{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6572c153",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import cv2\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "VIDEO_NUMBER = \"3\"\n",
    "PROJECT_ROOT = Path().resolve().parent.parent\n",
    "INPUT_VIDEO_PATH = str(\n",
    "    PROJECT_ROOT\n",
    "    / \"data\"\n",
    "    / f\"recording_{VIDEO_NUMBER}\"\n",
    "    / f\"Recording_{VIDEO_NUMBER}.mp4\"\n",
    ")\n",
    "INPUT_CSV_PATH = str(\n",
    "    PROJECT_ROOT\n",
    "    / \"data\"\n",
    "    / \"auxiliary_data\"\n",
    "    / \"circle_positions\"\n",
    "    / f\"Adjusted_positions_new_{VIDEO_NUMBER}.csv\"\n",
    ")\n",
    "\n",
    "# Load video and CSV\n",
    "cap = cv2.VideoCapture(INPUT_VIDEO_PATH)\n",
    "if not cap.isOpened():\n",
    "    raise IOError(\"Error: Could not open video.\")\n",
    "df = pd.read_csv(INPUT_CSV_PATH)\n",
    "if df.empty:\n",
    "    raise ValueError(\"Error: The CSV file is empty.\")\n",
    "\n",
    "# Select frame and next frame\n",
    "frame_number = 80\n",
    "cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)\n",
    "ret, frame1 = cap.read()\n",
    "if not ret:\n",
    "    raise IOError(f\"Failed to read frame {frame_number}.\")\n",
    "ret, frame2 = cap.read()\n",
    "if not ret:\n",
    "    raise IOError(f\"Failed to read frame {frame_number + 1}.\")\n",
    "\n",
    "# Extract ball center and radius\n",
    "df_row1 = df.iloc[frame_number]\n",
    "df_row2 = df.iloc[frame_number + 1]\n",
    "\n",
    "ball_center1 = np.array([int(df_row1[\"x\"]), int(df_row1[\"y\"])])\n",
    "ball_center2 = np.array([int(df_row2[\"x\"]), int(df_row2[\"y\"])])\n",
    "ball_radius = int(df_row1[\"radius\"])\n",
    "\n",
    "# Compute ROI bounds\n",
    "offset = 2\n",
    "x_min1 = max(ball_center1[0] - ball_radius - offset, 0)\n",
    "x_max1 = min(ball_center1[0] + ball_radius + offset, frame1.shape[1])\n",
    "y_min1 = max(ball_center1[1] - ball_radius - offset, 0)\n",
    "y_max1 = min(ball_center1[1] + ball_radius + offset, frame1.shape[0])\n",
    "\n",
    "x_min2 = max(ball_center2[0] - ball_radius - offset, 0)\n",
    "x_max2 = min(ball_center2[0] + ball_radius + offset, frame2.shape[1])\n",
    "y_min2 = max(ball_center2[1] - ball_radius - offset, 0)\n",
    "y_max2 = min(ball_center2[1] + ball_radius + offset, frame2.shape[0])\n",
    "\n",
    "\n",
    "# Crop ROI and convert to grayscale\n",
    "roi1 = frame1[y_min1:y_max1, x_min1:x_max1]\n",
    "roi2 = frame2[y_min2:y_max2, x_min2:x_max2]\n",
    "gray1 = cv2.cvtColor(roi1, cv2.COLOR_BGR2GRAY)\n",
    "gray2 = cv2.cvtColor(roi2, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# Compute center in ROI coordinates\n",
    "center_roi1 = np.array([ball_center1[0] - x_min1, ball_center1[1] - y_min1])\n",
    "center_roi2 = np.array([ball_center2[0] - x_min2, ball_center2[1] - y_min2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f849580f",
   "metadata": {},
   "source": [
    "### Feature Detection using Shi-Tomasi (Good Features to Track)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9cb0c833",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mask to focus within the ball\n",
    "mask = np.zeros_like(gray1)\n",
    "cv2.circle(mask, tuple(center_roi1.astype(int)), int(ball_radius * 0.8), 255, -1)\n",
    "\n",
    "# Parameters for Shi-Tomasi\n",
    "feature_params = dict(maxCorners=100, qualityLevel=0.001, minDistance=0, blockSize=3)\n",
    "\n",
    "# Detect corners\n",
    "p0 = cv2.goodFeaturesToTrack(gray1, mask=mask, **feature_params)\n",
    "if p0 is None:\n",
    "    raise ValueError(\"No features detected. Try relaxing parameters.\")\n",
    "\n",
    "# Visualize detected features\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.imshow(cv2.cvtColor(roi1, cv2.COLOR_BGR2RGB))\n",
    "ax.scatter(p0[:, 0, 0], p0[:, 0, 1], s=30, c=\"r\")\n",
    "ax.set_title(f\"Detected Features (Frame {frame_number})\")\n",
    "ax.axis(\"off\")\n",
    "plt.show()\n",
    "\n",
    "# Track points to next frame\n",
    "p1, status, _ = cv2.calcOpticalFlowPyrLK(\n",
    "    gray1,\n",
    "    gray2,\n",
    "    p0,\n",
    "    None,\n",
    "    winSize=(15, 15),\n",
    "    maxLevel=2,\n",
    "    criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5846c537",
   "metadata": {},
   "source": [
    "### Optical Flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2332b703",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lift 2D points to 3D on sphere surface\n",
    "old3d = []\n",
    "new3d = []\n",
    "for old_pt, new_pt, s in zip(p0.reshape(-1, 2), p1.reshape(-1, 2), status.reshape(-1)):\n",
    "    if s == 1:\n",
    "        ox, oy = old_pt - center_roi1\n",
    "        nx, ny = new_pt - center_roi2\n",
    "        oz = np.sqrt(max(ball_radius**2 - ox**2 - oy**2, 0))\n",
    "        nz = np.sqrt(max(ball_radius**2 - nx**2 - ny**2, 0))\n",
    "        old3d.append([ox, oy, oz])\n",
    "        new3d.append([nx, ny, nz])\n",
    "old3d = np.array(old3d)\n",
    "new3d = np.array(new3d)\n",
    "\n",
    "# Kabsch algorithm to find rotation matrix R\n",
    "# subtract centroids (but both centered at origin already)\n",
    "H = old3d.T @ new3d\n",
    "U, S, Vt = np.linalg.svd(H)\n",
    "R = Vt.T @ U.T\n",
    "# ensure right-handed\n",
    "if np.linalg.det(R) < 0:\n",
    "    Vt[-1, :] *= -1\n",
    "    R = Vt.T @ U.T\n",
    "\n",
    "# extract axis-angle from R\n",
    "theta = np.arccos((np.trace(R) - 1) / 2)\n",
    "axis = np.array([R[2, 1] - R[1, 2], R[0, 2] - R[2, 0], R[1, 0] - R[0, 1]])\n",
    "axis = axis / (2 * np.sin(theta))\n",
    "axis = axis / np.linalg.norm(axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c9524594",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find rotation pole on ball surface\n",
    "pole3d = axis * ball_radius\n",
    "opp_pole3d = -axis * ball_radius\n",
    "\n",
    "# Project to image (2D ROI coordinates, just take x and y)\n",
    "pole2d = center_roi1[:2] - pole3d[:2]\n",
    "opp_pole2d = center_roi1[:2] - opp_pole3d[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f71e5c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare visualization overlay\n",
    "mask = np.zeros_like(roi1)\n",
    "mask2 = np.zeros_like(roi2)\n",
    "# project axis to image plane (ignore z)\n",
    "a2d = axis[:2]\n",
    "a2d = a2d / np.linalg.norm(a2d)\n",
    "# draw axis line through center_roi\n",
    "length = ball_radius\n",
    "pt1 = (center_roi1 + a2d * length).astype(int)\n",
    "pt2 = (center_roi1 - a2d * length).astype(int)\n",
    "# cv2.line(mask, tuple(pt1), tuple(pt2), (255,255,0), 2)\n",
    "\n",
    "# draw tracked vectors\n",
    "for (new_pt,), (old_pt,), s in zip(p1, p0, status):\n",
    "    if s[0] == 1:\n",
    "        c = tuple(old_pt.astype(int))\n",
    "        a = tuple(new_pt.astype(int))\n",
    "        cv2.circle(mask, c, 1, (0, 0, 255), -1)\n",
    "        # cv2.circle(mask, a, 1, (255,0,0), -1)\n",
    "        # cv2.line(mask, c, a, (0, 255, 0), 1)\n",
    "        # cv2.circle(mask2, c, 1, (0,0,255), -1)\n",
    "        cv2.circle(mask2, a, 1, (255, 0, 0), -1)\n",
    "        cv2.line(mask2, c, a, (0, 255, 0), 1)\n",
    "\n",
    "# overlay and display\n",
    "output_roi1 = cv2.addWeighted(roi1, 1, mask, 1, 0)\n",
    "output_rgb1 = cv2.cvtColor(output_roi1, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "output_roi2 = cv2.addWeighted(roi2, 1, mask2, 1, 0)\n",
    "output_rgb2 = cv2.cvtColor(output_roi2, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# show the ball center\n",
    "# cv2.circle(output_rgb1, tuple(center_roi1), 1, (0, 0, 255), -1)\n",
    "# cv2.circle(output_rgb2, tuple(center_roi2), 1, (0, 0, 255), -1)\n",
    "\n",
    "# show the ball radius\n",
    "cv2.circle(output_rgb1, tuple(center_roi1), ball_radius, (0, 255, 0), 1)\n",
    "cv2.circle(output_rgb2, tuple(center_roi2), ball_radius, (0, 255, 0), 1)\n",
    "\n",
    "# show rotation points\n",
    "cv2.circle(\n",
    "    output_rgb1, tuple(pole2d.astype(int)), 2, (0, 0, 255), -1\n",
    ")  # Yellow = rotation pole\n",
    "cv2.circle(\n",
    "    output_rgb1, tuple(opp_pole2d.astype(int)), 2, (0, 0, 255), -1\n",
    ")  # Magenta = opposite pole\n",
    "\n",
    "# Display the two frames side by side\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 5))  # 1 row, 2 columns\n",
    "\n",
    "# First frame\n",
    "axes[0].imshow(output_rgb1)\n",
    "axes[0].axis(\"off\")\n",
    "axes[0].set_title(f\"Frame {frame_number}\")\n",
    "\n",
    "# Second frame\n",
    "axes[1].imshow(output_rgb2)\n",
    "axes[1].axis(\"off\")\n",
    "axes[1].set_title(f\"Frame {frame_number + 1}\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48a18635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import cv2\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import time\n",
    "from IPython.display import clear_output, display\n",
    "\n",
    "# --- Configurations ---\n",
    "VIDEO_NUMBER = \"3\"\n",
    "PROJECT_ROOT = Path().resolve().parent.parent\n",
    "INPUT_VIDEO_PATH = str(\n",
    "    PROJECT_ROOT\n",
    "    / \"data\"\n",
    "    / f\"recording_{VIDEO_NUMBER}\"\n",
    "    / f\"Recording_{VIDEO_NUMBER}.mp4\"\n",
    ")\n",
    "INPUT_CSV_PATH = str(\n",
    "    PROJECT_ROOT\n",
    "    / \"data\"\n",
    "    / \"auxiliary_data\"\n",
    "    / \"circle_positions\"\n",
    "    / f\"Adjusted_positions_new_{VIDEO_NUMBER}.csv\"\n",
    ")\n",
    "\n",
    "# --- Load video and CSV ---\n",
    "cap = cv2.VideoCapture(INPUT_VIDEO_PATH)\n",
    "fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "\n",
    "if not cap.isOpened():\n",
    "    raise IOError(\"Error: Could not open video.\")\n",
    "\n",
    "df = pd.read_csv(INPUT_CSV_PATH)\n",
    "if df.empty:\n",
    "    raise ValueError(\"Error: The CSV file is empty.\")\n",
    "\n",
    "frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "offset = 2\n",
    "\n",
    "# --- Process frame pairs ---\n",
    "for frame_number in range(frame_count - 1):\n",
    "    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)\n",
    "    ret, frame1 = cap.read()\n",
    "    if not ret:\n",
    "        print(f\"Failed to read frame {frame_number}.\")\n",
    "        continue\n",
    "\n",
    "    ret, frame2 = cap.read()\n",
    "    if not ret:\n",
    "        print(f\"Failed to read frame {frame_number + 1}.\")\n",
    "        continue\n",
    "\n",
    "    # --- Get CSV data for current frame pair ---\n",
    "    try:\n",
    "        df_row1 = df.iloc[frame_number]\n",
    "        df_row2 = df.iloc[frame_number + 1]\n",
    "\n",
    "        if (\n",
    "            df_row1[[\"x\", \"y\", \"radius\"]].isnull().any()\n",
    "            or df_row2[[\"x\", \"y\", \"radius\"]].isnull().any()\n",
    "        ):\n",
    "            continue\n",
    "    except IndexError:\n",
    "        print(f\"No CSV data for frame {frame_number}.\")\n",
    "        continue\n",
    "\n",
    "    # --- Compute ball centers and ROI bounds ---\n",
    "    ball_center1 = np.array([int(df_row1[\"x\"]), int(df_row1[\"y\"])])\n",
    "    ball_center2 = np.array([int(df_row2[\"x\"]), int(df_row2[\"y\"])])\n",
    "    ball_radius = int(df_row1[\"radius\"])\n",
    "\n",
    "    def roi_bounds(center, radius, frame_shape):\n",
    "        x_min = max(center[0] - radius - offset, 0)\n",
    "        x_max = min(center[0] + radius + offset, frame_shape[1])\n",
    "        y_min = max(center[1] - radius - offset, 0)\n",
    "        y_max = min(center[1] + radius + offset, frame_shape[0])\n",
    "        return x_min, x_max, y_min, y_max\n",
    "\n",
    "    x_min1, x_max1, y_min1, y_max1 = roi_bounds(ball_center1, ball_radius, frame1.shape)\n",
    "    x_min2, x_max2, y_min2, y_max2 = roi_bounds(ball_center2, ball_radius, frame2.shape)\n",
    "\n",
    "    roi1 = frame1[y_min1:y_max1, x_min1:x_max1]\n",
    "    roi2 = frame2[y_min2:y_max2, x_min2:x_max2]\n",
    "    gray1 = cv2.cvtColor(roi1, cv2.COLOR_BGR2GRAY)\n",
    "    gray2 = cv2.cvtColor(roi2, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    center_roi1 = ball_center1 - [x_min1, y_min1]\n",
    "    center_roi2 = ball_center2 - [x_min2, y_min2]\n",
    "\n",
    "    # --- Mask for feature detection ---\n",
    "    mask = np.zeros_like(gray1)\n",
    "    cv2.circle(mask, tuple(center_roi1.astype(int)), int(ball_radius * 0.8), 255, -1)\n",
    "\n",
    "    feature_params = dict(\n",
    "        maxCorners=100, qualityLevel=0.001, minDistance=0, blockSize=3\n",
    "    )\n",
    "\n",
    "    p0 = cv2.goodFeaturesToTrack(gray1, mask=mask, **feature_params)\n",
    "    if p0 is None:\n",
    "        raise ValueError(\"No features detected. Try relaxing parameters.\")\n",
    "\n",
    "    # --- Optical flow tracking ---\n",
    "    p1, status, _ = cv2.calcOpticalFlowPyrLK(\n",
    "        gray1,\n",
    "        gray2,\n",
    "        p0,\n",
    "        None,\n",
    "        winSize=(15, 15),\n",
    "        maxLevel=2,\n",
    "        criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03),\n",
    "    )\n",
    "\n",
    "    # --- Lift to 3D points ---\n",
    "    old3d, new3d = [], []\n",
    "    for old_pt, new_pt, s in zip(\n",
    "        p0.reshape(-1, 2), p1.reshape(-1, 2), status.reshape(-1)\n",
    "    ):\n",
    "        if s == 1:\n",
    "            ox, oy = old_pt - center_roi1\n",
    "            nx, ny = new_pt - center_roi2\n",
    "            oz = np.sqrt(max(ball_radius**2 - ox**2 - oy**2, 0))\n",
    "            nz = np.sqrt(max(ball_radius**2 - nx**2 - ny**2, 0))\n",
    "            old3d.append([ox, oy, oz])\n",
    "            new3d.append([nx, ny, nz])\n",
    "\n",
    "    old3d = np.array(old3d)\n",
    "    new3d = np.array(new3d)\n",
    "\n",
    "    # --- Kabsch algorithm for rotation ---\n",
    "    H = old3d.T @ new3d\n",
    "    U, S, Vt = np.linalg.svd(H)\n",
    "    R = Vt.T @ U.T\n",
    "    if np.linalg.det(R) < 0:\n",
    "        Vt[-1, :] *= -1\n",
    "        R = Vt.T @ U.T\n",
    "\n",
    "    theta = np.arccos((np.trace(R) - 1) / 2)\n",
    "    axis = np.array([R[2, 1] - R[1, 2], R[0, 2] - R[2, 0], R[1, 0] - R[0, 1]]) / (\n",
    "        2 * np.sin(theta)\n",
    "    )\n",
    "    axis /= np.linalg.norm(axis)\n",
    "\n",
    "    # --- Compute rotation poles ---\n",
    "    pole3d = axis * ball_radius\n",
    "    opp_pole3d = -pole3d\n",
    "\n",
    "    pole2d = center_roi1[:2] - pole3d[:2]\n",
    "    opp_pole2d = center_roi1[:2] - opp_pole3d[:2]\n",
    "\n",
    "    # --- Visualization overlay ---\n",
    "    mask = np.zeros_like(roi1)\n",
    "    mask2 = np.zeros_like(roi2)\n",
    "\n",
    "    for (new_pt,), (old_pt,), s in zip(p1, p0, status):\n",
    "        if s[0] == 1:\n",
    "            c = tuple(old_pt.astype(int))\n",
    "            a = tuple(new_pt.astype(int))\n",
    "            cv2.circle(mask, c, 1, (0, 0, 255), -1)\n",
    "            cv2.circle(mask2, a, 1, (255, 0, 0), -1)\n",
    "            cv2.line(mask2, c, a, (0, 255, 0), 1)\n",
    "\n",
    "    output_roi1 = cv2.addWeighted(roi1, 1, mask, 1, 0)\n",
    "    output_rgb1 = cv2.cvtColor(output_roi1, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "    output_roi2 = cv2.addWeighted(roi2, 1, mask2, 1, 0)\n",
    "    output_rgb2 = cv2.cvtColor(output_roi2, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "    cv2.circle(output_rgb1, tuple(center_roi1), ball_radius, (0, 255, 0), 1)\n",
    "    cv2.circle(output_rgb2, tuple(center_roi2), ball_radius, (0, 255, 0), 1)\n",
    "\n",
    "    cv2.circle(output_rgb1, tuple(pole2d.astype(int)), 2, (0, 0, 255), -1)\n",
    "    cv2.circle(output_rgb1, tuple(opp_pole2d.astype(int)), 2, (0, 0, 255), -1)\n",
    "\n",
    "    # --- Display results ---\n",
    "    clear_output(wait=True)\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axes[0].imshow(output_rgb1)\n",
    "    axes[0].axis(\"off\")\n",
    "    axes[0].set_title(f\"Frame {frame_number}\")\n",
    "    axes[1].imshow(output_rgb2)\n",
    "    axes[1].axis(\"off\")\n",
    "    axes[1].set_title(f\"Frame {frame_number + 1}\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    display(plt.gcf())\n",
    "    plt.close()\n",
    "    time.sleep(1 / fps)"
   ]
  }
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