Solar_Panel_Fault_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "mount_file_id": "1-fPgMZsOGAkIT7L6u8xka287yAM7qsYk",
      "authorship_tag": "ABX9TyM2Yf4aprQ2j3zjNI5kmeO5",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shivam-daksh/solar-panel-fault-detection/blob/main/Solar_Panel_Fault_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# clone YOLOv5 repository\n",
        "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
        "%cd yolov5\n",
        "!git reset --hard fbe67e465375231474a2ad80a4389efc77ecff99"
      ],
      "metadata": {
        "id": "LHSHHgrG2F9x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# install dependencies as necessary\n",
        "!pip install -qr requirements.txt  # install dependencies (ignore errors)\n",
        "import torch\n",
        "\n",
        "from IPython.display import Image, clear_output  # to display images\n",
        "from utils.downloads import attempt_download  # to download models/datasets\n",
        "\n",
        "# clear_output()\n",
        "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lUiOjdx42NJW",
        "outputId": "09c5a80b-1a48-4298-d5dc-3ef7520b66c9"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setup complete. Using torch 2.2.1+cu121 CPU\n"
          ]
        }
      ]
    },
    {
      "source": [
        "!pip install object_detection"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "Qw2tYcWJgpTG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !kill -9 -1"
      ],
      "metadata": {
        "id": "3lWJ8LY0gYPc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import json\n",
        "import cv2\n",
        "import shutil\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import Input, Flatten, Dense\n",
        "from tensorflow.keras import layers, models\n",
        "from tensorflow.keras.applications import VGG16\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from object_detection.utils import visualization_utils as vis_util\n",
        "from object_detection.utils import label_map_util\n",
        "\n",
        "# Replace with paths to your data and pre-trained model\n",
        "data_dir = \"...\"\n",
        "model_name = \"...\"  # Name of pre-trained model (e.g., 'ssd_mobilenet_v2')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "qJfiHRbRft-A",
        "outputId": "cfecde0d-e54f-4a26-9d8e-039691118474"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "cannot import name 'string_int_label_map_pb2' from 'object_detection.protos' (/usr/local/lib/python3.10/dist-packages/object_detection/protos/__init__.py)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-ef6a13a062a9>\u001b[0m in \u001b[0;36m<cell line: 18>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclassification_report\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mobject_detection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mvisualization_utils\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mvis_util\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mobject_detection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlabel_map_util\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# Replace with paths to your data and pre-trained model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/object_detection/utils/label_map_util.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotobuf\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtext_format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mobject_detection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotos\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstring_int_label_map_pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: cannot import name 'string_int_label_map_pb2' from 'object_detection.protos' (/usr/local/lib/python3.10/dist-packages/object_detection/protos/__init__.py)",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "mtNFwR50fo5B",
        "outputId": "0d2374f3-727c-4c5e-bcf3-959ac729650e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/data'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "shutil.copytree(\"/content/drive/MyDrive/AI ML DATA/solar/archive (3)/dataset_2/\",\"/content/data\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kvHqThcf_e8",
        "outputId": "4cef6aaf-527e-4e12-8fd0-75622412516e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Path to pre-trained model (.pb) file\n",
        "model_path = os.path.join(data_dir, 'models', model_name, 'frozen_inference_graph.pb')\n",
        "\n",
        "# Load the model\n",
        "detection_model = tf.saved_model.load(model_path)\n",
        "\n",
        "# Path to label map (.pbtxt) file\n",
        "category_index = label_map_util.create_category_index_from_labelmap(\n",
        "    os.path.join(data_dir, 'annotations', 'label_map.pbtxt'))\n"
      ],
      "metadata": {
        "id": "0wKs1mzQfxpV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def run_inference_on_image(image):\n",
        "  # Preprocess image for model input\n",
        "  image = np.expand_dims(image, axis=0)\n",
        "  image = np.uint8(255.0 * image)\n",
        "\n",
        "  # Run model inference\n",
        "  input_tensor = tf.convert_to_tensor(image)\n",
        "  detections = detection_model(input_tensor)\n",
        "\n",
        "  # Extract relevant outputs (boxes, scores, classes)\n",
        "  num_detections = int(detections['num_detections'][0])\n",
        "  detection_boxes = detections['detection_boxes'][0][:num_detections]\n",
        "  detection_scores = detections['detection_scores'][0][:num_detections]\n",
        "  detection_classes = np.array(detections['detection_classes'][0][:num_detections], dtype=np.uint8) + 1\n",
        "\n",
        "  return detection_boxes, detection_scores, detection_classes\n"
      ],
      "metadata": {
        "id": "NSRxuWiXf0KA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load your image\n",
        "image = ...\n",
        "\n",
        "# Run inference\n",
        "boxes, scores, classes = run_inference_on_image(image)\n",
        "\n",
        "# Visualize detections (optional)\n",
        "vis_util.visualize_boxes_and_labels_on_image_array(\n",
        "    image, boxes, classes, scores, category_index,\n",
        "    instance_mask=None,\n",
        "    use_normalized_coordinates=True,\n",
        "    line_thickness=8)\n",
        "\n",
        "# Display or save the image with detections\n",
        "...\n"
      ],
      "metadata": {
        "id": "roGH_9NBf2fA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# YOLOV8 CODE\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "X4MiLlMBCNyk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "HOME = os.getcwd()\n",
        "print(HOME)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NtF2ijqo7Wy7",
        "outputId": "707fe886-9b29-4020-9687-a3ee1b2597f2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pip install method (recommended)\n",
        "\n",
        "!pip install ultralytics==8.0.196\n",
        "\n",
        "from IPython import display\n",
        "display.clear_output()\n",
        "\n",
        "import ultralytics\n",
        "ultralytics.checks()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkD-SI8k7bzL",
        "outputId": "181c61df-e821-4fab-b08b-c2391da4d815"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Ultralytics YOLOv8.0.196 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 29.0/107.7 GB disk)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ultralytics import YOLO\n",
        "\n",
        "from IPython.display import display, Image"
      ],
      "metadata": {
        "id": "lgPJW-Zq7mN2"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shutil.rmtree(\"/content/__MACOSX\")"
      ],
      "metadata": {
        "id": "TYgAySsn7wpU"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "\n",
        "# Path to the zip file\n",
        "zip_file_path = '/content/drive/MyDrive/AI ML DATA/solar/data/data.zip'\n",
        "\n",
        "# Destination directory to extract the files\n",
        "extract_dir = '/content/'\n",
        "\n",
        "# Create a ZipFile object\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    # Extract all the contents of zip file into the specified directory\n",
        "    zip_ref.extractall(extract_dir)\n",
        "\n",
        "print(\"Extraction complete.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g0m7FAAq8X2N",
        "outputId": "3673f558-1c91-48e3-9614-3918906e8618"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extraction complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JftBZaEJiv_",
        "outputId": "0eed9e34-88eb-447b-84a4-82cbe0211639"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: nvidia-smi: command not found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd {HOME}\n",
        "!yolo task=detect mode=train model=yolov8s.pt data=/content/data/data.yaml epochs=20 imgsz=600"
      ],
      "metadata": {
        "id": "s5cvVUzZ9ved"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd {HOME}\n",
        "imgurl = \"/content/data/test/images/20170717_150451_jpg.rf.b2af1321ffe778cc4c642e354f5b84b9.jpg\"\n",
        "!yolo task=detect mode=predict model=yolov8s.pt conf=0.25 source={imgurl} save=True"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LyUosoSb_BsQ",
        "outputId": "24b3d663-925f-43ed-f064-41c71651da0c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov5\n",
            "Ultralytics YOLOv8.0.196 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "YOLOv8s summary (fused): 168 layers, 11156544 parameters, 0 gradients, 28.6 GFLOPs\n",
            "\n",
            "image 1/1 /content/data/test/images/20170717_150451_jpg.rf.b2af1321ffe778cc4c642e354f5b84b9.jpg: 640x640 1 toothbrush, 667.9ms\n",
            "Speed: 20.3ms preprocess, 667.9ms inference, 18.7ms postprocess per image at shape (1, 3, 640, 640)\n",
            "Results saved to \u001b[1mruns/detect/predict3\u001b[0m\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd {HOME}\n",
        "Image(filename='runs/detect/predict3/20170717_150451_jpg.rf.b2af1321ffe778cc4c642e354f5b84b9.jpg', height=600)\n",
        "# /content/yolov5/runs/detect/predict2/20170710_145114_jpg.rf.dddf919c3e547e6bff4170067ed771f3.jpg\n",
        "# /content/yolov5/runs/detect/predict3/20170717_150451_jpg.rf.b2af1321ffe778cc4c642e354f5b84b9.jpg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "id": "WJT-uydvA2No",
        "outputId": "edf8bd5c-d118-4925-b091-580c14d1fdeb"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov5\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "height": 600
            }
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tensorboard --logdir runs/detect/train2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "qyF5j1rPBnmj",
        "outputId": "aad612f3-74ed-464c-c091-c301772b58f5"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "invalid syntax (<ipython-input-28-067b92719ddc>, line 1)",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-28-067b92719ddc>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    tensorboard --logdir runs/detect/train2\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1Yiak_hkFCq1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}