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q_vZQuFlgliE"
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      "source": []
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    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/Mochoye/Licence-Plate-Detection-using-TensorFlow-Lite\n",
        "!pip install easyocr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g510McSeProD",
        "outputId": "750f71c7-3894-405b-effc-f2755407a162"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Licence-Plate-Detection-using-TensorFlow-Lite'...\n",
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            "Installing collected packages: pyclipper, ninja, python-bidi, easyocr\n",
            "Successfully installed easyocr-1.7.1 ninja-1.11.1 pyclipper-1.3.0.post5 python-bidi-0.4.2\n"
          ]
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    {
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnTKXXRRgcUp",
        "outputId": "afb0ceed-a7d9-4c4b-8183-1dbb6c27a584"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:easyocr.easyocr:Downloading detection model, please wait. This may take several minutes depending upon your network connection.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress: |██████████████████████████████████████████████████| 100.0% Complete"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:easyocr.easyocr:Downloading recognition model, please wait. This may take several minutes depending upon your network connection.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress: |██████████████████████████████████████████████████| 100.0% Complete"
          ]
        }
      ],
      "source": [
        "import easyocr\n",
        "import numpy as np\n",
        "import cv2\n",
        "import os\n",
        "import sys\n",
        "import glob\n",
        "import random\n",
        "import importlib.util\n",
        "from tensorflow.lite.python.interpreter import Interpreter\n",
        "\n",
        "\n",
        "reader = easyocr.Reader(['en'])\n",
        "\n",
        "modelpath = '/content/Licence-Plate-Detection-using-TensorFlow-Lite/custom_model_lite/detect.tflite'\n",
        "lblpath = '/content/Licence-Plate-Detection-using-TensorFlow-Lite/custom_model_lite/labelmap.txt'\n",
        "min_conf = 0.5\n",
        "input_video_path = '/content/Licence-Plate-Detection-using-TensorFlow-Lite/custom_model_lite/demo.mp4'\n",
        "output_video_path = 'output.mp4'  # Change this to your desired output path\n",
        "\n",
        "cap = cv2.VideoCapture(input_video_path)\n",
        "\n",
        "# Define the codec and create a VideoWriter object\n",
        "fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
        "frame_width = int(cap.get(3))\n",
        "frame_height = int(cap.get(4))\n",
        "out = cv2.VideoWriter(output_video_path, fourcc, 20.0, (frame_width, frame_height))\n",
        "\n",
        "interpreter = Interpreter(model_path=modelpath)\n",
        "interpreter.allocate_tensors()\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()\n",
        "height = input_details[0]['shape'][1]\n",
        "width = input_details[0]['shape'][2]\n",
        "\n",
        "float_input = (input_details[0]['dtype'] == np.float32)\n",
        "\n",
        "input_mean = 127.5\n",
        "input_std = 127.5\n",
        "\n",
        "crp_img = None  # Initialize variable to store the cropped image\n",
        "\n",
        "with open(lblpath, 'r') as f:\n",
        "    labels = [line.strip() for line in f.readlines()]\n",
        "\n",
        "while True:\n",
        "    ret, frame = cap.read()\n",
        "    if not ret:\n",
        "        break\n",
        "\n",
        "    image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
        "    imH, imW, _ = frame.shape\n",
        "    image_resized = cv2.resize(image_rgb, (width, height))\n",
        "    input_data = np.expand_dims(image_resized, axis=0)\n",
        "\n",
        "    # Normalize pixel values if using a floating model (i.e. if model is non-quantized)\n",
        "    if float_input:\n",
        "        input_data = (np.float32(input_data) - input_mean) / input_std\n",
        "\n",
        "    # Perform the actual detection by running the model with the image as input\n",
        "    interpreter.set_tensor(input_details[0]['index'], input_data)\n",
        "    interpreter.invoke()\n",
        "\n",
        "    boxes = interpreter.get_tensor(output_details[1]['index'])[0]  # Bounding box coordinates of detected objects\n",
        "    classes = interpreter.get_tensor(output_details[3]['index'])[0]  # Class index of detected objects\n",
        "    scores = interpreter.get_tensor(output_details[0]['index'])[0]  # Confidence of detected objects\n",
        "\n",
        "    detections = []\n",
        "\n",
        "    for i in range(len(scores)):\n",
        "        if (scores[i] > min_conf) and (scores[i] <= 1.0):\n",
        "            # Get bounding box coordinates and draw box\n",
        "            ymin = int(max(1, (boxes[i][0] * imH)))\n",
        "            xmin = int(max(1, (boxes[i][1] * imW)))\n",
        "            ymax = int(min(imH, (boxes[i][2] * imH)))\n",
        "            xmax = int(min(imW, (boxes[i][3] * imW)))\n",
        "\n",
        "            # Crop the detected region\n",
        "            crp_img = frame[ymin:ymax, xmin:xmax]\n",
        "\n",
        "            cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (10, 255, 0), 2)\n",
        "            object_name = labels[int(classes[i])]  # Look up object name from \"labels\" array using class index\n",
        "            label = '%s: %d%%' % (object_name, int(scores[i] * 100))  # Example: 'person: 72%'\n",
        "            labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)\n",
        "            label_ymin = max(ymin, labelSize[1] + 10)\n",
        "\n",
        "            result = reader.readtext(crp_img)\n",
        "            if len(result) == 0:\n",
        "              text = 'LicensePlate'\n",
        "            else:\n",
        "              text= result[0][-2]\n",
        "              font = cv2.FONT_HERSHEY_SIMPLEX\n",
        "\n",
        "\n",
        "            cv2.rectangle(frame, (xmin, label_ymin - labelSize[1] - 10), (xmin + labelSize[0], label_ymin + baseLine - 10), (255, 255, 255), cv2.FILLED)\n",
        "            cv2.putText(frame, text, (xmin, label_ymin - 7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2)\n",
        "            detections.append([object_name, scores[i], xmin, ymin, xmax, ymax])\n",
        "\n",
        "\n",
        "\n",
        "    out.write(frame)  # Write the frame to the output video\n",
        "\n",
        "cap.release()\n",
        "out.release()  # Release the output video\n",
        "cv2.destroyAllWindows()\n",
        "\n",
        "# The cropped image is now stored in the variable crp_img"
      ]
    },
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        "id": "_CwA6oYRiY80"
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