{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "23e1eb32",
   "metadata": {},
   "source": [
    "# Detection of the ball\n",
    "\n",
    "The idea here is:\n",
    "- I seach for the ball in the first rectangular of the image\n",
    "- When I detect a ball I restrict the detection for the successive ball whitin a rectangular not so much larger then the ball\n",
    "- I use the detected rectangle for the successive detection and I resize it with the new ball detected\n",
    "- And so on"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bdd7e1c",
   "metadata": {},
   "source": [
    "IMPORTANT: understand how to set the first rectangular in which detect the ball"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8a26dead",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import cv2\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display, clear_output\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4bc2cfff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Configuration ---\n",
    "VIDEO_NUM = \"3\"\n",
    "CONSISTENCY_THRESHOLD = 5\n",
    "POSITION_TOLERANCE = 35\n",
    "RADIUS_TOLERANCE = 10\n",
    "ROI_MARGIN = 50\n",
    "MAX_LOST_FRAMES = 2  # TODO: this can be used dynamically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1b6ed990",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Video opened: FPS=59.94005994005994, Total Frames=227\n"
     ]
    }
   ],
   "source": [
    "# --- Paths ---\n",
    "PROJECT_ROOT = Path().resolve().parent.parent\n",
    "VIDEO_PATH = (\n",
    "    PROJECT_ROOT / \"data\" / f\"recording_{VIDEO_NUM}\" / f\"Recording_{VIDEO_NUM}.mp4\"\n",
    ")\n",
    "CSV_PATH = (\n",
    "    PROJECT_ROOT\n",
    "    / \"data\"\n",
    "    / \"auxiliary_data\"\n",
    "    / \"lane_points\"\n",
    "    / f\"Lane_points_{VIDEO_NUM}.csv\"\n",
    ")\n",
    "\n",
    "# --- Load Data ---\n",
    "df = pd.read_csv(CSV_PATH)\n",
    "cap = cv2.VideoCapture(str(VIDEO_PATH))\n",
    "if not cap.isOpened():\n",
    "    raise IOError(\"Error opening video file!\")\n",
    "\n",
    "fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "print(f\"Video opened: FPS={fps}, Total Frames={total_frames}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "daa2b55a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- State Variables ---\n",
    "last_circle = None\n",
    "consistency_counter = 0\n",
    "lost_counter = 0\n",
    "use_roi = False\n",
    "search_roi = None\n",
    "frame_idx = 0\n",
    "first_time = True\n",
    "scale_factor = 0.6\n",
    "r_approx = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1de43fee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_points_for_frame(df: pd.DataFrame, frame_number: int) -> np.ndarray:\n",
    "    row = df[df[\"Frame\"] == frame_number]\n",
    "    if row.empty:\n",
    "        raise ValueError(f\"No points found for frame {frame_number}\")\n",
    "    return np.array(\n",
    "        [\n",
    "            [row[\"bottom_left_x\"].values[0], row[\"bottom_left_y\"].values[0]],\n",
    "            [row[\"bottom_right_x\"].values[0], row[\"bottom_right_y\"].values[0]],\n",
    "            [row[\"up_right_x\"].values[0], row[\"up_right_y\"].values[0]],\n",
    "            [row[\"up_left_x\"].values[0], row[\"up_left_y\"].values[0]],\n",
    "        ],\n",
    "        dtype=np.int32,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e3b6f0d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_modified_polygon(points: np.ndarray) -> np.ndarray:\n",
    "    top_indices = np.argsort(points[:, 1])[:2]\n",
    "    top_points = points[top_indices]\n",
    "    left_top, right_top = sorted(top_points, key=lambda pt: pt[0])\n",
    "\n",
    "    left_top_mod = [left_top[0] - 50, left_top[1] - 70]\n",
    "    right_top_mod = [right_top[0] + 50, right_top[1] - 70]\n",
    "\n",
    "    return np.array(\n",
    "        [\n",
    "            left_top_mod\n",
    "            if np.array_equal(pt, left_top)\n",
    "            else right_top_mod\n",
    "            if np.array_equal(pt, right_top)\n",
    "            else pt\n",
    "            for pt in points\n",
    "        ],\n",
    "        dtype=np.int32,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7f31b1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_upper_polygon(points: np.ndarray) -> np.ndarray:\n",
    "    top_indices = np.argsort(points[:, 1])[:2]\n",
    "    top_points = points[top_indices]\n",
    "    left_top, right_top = sorted(top_points, key=lambda pt: pt[0])\n",
    "\n",
    "    left_top_mod = [\n",
    "        left_top[0] - 500,\n",
    "        left_top[1] + 150,\n",
    "    ]  # TODO: calculate it in another way (proportions between the lines?)\n",
    "    right_top_mod = [\n",
    "        right_top[0] + 150,\n",
    "        right_top[1] + 150,\n",
    "    ]  # TODO: calculate it in another way (proportions between the lines?)\n",
    "\n",
    "    return np.array(\n",
    "        [\n",
    "            left_top_mod\n",
    "            if np.array_equal(pt, left_top)\n",
    "            else right_top_mod\n",
    "            if np.array_equal(pt, right_top)\n",
    "            else pt\n",
    "            for pt in points\n",
    "        ],\n",
    "        dtype=np.int32,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8f6f5d5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_background(\n",
    "    frame_number: int, df: pd.DataFrame, image: np.ndarray\n",
    ") -> tuple[np.ndarray, np.ndarray]:\n",
    "    points = get_points_for_frame(df, frame_number)\n",
    "    polygon = compute_modified_polygon(points)\n",
    "    mask = cv2.fillPoly(np.zeros(image.shape[:2], dtype=np.uint8), [polygon], 255)\n",
    "    result = cv2.bitwise_and(image, image, mask=mask)\n",
    "    return result, polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "96fe7325",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_roi(image: np.ndarray) -> np.ndarray:\n",
    "    return cv2.medianBlur(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2b89ab8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_approx_radius(df: pd.DataFrame) -> int:\n",
    "    points = get_points_for_frame(df, 0)\n",
    "    radius = int(np.linalg.norm(points[0] - points[1]) / 10)\n",
    "    return radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f624b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_circle(\n",
    "    preprocessed_img: np.ndarray, frame_number: int, r_approx: int\n",
    ") -> np.ndarray | None:\n",
    "    min_radius = int(r_approx * 0.65)\n",
    "    max_radius = int(r_approx)\n",
    "    print(f\"Frame {frame_number}: min radius={min_radius}, max radius={max_radius}\")\n",
    "\n",
    "    return cv2.HoughCircles(\n",
    "        preprocessed_img,\n",
    "        cv2.HOUGH_GRADIENT,\n",
    "        dp=1.2,\n",
    "        minDist=50,\n",
    "        param1=100,\n",
    "        param2=40,\n",
    "        minRadius=min_radius,\n",
    "        maxRadius=max_radius,\n",
    "    )\n",
    "\n",
    "\n",
    "def detect_circle_after_roi(\n",
    "    preprocessed_img: np.ndarray, frame_number: int, r_approx: int\n",
    ") -> np.ndarray | None:\n",
    "    min_radius = max(0, int(r_approx * 0.7))\n",
    "    max_radius = int(r_approx * 1.1)\n",
    "    print(f\"Frame {frame_number}: min radius={min_radius}, max radius={max_radius}\")\n",
    "\n",
    "    return cv2.HoughCircles(\n",
    "        preprocessed_img,\n",
    "        cv2.HOUGH_GRADIENT,\n",
    "        dp=1.2,\n",
    "        minDist=50,\n",
    "        param1=100,\n",
    "        param2=40,\n",
    "        minRadius=min_radius,\n",
    "        maxRadius=max_radius,\n",
    "    )\n",
    "\n",
    "\n",
    "def detect_circle_roi(\n",
    "    preprocessed_img: np.ndarray, frame_number: int, r_approx: int\n",
    ") -> np.ndarray | None:\n",
    "    min_radius = max(0, int(r_approx * 0.9))\n",
    "    max_radius = int(r_approx * 1.08)\n",
    "    print(f\"Frame {frame_number}: min radius={min_radius}, max radius={max_radius}\")\n",
    "\n",
    "    return cv2.HoughCircles(\n",
    "        preprocessed_img,\n",
    "        cv2.HOUGH_GRADIENT,\n",
    "        dp=1.2,\n",
    "        minDist=500,\n",
    "        param1=200,\n",
    "        param2=10,\n",
    "        minRadius=min_radius,\n",
    "        maxRadius=max_radius,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5b46d8a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_roi(\n",
    "    x: int, y: int, r: int, frame_shape, base_margin: int, scale_factor: float\n",
    ") -> tuple[int, int, int, int]:\n",
    "    height, width = frame_shape[:2]\n",
    "    dynamic_margin = max(int(r * scale_factor), 10)  # Ensure at least some margin\n",
    "\n",
    "    x_min = max(0, x - r - dynamic_margin)  # -10 to avoid cutting the circle\n",
    "    y_min = max(0, y - r - dynamic_margin)  # -10 to avoid cutting the circle\n",
    "    x_max = min(width, x + r + dynamic_margin)  # +10 to avoid cutting the circle\n",
    "    y_max = min(height, y + r + 3)\n",
    "    print(f\"ROI: width={x_max - x_min}, height={y_max - y_min}\")\n",
    "\n",
    "    return (x_min, y_min, x_max, y_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ac46ced6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End of video or cannot read frame.\n",
      "Processing complete.\n"
     ]
    }
   ],
   "source": [
    "while cap.isOpened():\n",
    "    ret, frame = cap.read()\n",
    "    if not ret:\n",
    "        print(\"End of video or cannot read frame.\")\n",
    "        break\n",
    "\n",
    "    try:\n",
    "        # Remove background\n",
    "        masked_frame, _ = remove_background(frame_idx, df, frame)\n",
    "\n",
    "        # Choice of the \"search region\"\n",
    "        if consistency_counter < CONSISTENCY_THRESHOLD and first_time:\n",
    "            # Search in the upper polygon\n",
    "            upper_polygon = compute_upper_polygon(get_points_for_frame(df, frame_idx))\n",
    "            upper_mask = np.zeros(frame.shape[:2], dtype=np.uint8)\n",
    "            cv2.fillPoly(upper_mask, [upper_polygon], 255)\n",
    "            search_region = cv2.bitwise_and(masked_frame, masked_frame, mask=upper_mask)\n",
    "            roi_offset = (0, 0)\n",
    "            preprocessed = preprocess_roi(search_region)\n",
    "            r_approx = compute_approx_radius(df)\n",
    "            circles = detect_circle(preprocessed, frame_idx, r_approx)\n",
    "        elif use_roi and search_roi:\n",
    "            # Detection in the ROI\n",
    "            x_min, y_min, x_max, y_max = search_roi\n",
    "            search_region = masked_frame[y_min:y_max, x_min:x_max]\n",
    "            roi_offset = (x_min, y_min)\n",
    "            preprocessed = preprocess_roi(search_region)\n",
    "            circles = detect_circle_roi(preprocessed, frame_idx, r_approx)\n",
    "        else:\n",
    "            # Fallback in the full masked frame\n",
    "            search_region = masked_frame\n",
    "            roi_offset = (0, 0)\n",
    "            preprocessed = preprocess_roi(search_region)\n",
    "            circles = detect_circle_after_roi(preprocessed, frame_idx, r_approx)\n",
    "\n",
    "        # Detect cicles in the search region\n",
    "        # preprocessed = preprocess_roi(search_region)\n",
    "        # circles = detect_circle(preprocessed, frame_idx, r_approx)\n",
    "        valid_circle = None\n",
    "\n",
    "        if circles is not None:\n",
    "            x, y, r = np.round(circles[0, 0]).astype(\"int\")\n",
    "            x_global, y_global, r_global = x + roi_offset[0], y + roi_offset[1], r\n",
    "            valid_circle = (x + roi_offset[0], y + roi_offset[1], r_global)\n",
    "\n",
    "        if valid_circle:\n",
    "            dx, dy, dr = (\n",
    "                np.abs(np.subtract(valid_circle, last_circle))\n",
    "                if last_circle\n",
    "                else (0, 0, 0)\n",
    "            )\n",
    "            if (\n",
    "                last_circle\n",
    "                and dx < POSITION_TOLERANCE\n",
    "                and dy < POSITION_TOLERANCE\n",
    "                and dr < RADIUS_TOLERANCE\n",
    "            ):\n",
    "                consistency_counter += 1\n",
    "            else:\n",
    "                consistency_counter = 1\n",
    "\n",
    "            last_circle = valid_circle\n",
    "            lost_counter = 0\n",
    "\n",
    "            # Draw circle\n",
    "            x_roi, y_roi = (\n",
    "                valid_circle[0] - roi_offset[0],\n",
    "                valid_circle[1] - roi_offset[1],\n",
    "            )\n",
    "            cv2.circle(search_region, (x_roi, y_roi), valid_circle[2], (0, 255, 0), 2)\n",
    "            cv2.circle(search_region, (x_roi, y_roi), 2, (0, 0, 255), 3)\n",
    "\n",
    "            if consistency_counter >= CONSISTENCY_THRESHOLD:\n",
    "                search_roi = update_roi(\n",
    "                    x_global, y_global, r_global, frame.shape, ROI_MARGIN, scale_factor\n",
    "                )\n",
    "                r_approx = r_global\n",
    "                first_time = False\n",
    "                use_roi = True\n",
    "        else:\n",
    "            lost_counter += 1\n",
    "            if lost_counter >= MAX_LOST_FRAMES:\n",
    "                use_roi = False\n",
    "                consistency_counter = 0\n",
    "                last_circle = None\n",
    "                search_roi = None\n",
    "                # r_approx = None\n",
    "\n",
    "        # Display result\n",
    "        display_frame = cv2.cvtColor(search_region, cv2.COLOR_BGR2RGB)  # preprocessed\n",
    "        clear_output(wait=True)\n",
    "        plt.figure(figsize=(10, 6))\n",
    "        plt.imshow(display_frame)\n",
    "        plt.title(f\"Search Region - Frame {frame_idx}\")\n",
    "        plt.axis(\"off\")\n",
    "        display(plt.gcf())\n",
    "        plt.close()\n",
    "        time.sleep(1 / fps)\n",
    "\n",
    "    except ValueError as e:\n",
    "        print(f\"Skipping frame {frame_idx}: {e}\")\n",
    "\n",
    "    frame_idx += 1\n",
    "\n",
    "cap.release()\n",
    "print(\"Processing complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c26281d",
   "metadata": {},
   "source": [
    "IMPROVEMENT: Rescrict the radius of the ball when we are detecting it in full track (camera calibration)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcdff092",
   "metadata": {},
   "source": [
    "[This code is implemented in Detection_3.py]"
   ]
  }
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