Visualizing_eRIT.ipynb

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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame 418 written to video\n",
      "Video saved to keypoints_flow_video.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Paths to directories\n",
    "images_dir = Path(\"images\")\n",
    "keypoints_dir = Path(\"NewOutputs/Keypoints\")\n",
    "matches_dir = Path(\"NewOutputs/Matches\")\n",
    "\n",
    "# Output video path\n",
    "output_video_path = \"keypoints_flow_video.mp4\"\n",
    "\n",
    "# Load image paths\n",
    "image_paths = sorted(images_dir.glob(\"*.png\"))\n",
    "keypoints_path = sorted(keypoints_dir.glob(\"*.txt\"))\n",
    "matches_path = sorted(matches_dir.glob(\"*.txt\"))\n",
    "\n",
    "# Function to load keypoints from file\n",
    "def load_keypoints(filepath):\n",
    "    return np.loadtxt(filepath, dtype=float)\n",
    "\n",
    "# Function to load matches from file\n",
    "def load_matches(filepath):\n",
    "    return np.loadtxt(filepath, dtype=int)\n",
    "\n",
    "# Function to assign unique colors\n",
    "def generate_colors(num_colors):\n",
    "    np.random.seed(42)\n",
    "    return np.random.randint(0, 255, size=(num_colors, 3))\n",
    "\n",
    "# Initialize tracking data structures\n",
    "tracked_keypoints = {}\n",
    "\n",
    "# Initialize colors for keypoints\n",
    "num_colors = 2048  # Assuming a maximum of 2048 keypoints\n",
    "colors = generate_colors(num_colors)\n",
    "\n",
    "# Create a VideoWriter object\n",
    "frame = cv2.imread(str(image_paths[0]))\n",
    "height, width, _ = frame.shape\n",
    "video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 1, (width * 2, height))\n",
    "\n",
    "if not video_writer.isOpened():\n",
    "    print(\"Error: VideoWriter not opened\")\n",
    "    exit()\n",
    "\n",
    "# Iterate through image pairs\n",
    "for i in tqdm(range(len(image_paths) - 1)):\n",
    "\n",
    "    img1_path = image_paths[i]\n",
    "    img2_path = image_paths[i + 1]\n",
    "\n",
    "    # Load images\n",
    "    img1 = cv2.imread(str(img1_path))\n",
    "    img2 = cv2.imread(str(img2_path))\n",
    "\n",
    "    # Check if images are loaded correctly\n",
    "    if img1 is None:\n",
    "        print(f\"Failed to load image: {img1_path}\")\n",
    "        continue\n",
    "    if img2 is None:\n",
    "        print(f\"Failed to load image: {img2_path}\")\n",
    "        continue\n",
    "\n",
    "    # Load keypoints and matches\n",
    "    kp1 = load_keypoints(keypoints_path[i])\n",
    "    kp2 = load_keypoints(keypoints_path[i+1])\n",
    "    matches = load_matches(matches_path[i])\n",
    "\n",
    "    # Check if keypoints and matches are loaded correctly\n",
    "    if kp1.size == 0 or kp2.size == 0 or matches.size == 0:\n",
    "        print(f\"Failed to load keypoints or matches for images {i} and {i+1}\")\n",
    "        continue\n",
    "\n",
    "    # Update tracking information\n",
    "    new_tracked_keypoints = {}\n",
    "    for idx1, idx2 in matches:\n",
    "        pt1 = (int(kp1[idx1][0]), int(kp1[idx1][1]))\n",
    "        pt2 = (int(kp2[idx2][0]), int(kp2[idx2][1]))\n",
    "        \n",
    "        # Assign a unique color to the tracked keypoint\n",
    "        if idx1 not in tracked_keypoints:\n",
    "            color = colors[idx1 % num_colors]\n",
    "            tracked_keypoints[idx1] = (pt1, color)\n",
    "        else:\n",
    "            color = tracked_keypoints[idx1][1]\n",
    "\n",
    "        # Update new tracked keypoints\n",
    "        new_tracked_keypoints[idx2] = (pt2, color)\n",
    "\n",
    "    # Draw keypoints and matches\n",
    "    for pt, color in tracked_keypoints.values():\n",
    "        cv2.circle(img1, pt, 3, color.tolist(), -1)\n",
    "\n",
    "    for pt, color in new_tracked_keypoints.values():\n",
    "        cv2.circle(img2, pt, 3, color.tolist(), -1)\n",
    "\n",
    "    for (idx1, idx2) in matches:\n",
    "        pt1 = (int(kp1[idx1][0]), int(kp1[idx1][1]))\n",
    "        pt2 = (int(kp2[idx2][0]), int(kp2[idx2][1]))\n",
    "        color = tracked_keypoints[idx1][1]\n",
    "        cv2.line(img1, pt1, pt2, color.tolist(), 1)\n",
    "        cv2.line(img2, pt1, pt2, color.tolist(), 1)\n",
    "\n",
    "    # Combine images side by side\n",
    "    combined_image = np.hstack((img1, img2))\n",
    "\n",
    "    # Debug: Save the frame as an image for verification\n",
    "    cv2.imwrite(f\"frame_{i}.png\", combined_image)\n",
    "\n",
    "    # Write frame to video\n",
    "    video_writer.write(combined_image)\n",
    "\n",
    "    # Debug: Check if the frame is being written\n",
    "    print(f\"Frame {i} written to video\")\n",
    "\n",
    "    # Update tracked keypoints\n",
    "    tracked_keypoints = new_tracked_keypoints\n",
    "\n",
    "# Release the VideoWriter object\n",
    "video_writer.release()\n",
    "print(f\"Video saved to {output_video_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34a3b7bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 420/420 [00:32<00:00, 12.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Video saved to keypoints_flow_video.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Paths to directories\n",
    "images_dir = Path(\"images\")\n",
    "keypoints_dir = Path(\"NewOutputs/Keypoints\")\n",
    "matches_dir = Path(\"NewOutputs/Matches\")\n",
    "\n",
    "# Output video path\n",
    "output_video_path = \"keypoints_flow_video.mp4\"\n",
    "\n",
    "# Load image paths\n",
    "image_paths = sorted(images_dir.glob(\"*.png\"))\n",
    "keypoints_path = sorted(keypoints_dir.glob(\"*.txt\"))\n",
    "matches_path = sorted(matches_dir.glob(\"*.txt\"))\n",
    "\n",
    "# Function to load keypoints from file\n",
    "def load_keypoints(filepath):\n",
    "    return np.loadtxt(filepath, dtype=float)\n",
    "\n",
    "# Function to load matches from file\n",
    "def load_matches(filepath):\n",
    "    return np.loadtxt(filepath, dtype=int)\n",
    "\n",
    "# Function to assign unique colors\n",
    "def generate_colors(num_colors):\n",
    "    np.random.seed(42)\n",
    "    return np.random.randint(0, 255, size=(num_colors, 3))\n",
    "\n",
    "# Initialize tracking data structures\n",
    "tracked_keypoints = {}\n",
    "\n",
    "# Initialize colors for keypoints\n",
    "num_colors = 2048  # Assuming a maximum of 2048 keypoints\n",
    "colors = generate_colors(num_colors)\n",
    "\n",
    "# Create a VideoWriter object\n",
    "frame = cv2.imread(str(image_paths[0]))\n",
    "height, width, _ = frame.shape\n",
    "video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 1, (width, height))\n",
    "\n",
    "if not video_writer.isOpened():\n",
    "    print(\"Error: VideoWriter not opened\")\n",
    "    exit()\n",
    "\n",
    "# Iterate through image pairs\n",
    "for i in tqdm(range(len(image_paths))):\n",
    "\n",
    "    img_path = image_paths[i]\n",
    "\n",
    "    # Load images\n",
    "    img = cv2.imread(str(img_path))\n",
    "\n",
    "    # Check if images are loaded correctly\n",
    "    if img is None:\n",
    "        print(f\"Failed to load image: {img_path}\")\n",
    "        continue\n",
    "\n",
    "    # Load keypoints and matches\n",
    "    kp = load_keypoints(keypoints_path[i])\n",
    "\n",
    "    # Check if keypoints are loaded correctly\n",
    "    if kp.size == 0:\n",
    "        print(f\"Failed to load keypoints for image {i}\")\n",
    "        continue\n",
    "\n",
    "    # Draw keypoints\n",
    "    for idx, (x, y) in enumerate(kp):\n",
    "        cv2.circle(img, (int(x), int(y)), 3, colors[idx % num_colors].tolist(), -1)\n",
    "\n",
    "    # Draw trajectories\n",
    "    if i > 0:\n",
    "        prev_kp = load_keypoints(keypoints_path[i - 1])\n",
    "        matches = load_matches(matches_path[i - 1])\n",
    "        \n",
    "        for (idx1, idx2) in matches:\n",
    "            pt1 = (int(prev_kp[idx1][0]), int(prev_kp[idx1][1]))\n",
    "            pt2 = (int(kp[idx2][0]), int(kp[idx2][1]))\n",
    "            color = colors[idx1 % num_colors].tolist()\n",
    "            cv2.line(img, pt1, pt2, color, 2)\n",
    "\n",
    "    # Write frame to video\n",
    "    video_writer.write(img)\n",
    "\n",
    "    # Debug: Save the frame as an image for verification\n",
    "    cv2.imwrite(f\"frame_{i}.png\", img)\n",
    "\n",
    "# Release the VideoWriter object\n",
    "video_writer.release()\n",
    "print(f\"Video saved to {output_video_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7a56e42a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 420/420 [00:51<00:00,  8.17it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Video saved to keypoints_full_trajectory_video.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Paths to directories\n",
    "images_dir = Path(\"images\")\n",
    "keypoints_dir = Path(\"NewOutputs/Keypoints\")\n",
    "matches_dir = Path(\"NewOutputs/Matches\")\n",
    "\n",
    "# Output video path\n",
    "output_video_path = \"keypoints_full_trajectory_video.mp4\"\n",
    "\n",
    "# Load image paths\n",
    "image_paths = sorted(images_dir.glob(\"*.png\"))\n",
    "keypoints_path = sorted(keypoints_dir.glob(\"*.txt\"))\n",
    "matches_path = sorted(matches_dir.glob(\"*.txt\"))\n",
    "\n",
    "# Function to load keypoints from file\n",
    "def load_keypoints(filepath):\n",
    "    return np.loadtxt(filepath, dtype=float)\n",
    "\n",
    "# Function to load matches from file\n",
    "def load_matches(filepath):\n",
    "    return np.loadtxt(filepath, dtype=int)\n",
    "\n",
    "# Function to assign unique colors\n",
    "def generate_colors(num_colors):\n",
    "    np.random.seed(42)\n",
    "    return np.random.randint(0, 255, size=(num_colors, 3))\n",
    "\n",
    "# Initialize colors for keypoints\n",
    "num_colors = 2048  # Assuming a maximum of 2048 keypoints\n",
    "colors = generate_colors(num_colors)\n",
    "\n",
    "# Create a VideoWriter object\n",
    "frame = cv2.imread(str(image_paths[0]))\n",
    "height, width, _ = frame.shape\n",
    "video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 5, (width, height))\n",
    "\n",
    "if not video_writer.isOpened():\n",
    "    print(\"Error: VideoWriter not opened\")\n",
    "    exit()\n",
    "\n",
    "# Initialize dictionary to keep track of keypoints trajectories\n",
    "trajectories = {}\n",
    "\n",
    "# Iterate through image pairs\n",
    "for i in tqdm(range(len(image_paths))):\n",
    "    img_path = image_paths[i]\n",
    "\n",
    "    # Load images\n",
    "    img = cv2.imread(str(img_path))\n",
    "\n",
    "    # Check if images are loaded correctly\n",
    "    if img is None:\n",
    "        print(f\"Failed to load image: {img_path}\")\n",
    "        continue\n",
    "\n",
    "    # Load keypoints\n",
    "    kp = load_keypoints(keypoints_path[i])\n",
    "\n",
    "    # Check if keypoints are loaded correctly\n",
    "    if kp.size == 0:\n",
    "        print(f\"Failed to load keypoints for image {i}\")\n",
    "        continue\n",
    "\n",
    "    # Initialize trajectories for the first frame\n",
    "    if i == 0:\n",
    "        for idx, (x, y) in enumerate(kp):\n",
    "            trajectories[idx] = [(int(x), int(y))]\n",
    "\n",
    "    # Update trajectories for subsequent frames\n",
    "    if i > 0:\n",
    "        prev_kp = load_keypoints(keypoints_path[i - 1])\n",
    "        matches = load_matches(matches_path[i - 1])\n",
    "        \n",
    "        new_trajectories = {}\n",
    "        for (idx1, idx2) in matches:\n",
    "            pt1 = (int(prev_kp[idx1][0]), int(prev_kp[idx1][1]))\n",
    "            pt2 = (int(kp[idx2][0]), int(kp[idx2][1]))\n",
    "            if idx1 in trajectories:\n",
    "                new_trajectories[idx2] = trajectories[idx1] + [pt2]\n",
    "            else:\n",
    "                new_trajectories[idx2] = [pt2]\n",
    "\n",
    "        trajectories = new_trajectories\n",
    "\n",
    "    # Draw keypoints\n",
    "    for idx, (x, y) in enumerate(kp):\n",
    "        cv2.circle(img, (int(x), int(y)), 3, colors[idx % num_colors].tolist(), -1)\n",
    "\n",
    "    # Draw trajectories\n",
    "    for idx, points in trajectories.items():\n",
    "        color = colors[idx % num_colors].tolist()\n",
    "        for j in range(len(points) - 1):\n",
    "            cv2.line(img, points[j], points[j + 1], color, 1)\n",
    "\n",
    "    # Write frame to video\n",
    "    video_writer.write(img)\n",
    "\n",
    "    # Debug: Save the frame as an image for verification\n",
    "    cv2.imwrite(f\"frame_{i}.png\", img)\n",
    "\n",
    "# Release the VideoWriter object\n",
    "video_writer.release()\n",
    "print(f\"Video saved to {output_video_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1324ecd1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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