Red Light Green Light Game.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bzu-5TjscSso"
   },
   "source": [
    "# Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1735795135041,
     "user": {
      "displayName": "Jose Aries De Los Santos",
      "userId": "09185922883375317245"
     },
     "user_tz": -480
    },
    "id": "8ELZVuRucUBv"
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "paKQ2og2cPMf"
   },
   "source": [
    "## Define and Specify paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 304,
     "status": "ok",
     "timestamp": 1735794666600,
     "user": {
      "displayName": "Jose Aries De Los Santos",
      "userId": "09185922883375317245"
     },
     "user_tz": -480
    },
    "id": "Eha-ncaIZKfc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory 'train_path1' created.\n",
      "Directory 'train_path2' created.\n",
      "Directory 'test_path' created.\n"
     ]
    }
   ],
   "source": [
    "#Define my paths\n",
    "def create_directory_if_not_exists(path):\n",
    "    # Check if the directory exists\n",
    "    if not os.path.exists(path):\n",
    "        # Create the directory since it does not exist\n",
    "        os.makedirs(path)\n",
    "        print(f\"Directory '{path}' created.\")\n",
    "    else:\n",
    "        print(f\"Directory '{path}' already exists.\")\n",
    "\n",
    "# Specify the path where you want to create the directory\n",
    "train_path1 = \"train_path1\"\n",
    "train_path2 = \"train_path2\"\n",
    "test_path = \"test_path\"\n",
    "create_directory_if_not_exists(train_path1)\n",
    "create_directory_if_not_exists(train_path2)\n",
    "create_directory_if_not_exists(test_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ndiIWSp9UF0v"
   },
   "source": [
    "# Extract Video Frames for Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 224081,
     "status": "ok",
     "timestamp": 1735795135040,
     "user": {
      "displayName": "Jose Aries De Los Santos",
      "userId": "09185922883375317245"
     },
     "user_tz": -480
    },
    "id": "oONiu5qXbvi-"
   },
   "outputs": [],
   "source": [
    "def extract_frames(video_path, output_folder):\n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "    frame_count = 0\n",
    "\n",
    "    if not os.path.exists(output_folder):\n",
    "        os.makedirs(output_folder)\n",
    "\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "        frame_path = os.path.join(output_folder, f\"frame_{frame_count:04d}.png\")\n",
    "        cv2.imwrite(frame_path, frame)\n",
    "        frame_count += 1\n",
    "\n",
    "    cap.release()\n",
    "\n",
    "video_train1 = \"training-video-001.mp4\"\n",
    "video_train2 = \"training-video-002.mp4\"\n",
    "video_test = \"testing-video-001.mp4\"\n",
    "\n",
    "extract_frames(video_train1, train_path1)\n",
    "extract_frames(video_train2, train_path2)\n",
    "extract_frames(video_test, test_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zO1lUBFvdJ9h"
   },
   "source": [
    "# A walkthrough inside the directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 805,
     "status": "ok",
     "timestamp": 1735795366422,
     "user": {
      "displayName": "Jose Aries De Los Santos",
      "userId": "09185922883375317245"
     },
     "user_tz": -480
    },
    "id": "OstO3tEvdJMX",
    "outputId": "559b382b-8a6b-4421-fac8-85d3ad910638"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of image files train_path1 folder: 1208\n",
      "Total number of image files train_path2 folder 1208\n",
      "Total number of image files test_path folder 1208\n"
     ]
    }
   ],
   "source": [
    "def count_images(path, ext=\".png\"):\n",
    "  jpg_count = 0\n",
    "  for item in os.listdir(path):\n",
    "    full_path = os.path.join(path, item)\n",
    "    if os.path.isfile(full_path) and item.lower().endswith(ext):\n",
    "      jpg_count += 1\n",
    "    elif os.path.isdir(full_path):\n",
    "      jpg_count += count_images(full_path)\n",
    "  return jpg_count\n",
    "\n",
    "\n",
    "def look_at_dir(path):\n",
    "  for dirpath, dirnames, filenames in os.walk(path):\n",
    "    print(f\"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.\")\n",
    "\n",
    "count_train_path1 = count_images(train_path1)\n",
    "count_train_path2 = count_images(train_path2)\n",
    "count_test_path = count_images(test_path)\n",
    "\n",
    "print(f\"Total number of image files train_path1 folder: {count_train_path1}\")\n",
    "print(f\"Total number of image files train_path2 folder {count_train_path2}\")\n",
    "print(f\"Total number of image files test_path folder {count_test_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hOvAFw-qeSrM"
   },
   "source": [
    "Given that you have 1208 frames extracted from a 40-second video, we can calculate the frame rate (FPS) using the following formula:\n",
    "\n",
    "$$FPS = \\frac{\\text{Number of Frames}}{\\text{Video duration in seconds}} = \\frac{1208}{40} = 30.2 \\text{  frame rate per second}$$\n",
    "\n",
    "Thus, the videos must be recorded in a 30 FPS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NRJEQT--XleI"
   },
   "source": [
    "# Background Subtraction and Contour Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 359072,
     "status": "ok",
     "timestamp": 1735796093449,
     "user": {
      "displayName": "Jose Aries De Los Santos",
      "userId": "09185922883375317245"
     },
     "user_tz": -480
    },
    "id": "vCgNzZPhXoe9"
   },
   "outputs": [],
   "source": [
    "# Initialize background subtractor\n",
    "fgbg = cv2.createBackgroundSubtractorMOG2()\n",
    "\n",
    "# Function to calculate the centroid of a contour\n",
    "def get_centroid(contour):\n",
    "    M = cv2.moments(contour)\n",
    "    if M['m00'] != 0:\n",
    "        cx = int(M['m10'] / M['m00'])\n",
    "        cy = int(M['m01'] / M['m00'])\n",
    "        return (cx, cy)\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Track player positions across frames\n",
    "def track_players(frames_folder):\n",
    "    frame_files = sorted([os.path.join(frames_folder, f) for f in os.listdir(frames_folder) if f.endswith('.png')])\n",
    "    player_positions = {}\n",
    "\n",
    "    for frame_file in frame_files:\n",
    "        frame = cv2.imread(frame_file)\n",
    "        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "        fgmask = fgbg.apply(gray)\n",
    "        contours, _ = cv2.findContours(fgmask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "        frame_centroids = []\n",
    "        for contour in contours:\n",
    "            if cv2.contourArea(contour) > 500:\n",
    "                centroid = get_centroid(contour)\n",
    "                if centroid:\n",
    "                    frame_centroids.append(centroid)\n",
    "\n",
    "        # Logic to associate centroids with player IDs\n",
    "        for i, centroid in enumerate(frame_centroids):\n",
    "            player_positions.setdefault(i, []).append(centroid)\n",
    "\n",
    "        # Display frame with centroids (optional)\n",
    "        for centroid in frame_centroids:\n",
    "            cv2.circle(frame, centroid, 5, (0, 255, 0), -1)\n",
    "\n",
    "    return player_positions\n",
    "\n",
    "##Calculate Player Positions\n",
    "player_positions1 = track_players(train_path1)\n",
    "player_positions2 = track_players(train_path2)\n",
    "player_positions_test = track_players(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import islice\n",
    "\n",
    "# Get the first 5 entries from the dictionary\n",
    "player_positions1 = dict(islice(player_positions1.items(), 5))\n",
    "player_positions2 = dict(islice(player_positions2.items(), 5))\n",
    "player_positions_test = dict(islice(player_positions_test.items(), 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save player positions to files\n",
    "with open('player_positions1.pkl', 'wb') as f:\n",
    "    pickle.dump(player_positions1, f)\n",
    "with open('player_positions2.pkl', 'wb') as f:\n",
    "    pickle.dump(player_positions2, f)\n",
    "with open('player_positions_test.pkl', 'wb') as f:\n",
    "    pickle.dump(player_positions_test, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization Attemp for the Centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "RJvTkeo6hEac",
    "outputId": "d697fdfc-73d5-4684-8f54-abf19348f1a9"
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "\n",
    "def visualize_player_positions(frames_folder, player_positions):\n",
    "    frame_files = sorted([os.path.join(frames_folder, f) for f in os.listdir(frames_folder) if f.endswith('.png')])\n",
    "    \n",
    "    for i, frame_file in enumerate(frame_files):\n",
    "        frame = cv2.imread(frame_file)\n",
    "        \n",
    "        # Draw the centroids on the frame\n",
    "        for player_id, positions in player_positions.items():\n",
    "            if i < len(positions):\n",
    "                centroid = positions[i]\n",
    "                cv2.circle(frame, centroid, 5, (0, 255, 0), -1)  # Draw the centroid\n",
    "                \n",
    "                # Optionally, display player ID near the centroid\n",
    "                cv2.putText(frame, str(player_id), (centroid[0] + 10, centroid[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\n",
    "        \n",
    "        # Display the frame\n",
    "        cv2.imshow('Frame with Tracked Positions', frame)\n",
    "        if cv2.waitKey(30) & 0xFF == 27:  # Press 'Esc' to exit\n",
    "            break\n",
    "\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "# Example usage (visualize the pre-calculated player positions)\n",
    "visualize_player_positions(train_path1, player_positions1)\n",
    "# visualize_player_positions('training_frames2', player_positions2)\n",
    "# visualize_player_positions('test_frames', player_positions_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_phase(frame, template, threshold=0.7):\n",
    "    \"\"\"Detect phase (move/stop) based on template matching.\"\"\"\n",
    "    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    res = cv2.matchTemplate(gray_frame, template, cv2.TM_CCOEFF_NORMED)\n",
    "    loc = np.where(res >= threshold)\n",
    "    if len(loc[0]) > 0:\n",
    "        return 'stop'\n",
    "    else:\n",
    "        return 'move'\n",
    "\n",
    "# Load the template image of the doll looking at the players\n",
    "template = cv2.imread('stopped.png', cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "# Classify each frame\n",
    "def classify_frames_with_template(frames_folder, template):\n",
    "    frame_files = sorted([os.path.join(frames_folder, f) for f in os.listdir(frames_folder) if f.endswith('.png')])\n",
    "    phases = {}\n",
    "\n",
    "    for frame_file in frame_files:\n",
    "        frame = cv2.imread(frame_file)\n",
    "        phase = detect_phase(frame, template)\n",
    "        phases[frame_file] = phase\n",
    "\n",
    "    return phases\n",
    "\n",
    "# Example usage\n",
    "phases1 = classify_frames_with_template(train_path1, template)\n",
    "phases2 = classify_frames_with_template(train_path2, template)\n",
    "phases_test = classify_frames_with_template(test_path, template)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Include ROI in Detection phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phases (Training 1): {'train_path1/frame_0000.png': 'move', 'train_path1/frame_0001.png': 'move', 'train_path1/frame_0002.png': 'move', 'train_path1/frame_0003.png': 'move', 'train_path1/frame_0004.png': 'move', 'train_path1/frame_0005.png': 'move', 'train_path1/frame_0006.png': 'move', 'train_path1/frame_0007.png': 'move', 'train_path1/frame_0008.png': 'move', 'train_path1/frame_0009.png': 'move', 'train_path1/frame_0010.png': 'move', 'train_path1/frame_0011.png': 'move', 'train_path1/frame_0012.png': 'move', 'train_path1/frame_0013.png': 'move', 'train_path1/frame_0014.png': 'move', 'train_path1/frame_0015.png': 'move', 'train_path1/frame_0016.png': 'move', 'train_path1/frame_0017.png': 'move', 'train_path1/frame_0018.png': 'move', 'train_path1/frame_0019.png': 'move', 'train_path1/frame_0020.png': 'move', 'train_path1/frame_0021.png': 'move', 'train_path1/frame_0022.png': 'move', 'train_path1/frame_0023.png': 'move', 'train_path1/frame_0024.png': 'move', 'train_path1/frame_0025.png': 'move', 'train_path1/frame_0026.png': 'move', 'train_path1/frame_0027.png': 'move', 'train_path1/frame_0028.png': 'move', 'train_path1/frame_0029.png': 'move', 'train_path1/frame_0030.png': 'move', 'train_path1/frame_0031.png': 'move', 'train_path1/frame_0032.png': 'move', 'train_path1/frame_0033.png': 'move', 'train_path1/frame_0034.png': 'move', 'train_path1/frame_0035.png': 'move', 'train_path1/frame_0036.png': 'move', 'train_path1/frame_0037.png': 'move', 'train_path1/frame_0038.png': 'move', 'train_path1/frame_0039.png': 'move', 'train_path1/frame_0040.png': 'move', 'train_path1/frame_0041.png': 'move', 'train_path1/frame_0042.png': 'move', 'train_path1/frame_0043.png': 'move', 'train_path1/frame_0044.png': 'move', 'train_path1/frame_0045.png': 'move', 'train_path1/frame_0046.png': 'move', 'train_path1/frame_0047.png': 'move', 'train_path1/frame_0048.png': 'move', 'train_path1/frame_0049.png': 'move', 'train_path1/frame_0050.png': 'move', 'train_path1/frame_0051.png': 'move', 'train_path1/frame_0052.png': 'move', 'train_path1/frame_0053.png': 'move', 'train_path1/frame_0054.png': 'move', 'train_path1/frame_0055.png': 'move', 'train_path1/frame_0056.png': 'move', 'train_path1/frame_0057.png': 'move', 'train_path1/frame_0058.png': 'move', 'train_path1/frame_0059.png': 'move', 'train_path1/frame_0060.png': 'move', 'train_path1/frame_0061.png': 'move', 'train_path1/frame_0062.png': 'move', 'train_path1/frame_0063.png': 'move', 'train_path1/frame_0064.png': 'move', 'train_path1/frame_0065.png': 'move', 'train_path1/frame_0066.png': 'move', 'train_path1/frame_0067.png': 'move', 'train_path1/frame_0068.png': 'move', 'train_path1/frame_0069.png': 'move', 'train_path1/frame_0070.png': 'move', 'train_path1/frame_0071.png': 'move', 'train_path1/frame_0072.png': 'move', 'train_path1/frame_0073.png': 'move', 'train_path1/frame_0074.png': 'move', 'train_path1/frame_0075.png': 'move', 'train_path1/frame_0076.png': 'move', 'train_path1/frame_0077.png': 'move', 'train_path1/frame_0078.png': 'move', 'train_path1/frame_0079.png': 'move', 'train_path1/frame_0080.png': 'move', 'train_path1/frame_0081.png': 'move', 'train_path1/frame_0082.png': 'move', 'train_path1/frame_0083.png': 'move', 'train_path1/frame_0084.png': 'move', 'train_path1/frame_0085.png': 'move', 'train_path1/frame_0086.png': 'move', 'train_path1/frame_0087.png': 'move', 'train_path1/frame_0088.png': 'move', 'train_path1/frame_0089.png': 'move', 'train_path1/frame_0090.png': 'move', 'train_path1/frame_0091.png': 'move', 'train_path1/frame_0092.png': 'move', 'train_path1/frame_0093.png': 'move', 'train_path1/frame_0094.png': 'move', 'train_path1/frame_0095.png': 'move', 'train_path1/frame_0096.png': 'move', 'train_path1/frame_0097.png': 'move', 'train_path1/frame_0098.png': 'move', 'train_path1/frame_0099.png': 'move', 'train_path1/frame_0100.png': 'move', 'train_path1/frame_0101.png': 'move', 'train_path1/frame_0102.png': 'move', 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     ]
    }
   ],
   "source": [
    "# Load the template image (ROI)\n",
    "template = cv2.imread('roi_template.png', cv2.IMREAD_GRAYSCALE)\n",
    "template_height, template_width = template.shape\n",
    "\n",
    "# Define the Region of Interest (ROI) in the frame where the doll is expected to be located\n",
    "# Adjust these values based on the location of the doll in your frames\n",
    "roi_x, roi_y, roi_width, roi_height = 0, 0, 500, 1080\n",
    "\n",
    "# Function to detect the doll's phase (move/stop) using ROI\n",
    "def detect_phase(frame, template, threshold=0.8):\n",
    "    \"\"\"Detect phase (move/stop) based on template matching within ROI.\"\"\"\n",
    "    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    \n",
    "    # Extract the ROI from the frame\n",
    "    roi = gray_frame[roi_y:roi_y + roi_height, roi_x:roi_x + roi_width]\n",
    "    \n",
    "    res = cv2.matchTemplate(roi, template, cv2.TM_CCOEFF_NORMED)\n",
    "    _, max_val, _, max_loc = cv2.minMaxLoc(res)\n",
    "\n",
    "    # Adjust coordinates to the original frame\n",
    "    top_left = (max_loc[0] + roi_x, max_loc[1] + roi_y)\n",
    "    bottom_right = (top_left[0] + template_width, top_left[1] + template_height)\n",
    "\n",
    "    # Visualize the matching result\n",
    "    cv2.rectangle(frame, top_left, bottom_right, (0, 255, 0), 2)\n",
    "    cv2.imshow('Template Matching', frame)\n",
    "    cv2.waitKey(1)  # Display for a short period\n",
    "\n",
    "    if max_val >= threshold:\n",
    "        return 'stop'\n",
    "    else:\n",
    "        return 'move'\n",
    "\n",
    "# Function to classify phases for each frame\n",
    "def classify_frames_with_template(frames_folder, template, threshold=0.8):\n",
    "    frame_files = sorted([os.path.join(frames_folder, f) for f in os.listdir(frames_folder) if f.endswith('.png')])\n",
    "    phases = {}\n",
    "\n",
    "    for frame_file in frame_files:\n",
    "        frame = cv2.imread(frame_file)\n",
    "        phase = detect_phase(frame, template, threshold)\n",
    "        phases[frame_file] = phase\n",
    "\n",
    "    cv2.destroyAllWindows()\n",
    "    return phases\n",
    "\n",
    "# Example usage\n",
    "# Replace 'train_path1', 'train_path2', and 'test_path' with the actual paths to your frames folders\n",
    "phases1 = classify_frames_with_template(train_path1, template)\n",
    "phases2 = classify_frames_with_template(train_path2, template)\n",
    "phases_test = classify_frames_with_template(test_path, template)\n",
    "\n",
    "# Print the detected phases for verification\n",
    "print(f\"Phases (Training 1): {phases1}\")\n",
    "print(f\"Phases (Training 2): {phases2}\")\n",
    "print(f\"Phases (Test): {phases_test}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def calculate_distance(point1, point2):\n",
    "#     \"\"\"Calculate Euclidean distance between two points.\"\"\"\n",
    "#     return np.sqrt((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2)\n",
    "\n",
    "# def average_rate_of_motion(player_positions):\n",
    "#     \"\"\"Calculate the average rate of motion per player.\"\"\"\n",
    "#     average_motion = {}\n",
    "#     for player_id, positions in player_positions.items():\n",
    "#         if len(positions) > 1:\n",
    "#             distances = [calculate_distance(positions[i], positions[i + 1]) for i in range(len(positions) - 1)]\n",
    "#             average_motion[player_id] = np.mean(distances)\n",
    "#     return average_motion\n",
    "\n",
    "# def relative_distance_progressed(player_positions):\n",
    "#     \"\"\"Calculate the cumulative distance each player manages to advance per movement cycle.\"\"\"\n",
    "#     cumulative_distance = {}\n",
    "#     for player_id, positions in player_positions.items():\n",
    "#         if len(positions) > 1:\n",
    "#             distances = [calculate_distance(positions[i], positions[i + 1]) for i in range(len(positions) - 1)]\n",
    "#             cumulative_distance[player_id] = np.sum(distances)\n",
    "#     return cumulative_distance\n",
    "\n",
    "\n",
    "# def consistency_in_motion_patterns(player_positions):\n",
    "#     \"\"\"Identify patterns in players’ motions.\"\"\"\n",
    "#     motion_patterns = {}\n",
    "#     for player_id, positions in player_positions.items():\n",
    "#         if len(positions) > 1:\n",
    "#             distances = [calculate_distance(positions[i], positions[i + 1]) for i in range(len(positions) - 1)]\n",
    "#             variations = np.std(distances)  # Standard deviation as a measure of variation\n",
    "#             motion_patterns[player_id] = variations\n",
    "#     return motion_patterns\n",
    "\n",
    "\n",
    "# def filter_movements_by_phase(player_positions, phases):\n",
    "#     \"\"\"Filter out player movements during 'stop' phases.\"\"\"\n",
    "#     filtered_positions = {}\n",
    "\n",
    "#     for player_id, positions in player_positions.items():\n",
    "#         filtered_positions[player_id] = []\n",
    "#         for i, position in enumerate(positions):\n",
    "#             frame_file = f'frame_{i:04d}.png'\n",
    "#             if phases.get(frame_file) == 'move':\n",
    "#                 filtered_positions[player_id].append(position)\n",
    "\n",
    "#     return filtered_positions\n",
    "\n",
    "# # Example usage\n",
    "# filtered_positions1 = filter_movements_by_phase(player_positions1, phases1)\n",
    "# filtered_positions2 = filter_movements_by_phase(player_positions2, phases2)\n",
    "# filtered_positions_test = filter_movements_by_phase(player_positions_test, phases_test)\n",
    "\n",
    "# # Recalculate metrics using filtered positions\n",
    "# avg_motion1 = average_rate_of_motion(filtered_positions1)\n",
    "# avg_motion2 = average_rate_of_motion(filtered_positions2)\n",
    "# avg_motion_test = average_rate_of_motion(filtered_positions_test)\n",
    "\n",
    "# distance_progressed1 = relative_distance_progressed(filtered_positions1)\n",
    "# distance_progressed2 = relative_distance_progressed(filtered_positions2)\n",
    "# distance_progressed_test = relative_distance_progressed(filtered_positions_test)\n",
    "\n",
    "# motion_patterns1 = consistency_in_motion_patterns(filtered_positions1)\n",
    "# motion_patterns2 = consistency_in_motion_patterns(filtered_positions2)\n",
    "# motion_patterns_test = consistency_in_motion_patterns(filtered_positions_test)\n",
    "\n",
    "# print(\"Average Rate of Motion per Player (Training 1):\", avg_motion1)\n",
    "# print(\"Average Rate of Motion per Player (Training 2):\", avg_motion2)\n",
    "# print(\"Average Rate of Motion per Player (Test):\", avg_motion_test)\n",
    "\n",
    "# print(\"Relative Distance Progressed (Training 1):\", distance_progressed1)\n",
    "# print(\"Relative Distance Progressed (Training 2):\", distance_progressed2)\n",
    "# print(\"Relative Distance Progressed (Test):\", distance_progressed_test)\n",
    "\n",
    "# print(\"Consistency in Motion Patterns (Training 1):\", motion_patterns1)\n",
    "# print(\"Consistency in Motion Patterns (Training 2):\", motion_patterns2)\n",
    "# print(\"Consistency in Motion Patterns (Test):\", motion_patterns_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metric Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Rate of Motion per Player (Training 1): {0: 10.508346711911326, 1: 16.419582815011285, 2: 19.292723434254825, 3: 17.830274200965302, 4: 35.060725765241216}\n",
      "Average Rate of Motion per Player (Training 2): {0: 4.635162183584238, 1: 12.269180161131974, 2: 9.093452311228448, 3: 12.29867883436366, 4: 12.35619026641398}\n",
      "Average Rate of Motion per Player (Test): {0: 4.435680144474085, 1: 11.152579889903972, 2: 9.992304474710755, 3: 12.194437802381323, 4: 21.93522595310478}\n",
      "Relative Distance Progressed (Training 1): {0: 11758.839970628775, 1: 18340.674004367607, 2: 21183.410330811796, 3: 18614.806265807776, 4: 35867.12245784176}\n",
      "Relative Distance Progressed (Training 2): {0: 5339.706835489042, 1: 14121.826365462903, 2: 10184.666588575863, 3: 13663.832184978026, 4: 13171.698823997303}\n",
      "Relative Distance Progressed (Test): {0: 5101.032166145197, 1: 12814.314293499665, 2: 11411.211710119682, 3: 13804.103592295658, 4: 24611.323519383564}\n",
      "Consistency in Motion Patterns (Training 1): {0: 76.12104109783554, 1: 62.50293303271597, 2: 89.69661638025578, 3: 62.37477032630571, 4: 127.0276470839038}\n",
      "Consistency in Motion Patterns (Training 2): {0: 31.299413610564716, 1: 61.66173923305922, 2: 43.52260516993094, 3: 55.22856456684071, 4: 50.46861063731745}\n",
      "Consistency in Motion Patterns (Test): {0: 25.529002312021962, 1: 53.14100441243965, 2: 47.807495786604974, 3: 55.343703755179746, 4: 92.30013845794562}\n"
     ]
    }
   ],
   "source": [
    "def calculate_distance(point1, point2):\n",
    "    \"\"\"Calculate Euclidean distance between two points.\"\"\"\n",
    "    return np.sqrt((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2)\n",
    "\n",
    "def average_rate_of_motion(player_positions):\n",
    "    \"\"\"Calculate the average rate of motion per player.\"\"\"\n",
    "    average_motion = {}\n",
    "    for player_id, positions in player_positions.items():\n",
    "        if len(positions) > 1:\n",
    "            distances = [calculate_distance(positions[i], positions[i + 1]) \n",
    "                         for i in range(len(positions) - 1)]\n",
    "            average_motion[player_id] = np.mean(distances)\n",
    "    return average_motion\n",
    "\n",
    "def relative_distance_progressed(player_positions):\n",
    "    \"\"\"Calculate the cumulative distance each player manages to advance per movement cycle.\"\"\"\n",
    "    cumulative_distance = {}\n",
    "    for player_id, positions in player_positions.items():\n",
    "        if len(positions) > 1:\n",
    "            distances = [calculate_distance(positions[i], positions[i + 1]) \n",
    "                         for i in range(len(positions) - 1)]\n",
    "            cumulative_distance[player_id] = np.sum(distances)\n",
    "    return cumulative_distance\n",
    "\n",
    "def consistency_in_motion_patterns(player_positions):\n",
    "    \"\"\"Identify patterns in players’ motions.\"\"\"\n",
    "    motion_patterns = {}\n",
    "    for player_id, positions in player_positions.items():\n",
    "        if len(positions) > 1:\n",
    "            distances = [calculate_distance(positions[i], positions[i + 1]) \n",
    "                         for i in range(len(positions) - 1)]\n",
    "            variations = np.std(distances)  # Standard deviation as a measure of variation\n",
    "            motion_patterns[player_id] = variations\n",
    "    return motion_patterns\n",
    "\n",
    "\n",
    "# Calculate metrics without filtering\n",
    "avg_motion1 = average_rate_of_motion(player_positions1)\n",
    "avg_motion2 = average_rate_of_motion(player_positions2)\n",
    "avg_motion_test = average_rate_of_motion(player_positions_test)\n",
    "\n",
    "distance_progressed1 = relative_distance_progressed(player_positions1)\n",
    "distance_progressed2 = relative_distance_progressed(player_positions2)\n",
    "distance_progressed_test = relative_distance_progressed(player_positions_test)\n",
    "\n",
    "motion_patterns1 = consistency_in_motion_patterns(player_positions1)\n",
    "motion_patterns2 = consistency_in_motion_patterns(player_positions2)\n",
    "motion_patterns_test = consistency_in_motion_patterns(player_positions_test)\n",
    "\n",
    "print(\"Average Rate of Motion per Player (Training 1):\", avg_motion1)\n",
    "print(\"Average Rate of Motion per Player (Training 2):\", avg_motion2)\n",
    "print(\"Average Rate of Motion per Player (Test):\", avg_motion_test)\n",
    "\n",
    "print(\"Relative Distance Progressed (Training 1):\", distance_progressed1)\n",
    "print(\"Relative Distance Progressed (Training 2):\", distance_progressed2)\n",
    "print(\"Relative Distance Progressed (Test):\", distance_progressed_test)\n",
    "\n",
    "print(\"Consistency in Motion Patterns (Training 1):\", motion_patterns1)\n",
    "print(\"Consistency in Motion Patterns (Training 2):\", motion_patterns2)\n",
    "print(\"Consistency in Motion Patterns (Test):\", motion_patterns_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_player_positions_graph(player_positions, title=\"Player Positions Over Time\"):\n",
    "    \"\"\"\n",
    "    This function plots player positions (trajectories) on a graph over time (across frames).\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    for player_id, positions in player_positions.items():\n",
    "        x_positions = [pos[0] for pos in positions]  # Extract x coordinates\n",
    "        y_positions = [pos[1] for pos in positions]  # Extract y coordinates\n",
    "        plt.plot(x_positions, y_positions, label=f\"Player {player_id}\", marker='o')\n",
    "    \n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"X Position\")\n",
    "    plt.ylabel(\"Y Position\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "    \n",
    "# plot_player_positions_graph(player_positions1, title=\"Player Positions (Training 1)\")\n",
    "plot_player_positions_graph(player_positions2, title=\"Player Positions (Training 2)\")\n",
    "# plot_player_positions_graph(player_positions_test, title=\"Player Positions (Test)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Suggested CNN Model (for now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "\n",
    "\n",
    "class guard_classifier(nn.Module):\n",
    "    def __init__(self, input_channels: int, output_shape: int):\n",
    "        super().__init__()\n",
    "        self.conv_block_1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=input_channels,\n",
    "                      out_channels=32,\n",
    "                      kernel_size=(3,3),\n",
    "                      stride=1, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(in_channels=32,\n",
    "                      out_channels=32,\n",
    "                      kernel_size=(3,3),\n",
    "                      stride=1, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=(2,2)),\n",
    "        )\n",
    "        self.conv_block_2 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=32,\n",
    "                      out_channels=64,\n",
    "                      kernel_size=(3,3),\n",
    "                      stride=1, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(in_channels=64,\n",
    "                      out_channels=64,\n",
    "                      kernel_size=(3,3),\n",
    "                      stride=1, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=(2,2)),\n",
    "        )\n",
    "        self.conv_block_3 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=64,\n",
    "                      out_channels=128,\n",
    "                      kernel_size=(3,3),\n",
    "                      stride=1, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(in_channels=128,\n",
    "                      out_channels=128,\n",
    "                      kernel_size=(3,3),\n",
    "                      stride=1, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=(2,2)),\n",
    "        )\n",
    "        # Calculate the size of the flattened features after the convolutional layers\n",
    "        # Assuming input size of 1920x1080, after three max pooling layers (each 2x2),\n",
    "        # the size will be reduced to 240x135\n",
    "        flattened_size = 128 * 240 * 135\n",
    "\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(in_features=flattened_size,\n",
    "                      out_features=256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=256,\n",
    "                      out_features=256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=256,\n",
    "                      out_features=output_shape)\n",
    "        )\n",
    "\n",
    "    def forward(self, x: torch.Tensor):\n",
    "        x = self.conv_block_1(x)\n",
    "        x = self.conv_block_2(x)\n",
    "        x = self.conv_block_3(x)\n",
    "        x = self.classifier(x)\n",
    "        return x\n",
    "\n",
    "model = guard_classifier(input_channels=3, output_shape=2)\n",
    "print(model)"
   ]
  }
 ],
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