BE5740_Final_Algorithm_pt1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dJeId6KodKft"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Installing necessary packages\n",
        "\n",
        "!pip install torch torchvision\n",
        "!pip install light-the-torch >> /.tmp\n",
        "!ltt install torch torchvision >> /.tmp\n",
        "!pip install fastai --upgrade >> /.tmp"
      ],
      "metadata": {
        "id": "nUEgrbM_BpvF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "38a8957a-417a-4532-fa06-430ebc68370a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n# Installing necessary packages\\n\\n!pip install torch torchvision\\n!pip install light-the-torch >> /.tmp\\n!ltt install torch torchvision >> /.tmp\\n!pip install fastai --upgrade >> /.tmp\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing necessary packages\n",
        "import pandas as pd\n",
        "import os\n",
        "import glob\n",
        "from torch.utils.data import Dataset\n",
        "from PIL import Image\n",
        "import torch\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader, random_split\n",
        "from google.colab import drive\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import DataLoader, WeightedRandomSampler, TensorDataset\n",
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "xCFCgc0hElO5"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mounting the Google Drive to import all the necessary files\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9EyJHczion4",
        "outputId": "b8104868-a58a-4ca8-e520-4cd3c5cc2706"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting up the DataFrames containing all the necessary images and labels\n",
        "\n",
        "img_dir = '/content/drive/Shareddrives/BE5740/Dataset/spectrograms_zip/spectrograms'\n",
        "\n",
        "labels_csv_path = '/content/drive/Shareddrives/BE5740/Dataset/mood/combined_labels.csv'\n",
        "combined_labels_df = pd.read_csv(labels_csv_path)\n",
        "combined_labels_df = pd.read_csv(labels_csv_path)\n",
        "combined_labels_df['image_path'] = combined_labels_df['filename'].apply(lambda x: os.path.join(img_dir, x))\n",
        "combined_labels_df = combined_labels_df[combined_labels_df['image_path'].apply(os.path.isfile)]\n",
        "\n",
        "print(combined_labels_df.head())"
      ],
      "metadata": {
        "id": "9ts3VpvMFj-G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66bca502-48a7-4cbb-c485-486aefeea494"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          filename  label participant_id  \\\n",
            "977  307_seg_1.png      1            307   \n",
            "978  307_seg_2.png      1            307   \n",
            "979  307_seg_3.png      1            307   \n",
            "980  307_seg_4.png      1            307   \n",
            "981  307_seg_5.png      0            307   \n",
            "\n",
            "                                            image_path  \n",
            "977  /content/drive/Shareddrives/BE5740/Dataset/spe...  \n",
            "978  /content/drive/Shareddrives/BE5740/Dataset/spe...  \n",
            "979  /content/drive/Shareddrives/BE5740/Dataset/spe...  \n",
            "980  /content/drive/Shareddrives/BE5740/Dataset/spe...  \n",
            "981  /content/drive/Shareddrives/BE5740/Dataset/spe...  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting up a labeled dataset for training\n",
        "\n",
        "class LabeledDataset(Dataset):\n",
        "    def __init__(self, annotations_df, transform=None):\n",
        "        self.img_labels = annotations_df\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.img_labels)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_path = self.img_labels.iloc[idx]['image_path']\n",
        "        image = Image.open(img_path).convert('L')  # 'L' mode for grayscale\n",
        "        label = self.img_labels.iloc[idx]['label']\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "        return image, label"
      ],
      "metadata": {
        "id": "NKRyjYbxIAG0"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a transform for our datasets\n",
        "\n",
        "transform = transforms.Compose([\n",
        "    transforms.Grayscale(num_output_channels=1),\n",
        "    transforms.Resize((128, 128)),\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "\n",
        "# Transforming the datasets using the transform created\n",
        "dataset = LabeledDataset(annotations_df=combined_labels_df, transform=transform)\n",
        "\n",
        "# Setting up the train/test split\n",
        "train_size = int(0.9 * len(dataset))\n",
        "val_size = len(dataset) - train_size\n",
        "train_dataset, val_dataset = random_split(dataset, [train_size, val_size])\n",
        "\n",
        "# Creating dataloaders to iterate through the training and testing data\n",
        "train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=128, shuffle=False)"
      ],
      "metadata": {
        "id": "mKhQgFCjIH-2"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a function to show a few of the images to ensure our transform of the datasets worked\n",
        "def show_images(dataloader, num_images=6):\n",
        "    # Get a batch of images and labels\n",
        "    images, labels = next(iter(dataloader))\n",
        "\n",
        "    # Set up the figure\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    for i in range(num_images):\n",
        "        ax = plt.subplot(1, num_images, i + 1)\n",
        "        plt.tight_layout()\n",
        "        ax.set_title(f'Label: {labels[i].item()}')\n",
        "        ax.axis('off')\n",
        "\n",
        "        # Display the image; images are in grayscale\n",
        "        plt.imshow(images[i].squeeze(), cmap='gray')  # Squeeze removes single-dimensional entries from the shape\n",
        "\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "id": "2kFHQnx6JPSk"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Showing 6 images as a sanity check\n",
        "show_images(train_loader, num_images=6)"
      ],
      "metadata": {
        "id": "OyAV11niJSIu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "outputId": "15250c40-aeaa-43d3-db5d-c7f7d86e6ce6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a function for our CNN to create our model\n",
        "class BinaryCNN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(BinaryCNN, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n",
        "        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n",
        "        self.fc1 = nn.Linear(64 * 16 * 16, 512)\n",
        "        self.fc2 = nn.Linear(512, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = self.pool(F.relu(self.conv3(x)))\n",
        "        x = x.view(-1, 64 * 16 * 16)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = torch.sigmoid(self.fc2(x))\n",
        "        return x"
      ],
      "metadata": {
        "id": "sQdoiSz1ITnL"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing the model\n",
        "model = BinaryCNN()\n",
        "model.load_state_dict(torch.load('/content/drive/Shareddrives/BE5740/model_weights_0422_3.pth'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yts0fuwkgSBQ",
        "outputId": "9b741fcf-0ef9-4c5d-f274-9f6618fae6b4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_counts = [21257, 15702]  # Number of samples for each class\n",
        "num_samples = sum(class_counts) # Total number of samples\n",
        "class_weights = [num_samples / class_count for class_count in class_counts] # Creating weights for the model\n",
        "\n",
        "# Getting all the weights and applying them to the sampler for the training dataloader\n",
        "sample_weights = [class_weights[label] for _, label in tqdm(train_dataset)]\n",
        "sampler = WeightedRandomSampler(sample_weights, num_samples=len(sample_weights), replacement=True)\n",
        "\n",
        "# Creating the dataloader with training data\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, sampler=sampler)\n",
        "\n",
        "# Creating the criterion and optimizer for the model\n",
        "criterion = nn.BCELoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    model.cuda()\n",
        "\n",
        "# Training loop\n",
        "num_epochs = 5\n",
        "loss_values = []\n",
        "\n",
        "# Training loop for the model\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "    for images, labels in tqdm(train_loader):\n",
        "        if torch.cuda.is_available():\n",
        "            images = images.cuda()\n",
        "            labels = labels.cuda()\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        labels = labels.float().unsqueeze(1)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item() * images.size(0)\n",
        "\n",
        "    epoch_loss = running_loss / len(train_loader.dataset)\n",
        "    loss_values.append(epoch_loss)\n",
        "    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {epoch_loss:.4f}')"
      ],
      "metadata": {
        "id": "x82LMFsAKjzH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "283f0d0b-e1b4-4726-fcd5-d40db3d30920"
      },
      "execution_count": 11,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 5866/5866 [13:22<00:00,  7.31it/s]\n",
            "100%|██████████| 184/184 [00:25<00:00,  7.33it/s]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5, Loss: 1.6048\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 184/184 [00:23<00:00,  7.75it/s]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 2/5, Loss: 0.4915\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 184/184 [00:23<00:00,  7.74it/s]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 3/5, Loss: 0.3238\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 184/184 [00:24<00:00,  7.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 4/5, Loss: 0.1494\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 184/184 [00:24<00:00,  7.53it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 5/5, Loss: 0.1597\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the training loss over the number of epochs\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(loss_values, label='Training Loss')\n",
        "plt.title('Loss vs. Epochs')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "z1YZL4j8Kmzr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "d0284f32-ea11-414e-99f2-e1c243dab72a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the weights for the model\n",
        "model_path = '/content/drive/Shareddrives/BE5740/model_weights_0422_3.pth'\n",
        "model = BinaryCNN()\n",
        "model.load_state_dict(torch.load(model_path))\n",
        "\n",
        "# Move model to GPU if available\n",
        "if torch.cuda.is_available():\n",
        "    model.cuda()\n",
        "\n",
        "model.eval()  # Set the model to evaluation mode"
      ],
      "metadata": {
        "id": "m1FZUQ4lNT2Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2be7a3ad-0b3a-4878-c7d8-e434e77d8dc7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BinaryCNN(\n",
              "  (conv1): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (fc1): Linear(in_features=16384, out_features=512, bias=True)\n",
              "  (fc2): Linear(in_features=512, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function for testing the model\n",
        "def test_model(loader, model):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    predictions = []  # List to store all predictions\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for images, labels in tqdm(loader):\n",
        "            if torch.cuda.is_available():\n",
        "                images = images.cuda()\n",
        "                labels = labels.cuda()\n",
        "\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            predictions.extend(predicted.cpu().numpy())  # Store predictions\n",
        "\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "    accuracy = 100 * correct / total\n",
        "    print(f'Accuracy of the model on the test images: {accuracy}%')\n",
        "    return np.array(predictions)"
      ],
      "metadata": {
        "id": "EPvWuyUzIcyp"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing the model\n",
        "a = test_model(val_loader, model)"
      ],
      "metadata": {
        "id": "F_qzyTNx_vWk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79064447-9cdc-4730-8726-625f4d645f7b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 6/6 [00:10<00:00,  1.67s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the model on the test images: 57.20858895705521%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the sum to ensure a sensible amount of 1s compare to 0s outputted\n",
        "sum(a)"
      ],
      "metadata": {
        "id": "9qoRxBiVD72h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0f06e61-3cf9-4a80-9ed1-d2e756e79e9b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the path for the test directory and create the dataframes\n",
        "test_dir = '/content/drive/Shareddrives/BE5740/Dataset/tests/380'\n",
        "\n",
        "test_csv_path = '/content/drive/Shareddrives/BE5740/Dataset/tests/380_labels.csv'\n",
        "test_labels_df = pd.read_csv(test_csv_path)\n",
        "test_labels_df['image_path'] = test_labels_df['filename'].apply(lambda x: os.path.join(test_dir, x))\n",
        "test_labels_df = test_labels_df[test_labels_df['image_path'].apply(os.path.isfile)]\n",
        "\n",
        "print(test_labels_df.head())\n",
        "\n",
        "# Initialize the dataset with the updated transformation\n",
        "test_dataset = LabeledDataset(annotations_df=test_labels_df, transform=transform)\n",
        "\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)"
      ],
      "metadata": {
        "id": "dd5g1ZI0_nOL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f32f776-04f0-423b-fac6-09f23da3e868"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        filename  label  net_mood  \\\n",
            "0  380_seg_1.png      0    -115.0   \n",
            "1  380_seg_2.png      1     115.0   \n",
            "2  380_seg_3.png      1     102.0   \n",
            "3  380_seg_4.png      0    -102.0   \n",
            "4  380_seg_5.png      0    -142.0   \n",
            "\n",
            "                                          image_path  \n",
            "0  /content/drive/Shareddrives/BE5740/Dataset/tes...  \n",
            "1  /content/drive/Shareddrives/BE5740/Dataset/tes...  \n",
            "2  /content/drive/Shareddrives/BE5740/Dataset/tes...  \n",
            "3  /content/drive/Shareddrives/BE5740/Dataset/tes...  \n",
            "4  /content/drive/Shareddrives/BE5740/Dataset/tes...  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the model with the testing data\n",
        "a = test_model(test_loader, model)"
      ],
      "metadata": {
        "id": "XVwmAZDvCFVy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ddaad773-665b-40b1-f71a-ff7013f358ba"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [03:21<00:00, 12.61s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the model on the test images: 81.67006109979633%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View all outputs as a sanity check\n",
        "print(a)"
      ],
      "metadata": {
        "id": "1GpNlYEZEqTP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33ff0ff9-b3c2-4fcc-bd89-60c3b7ac1601"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View the ground truth labels to compare to our predictions\n",
        "print(test_labels_df['label'].to_numpy())"
      ],
      "metadata": {
        "id": "maUiD5OIEyQL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "600c787d-5c83-4d52-de89-28edf99bf773"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 1 0 0 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 0 0 0 0 1 0 1\n",
            " 1 1 1 0 1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0\n",
            " 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0\n",
            " 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1\n",
            " 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0\n",
            " 0 0 0 0 0 0 0 1 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_net_mood(df, weights):\n",
        "    net_mood = sum(df[f'AU{au:02d}_c'] * weight for au, weight in weights.items())\n",
        "    return net_mood\n",
        "\n",
        "def process_and_label_images(facs_path, output_spectrogram_path, weights):\n",
        "    facs = pd.read_csv(facs_path)\n",
        "    facs['interval'] = (facs['frame'] - 1) // 120\n",
        "    c_columns = facs.filter(regex='_c$')  # Simplified column filtering\n",
        "    df = c_columns.groupby(facs['interval']).sum()\n",
        "    df['net_mood'] = calculate_net_mood(df, weights)\n",
        "\n",
        "\n",
        "    df['label'] = (df['net_mood'] > 0).astype(int)\n",
        "\n",
        "    participant_id = facs_path.split('/')[-2]  # Changed to capture directory name\n",
        "    labels_df = pd.DataFrame({\n",
        "        'filename': [f\"{participant_id}_seg_{i+1}.png\" for i in range(len(df))],\n",
        "        'label': df['label'],\n",
        "        'net_mood':df['net_mood']\n",
        "    })\n",
        "\n",
        "    # Ensure the directory exists before saving\n",
        "    os.makedirs(output_spectrogram_path, exist_ok=True)\n",
        "    labels_df.to_csv(os.path.join(output_spectrogram_path, f\"{participant_id}_labels.csv\"), index=False)\n",
        "\n",
        "def batch_csv_labeling(root_dir, output_spectrogram_path, weights):\n",
        "    for root, dirs, files in os.walk(root_dir):\n",
        "        for file in files:\n",
        "            if file.endswith(\"_Pose_gaze_AUs.csv\"):\n",
        "                facs_path = os.path.join(root, file)\n",
        "                process_and_label_images(facs_path, output_spectrogram_path, weights)\n",
        "                print(f\"Processed {file}\")"
      ],
      "metadata": {
        "id": "6pv5uTzvFgKG"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weights = {1: -2, 4: -2, 15: -2, 6: 3, 12: 4, 2: 0, 5: 0, 7: -1, 20: 0, 26: -1}\n",
        "\n",
        "root_directory = '/content/drive/Shareddrives/BE5740/Dataset/All files/300-380/330'\n",
        "output_directory = '/content/drive/Shareddrives/BE5740/Dataset/tests'\n",
        "batch_csv_labeling(root_directory, output_directory, weights)"
      ],
      "metadata": {
        "id": "cG2Yu5kzGGra",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1312a8f4-5333-4894-8cc2-f92fbfa226ad"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed 330_OpenFace2.1.0_Pose_gaze_AUs.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def normalize_data(values):\n",
        "    min_val = min(values)\n",
        "    max_val = max(values)\n",
        "    normalized = [(x - min_val) / (max_val - min_val) for x in values]\n",
        "    return normalized"
      ],
      "metadata": {
        "id": "eh58gHacHxTc"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/Shareddrives/BE5740/Dataset/tests/330_labels.csv')\n",
        "y = df['label'].to_numpy()\n",
        "z = df['net_mood'].to_numpy()\n",
        "z = normalize_data(z)\n",
        "x = np.arange(len(y))*4\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.scatter(x,y, label = 'happiness')\n",
        "plt.scatter(x,z, label='net mood')\n",
        "plt.title('Mood calculation')\n",
        "\n",
        "plt.xlabel('seconds')\n",
        "plt.ylabel('happiness score')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "ozrTUp0OGmmA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "d220eaa4-5ceb-4c92-823d-c6a1765b1b07"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "saAkesiasgIX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}