LogoDetection-PreTrained-Model.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "n5f31B_ncrOz",
        "outputId": "66cf7bec-8140-4690-9284-7c4481eef347"
      },
      "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<2,>=0.5->tensorboard>=2.4.1->yolov5) (3.2.2)\n",
            "done\n"
          ]
        }
      ],
      "source": [
        "!pip install av\n",
        "!pip install torch torchvision\n",
        "!pip install opencv-python\n",
        "!pip install yolov5\n",
        "\n",
        "import av\n",
        "import torch\n",
        "from yolov5 import YOLOv5\n",
        "import json\n",
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "print(\"done\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ZjiUEtJmvcg"
      },
      "outputs": [],
      "source": [
        "# Function to extract frames\n",
        "def extract_frames(video_path, interval=1):\n",
        "    container = av.open(video_path)\n",
        "    frames = []\n",
        "    timestamps = []\n",
        "    for frame in container.decode(video=0):\n",
        "        # Extract frames based on the specified interval\n",
        "        if frame.time >= len(timestamps) * interval:\n",
        "            frames.append(frame.to_image())\n",
        "            timestamps.append(frame.time)\n",
        "    return frames, timestamps\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "34sYUMhAm232"
      },
      "outputs": [],
      "source": [
        "# Function to detect logos\n",
        "def detect_logos(model, frames, timestamps):\n",
        "    pepsi_timestamps = []\n",
        "    cocacola_timestamps = []\n",
        "\n",
        "    for i, frame in enumerate(frames):\n",
        "        frame = np.array(frame)  # Convert PIL image to numpy array\n",
        "        results = model(frame)  # Get predictions from the model\n",
        "        predictions = results.pred[0].cpu().numpy()  # Convert predictions to numpy array\n",
        "\n",
        "        for pred in predictions:\n",
        "            # The format of pred is [x1, y1, x2, y2, confidence, class]\n",
        "            class_id = int(pred[5])\n",
        "            if class_id == 0:  # Assuming class 0 is Pepsi\n",
        "                pepsi_timestamps.append(timestamps[i])\n",
        "            elif class_id == 1:  # Assuming class 1 is CocaCola\n",
        "                cocacola_timestamps.append(timestamps[i])\n",
        "\n",
        "    return pepsi_timestamps, cocacola_timestamps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nFqysGRknCzm"
      },
      "outputs": [],
      "source": [
        "# Function to save results\n",
        "def save_results(pepsi_timestamps, cocacola_timestamps, output_path='results.json'):\n",
        "    results = {\n",
        "        \"Pepsi_pts\": pepsi_timestamps,\n",
        "        \"CocaCola_pts\": cocacola_timestamps\n",
        "    }\n",
        "    with open(output_path, 'w') as f:\n",
        "        json.dump(results, f)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DvpYYARKnQ2U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "outputId": "fc0b6c77-af2e-4655-a5cb-54876ec4378a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cache found in /root/.cache/torch/hub/ultralytics_yolov5_master\n",
            "YOLOv5 🚀 2024-7-13 Python-3.10.12 torch-2.3.0+cu121 CPU\n",
            "\n",
            "Fusing layers... \n",
            "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n",
            "Adding AutoShape... \n"
          ]
        }
      ],
      "source": [
        "# Load model\n",
        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # Download and load pre-trained YOLOv5s model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O3F-uqlgoYy5"
      },
      "outputs": [],
      "source": [
        "# Extract frames from video\n",
        "video_path = '/content/Coke_Pepsi_Video.mp4'  # Use the uploaded file path\n",
        "frames, timestamps = extract_frames(video_path, interval=1)  # Adjust interval as needed\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sqOUUSckpha3",
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "# Detect logos\n",
        "pepsi_timestamps, cocacola_timestamps = detect_logos(model, frames, timestamps)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save results to JSON\n",
        "save_results(pepsi_timestamps, cocacola_timestamps)\n",
        "\n",
        "print(\"Logo detection completed and results have been saved to a new file-'results.json'\")"
      ],
      "metadata": {
        "id": "yA8_xefCy_Pd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58162b0e-9978-4cc6-baf1-884ae3b55d5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logo detection completed and results have been saved to a new file-'results.json'\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOGRMGUulp16zVEYl+5jpuo"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}