BDIX/predict_save at main · MrTimonM/BDIX · GitHub

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{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN39wo1GCbWb3JtEhh1Jmzk"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "EDTnYiGtWLv0"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import load_model\n",
        "from PIL import Image\n",
        "import time"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!curl -LO https://github.com/MrTimonM/cap-dataset/releases/download/v1.11/Images.zip\n",
        "!unzip -qq Images.zip\n",
        "!curl -LO https://github.com/MrTimonM/cap-dataset/releases/download/v1.11/saved_model.zip\n",
        "!unzip saved_model.zip -d saved_model"
      ],
      "metadata": {
        "id": "K_mvf-dDWZT2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import load_model\n",
        "from PIL import Image\n",
        "import time\n",
        "\n",
        "# Load the saved model\n",
        "model = load_model('saved_model')\n",
        "\n",
        "# Define the directory where the images are located\n",
        "image_dir = 'Images'\n",
        "predicted_dir = 'predicted'\n",
        "\n",
        "# Create the predicted directory if it doesn't exist\n",
        "if not os.path.exists(predicted_dir):\n",
        "    os.makedirs(predicted_dir)\n",
        "\n",
        "# Function to parse label from filename\n",
        "def parse_label(filename):\n",
        "    return filename.split('.')[0]  # Assuming label is the part before the first '.'\n",
        "\n",
        "# Function to format the predicted label\n",
        "def format_predicted_label(prediction):\n",
        "    return ''.join(map(lambda x: chr(np.argmax(x)), prediction[0]))\n",
        "\n",
        "# Initialize total time taken\n",
        "total_time = 0\n",
        "\n",
        "# Iterate through all images in the directory\n",
        "for filename in os.listdir(image_dir):\n",
        "    filepath = os.path.join(image_dir, filename)\n",
        "    # Load and preprocess the image\n",
        "    image = Image.open(filepath)\n",
        "    image = image.resize((270, 130))  # Resize to match model input shape\n",
        "    image = np.array(image) / 255.0  # Normalize pixel values\n",
        "    image = np.expand_dims(image, axis=0)  # Add batch dimension\n",
        "\n",
        "    # Predict label for the image\n",
        "    start_time = time.time()\n",
        "    prediction = model.predict(image)\n",
        "    end_time = time.time()\n",
        "\n",
        "    # Calculate time taken for prediction\n",
        "    time_taken = end_time - start_time\n",
        "    total_time += time_taken\n",
        "\n",
        "    # Format predicted label\n",
        "    predicted_label = format_predicted_label(prediction)\n",
        "\n",
        "    # Rename the image file with the predicted label and save it in the predicted directory\n",
        "    new_filename = predicted_label + '.png'\n",
        "    new_filepath = os.path.join(predicted_dir, new_filename)\n",
        "    # Convert the NumPy array back to a PIL image\n",
        "    image_to_save = Image.fromarray((image[0] * 255).astype(np.uint8))\n",
        "\n",
        "    # Save the image with the predicted label as the filename in the predicted directory\n",
        "    new_filename = predicted_label + '.png'\n",
        "    new_filepath = os.path.join(predicted_dir, new_filename)\n",
        "    image_to_save.save(new_filepath)\n",
        "\n",
        "    # Print prediction output and time taken\n",
        "    print(f\"Predicted label for {filename}: {predicted_label} - Time taken: {time_taken:.4f} seconds\")\n",
        "\n",
        "print(\"Prediction and renaming completed successfully.\")\n",
        "print(f\"Total time taken for {len(os.listdir(image_dir))} images: {total_time:.4f} seconds\")\n"
      ],
      "metadata": {
        "id": "iTgD8z_EWjyM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}