diff --git a/Micromobility Lane Recognition/Dataset/Organized_dataset.csv b/Micromobility Lane Recognition/Dataset/Organized_dataset.csv
new file mode 100644
index 000000000..3562b9bd8
--- /dev/null
+++ b/Micromobility Lane Recognition/Dataset/Organized_dataset.csv
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diff --git a/Micromobility Lane Recognition/Dataset/README.md b/Micromobility Lane Recognition/Dataset/README.md
new file mode 100644
index 000000000..7833f8ba4
--- /dev/null
+++ b/Micromobility Lane Recognition/Dataset/README.md
@@ -0,0 +1,5 @@
+The Dataset was so messed up so here's an organzied csv file that contains all the image paths and their labels
+Organized_dataset.csv
+
+and here's the link to the dataset on kaggle webiste:
+https://www.kaggle.com/datasets/kaundanyachinmaya07/micromobility-lane-recognition-dataset
diff --git a/Micromobility Lane Recognition/Images/Bar-Chart-Label-Distribution.png b/Micromobility Lane Recognition/Images/Bar-Chart-Label-Distribution.png
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diff --git a/Micromobility Lane Recognition/Images/Dublin01-GH010157-000004_anon.png b/Micromobility Lane Recognition/Images/Dublin01-GH010157-000004_anon.png
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diff --git a/Micromobility Lane Recognition/Images/Grouped-Bar-Chart-for-Weather-Conditions.png b/Micromobility Lane Recognition/Images/Grouped-Bar-Chart-for-Weather-Conditions.png
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diff --git a/Micromobility Lane Recognition/Images/Paris-Paris_Dott02-000005_anon.png b/Micromobility Lane Recognition/Images/Paris-Paris_Dott02-000005_anon.png
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diff --git a/Micromobility Lane Recognition/Images/Pie-Chart-for-Surface-Type-Distribution.png b/Micromobility Lane Recognition/Images/Pie-Chart-for-Surface-Type-Distribution.png
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diff --git a/Micromobility Lane Recognition/Images/Singapore-Singapore-000023_anon.png b/Micromobility Lane Recognition/Images/Singapore-Singapore-000023_anon.png
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diff --git a/Micromobility Lane Recognition/Images/Stockholm-GH010128-000002_anon.png b/Micromobility Lane Recognition/Images/Stockholm-GH010128-000002_anon.png
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diff --git a/Micromobility Lane Recognition/Model/.ipynb_checkpoints/models-checkpoint.ipynb b/Micromobility Lane Recognition/Model/.ipynb_checkpoints/models-checkpoint.ipynb
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index 000000000..7e4c25fc3
--- /dev/null
+++ b/Micromobility Lane Recognition/Model/.ipynb_checkpoints/models-checkpoint.ipynb
@@ -0,0 +1,1901 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+ "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:26:03.053703Z",
+ "iopub.status.busy": "2024-06-18T13:26:03.053301Z",
+ "iopub.status.idle": "2024-06-18T13:26:11.109279Z",
+ "shell.execute_reply": "2024-06-18T13:26:11.108088Z",
+ "shell.execute_reply.started": "2024-06-18T13:26:03.053674Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd \n",
+ "from PIL import Image\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "import torch.nn as nn\n",
+ "import torch.optim as optim\n",
+ "import torch.nn.functional as F\n",
+ "from torchvision import transforms\n",
+ "\n",
+ "import torchvision.models as models\n",
+ "\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.utils import shuffle\n",
+ "import cv2\n",
+ "import os\n",
+ "\n",
+ "import pickle\n",
+ "\n",
+ "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:05.522351Z",
+ "iopub.status.busy": "2024-06-18T13:39:05.520337Z",
+ "iopub.status.idle": "2024-06-18T13:39:05.567549Z",
+ "shell.execute_reply": "2024-06-18T13:39:05.566444Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:05.522302Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "We have 18 Features\n",
+ "frame Type: int64\n",
+ "road Type: int64\n",
+ "sidewalk Type: int64\n",
+ "bikelane Type: int64\n",
+ "asphalt Type: int64\n",
+ "concrete Type: int64\n",
+ "cobblestone Type: int64\n",
+ "dirt Type: int64\n",
+ "grass Type: int64\n",
+ "day Type: int64\n",
+ "night Type: int64\n",
+ "indoor Type: int64\n",
+ "sunny Type: int64\n",
+ "cloudy Type: int64\n",
+ "raining Type: int64\n",
+ "on Type: float64\n",
+ "off Type: float64\n",
+ "Unnamed: 17 Type: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_DC = pd.read_csv('/kaggle/input/micromobility-lane-recognition-dataset/MLRD/labels/WashingtonDC/washingtonDC-01.csv')\n",
+ "\n",
+ "print(f'We have {len(df_DC.columns)} Features')\n",
+ "_ = [print(col, 'Type:', df_DC[col].dtype) for col in df_DC.columns]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:06.918969Z",
+ "iopub.status.busy": "2024-06-18T13:39:06.918562Z",
+ "iopub.status.idle": "2024-06-18T13:39:06.947838Z",
+ "shell.execute_reply": "2024-06-18T13:39:06.946800Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:06.918929Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ " frame road sidewalk bikelane asphalt concrete cobblestone dirt \\\n",
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+ " grass day night indoor sunny cloudy raining on off Unnamed: 17 \n",
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+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_DC.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:07.741654Z",
+ "iopub.status.busy": "2024-06-18T13:39:07.741233Z",
+ "iopub.status.idle": "2024-06-18T13:39:07.747955Z",
+ "shell.execute_reply": "2024-06-18T13:39:07.746767Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:07.741622Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Columns: ['frame', 'road', 'sidewalk', 'bikelane', 'asphalt', 'concrete', 'cobblestone', 'dirt', 'grass', 'day', 'night', 'indoor', 'sunny', 'cloudy', 'raining', 'on', 'off', 'Unnamed: 17']\n"
+ ]
+ }
+ ],
+ "source": [
+ "columns = df_DC.columns.tolist()\n",
+ "print('Columns:', columns)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Organize the Dataset into One CSV File"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Dataset is so messy, that we have to extract all image paths and put them into one new organized csv file"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Extract all Image Directories Paths"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:10.015376Z",
+ "iopub.status.busy": "2024-06-18T13:39:10.014966Z",
+ "iopub.status.idle": "2024-06-18T13:39:10.118330Z",
+ "shell.execute_reply": "2024-06-18T13:39:10.117191Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:10.015346Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# You Can Edit this to get the frames directory in the dataset directory \n",
+ "frames_path = '/kaggle/input/micromobility-lane-recognition-dataset/MLRD/frames'\n",
+ "# You Can Edit this to get the labels directory in the dataset directory\n",
+ "labels_path = '/kaggle/input/micromobility-lane-recognition-dataset/MLRD/labels'\n",
+ "\n",
+ "\n",
+ "# Dublin01\n",
+ "dublin_dirs = []\n",
+ "curr_dir = 'Dublin01'\n",
+ "input_image_path = os.path.join(frames_path, curr_dir)\n",
+ "input_label_path = os.path.join(labels_path, curr_dir)\n",
+ "dublin_dirs = [os.path.join(input_image_path, d) for d in os.listdir(input_image_path) if d != 'journeys']\n",
+ "\n",
+ "curr_dir = 'journeys'\n",
+ "journey_dirs = []\n",
+ "input_image_path = os.path.join(input_image_path, curr_dir)\n",
+ "for d1 in os.listdir(input_image_path):\n",
+ " d1_path = os.path.join(input_image_path, d1)\n",
+ " for d2 in os.listdir(d1_path):\n",
+ " d2_path = os.path.join(d1_path, d2)\n",
+ " for d3 in os.listdir(d2_path):\n",
+ " d3_path = os.path.join(d2_path, d3)\n",
+ " journey_dirs.append(d3_path)\n",
+ "\n",
+ "dublin_dirs.extend(journey_dirs)\n",
+ "\n",
+ "\n",
+ "#Northampton02\n",
+ "northampton_dirs = []\n",
+ "northampton_dirs.append(os.path.join(frames_path, 'Northampton02/Northampton02-vid-14-15-17'))\n",
+ "\n",
+ "#Northampton04\n",
+ "curr_dir = os.path.join(frames_path, 'Northampton04')\n",
+ "for d in os.listdir(curr_dir):\n",
+ " d_path = os.path.join(curr_dir, d)\n",
+ " northampton_dirs.append(d_path)\n",
+ "\n",
+ "#Paris\n",
+ "curr_dir = os.path.join(frames_path, 'Paris')\n",
+ "paris_dirs = []\n",
+ "paris_dirs.append(os.path.join(curr_dir, 'Paris_Dott02'))\n",
+ "paris_dirs.append(os.path.join(curr_dir, 'Paris_Dott03'))\n",
+ "\n",
+ "#Singapore\n",
+ "curr_dir = os.path.join(frames_path, 'Singapore/Singapore')\n",
+ "singapore_dir = []\n",
+ "singapore_dir.append(curr_dir)\n",
+ "\n",
+ "#Stockholm\n",
+ "curr_dir = os.path.join(frames_path, 'Stockholm')\n",
+ "stockholm_dirs = [os.path.join(curr_dir, d) for d in os.listdir(curr_dir)]\n",
+ "\n",
+ "#WashingtonDC\n",
+ "curr_dir = os.path.join(frames_path, 'WashingtonDC/washingtonDC-01')\n",
+ "DC_dir = []\n",
+ "DC_dir.append(curr_dir)\n",
+ "\n",
+ "\n",
+ "all_dir_paths = []\n",
+ "all_dir_paths.extend(dublin_dirs)\n",
+ "all_dir_paths.extend(northampton_dirs)\n",
+ "all_dir_paths.extend(paris_dirs)\n",
+ "all_dir_paths.extend(singapore_dir)\n",
+ "all_dir_paths.extend(stockholm_dirs)\n",
+ "all_dir_paths.extend(DC_dir)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create a New Directory for the Organzied Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:11.061438Z",
+ "iopub.status.busy": "2024-06-18T13:39:11.061038Z",
+ "iopub.status.idle": "2024-06-18T13:39:11.067439Z",
+ "shell.execute_reply": "2024-06-18T13:39:11.066247Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:11.061409Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "root_dir = os.getcwd()\n",
+ "os.makedirs(os.path.join(root_dir, 'Dataset'), exist_ok=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Extract Image Paths"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:12.524218Z",
+ "iopub.status.busy": "2024-06-18T13:39:12.523748Z",
+ "iopub.status.idle": "2024-06-18T13:39:12.530257Z",
+ "shell.execute_reply": "2024-06-18T13:39:12.529040Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:12.524183Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def extract_images(dir_path):\n",
+ " images = [f for f in os.listdir(dir_path) if f.endswith('png')]\n",
+ " return images"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Drop NaN Columns + Extract Labels \n",
+ " \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:13.082214Z",
+ "iopub.status.busy": "2024-06-18T13:39:13.081768Z",
+ "iopub.status.idle": "2024-06-18T13:39:13.090265Z",
+ "shell.execute_reply": "2024-06-18T13:39:13.088898Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:13.082169Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def extract_labels(d_path):\n",
+ " csv_file_path = d_path.replace('frames', 'labels') + '.csv'\n",
+ " df_labels = pd.read_csv(csv_file_path)\n",
+ "\n",
+ " if 'frame' in df_labels.columns:\n",
+ " df_labels.set_index('frame', inplace=True)\n",
+ " # Reset index to start from 1\n",
+ " df_labels.index = range(1, len(df_labels) + 1)\n",
+ "\n",
+ " # Drop 'frame' column if it exists\n",
+ " if 'frame' in df_labels.columns:\n",
+ " df_labels.drop('frame', axis=1, inplace=True)\n",
+ "\n",
+ " # Drop columns with NaN values\n",
+ " df_labels.dropna(axis=1, inplace=True)\n",
+ "\n",
+ " # Convert each row of DataFrame into a list of labels\n",
+ " all_labels = df_labels.values.tolist()\n",
+ "\n",
+ " return all_labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:14.166247Z",
+ "iopub.status.busy": "2024-06-18T13:39:14.165815Z",
+ "iopub.status.idle": "2024-06-18T13:39:14.172410Z",
+ "shell.execute_reply": "2024-06-18T13:39:14.170970Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:14.166216Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def combine_path_and_labels(image_paths, label_lists):\n",
+ " combined_data = []\n",
+ "\n",
+ " for path, labels in zip(image_paths, label_lists):\n",
+ " combined_data.append([path] + labels)\n",
+ "\n",
+ " return combined_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:14.445929Z",
+ "iopub.status.busy": "2024-06-18T13:39:14.445515Z",
+ "iopub.status.idle": "2024-06-18T13:39:22.155658Z",
+ "shell.execute_reply": "2024-06-18T13:39:22.154272Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:14.445883Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing complete for directory 1 / 65\n",
+ "Processing complete for directory 2 / 65\n",
+ "Processing complete for directory 3 / 65\n",
+ "Processing complete for directory 4 / 65\n",
+ "Processing complete for directory 5 / 65\n",
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+ "Processing complete for directory 11 / 65\n",
+ "Processing complete for directory 12 / 65\n",
+ "Processing complete for directory 13 / 65\n",
+ "Processing complete for directory 14 / 65\n",
+ "Processing complete for directory 15 / 65\n",
+ "Processing complete for directory 16 / 65\n",
+ "Processing complete for directory 17 / 65\n",
+ "Processing complete for directory 18 / 65\n",
+ "Processing complete for directory 19 / 65\n",
+ "Processing complete for directory 20 / 65\n",
+ "Processing complete for directory 21 / 65\n",
+ "Processing complete for directory 22 / 65\n",
+ "Processing complete for directory 23 / 65\n",
+ "Processing complete for directory 24 / 65\n",
+ "Processing complete for directory 25 / 65\n",
+ "Processing complete for directory 26 / 65\n",
+ "Processing complete for directory 27 / 65\n",
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+ "Processing complete for directory 49 / 65\n",
+ "Processing complete for directory 50 / 65\n",
+ "Processing complete for directory 51 / 65\n",
+ "Processing complete for directory 52 / 65\n",
+ "Processing complete for directory 53 / 65\n",
+ "Processing complete for directory 54 / 65\n",
+ "Processing complete for directory 55 / 65\n",
+ "Processing complete for directory 56 / 65\n",
+ "Processing complete for directory 57 / 65\n",
+ "Processing complete for directory 58 / 65\n",
+ "Processing complete for directory 59 / 65\n",
+ "Processing complete for directory 60 / 65\n",
+ "Processing complete for directory 61 / 65\n",
+ "Processing complete for directory 62 / 65\n",
+ "Processing complete for directory 63 / 65\n",
+ "Processing complete for directory 64 / 65\n",
+ "Processing complete for directory 65 / 65\n"
+ ]
+ }
+ ],
+ "source": [
+ "output_rows = []\n",
+ "# city_image_counts = {city: 0 for city in cities}\n",
+ "\n",
+ "for d_num, d_path in enumerate(all_dir_paths, start=1):\n",
+ "\n",
+ " # Extract images\n",
+ " images_names = extract_images(d_path)\n",
+ " images_paths = [os.path.join(d_path, image_name) for image_name in images_names]\n",
+ "\n",
+ " # Extract labels\n",
+ " all_labels = extract_labels(d_path)\n",
+ "\n",
+ " # Combine each image path + labels\n",
+ " output_rows_per_path = combine_path_and_labels(images_paths, all_labels)\n",
+ "\n",
+ " # Append to output_rows\n",
+ " output_rows.extend(output_rows_per_path)\n",
+ "\n",
+ " print(f\"Processing complete for directory {d_num} / {len(all_dir_paths)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create a New CSV File"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:22.157746Z",
+ "iopub.status.busy": "2024-06-18T13:39:22.157411Z",
+ "iopub.status.idle": "2024-06-18T13:39:22.163087Z",
+ "shell.execute_reply": "2024-06-18T13:39:22.161721Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:22.157718Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Load and preprocess data\n",
+ "organized_dataset_csv = os.path.join(root_dir, 'Dataset/Organized_dataset.csv')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define the New Columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:33.993090Z",
+ "iopub.status.busy": "2024-06-18T13:41:33.992605Z",
+ "iopub.status.idle": "2024-06-18T13:41:33.998449Z",
+ "shell.execute_reply": "2024-06-18T13:41:33.997384Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:33.993055Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "new_labels= columns[1:-3]\n",
+ "new_columns = ['image_path'] + new_labels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Write the Dataset into the New CSV File"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:34.354929Z",
+ "iopub.status.busy": "2024-06-18T13:41:34.354538Z",
+ "iopub.status.idle": "2024-06-18T13:41:34.863591Z",
+ "shell.execute_reply": "2024-06-18T13:41:34.862381Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:34.354885Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "output_df = pd.DataFrame(output_rows, columns=new_columns)\n",
+ "output_df.to_csv(organized_dataset_csv, index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read the New CSV File"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:36.703674Z",
+ "iopub.status.busy": "2024-06-18T13:41:36.703301Z",
+ "iopub.status.idle": "2024-06-18T13:41:36.806002Z",
+ "shell.execute_reply": "2024-06-18T13:41:36.805026Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:36.703646Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(organized_dataset_csv)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Show the new Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:38.735836Z",
+ "iopub.status.busy": "2024-06-18T13:41:38.734652Z",
+ "iopub.status.idle": "2024-06-18T13:41:38.751366Z",
+ "shell.execute_reply": "2024-06-18T13:41:38.750202Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:38.735797Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
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+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get to Know the Dataset - EDA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:44:35.383656Z",
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+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Bar Chart for Label Distribution \n",
+ "label_columns = new_labels\n",
+ "label_counts = df[label_columns].sum()\n",
+ "\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "label_counts.plot(kind='bar')\n",
+ "plt.title('Label Distribution')\n",
+ "plt.xlabel('Labels')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:46:49.088953Z",
+ "iopub.status.busy": "2024-06-18T13:46:49.088453Z",
+ "iopub.status.idle": "2024-06-18T13:46:49.547106Z",
+ "shell.execute_reply": "2024-06-18T13:46:49.545961Z",
+ "shell.execute_reply.started": "2024-06-18T13:46:49.088898Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Grouped Bar Chart for Time of Day\n",
+ "\n",
+ "day_night_counts = df.groupby('day')[label_columns].sum().T\n",
+ "night_counts = df.groupby('night')[label_columns].sum().T\n",
+ "\n",
+ "day_night_counts.plot(kind='bar', figsize=(12, 8))\n",
+ "plt.title('Label Distribution by Time of Day')\n",
+ "plt.xlabel('Labels')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.legend(['Day', 'Night'])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:47:23.091671Z",
+ "iopub.status.busy": "2024-06-18T13:47:23.091277Z",
+ "iopub.status.idle": "2024-06-18T13:47:23.759664Z",
+ "shell.execute_reply": "2024-06-18T13:47:23.758584Z",
+ "shell.execute_reply.started": "2024-06-18T13:47:23.091640Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Grouped Bar Chart for Weather Condition\n",
+ "\n",
+ "weather_conditions = ['sunny', 'cloudy', 'raining']\n",
+ "weather_counts = {condition: df[df[condition] == 1][label_columns].sum() for condition in weather_conditions}\n",
+ "\n",
+ "weather_df = pd.DataFrame(weather_counts)\n",
+ "weather_df.plot(kind='bar', figsize=(12, 8))\n",
+ "plt.title('Label Distribution by Weather Condition')\n",
+ "plt.xlabel('Labels')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.legend(weather_conditions)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:48:44.928985Z",
+ "iopub.status.busy": "2024-06-18T13:48:44.928484Z",
+ "iopub.status.idle": "2024-06-18T13:48:45.164967Z",
+ "shell.execute_reply": "2024-06-18T13:48:45.163439Z",
+ "shell.execute_reply.started": "2024-06-18T13:48:44.928896Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Pie Chart for Sufrace Type Distribution\n",
+ "\n",
+ "surface_labels = ['asphalt', 'concrete', 'cobblestone', 'dirt', 'grass']\n",
+ "surface_counts = df[surface_labels].sum()\n",
+ "\n",
+ "plt.figure(figsize=(8, 8))\n",
+ "surface_counts.plot(kind='pie', autopct='%1.1f%%')\n",
+ "plt.title('Surface Type Distribution')\n",
+ "plt.ylabel('')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Prepare the Data before Passing it to the Models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### For CNN and ResNet Models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:41.435739Z",
+ "iopub.status.busy": "2024-06-18T13:41:41.435348Z",
+ "iopub.status.idle": "2024-06-18T13:41:41.444477Z",
+ "shell.execute_reply": "2024-06-18T13:41:41.443105Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:41.435710Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Custom dataset class\n",
+ "class ImageDataset(Dataset):\n",
+ " def __init__(self, dataframe, transform=None):\n",
+ " self.dataframe = dataframe\n",
+ " self.transform = transform\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.dataframe)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " img_name = self.dataframe.iloc[idx, 0]\n",
+ " image = Image.open(img_name).convert('RGB')\n",
+ " labels = self.dataframe.iloc[idx, 1:].values.astype('float32')\n",
+ " \n",
+ " if self.transform:\n",
+ " image = self.transform(image) \n",
+ " \n",
+ " \n",
+ " return {'image': image, 'labels': torch.tensor(labels)}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Split the dataset into 80% Training and 20% Testing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:45.483018Z",
+ "iopub.status.busy": "2024-06-18T13:41:45.482561Z",
+ "iopub.status.idle": "2024-06-18T13:41:45.506669Z",
+ "shell.execute_reply": "2024-06-18T13:41:45.503258Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:45.482982Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:47.336940Z",
+ "iopub.status.busy": "2024-06-18T13:41:47.336502Z",
+ "iopub.status.idle": "2024-06-18T13:41:47.343369Z",
+ "shell.execute_reply": "2024-06-18T13:41:47.342100Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:47.336894Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((128, 128)),\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize((0.5,), (0.5,))\n",
+ "])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Transform the Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:48.582690Z",
+ "iopub.status.busy": "2024-06-18T13:41:48.582285Z",
+ "iopub.status.idle": "2024-06-18T13:41:48.587670Z",
+ "shell.execute_reply": "2024-06-18T13:41:48.586546Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:48.582658Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "train_dataset = ImageDataset(train_df, transform=transform)\n",
+ "test_dataset = ImageDataset(test_df, transform=transform)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define some hyperparameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:43:00.160798Z",
+ "iopub.status.busy": "2024-06-18T13:43:00.160337Z",
+ "iopub.status.idle": "2024-06-18T13:43:00.165953Z",
+ "shell.execute_reply": "2024-06-18T13:43:00.164793Z",
+ "shell.execute_reply.started": "2024-06-18T13:43:00.160765Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "batch_size = 32\n",
+ "img_size = (224, 224)\n",
+ "epochs = 10"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load the Transformed Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:43:07.332674Z",
+ "iopub.status.busy": "2024-06-18T13:43:07.332297Z",
+ "iopub.status.idle": "2024-06-18T13:43:07.339106Z",
+ "shell.execute_reply": "2024-06-18T13:43:07.337751Z",
+ "shell.execute_reply.started": "2024-06-18T13:43:07.332647Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)\n",
+ "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Declare the Models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Model 1: CNN"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-12T11:53:19.536299Z",
+ "iopub.status.busy": "2024-06-12T11:53:19.535305Z",
+ "iopub.status.idle": "2024-06-12T11:53:19.546671Z",
+ "shell.execute_reply": "2024-06-12T11:53:19.545283Z",
+ "shell.execute_reply.started": "2024-06-12T11:53:19.536261Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "class SimpleCNN(nn.Module):\n",
+ " def __init__(self):\n",
+ " super(SimpleCNN, self).__init__()\n",
+ " self.conv1 = nn.Conv2d(3, 32, 3, padding=1)\n",
+ " self.pool = nn.MaxPool2d(2, 2)\n",
+ " self.conv2 = nn.Conv2d(32, 64, 3, padding=1)\n",
+ " self.fc1 = nn.Linear(64 * 32 * 32, 128)\n",
+ " self.fc2 = nn.Linear(128, 14)\n",
+ " self.sigmoid = nn.Sigmoid()\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 = x.view(-1, 64 * 32 * 32)\n",
+ " x = F.relu(self.fc1(x))\n",
+ " x = self.sigmoid(self.fc2(x))\n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-12T11:53:19.704571Z",
+ "iopub.status.busy": "2024-06-12T11:53:19.704112Z",
+ "iopub.status.idle": "2024-06-12T11:53:19.814940Z",
+ "shell.execute_reply": "2024-06-12T11:53:19.813732Z",
+ "shell.execute_reply.started": "2024-06-12T11:53:19.704535Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "cnn_model = SimpleCNN()\n",
+ "criterion = nn.BCELoss()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Model 2: ResNet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-12T11:53:20.748540Z",
+ "iopub.status.busy": "2024-06-12T11:53:20.748091Z",
+ "iopub.status.idle": "2024-06-12T11:53:21.732992Z",
+ "shell.execute_reply": "2024-06-12T11:53:21.731712Z",
+ "shell.execute_reply.started": "2024-06-12T11:53:20.748506Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+ " warnings.warn(\n",
+ "/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
+ " warnings.warn(msg)\n"
+ ]
+ }
+ ],
+ "source": [
+ "resnet_model = models.resnet50(pretrained=True)\n",
+ "num_ftrs = resnet_model.fc.in_features\n",
+ "resnet_model.fc = nn.Sequential(\n",
+ " nn.Linear(num_ftrs, 14),\n",
+ " nn.Sigmoid()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Model 3: VGG16"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-12T11:53:23.563340Z",
+ "iopub.status.busy": "2024-06-12T11:53:23.562861Z",
+ "iopub.status.idle": "2024-06-12T11:53:31.875275Z",
+ "shell.execute_reply": "2024-06-12T11:53:31.874077Z",
+ "shell.execute_reply.started": "2024-06-12T11:53:23.563304Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
+ " warnings.warn(msg)\n"
+ ]
+ }
+ ],
+ "source": [
+ "vgg16_model = models.vgg16(pretrained=True)\n",
+ "num_ftrs = vgg16_model.classifier[6].in_features\n",
+ "vgg16_model.classifier[6] = nn.Sequential(\n",
+ " nn.Linear(num_ftrs, 14),\n",
+ " nn.Sigmoid()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Declare the Optimizers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-12T11:53:31.877956Z",
+ "iopub.status.busy": "2024-06-12T11:53:31.877551Z",
+ "iopub.status.idle": "2024-06-12T11:53:31.886363Z",
+ "shell.execute_reply": "2024-06-12T11:53:31.885198Z",
+ "shell.execute_reply.started": "2024-06-12T11:53:31.877924Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "cnn_optimizer = optim.Adam(cnn_model.parameters(), lr=0.001)\n",
+ "resnet_optimizer = optim.Adam(resnet_model.parameters(), lr=0.001)\n",
+ "vgg16_optimizer = optim.Adam(vgg16_model.parameters(), lr=0.001)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define Early Stopping Class - To Prevent Overfitting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-12T11:53:31.888383Z",
+ "iopub.status.busy": "2024-06-12T11:53:31.887958Z",
+ "iopub.status.idle": "2024-06-12T11:53:31.904451Z",
+ "shell.execute_reply": "2024-06-12T11:53:31.903175Z",
+ "shell.execute_reply.started": "2024-06-12T11:53:31.888341Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "class EarlyStopping:\n",
+ " def __init__(self, patience=3, verbose=False):\n",
+ " self.patience = patience\n",
+ " self.verbose = verbose\n",
+ " self.counter = 0\n",
+ " self.best_score = None\n",
+ " self.early_stop = False\n",
+ " self.val_loss_min = float('inf')\n",
+ "\n",
+ " def __call__(self, val_loss, model):\n",
+ " score = -val_loss\n",
+ "\n",
+ " if self.best_score is None:\n",
+ " self.best_score = score\n",
+ " self.save_checkpoint(val_loss, model)\n",
+ " elif score < self.best_score:\n",
+ " self.counter += 1\n",
+ " if self.verbose:\n",
+ " print(f'EarlyStopping counter: {self.counter} out of {self.patience}')\n",
+ " if self.counter >= self.patience:\n",
+ " self.early_stop = True\n",
+ " else:\n",
+ " self.best_score = score\n",
+ " self.save_checkpoint(val_loss, model)\n",
+ " self.counter = 0\n",
+ "\n",
+ " def save_checkpoint(self, val_loss, model):\n",
+ " if self.verbose:\n",
+ " print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')\n",
+ " torch.save(model.state_dict(), 'checkpoint.pt')\n",
+ " self.val_loss_min = val_loss\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Train the Models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-12T11:53:31.907627Z",
+ "iopub.status.busy": "2024-06-12T11:53:31.907203Z",
+ "iopub.status.idle": "2024-06-12T11:53:31.926296Z",
+ "shell.execute_reply": "2024-06-12T11:53:31.925067Z",
+ "shell.execute_reply.started": "2024-06-12T11:53:31.907580Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def train_model(model, dataloader, criterion, optimizer, num_epochs=10, threshold=0.5, patience=3):\n",
+ " train_losses = []\n",
+ " val_losses = []\n",
+ " train_accuracies = []\n",
+ " val_accuracies = []\n",
+ "\n",
+ " early_stopping = EarlyStopping(patience=patience, verbose=True)\n",
+ " \n",
+ " for epoch in range(num_epochs):\n",
+ " # Training phase\n",
+ " model.train()\n",
+ " running_loss = 0.0\n",
+ " correct_predictions = 0\n",
+ " total_predictions = 0\n",
+ " \n",
+ " for batch in dataloader['train']:\n",
+ " images = batch['image']\n",
+ " labels = batch['labels'].float() # Ensure labels are float32\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ " outputs = model(images)\n",
+ " loss = criterion(outputs, labels)\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " \n",
+ " running_loss += loss.item()\n",
+ " \n",
+ " # Calculate training accuracy\n",
+ " preds = outputs > threshold\n",
+ " correct_predictions += (preds == labels).sum().item()\n",
+ " total_predictions += labels.numel()\n",
+ " \n",
+ " train_loss = running_loss / len(dataloader['train'])\n",
+ " train_losses.append(train_loss)\n",
+ " train_accuracy = correct_predictions / total_predictions\n",
+ " train_accuracies.append(train_accuracy)\n",
+ " \n",
+ " # Validation phase\n",
+ " model.eval()\n",
+ " val_loss = 0.0\n",
+ " correct_predictions = 0\n",
+ " total_predictions = 0\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for batch in dataloader['val']:\n",
+ " images = batch['image']\n",
+ " labels = batch['labels'].float() # Ensure labels are float32\n",
+ " outputs = model(images)\n",
+ " loss = criterion(outputs, labels)\n",
+ " val_loss += loss.item()\n",
+ " \n",
+ " # Calculate validation accuracy\n",
+ " preds = outputs > threshold\n",
+ " correct_predictions += (preds == labels).sum().item()\n",
+ " total_predictions += labels.numel()\n",
+ " \n",
+ " val_loss = val_loss / len(dataloader['val'])\n",
+ " val_losses.append(val_loss)\n",
+ " val_accuracy = correct_predictions / total_predictions\n",
+ " val_accuracies.append(val_accuracy)\n",
+ " \n",
+ " print(f'Epoch {epoch+1}, Train Loss: {train_loss}, Train Accuracy: {train_accuracy}, Val Loss: {val_loss}, Val Accuracy: {val_accuracy}')\n",
+ " \n",
+ " early_stopping(val_loss, model)\n",
+ " \n",
+ " if early_stopping.early_stop:\n",
+ " print(\"Early stopping triggered\")\n",
+ " break\n",
+ " \n",
+ " model.load_state_dict(torch.load('checkpoint.pt'))\n",
+ " return train_losses, val_losses, train_accuracies, val_accuracies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-12T11:53:31.927980Z",
+ "iopub.status.busy": "2024-06-12T11:53:31.927630Z",
+ "iopub.status.idle": "2024-06-12T11:53:31.944102Z",
+ "shell.execute_reply": "2024-06-12T11:53:31.942826Z",
+ "shell.execute_reply.started": "2024-06-12T11:53:31.927942Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "dataloader = {'train': train_loader, 'val': test_loader}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Train Model 1 - CNN"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cnn_train_losses, cnn_val_losses, cnn_train_accuracies, cnn_val_accuracies = train_model(cnn_model, dataloader, criterion, cnn_optimizer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Train Model 2 - ResNet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "resnet_train_losses, resnet_val_losses, resnet_train_accuracies, resnet_val_accuracies = train_model(resnet_model, dataloader, criterion, resnet_optimizer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Train Model 3 - VGG16"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-12T11:53:36.248472Z",
+ "iopub.status.busy": "2024-06-12T11:53:36.248036Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+ " self.pid = os.fork()\n"
+ ]
+ }
+ ],
+ "source": [
+ "vgg16_train_losses, vgg16_val_losses, vgg16_train_accuracies, vgg16_val_accuracies = train_model(vgg16_model, dataloader, criterion, vgg16_optimizer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plot the Training and Validation Accuracies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot CNN accuracies \n",
+ "plt.figure(figsize=(10, 5)) \n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.plot(cnn_train_accuracies, label='CNN Train Accuracy') \n",
+ "plt.plot(cnn_val_accuracies, label='CNN Val Accuracy')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.title('CNN Training and Validation Accuracy')\n",
+ "plt.legend()\n",
+ "\n",
+ "# Plot ResNet accuracies\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.plot(resnet_train_accuracies, label='ResNet Train Accuracy')\n",
+ "plt.plot(resnet_val_accuracies, label='ResNet Val Accuracy')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.title('ResNet Training and Validation Accuracy')\n",
+ "plt.legend() \n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Plot VGG16 accuracies\n",
+ "plt.figure(figsize=(10, 5))\n",
+ "plt.plot(vgg16_train_accuracies, label='Train Accuracy')\n",
+ "plt.plot(vgg16_val_accuracies, label='Validation Accuracy')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.title('Training and Validation Accuracy per Epoch')\n",
+ "plt.legend()\n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "______________________________________________________________________________"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Try an approach other than DL approaches and see the difference - Logistic Regression"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Prepare the Data for it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "_kg_hide-input": false,
+ "_kg_hide-output": true
+ },
+ "outputs": [],
+ "source": [
+ "def extract_features(image_path):\n",
+ " image = cv2.imread(image_path)\n",
+ " image = cv2.resize(image, (128, 128)) # Resize image\n",
+ " return image.flatten()\n",
+ "\n",
+ "features = np.array([extract_features(path) for path in df['image_path']])\n",
+ "labels = df.iloc[:, 1:].values\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)\n",
+ "\n",
+ "# Convert multi-labels to a single multi-label binary vector\n",
+ "y_train_binary = np.array([np.argmax(labels) for labels in y_train])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Fit Dataset Batches to the Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "batch_size = 1000\n",
+ "\n",
+ "# Shuffle the data\n",
+ "X_train_shuffled, y_train_shuffled = shuffle(X_train, y_train_binary, random_state=42)\n",
+ "\n",
+ "# Determine the number of batches\n",
+ "num_batches = len(X_train) // batch_size\n",
+ "\n",
+ "# Train the model on each batch\n",
+ "for i in range(num_batches):\n",
+ " start_idx = i * batch_size\n",
+ " end_idx = (i + 1) * batch_size\n",
+ " X_batch = X_train_shuffled[start_idx:end_idx]\n",
+ " y_batch = y_train_shuffled[start_idx:end_idx]\n",
+ " \n",
+ " print(f\"Fitting batch {i+1}...\")\n",
+ " logreg.fit(X_batch, y_batch)\n",
+ " print(f\"Batch {i+1} fitted successfully.\")\n",
+ "\n",
+ "# Train the model on the remaining data (if any)\n",
+ "if len(X_train) % batch_size != 0:\n",
+ " print(f\"Fitting batch {num_batches+1} (remaining data)...\")\n",
+ " logreg.fit(X_train_shuffled[num_batches * batch_size:], y_train_shuffled[num_batches * batch_size:])\n",
+ " print(f\"Batch {num_batches+1} fitted successfully.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Evaluate the Model - Logistic Regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define batch size for testing\n",
+ "test_batch_size = 1000\n",
+ "y_test_binary = np.array([np.argmax(labels) for labels in y_test])\n",
+ "\n",
+ "# Shuffle the test data\n",
+ "X_test_shuffled, y_test_shuffled = shuffle(X_test, y_test_binary, random_state=42)\n",
+ "\n",
+ "# Determine the number of batches for testing\n",
+ "num_test_batches = len(X_test) // test_batch_size\n",
+ "\n",
+ "# Initialize empty array for predicted labels\n",
+ "y_pred_test = np.array([])\n",
+ "\n",
+ "# Predict on each batch of test data\n",
+ "for i in range(num_test_batches):\n",
+ " start_idx = i * test_batch_size\n",
+ " end_idx = (i + 1) * test_batch_size\n",
+ " X_test_batch = X_test_shuffled[start_idx:end_idx]\n",
+ " y_pred_test_batch = logreg.predict(X_test_batch)\n",
+ " y_pred_test = np.concatenate((y_pred_test, y_pred_test_batch))\n",
+ "\n",
+ "# Predict on the remaining test data (if any)\n",
+ "if len(X_test) % test_batch_size != 0:\n",
+ " remaining_test_data = X_test_shuffled[num_test_batches * test_batch_size:]\n",
+ " y_pred_test_remaining = logreg.predict(remaining_test_data)\n",
+ " y_pred_test = np.concatenate((y_pred_test, y_pred_test_remaining))\n",
+ "\n",
+ "# Calculate evaluation metrics for the test set\n",
+ "# Calculate evaluation metrics for the test set\n",
+ "precision_test = precision_score(y_test_binary, y_pred_test, average='micro')\n",
+ "recall_test = recall_score(y_test_binary, y_pred_test, average='micro')\n",
+ "f1_test = f1_score(y_test_binary, y_pred_test, average='micro')\n",
+ "\n",
+ "print(f\"Test Set Precision (Micro Average): {precision_test}\")\n",
+ "print(f\"Test Set Recall (Micro Average): {recall_test}\")\n",
+ "print(f\"Test Set F1 Score (Micro Average): {f1_test}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "-------------------------------------------------------------------------------------------------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## **Model - Train Accuracy - Testing Accuracy**\n",
+ "* ## CNN 98% 88%\n",
+ "* ## ResNet 94% 90%\n",
+ "* ## VGG16 - -\n",
+ "* ## Linear Regression 58% 10%"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# The Best Model would Be **ResNet** with accuracy 90%"
+ ]
+ }
+ ],
+ "metadata": {
+ "kaggle": {
+ "accelerator": "none",
+ "dataSources": [
+ {
+ "datasetId": 4908132,
+ "sourceId": 8267571,
+ "sourceType": "datasetVersion"
+ }
+ ],
+ "dockerImageVersionId": 30698,
+ "isGpuEnabled": false,
+ "isInternetEnabled": true,
+ "language": "python",
+ "sourceType": "notebook"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Micromobility Lane Recognition/Model/README.md b/Micromobility Lane Recognition/Model/README.md
new file mode 100644
index 000000000..05e639d82
--- /dev/null
+++ b/Micromobility Lane Recognition/Model/README.md
@@ -0,0 +1,125 @@
+# Micromobility Lane Recognition
+
+## 🎯 Goal
+The main purpose of this project is to **recognize weather condition, lane type, and day time of lines for micromobility** from the dataset (mentioned below) using various image detection/recognition models and comparing their accuracy.
+
+## 🧵 Dataset
+
+The link to the dataset is given below :-
+
+**Link :- *https://www.kaggle.com/datasets/kaundanyachinmaya07/micromobility-lane-recognition-dataset*
+
+## 🧾 Description
+
+This project involves the comparative analysis of three Keras image detection models, namely VGG16, InceptionV3, and MobileNet, applied to a specific dataset. The dataset consists of annotated images related to a particular domain, and the objectives include training and evaluating these models to compare their accuracy scores and performance metrics. Additionally, exploratory data analysis (EDA) techniques are employed to understand the dataset's characteristics, explore class distributions, detect imbalances, and identify areas for potential improvement. The methodology encompasses data preparation, model training, evaluation, comparative analysis of accuracy and performance metrics, and visualization of EDA insights.
+
+## 🧮 What I had done!
+
+### 1. Data Loading and Preparation:
+ Loaded the dataset from each directory and mapping them into 1 organized csv file.
+ Loaded the dataset from the new organized csv file, containing image paths and corresponding labels, into a pandas DataFrame for easy manipulation and analysis.
+
+### 2. Exploratory Data Analysis (EDA):
+ Bar Chart for Label Distribution: Created a bar chart to visualize the frequency distribution of different labels in the dataset.
+
+ Grouped Bar Chart for Time of Day: Created a bar chart to visualize the frequency distribution of the labels grouped by the time of the day: 'Day' and 'Night'.
+
+ Grouped Bar Chart for Weather Conditions: Created a bar chart to visualize the frequency distribution of the labels grouped by the time of the day: 'Sunny', 'Cloudy', and 'Raining'.
+
+ Pie Chart for Label Distribution: Generated a pie chart to represent the proportion of each surface type:'Asphalt', 'Concrete', 'Cobblestone', 'Dirt', and 'Grass'.
+
+### 3. Data Analysis:
+ Constructed a Label Co-Occurrence Matrix, by analyzing which labels tend to occur together, that helped in understanding potential correlations between labels.
+
+### 4. Data Preprocessing:
+ Dropped Nan Columns.
+ Resized, transformed and normalized the images.
+
+### 5. Model Training:
+ Divided the dataset into batches.
+ Iterated through multiple models (CNN, ResNet50, VGG16, Logistic Regression).
+ Used Early Stopping to prevent overfitting.
+
+
+## 🚀 Models Implemented
+
+Trained the dataset on various models , each of their summary is as follows :-
+
+### CNN
+I utilized Convolutional Neural Networks to enhance image recongintion and classification.
+
+### ResNet50
+I leveraged ResNet50, a deep Convolutional Neural Network, to improve image recognition and classification.
+
+### VGG16
+I tried to utilize the VGG16 (Visual Geometry Group) architecture, which have deeper and complex structures, but it took so long before being able to get any results.
+
+### Logistic Regression
+I used it just to make the difference clear between ML and DL approaches regarding Computer Vision Problems, it took time less than the other DL approaches, but the accuracy was so bad, because it couldn't capture enough details like the other DL models.
+
+
+## 📚 Libraries Needed
+
+1. **NumPy**: Fundamental package for numerical computing.
+2. **Pandas**: Data analysis and manipulation library.
+3. **Scikit-Learn**: Machine learning library for classification, regression, and clustering.
+4. **Matplotlib**: Plotting library for creating visualizations.
+5. **Pillow (PIL)**: Python Imaging Library for image processing.
+6. **OpenCV**: Library for computer vision tasks.
+7. **PyTorch**: Deep learning framework for building and training neural networks.
+8. **TorchVision**: Computer vision library that works with PyTorch, providing datasets, model architectures, and image transformations.
+9. **Pickle**: Python module for serializing and de-serializing objects.
+
+## 📊 Exploratory Data Analysis Results
+
+### Bar Charts :-
+
+- **Bar Chart for Label Distribution**: Created a bar chart to visualize the frequency distribution of different labels in the dataset.
+
+![alt text](../Images/Bar-Chart-Label-Distribution.png)
+
+- **Grouped Bar Chart for Time of Day**: Created a bar chart to visualize the frequency distribution of the labels grouped by the time of the day: 'Day' and 'Night'.
+
+![alt text](../Images/Grouped-Bar-Chart-for-Time-of-Day.png)
+
+- **Grouped Bar Chart for Weather Conditions**: Created a bar chart to visualize the frequency distribution of the labels grouped by the time of the day: 'Sunny', 'Cloudy', and 'Raining'.
+
+![alt text](../Images/Grouped-Bar-Chart-for-Weather-Conditions.png)
+
+### Pie Chart :-
+
+- **Pie Chart for Label Distribution**: Generated a pie chart to represent the proportion of each surface type:'Asphalt', 'Concrete', 'Cobblestone', 'Dirt', and 'Grass'.
+
+![alt text](../Images/Pie-Chart-for-Surface-Type-Distribution.png)
+
+### Label Co-Occurrence Matrix
+
+![alt text](../Images/co-occurrence-heatmap.png)
+
+## 📈 Performance of the Models based on the Accuracy Scores
+
+| Models | Accuracy Scores|
+|--------------------|------------|
+|CNN | 88% (Training Accuracy: 0.98) |
+|ResNet50 | 90% (Training Accuracy: 0.94) |
+|VGG16 | --% (Training Accuracy: ----) |
+|Logistic Regression | 10% (Training Accuracy: 0.58) |
+
+![alt text](../Images/CNN_ResNet_Accuracies.png)
+
+## 📢 Conclusion
+
+**Accoding to the accuracy scores it can be concluded that ResNet and a simple CNN architecture were able to perform good on this dataset.**
+
+While VGG16 took so much time so it was concluded.
+
+
+## ✒️ Your Signature
+
+Full name:- Basma Mahmoud
+Github ID:- https://github.com/Basma2423
+Email ID :- mayarbasma2423@gmail.com
+Participant Role :- Contributor / GSSOC (Girl Script Summer of Code ) - 2024
+
+
+Helping Reference for the README.md file: https://github.com/abhisheks008/DL-Simplified/blob/main/Rick%20and%20Morty%20Character%20Image%20Detection/Models/README.md
diff --git a/Micromobility Lane Recognition/Model/models.ipynb b/Micromobility Lane Recognition/Model/models.ipynb
new file mode 100644
index 000000000..3ac73e676
--- /dev/null
+++ b/Micromobility Lane Recognition/Model/models.ipynb
@@ -0,0 +1,2072 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+ "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd \n",
+ "from PIL import Image\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "import torch.nn as nn\n",
+ "import torch.optim as optim\n",
+ "import torch.nn.functional as F\n",
+ "from torchvision import transforms\n",
+ "\n",
+ "import torchvision.models as models\n",
+ "\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.utils import shuffle\n",
+ "import cv2\n",
+ "import os\n",
+ "\n",
+ "import pickle\n",
+ "\n",
+ "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:05.522351Z",
+ "iopub.status.busy": "2024-06-18T13:39:05.520337Z",
+ "iopub.status.idle": "2024-06-18T13:39:05.567549Z",
+ "shell.execute_reply": "2024-06-18T13:39:05.566444Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:05.522302Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "We have 18 Features\n",
+ "frame Type: int64\n",
+ "road Type: int64\n",
+ "sidewalk Type: int64\n",
+ "bikelane Type: int64\n",
+ "asphalt Type: int64\n",
+ "concrete Type: int64\n",
+ "cobblestone Type: int64\n",
+ "dirt Type: int64\n",
+ "grass Type: int64\n",
+ "day Type: int64\n",
+ "night Type: int64\n",
+ "indoor Type: int64\n",
+ "sunny Type: int64\n",
+ "cloudy Type: int64\n",
+ "raining Type: int64\n",
+ "on Type: float64\n",
+ "off Type: float64\n",
+ "Unnamed: 17 Type: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_DC = pd.read_csv('/kaggle/input/micromobility-lane-recognition-dataset/MLRD/labels/WashingtonDC/washingtonDC-01.csv')\n",
+ "\n",
+ "print(f'We have {len(df_DC.columns)} Features')\n",
+ "_ = [print(col, 'Type:', df_DC[col].dtype) for col in df_DC.columns]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:06.918969Z",
+ "iopub.status.busy": "2024-06-18T13:39:06.918562Z",
+ "iopub.status.idle": "2024-06-18T13:39:06.947838Z",
+ "shell.execute_reply": "2024-06-18T13:39:06.946800Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:06.918929Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_DC.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:07.741654Z",
+ "iopub.status.busy": "2024-06-18T13:39:07.741233Z",
+ "iopub.status.idle": "2024-06-18T13:39:07.747955Z",
+ "shell.execute_reply": "2024-06-18T13:39:07.746767Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:07.741622Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Columns: ['frame', 'road', 'sidewalk', 'bikelane', 'asphalt', 'concrete', 'cobblestone', 'dirt', 'grass', 'day', 'night', 'indoor', 'sunny', 'cloudy', 'raining', 'on', 'off', 'Unnamed: 17']\n"
+ ]
+ }
+ ],
+ "source": [
+ "columns = df_DC.columns.tolist()\n",
+ "print('Columns:', columns)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Organize the Dataset into One CSV File"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Dataset is so messy, that we have to extract all image paths and put them into one new organized csv file"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Extract all Image Directories Paths"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:10.015376Z",
+ "iopub.status.busy": "2024-06-18T13:39:10.014966Z",
+ "iopub.status.idle": "2024-06-18T13:39:10.118330Z",
+ "shell.execute_reply": "2024-06-18T13:39:10.117191Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:10.015346Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# You Can Edit this to get the frames directory in the dataset directory \n",
+ "frames_path = '/kaggle/input/micromobility-lane-recognition-dataset/MLRD/frames'\n",
+ "# You Can Edit this to get the labels directory in the dataset directory\n",
+ "labels_path = '/kaggle/input/micromobility-lane-recognition-dataset/MLRD/labels'\n",
+ "\n",
+ "\n",
+ "# Dublin01\n",
+ "dublin_dirs = []\n",
+ "curr_dir = 'Dublin01'\n",
+ "input_image_path = os.path.join(frames_path, curr_dir)\n",
+ "input_label_path = os.path.join(labels_path, curr_dir)\n",
+ "dublin_dirs = [os.path.join(input_image_path, d) for d in os.listdir(input_image_path) if d != 'journeys']\n",
+ "\n",
+ "curr_dir = 'journeys'\n",
+ "journey_dirs = []\n",
+ "input_image_path = os.path.join(input_image_path, curr_dir)\n",
+ "for d1 in os.listdir(input_image_path):\n",
+ " d1_path = os.path.join(input_image_path, d1)\n",
+ " for d2 in os.listdir(d1_path):\n",
+ " d2_path = os.path.join(d1_path, d2)\n",
+ " for d3 in os.listdir(d2_path):\n",
+ " d3_path = os.path.join(d2_path, d3)\n",
+ " journey_dirs.append(d3_path)\n",
+ "\n",
+ "dublin_dirs.extend(journey_dirs)\n",
+ "\n",
+ "\n",
+ "#Northampton02\n",
+ "northampton_dirs = []\n",
+ "northampton_dirs.append(os.path.join(frames_path, 'Northampton02/Northampton02-vid-14-15-17'))\n",
+ "\n",
+ "#Northampton04\n",
+ "curr_dir = os.path.join(frames_path, 'Northampton04')\n",
+ "for d in os.listdir(curr_dir):\n",
+ " d_path = os.path.join(curr_dir, d)\n",
+ " northampton_dirs.append(d_path)\n",
+ "\n",
+ "#Paris\n",
+ "curr_dir = os.path.join(frames_path, 'Paris')\n",
+ "paris_dirs = []\n",
+ "paris_dirs.append(os.path.join(curr_dir, 'Paris_Dott02'))\n",
+ "paris_dirs.append(os.path.join(curr_dir, 'Paris_Dott03'))\n",
+ "\n",
+ "#Singapore\n",
+ "curr_dir = os.path.join(frames_path, 'Singapore/Singapore')\n",
+ "singapore_dir = []\n",
+ "singapore_dir.append(curr_dir)\n",
+ "\n",
+ "#Stockholm\n",
+ "curr_dir = os.path.join(frames_path, 'Stockholm')\n",
+ "stockholm_dirs = [os.path.join(curr_dir, d) for d in os.listdir(curr_dir)]\n",
+ "\n",
+ "#WashingtonDC\n",
+ "curr_dir = os.path.join(frames_path, 'WashingtonDC/washingtonDC-01')\n",
+ "DC_dir = []\n",
+ "DC_dir.append(curr_dir)\n",
+ "\n",
+ "\n",
+ "all_dir_paths = []\n",
+ "all_dir_paths.extend(dublin_dirs)\n",
+ "all_dir_paths.extend(northampton_dirs)\n",
+ "all_dir_paths.extend(paris_dirs)\n",
+ "all_dir_paths.extend(singapore_dir)\n",
+ "all_dir_paths.extend(stockholm_dirs)\n",
+ "all_dir_paths.extend(DC_dir)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create a New Directory for the Organzied Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:11.061438Z",
+ "iopub.status.busy": "2024-06-18T13:39:11.061038Z",
+ "iopub.status.idle": "2024-06-18T13:39:11.067439Z",
+ "shell.execute_reply": "2024-06-18T13:39:11.066247Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:11.061409Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "root_dir = os.getcwd()\n",
+ "os.makedirs(os.path.join(root_dir, 'Dataset'), exist_ok=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Extract Image Paths"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:12.524218Z",
+ "iopub.status.busy": "2024-06-18T13:39:12.523748Z",
+ "iopub.status.idle": "2024-06-18T13:39:12.530257Z",
+ "shell.execute_reply": "2024-06-18T13:39:12.529040Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:12.524183Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def extract_images(dir_path):\n",
+ " images = [f for f in os.listdir(dir_path) if f.endswith('png')]\n",
+ " return images"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Drop NaN Columns + Extract Labels \n",
+ " \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:13.082214Z",
+ "iopub.status.busy": "2024-06-18T13:39:13.081768Z",
+ "iopub.status.idle": "2024-06-18T13:39:13.090265Z",
+ "shell.execute_reply": "2024-06-18T13:39:13.088898Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:13.082169Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def extract_labels(d_path):\n",
+ " csv_file_path = d_path.replace('frames', 'labels') + '.csv'\n",
+ " df_labels = pd.read_csv(csv_file_path)\n",
+ "\n",
+ " if 'frame' in df_labels.columns:\n",
+ " df_labels.set_index('frame', inplace=True)\n",
+ " # Reset index to start from 1\n",
+ " df_labels.index = range(1, len(df_labels) + 1)\n",
+ "\n",
+ " # Drop 'frame' column if it exists\n",
+ " if 'frame' in df_labels.columns:\n",
+ " df_labels.drop('frame', axis=1, inplace=True)\n",
+ "\n",
+ " # Drop columns with NaN values\n",
+ " df_labels.dropna(axis=1, inplace=True)\n",
+ "\n",
+ " # Convert each row of DataFrame into a list of labels\n",
+ " all_labels = df_labels.values.tolist()\n",
+ "\n",
+ " return all_labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:14.166247Z",
+ "iopub.status.busy": "2024-06-18T13:39:14.165815Z",
+ "iopub.status.idle": "2024-06-18T13:39:14.172410Z",
+ "shell.execute_reply": "2024-06-18T13:39:14.170970Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:14.166216Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def combine_path_and_labels(image_paths, label_lists):\n",
+ " combined_data = []\n",
+ "\n",
+ " for path, labels in zip(image_paths, label_lists):\n",
+ " combined_data.append([path] + labels)\n",
+ "\n",
+ " return combined_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:14.445929Z",
+ "iopub.status.busy": "2024-06-18T13:39:14.445515Z",
+ "iopub.status.idle": "2024-06-18T13:39:22.155658Z",
+ "shell.execute_reply": "2024-06-18T13:39:22.154272Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:14.445883Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing complete for directory 1 / 65\n",
+ "Processing complete for directory 2 / 65\n",
+ "Processing complete for directory 3 / 65\n",
+ "Processing complete for directory 4 / 65\n",
+ "Processing complete for directory 5 / 65\n",
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+ "Processing complete for directory 8 / 65\n",
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+ "Processing complete for directory 10 / 65\n",
+ "Processing complete for directory 11 / 65\n",
+ "Processing complete for directory 12 / 65\n",
+ "Processing complete for directory 13 / 65\n",
+ "Processing complete for directory 14 / 65\n",
+ "Processing complete for directory 15 / 65\n",
+ "Processing complete for directory 16 / 65\n",
+ "Processing complete for directory 17 / 65\n",
+ "Processing complete for directory 18 / 65\n",
+ "Processing complete for directory 19 / 65\n",
+ "Processing complete for directory 20 / 65\n",
+ "Processing complete for directory 21 / 65\n",
+ "Processing complete for directory 22 / 65\n",
+ "Processing complete for directory 23 / 65\n",
+ "Processing complete for directory 24 / 65\n",
+ "Processing complete for directory 25 / 65\n",
+ "Processing complete for directory 26 / 65\n",
+ "Processing complete for directory 27 / 65\n",
+ "Processing complete for directory 28 / 65\n",
+ "Processing complete for directory 29 / 65\n",
+ "Processing complete for directory 30 / 65\n",
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+ "Processing complete for directory 32 / 65\n",
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+ "Processing complete for directory 40 / 65\n",
+ "Processing complete for directory 41 / 65\n",
+ "Processing complete for directory 42 / 65\n",
+ "Processing complete for directory 43 / 65\n",
+ "Processing complete for directory 44 / 65\n",
+ "Processing complete for directory 45 / 65\n",
+ "Processing complete for directory 46 / 65\n",
+ "Processing complete for directory 47 / 65\n",
+ "Processing complete for directory 48 / 65\n",
+ "Processing complete for directory 49 / 65\n",
+ "Processing complete for directory 50 / 65\n",
+ "Processing complete for directory 51 / 65\n",
+ "Processing complete for directory 52 / 65\n",
+ "Processing complete for directory 53 / 65\n",
+ "Processing complete for directory 54 / 65\n",
+ "Processing complete for directory 55 / 65\n",
+ "Processing complete for directory 56 / 65\n",
+ "Processing complete for directory 57 / 65\n",
+ "Processing complete for directory 58 / 65\n",
+ "Processing complete for directory 59 / 65\n",
+ "Processing complete for directory 60 / 65\n",
+ "Processing complete for directory 61 / 65\n",
+ "Processing complete for directory 62 / 65\n",
+ "Processing complete for directory 63 / 65\n",
+ "Processing complete for directory 64 / 65\n",
+ "Processing complete for directory 65 / 65\n"
+ ]
+ }
+ ],
+ "source": [
+ "output_rows = []\n",
+ "# city_image_counts = {city: 0 for city in cities}\n",
+ "\n",
+ "for d_num, d_path in enumerate(all_dir_paths, start=1):\n",
+ "\n",
+ " # Extract images\n",
+ " images_names = extract_images(d_path)\n",
+ " images_paths = [os.path.join(d_path, image_name) for image_name in images_names]\n",
+ "\n",
+ " # Extract labels\n",
+ " all_labels = extract_labels(d_path)\n",
+ "\n",
+ " # Combine each image path + labels\n",
+ " output_rows_per_path = combine_path_and_labels(images_paths, all_labels)\n",
+ "\n",
+ " # Append to output_rows\n",
+ " output_rows.extend(output_rows_per_path)\n",
+ "\n",
+ " print(f\"Processing complete for directory {d_num} / {len(all_dir_paths)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create a New CSV File"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:39:22.157746Z",
+ "iopub.status.busy": "2024-06-18T13:39:22.157411Z",
+ "iopub.status.idle": "2024-06-18T13:39:22.163087Z",
+ "shell.execute_reply": "2024-06-18T13:39:22.161721Z",
+ "shell.execute_reply.started": "2024-06-18T13:39:22.157718Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Load and preprocess data\n",
+ "organized_dataset_csv = os.path.join(root_dir, 'Dataset/Organized_dataset.csv')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define the New Columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:33.993090Z",
+ "iopub.status.busy": "2024-06-18T13:41:33.992605Z",
+ "iopub.status.idle": "2024-06-18T13:41:33.998449Z",
+ "shell.execute_reply": "2024-06-18T13:41:33.997384Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:33.993055Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "new_labels= columns[1:-3]\n",
+ "new_columns = ['image_path'] + new_labels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Write the Dataset into the New CSV File"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:34.354929Z",
+ "iopub.status.busy": "2024-06-18T13:41:34.354538Z",
+ "iopub.status.idle": "2024-06-18T13:41:34.863591Z",
+ "shell.execute_reply": "2024-06-18T13:41:34.862381Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:34.354885Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "output_df = pd.DataFrame(output_rows, columns=new_columns)\n",
+ "output_df.to_csv(organized_dataset_csv, index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read the New CSV File"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:36.703674Z",
+ "iopub.status.busy": "2024-06-18T13:41:36.703301Z",
+ "iopub.status.idle": "2024-06-18T13:41:36.806002Z",
+ "shell.execute_reply": "2024-06-18T13:41:36.805026Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:36.703646Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(organized_dataset_csv)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Show the new Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:41:38.735836Z",
+ "iopub.status.busy": "2024-06-18T13:41:38.734652Z",
+ "iopub.status.idle": "2024-06-18T13:41:38.751366Z",
+ "shell.execute_reply": "2024-06-18T13:41:38.750202Z",
+ "shell.execute_reply.started": "2024-06-18T13:41:38.735797Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
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+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get to Know the Dataset - EDA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-06-18T13:44:35.383656Z",
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+ "outputs": [
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",
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