{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5e46d2e1-d240-4e0f-bf12-d5dd67cd0c56",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import argparse\n",
    "import os\n",
    "import os.path as osp\n",
    "import time\n",
    "import cv2\n",
    "import torch\n",
    "import numpy as np\n",
    "from loguru import logger\n",
    "from collections import defaultdict, Counter\n",
    "\n",
    "from mmdet.apis import async_inference_detector, inference_detector\n",
    "from mmdet.apis.inference import init_detector\n",
    "\n",
    "\n",
    "from yolox.utils.visualize import plot_tracking, _COLORS\n",
    "from yolox.tracker.tp_tracker_interpolation import TPTracker\n",
    "from yolox.tracking_utils.timer import Timer\n",
    "\n",
    "from model import SocialImplicit\n",
    "from CFG import CFG\n",
    "import numpy as np\n",
    "from scipy.fftpack import fft, ifft\n",
    "\n",
    "IMAGE_EXT = [\".jpg\", \".jpeg\", \".webp\", \".bmp\", \".png\"]\n",
    "\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# clear_output(wait=False)\n",
    "\n",
    "from IPython.display import display, Javascript\n",
    "\n",
    "def clear_all_output():\n",
    "    display(Javascript('IPython.notebook.clear_all_output()'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49a53d22-2616-4d9b-b67c-e044eb09c8a4",
   "metadata": {},
   "source": [
    "# 1. Single-Cam Trajectory Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07a05393-5d44-4d46-af3c-f4865c01eced",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_parser():\n",
    "    parser = argparse.ArgumentParser(\"Trajectory-Prediction Track Demo!\")\n",
    "    parser.add_argument(\n",
    "        \"demo\", default=\"video\", help=\"demo type, eg. image, video and webcam\"\n",
    "    )\n",
    "    \n",
    "    ########### mmdet bbox detector ##############\n",
    "    parser.add_argument('video', help='Video file path')\n",
    "    # parser.add_argument('config', help='Config file')\n",
    "    # parser.add_argument('checkpoint', help='Checkpoint file')\n",
    "    parser.add_argument(\n",
    "        '--device', default='cuda:0', help='Device used for inference')\n",
    "    ##############################################\n",
    "    \n",
    "    ########### trajectory predictor ##############\n",
    "    parser.add_argument('--tp_weight', type=str, help='trajectory prediction model weight')\n",
    "    ##############################################\n",
    "\n",
    "    parser.add_argument('--save_result', type=str, help='Output video file')\n",
    "    parser.add_argument('--save_vid', default=False, help='Output video file')\n",
    "    \n",
    "    # tracking args\n",
    "    parser.add_argument(\"--track_thresh\", type=float, default=0.8, help=\"tracking confidence threshold\")\n",
    "    parser.add_argument(\"--track_buffer\", type=int, default=150, help=\"the frames for keep lost tracks\")\n",
    "    parser.add_argument(\"--match_thresh\", type=float, default=0.8, help=\"matching threshold for tracking\")\n",
    "    # parser.add_argument(\n",
    "    #     \"--aspect_ratio_thresh\", type=float, default=1.6,\n",
    "    #     help=\"threshold for filtering out boxes of which aspect ratio are above the given value.\"\n",
    "    # )\n",
    "    # parser.add_argument('--min_box_area', type=float, default=10, help='filter out tiny boxes')\n",
    "    # parser.add_argument(\"--mot20\", dest=\"mot20\", default=False, action=\"store_true\", help=\"test mot20.\")\n",
    "    return parser\n",
    "\n",
    "args_list = [\n",
    "    'video',\n",
    "    '/data/autocon/hd_dataset/site1_093448',  # replace with actual path\n",
    "    # './weights/yolox_x.py',  # replace with actual path\n",
    "    # './weights/epoch_250.pth',  # replace with actual path\n",
    "    '--device', 'cuda:0',\n",
    "    '--tp_weight', './weights/tp_best.pth',  # replace with actual value\n",
    "    '--save_result', './site1_093448-result',  # replace with actual path\n",
    "    '--save_vid', 'False',  # or 'False'\n",
    "    '--track_thresh', '0.8',\n",
    "    '--track_buffer', '30',\n",
    "    '--match_thresh', '0.8',\n",
    "    # '--min_box_area', '10',\n",
    "    # Add any other argument values you want to set\n",
    "]\n",
    "\n",
    "args = make_parser().parse_args(args_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd5700b3-41ee-476b-aef5-81efc8bc7d2b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def mmdet3x_convert(results, threshold):\n",
    "    det_result_list = []\n",
    "   \n",
    "    confidence_score = results.pred_instances.scores.tolist()\n",
    "    for i, conf in enumerate(confidence_score):\n",
    "        if conf >= threshold:\n",
    "            # print(conf)\n",
    "            \n",
    "            extracted_box = results.pred_instances.bboxes[i].cpu().tolist()\n",
    "            extracted_label = results.pred_instances.labels[i].cpu().tolist()\n",
    "            \n",
    "            class_name = 0\n",
    "            \n",
    "            temp = [float(extracted_box[0]),\n",
    "                     float(extracted_box[1]),\n",
    "                     float(extracted_box[2]),\n",
    "                     float(extracted_box[3]), float(0.9), class_name]\n",
    "            \n",
    "            det_result_list.append(temp)\n",
    "            \n",
    "    return np.array(det_result_list)\n",
    "\n",
    "def mmdet3x_convert_pretrain(results, threshold):\n",
    "    det_result_list = []\n",
    "\n",
    "    permit_class_list = [0, 7]\n",
    "    class_labels = results.pred_instances.labels.tolist()\n",
    "    \n",
    "    confidence_score = results.pred_instances.scores.tolist()\n",
    "    for i, conf in enumerate(confidence_score):\n",
    "        if conf >= threshold and class_labels[i] in permit_class_list:\n",
    "            # print(conf)\n",
    "            \n",
    "            extracted_box = results.pred_instances.bboxes[i].cpu().tolist()\n",
    "            extracted_label = results.pred_instances.labels[i].cpu().tolist()\n",
    "            \n",
    "            class_name = 0\n",
    "            \n",
    "            temp = [float(extracted_box[0]),\n",
    "                     float(extracted_box[1]),\n",
    "                     float(extracted_box[2]),\n",
    "                     float(extracted_box[3]), float(0.9), class_name]\n",
    "            \n",
    "            det_result_list.append(temp)\n",
    "            \n",
    "    return np.array(det_result_list)\n",
    "\n",
    "def compute_iou(boxA, boxB):\n",
    "    # Compute the coordinates of the intersection rectangle\n",
    "    xA = max(boxA[0], boxB[0])\n",
    "    yA = max(boxA[1], boxB[1])\n",
    "    xB = min(boxA[2], boxB[2])\n",
    "    yB = min(boxA[3], boxB[3])\n",
    "    \n",
    "    # Compute the area of intersection\n",
    "    interArea = max(0, xB - xA) * max(0, yB - yA)\n",
    "    \n",
    "    # Compute the area of both boxes\n",
    "    boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])\n",
    "    boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])\n",
    "    \n",
    "    # Compute IoU\n",
    "    iou = interArea / float(boxAArea + boxBArea - interArea)\n",
    "    \n",
    "    return iou\n",
    "\n",
    "def nms(boxes, overlap_threshold=0.5):\n",
    "    if len(boxes) == 0:\n",
    "        return []\n",
    "    \n",
    "    pick = []\n",
    "    \n",
    "    # Extract coordinates and scores from the boxes\n",
    "    x1 = boxes[:,0]\n",
    "    y1 = boxes[:,1]\n",
    "    x2 = boxes[:,2]\n",
    "    y2 = boxes[:,3]\n",
    "    scores = boxes[:,4]\n",
    "    \n",
    "    # Compute the area of each box and sort the scores in descending order\n",
    "    area = (x2 - x1) * (y2 - y1)\n",
    "    indices = np.argsort(scores)[::-1]\n",
    "    \n",
    "    while len(indices) > 0:\n",
    "        # Take the index of the box with the highest score\n",
    "        current = indices[0]\n",
    "        pick.append(current)\n",
    "        \n",
    "        # Compute IoU between the box with the highest score and the other boxes\n",
    "        ious = np.array([compute_iou(boxes[current], boxes[i]) for i in indices[1:]])\n",
    "        \n",
    "        # Get the indices of boxes that are overlapping less than the threshold with the current box\n",
    "        remove_indices = np.where(ious > overlap_threshold)[0] + 1\n",
    "        \n",
    "        # Remove the current box and the overlapping boxes\n",
    "        indices = np.delete(indices, remove_indices)\n",
    "        indices = np.delete(indices, 0)\n",
    "        \n",
    "    return boxes[pick]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b0d3ed36-7b75-4495-a26e-9dccfeb1d2bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame_img_list = []\n",
    "traj_list = []\n",
    "\n",
    "def imageflow_demo(det_model, vis_folder, current_time, args, tp_model):\n",
    "    \n",
    "    tracker_list = []\n",
    "    \n",
    "    temp = []\n",
    "    channel_list = [file for file in os.listdir(args.video)]\n",
    "    for channel in channel_list:\n",
    "        d = os.path.join(args.video, channel)\n",
    "        if os.path.isdir(d) and not channel.startswith('.'):\n",
    "            temp.append(channel)\n",
    "    \n",
    "    for channel in temp:\n",
    "        video_path = os.path.join(os.path.join(args.video, channel, 'video.mp4'))\n",
    "    \n",
    "        cap = cv2.VideoCapture(video_path)\n",
    "        width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # float\n",
    "        height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # float\n",
    "        fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "        timestamp = time.strftime(\"%Y_%m_%d_%H_%M_%S\", current_time)\n",
    "        \n",
    "        if args.save_vid:\n",
    "            save_folder = osp.join(vis_folder, channel)\n",
    "            os.makedirs(save_folder, exist_ok=True)\n",
    "            save_path = osp.join(save_folder,\"inference_vid.mp4\")\n",
    "\n",
    "            logger.info(f\"video save_path is {save_path}\")\n",
    "            vid_writer = cv2.VideoWriter(\n",
    "                save_path, cv2.VideoWriter_fourcc(*\"mp4v\"), fps, (int(width), int(height))\n",
    "            )\n",
    "        \n",
    "        tracker = TPTracker(tp_model, args, frame_rate=30)\n",
    "        timer = Timer()\n",
    "        frame_id = 0\n",
    "        results = []\n",
    "        \n",
    "        while True:\n",
    "            if frame_id % 30 == 0:\n",
    "                logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))\n",
    "            ret_val, frame = cap.read()\n",
    "            if ret_val:\n",
    "                \n",
    "                timer.tic()\n",
    "                worker_det_results = inference_detector(det_model[0], frame)\n",
    "                vehicle_det_results = inference_detector(det_model[1], frame)\n",
    "                pretrained_det_results = inference_detector(det_model[2], frame)\n",
    "                \n",
    "                threshold = 0.15\n",
    "                worker_class_dic = {0: 8, 1: 9}\n",
    "                vehicle_class_dic = {0 : 0, 1 : 1, 2 : 2, 3 : 3, 4 : 4, 5 : 5, 6 : 6, 7 : 7}\n",
    "                \n",
    "                worker_results = mmdet3x_convert(worker_det_results, threshold)\n",
    "                vehicle_results = mmdet3x_convert(vehicle_det_results, threshold)\n",
    "                pretrained_results = mmdet3x_convert_pretrain(pretrained_det_results, threshold)\n",
    "                \n",
    "                # *** dets format [x1,y1,x2,y2,score,class_id]\n",
    "                all_results = [result for result in [vehicle_results, worker_results, pretrained_results] if result.size > 0]\n",
    "\n",
    "                if len(all_results) == 0:\n",
    "                    final_boxes = np.array([], dtype=float64)\n",
    "                else:\n",
    "                    combined_boxes = np.concatenate(all_results, axis=0)\n",
    "                    final_boxes = nms(combined_boxes)\n",
    "\n",
    "                dets_arr = np.array(final_boxes)\n",
    "\n",
    "                # ##############\n",
    "                # print(\"frame # : \", frame_id)\n",
    "                # print(dets_arr)\n",
    "                # ##############\n",
    "              \n",
    "                if dets_arr is not None:\n",
    "                    # Run tracker\n",
    "                    online_targets = tracker.update(dets_arr, frame)\n",
    "                    \n",
    "                    online_tlwhs = []\n",
    "                    online_ids = []\n",
    "                    online_scores = []\n",
    "                    online_traj = []\n",
    "                    for t in online_targets:\n",
    "                        tlwh = t.tlwh\n",
    "                        tid = t.track_id\n",
    "                        # vertical = tlwh[2] / tlwh[3] > args.aspect_ratio_thresh\n",
    "                        online_tlwhs.append(tlwh)\n",
    "                        online_ids.append(tid)\n",
    "                        online_scores.append(t.score)\n",
    "                        online_traj.append(t.tlwh_queue.to_array())\n",
    "                        results.append(\n",
    "                            f\"{frame_id},{tid},{tlwh[0]:.2f},{tlwh[1]:.2f},{tlwh[2]:.2f},{tlwh[3]:.2f},{t.score:.2f},-1,-1,-1\\n\"\n",
    "                        )\n",
    "                    timer.toc()\n",
    "                    \n",
    "                    online_im = plot_tracking(\n",
    "                        frame, online_tlwhs, online_ids, online_traj, frame_id=frame_id + 1, fps=1. / timer.average_time\n",
    "                    )\n",
    "                else:\n",
    "                    timer.toc()\n",
    "                    online_im = frame\n",
    "                if args.save_vid:\n",
    "                    vid_writer.write(online_im)\n",
    "                ch = cv2.waitKey(1)\n",
    "                if ch == 27 or ch == ord(\"q\") or ch == ord(\"Q\"):\n",
    "                    break\n",
    "            else:\n",
    "                break\n",
    "                \n",
    "            if frame_id == 51:\n",
    "                frame_img_list.append(online_im) \n",
    "                traj_list.append(online_traj)\n",
    "                break    \n",
    "            \n",
    "            frame_id += 1\n",
    "\n",
    "        if args.save_result:\n",
    "            res_file = osp.join(save_folder, f\"{timestamp}.txt\")\n",
    "            with open(res_file, 'w') as f:\n",
    "                f.writelines(results)\n",
    "            logger.info(f\"save results to {res_file}\")\n",
    "            \n",
    "        tracker_list.append(tracker)\n",
    "        \n",
    "    return tracker_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb5199b5-c0b0-4b5b-92e5-d421c160a149",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-08-18 15:01:33.964\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m23\u001b[0m - \u001b[1mArgs: Namespace(demo='video', device='cuda:0', match_thresh=0.8, save_result='./site1_093448-result', save_vid='False', tp_weight='./weights/tp_best.pth', track_buffer=30, track_thresh=0.8, video='/data/autocon/hd_dataset/site1_093448')\u001b[0m\n",
      "\u001b[32m2023-08-18 15:01:34.079\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m29\u001b[0m - \u001b[1mvideo save_path is ./site1_093448-result/track_vis/c5/inference_vid.mp4\u001b[0m\n",
      "\u001b[32m2023-08-18 15:01:34.082\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m41\u001b[0m - \u001b[1mProcessing frame 0 (100000.00 fps)\u001b[0m\n",
      "/opt/anaconda3/envs/mmyolo/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /opt/conda/conda-bld/pytorch_1639180588308/work/aten/src/ATen/native/TensorShape.cpp:2157.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "\u001b[32m2023-08-18 15:01:38.500\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m41\u001b[0m - \u001b[1mProcessing frame 30 (8.18 fps)\u001b[0m\n",
      "\u001b[32m2023-08-18 15:01:40.800\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m120\u001b[0m - \u001b[1msave results to ./site1_093448-result/track_vis/c5/2023_08_18_15_01_33.txt\u001b[0m\n",
      "\u001b[32m2023-08-18 15:01:40.868\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m29\u001b[0m - \u001b[1mvideo save_path is ./site1_093448-result/track_vis/c3/inference_vid.mp4\u001b[0m\n",
      "\u001b[32m2023-08-18 15:01:40.872\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m41\u001b[0m - \u001b[1mProcessing frame 0 (100000.00 fps)\u001b[0m\n",
      "\u001b[32m2023-08-18 15:01:44.381\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m41\u001b[0m - \u001b[1mProcessing frame 30 (10.71 fps)\u001b[0m\n",
      "\u001b[32m2023-08-18 15:01:46.824\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m120\u001b[0m - \u001b[1msave results to ./site1_093448-result/track_vis/c3/2023_08_18_15_01_33.txt\u001b[0m\n",
      "\u001b[32m2023-08-18 15:01:46.907\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m29\u001b[0m - \u001b[1mvideo save_path is ./site1_093448-result/track_vis/c1/inference_vid.mp4\u001b[0m\n",
      "\u001b[32m2023-08-18 15:01:46.910\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m41\u001b[0m - \u001b[1mProcessing frame 0 (100000.00 fps)\u001b[0m\n",
      "\u001b[32m2023-08-18 15:01:50.297\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m41\u001b[0m - \u001b[1mProcessing frame 30 (11.06 fps)\u001b[0m\n",
      "\u001b[32m2023-08-18 15:01:52.811\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mimageflow_demo\u001b[0m:\u001b[36m120\u001b[0m - \u001b[1msave results to ./site1_093448-result/track_vis/c1/2023_08_18_15_01_33.txt\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "if args.save_result:\n",
    "    output_dir = osp.join(args.save_result)\n",
    "    os.makedirs(output_dir, exist_ok=True)\n",
    "    vis_folder = osp.join(output_dir, \"track_vis\")\n",
    "    os.makedirs(vis_folder, exist_ok=True)\n",
    "\n",
    "vehicle_config = \"/data/autocon/detection/weights/yolov8x_vehicle.py\"\n",
    "vehicle_checkpoint = '/data/autocon/detection/weights/yolov8x_vehicle_best.pth'\n",
    "vehicle_model = init_detector(vehicle_config, vehicle_checkpoint, device='cuda:0')\n",
    "\n",
    "worker_config = \"/data/autocon/detection/weights/yolov8x_signalman.py\"\n",
    "worker_checkpoint = '/data/autocon/detection/weights/yolov8x_signalman.pth'\n",
    "worker_model = init_detector(worker_config, worker_checkpoint, device='cuda:0')\n",
    "\n",
    "pretrained_config = \"/data/autocon/detection/mmyolo/configs/yolov8/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco.py\"\n",
    "pretrained_checkpoint = '/data/autocon/detection/weights/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco_20230217_120411-079ca8d1.pth'\n",
    "pretrained_model = init_detector(pretrained_config, pretrained_checkpoint, device='cuda:0')\n",
    "\n",
    "det_model = (worker_model, vehicle_model, pretrained_model)\n",
    "\n",
    "clear_output(wait=False)\n",
    "\n",
    "logger.info(\"Args: {}\".format(args))\n",
    "\n",
    "tp_model = SocialImplicit(spatial_input=CFG[\"spatial_input\"],\n",
    "                          spatial_output=CFG[\"spatial_output\"],\n",
    "                          temporal_input=CFG[\"temporal_input\"],\n",
    "                          temporal_output=CFG[\"temporal_output\"],\n",
    "                          bins=CFG[\"bins\"],\n",
    "                          noise_weight=CFG[\"noise_weight\"]).cuda()\n",
    "tp_model.load_state_dict(torch.load(args.tp_weight))\n",
    "tp_model.cuda().double()\n",
    "tp_model.eval()\n",
    "\n",
    "current_time = time.localtime()\n",
    "\n",
    "if args.demo == \"video\":\n",
    "    tracker_list = imageflow_demo(det_model, vis_folder, current_time, args, tp_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb8120f8-1ce7-4a07-a927-2c7ec91807eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.interpolate import interp1d\n",
    "\n",
    "def inf_interpolated_coor(data):\n",
    "    \n",
    "    # Extract middle_x and right_bottom_y from tracklets\n",
    "    middle_x = {}\n",
    "    bottom_y = {}\n",
    "    \n",
    "    for frame, tracklet in data.items():\n",
    "        mid_x = tracklet[0] + tracklet[2] / 2\n",
    "        bot_y = tracklet[1] + tracklet[3]\n",
    "        middle_x[frame] = mid_x\n",
    "        bottom_y[frame] = bot_y\n",
    "    \n",
    "    # Interpolate missing frames\n",
    "    all_frames = list(range(min(middle_x.keys()), max(middle_x.keys()) + 1))\n",
    "    middle_x_interpolator = interp1d(list(middle_x.keys()), list(middle_x.values()), kind=\"linear\", fill_value=\"extrapolate\")\n",
    "    bottom_y_interpolator = interp1d(list(bottom_y.keys()), list(bottom_y.values()), kind=\"linear\", fill_value=\"extrapolate\")\n",
    "    \n",
    "    middle_x_interpolated = {frame: middle_x_interpolator(frame) for frame in all_frames}\n",
    "    bottom_y_interpolated = {frame: bottom_y_interpolator(frame) for frame in all_frames}\n",
    "    \n",
    "    # Extract representative positions for every 5 frames\n",
    "    representative_positions = []\n",
    "    \n",
    "    for frame in range(min(all_frames), max(all_frames) + 1, 5):\n",
    "        if frame in middle_x_interpolated:\n",
    "            representative_positions.append([middle_x_interpolated[frame], bottom_y_interpolated[frame]])\n",
    "\n",
    "    return representative_positions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eea22d8-ed95-4082-b13f-5668db56964b",
   "metadata": {},
   "source": [
    "# Tracklet Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f621bb58-1864-4424-82d0-057d1f650486",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def give_color(tracking_id):\n",
    "    return [int(item) for item in 255*_COLORS[tracking_id]]\n",
    "\n",
    "def draw_tracklet(img, tracklets):\n",
    "    result_img = img.copy()\n",
    "\n",
    "    interpolated_tracklets = {}\n",
    "    \n",
    "    for idx,tracklet in enumerate(tracklets):\n",
    "        interpolated_tracklet = inf_interpolated_coor(tracklet.tlwh_log)\n",
    "    \n",
    "        x_coords = []\n",
    "        y_coords = []\n",
    "        for item in interpolated_tracklet:\n",
    "            c_x, b_y= item[0],item[1]\n",
    "            x_coords.append(int(c_x))\n",
    "            y_coords.append(int(b_y))\n",
    "\n",
    "        interpolated_tracklets[tracklet.track_id] = [x_coords,y_coords]\n",
    "\n",
    "        now_c_x = int(tracklet._tlwh[0] + (tracklet._tlwh[2]/2))\n",
    "        now_b_y = int(tracklet._tlwh[1] + tracklet._tlwh[3])\n",
    "        x_coords.append(now_c_x)\n",
    "        y_coords.append(now_b_y)\n",
    "        \n",
    "        # color = (255,0,0)\n",
    "        color = give_color(tracklet.track_id%100)\n",
    "        \n",
    "        # Draw the points and connect them with a line\n",
    "        for i in range(len(x_coords)-1):\n",
    "            # Draw point\n",
    "            result_img = cv2.circle(result_img, (x_coords[i], y_coords[i]), 4, color, -1)\n",
    "            # Draw line\n",
    "            result_img = cv2.line(result_img, (x_coords[i], y_coords[i]), (x_coords[i+1], y_coords[i+1]), color, 3)\n",
    "        # Draw the last point\n",
    "        result_img = cv2.circle(result_img, (x_coords[-1], y_coords[-1]), 4, color, -1)\n",
    "    \n",
    "    return result_img, interpolated_tracklets\n",
    "\n",
    "cmap = ['red', 'green', 'blue', 'cyan', 'magenta', 'yellow']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1218eb1-f466-48bd-9213-53a5e8f2bbd5",
   "metadata": {},
   "source": [
    "#### 1. 경로 보간\n",
    "#### 2. 보간 경로 점 중심으로 그리기 (색상은 ID기반)\n",
    "#### 3. 보간경로 점을 input(8개) 으로 주고 경로 예측(12개)\n",
    "#### 4. 예측경로 그리기 (색상은 빨간색)\n",
    "#### 5. ADE/FDE 평가 (**실제 트래킹 된 결과값으로 평가하기)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3574182-521b-4e51-836f-02ac3b4f0bb2",
   "metadata": {},
   "source": [
    "### VIS for cam#1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "35192abe-1e1f-4f48-8f9d-2de7628a2533",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 경로 보간 및\n",
    "# 2. 보간 경로 그리기\n",
    "cam1_result_img, interpolated_tracklets1 = draw_tracklet(frame_img_list[0], tracker_list[0].tracked_stracks) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7fd6a684-f58b-4b92-b5ab-8ce2fa5471b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 경로 예측\n",
    "# 4. 예측 경로 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "79b1f20b-7cdb-4869-8d9d-a6872c0b07cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. 예측 경로 평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f8f9ee92-6ecd-4d5f-a319-007f7829472e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('result1.png',cam1_result_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff1388c-b6d9-4d0f-b6e8-2a6a815982c4",
   "metadata": {},
   "source": [
    "### VIS for cam#2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4955cb43-588d-4e59-99e3-d1c55741ca44",
   "metadata": {},
   "outputs": [],
   "source": [
    "cam2_result_img, interpolated_tracklets2 = draw_tracklet(frame_img_list[1], tracker_list[1].tracked_stracks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1b012840-ee2d-4018-be6d-1c1fe1196c37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('result2.png',cam2_result_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ba3a47-6d93-43e5-8497-99d1cc69df5f",
   "metadata": {},
   "source": [
    "### Vis for cam#3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e8dd0b67-4761-4d1b-a03a-6812c8e474e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cam3_result_img, interpolated_tracklets3 = draw_tracklet(frame_img_list[2], tracker_list[2].tracked_stracks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6c731ccb-706a-4d1c-bf97-6b6151a5e387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('result3.png',cam3_result_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2028ba16-d5db-4a90-89f8-9d56e2f27e8d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d4754ff7-17f3-458e-ba17-d5267ba2ed36",
   "metadata": {},
   "source": [
    "# 2. Trajectory Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1d1746a2-110b-47f0-a696-acd2921c5c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from model import SocialImplicit\n",
    "from CFG import CFG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "706bda42-f106-4baf-8f35-57049c4cce41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.clear_all_output()"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp_model = SocialImplicit(spatial_input=CFG[\"spatial_input\"],\n",
    "                          spatial_output=CFG[\"spatial_output\"],\n",
    "                          temporal_input=CFG[\"temporal_input\"],\n",
    "                          temporal_output=CFG[\"temporal_output\"],\n",
    "                          bins=CFG[\"bins\"],\n",
    "                          noise_weight=CFG[\"noise_weight\"]).cuda()\n",
    "tp_model.load_state_dict(torch.load(args.tp_weight))\n",
    "tp_model.cuda().double()\n",
    "tp_model.eval()\n",
    "\n",
    "clear_all_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "17a96241-3dc0-4e33-8797-21a6a702f86e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([[[696, 696, 693, 697, 694, 694, 695, 694, 695, 696, 695, 695], [707, 707, 707, 707, 707, 708, 707, 707, 707, 707, 707, 707]], [[918, 917, 918, 920, 923, 922, 922, 925, 925, 925, 926, 926], [684, 684, 683, 683, 683, 684, 684, 683, 684, 684, 684, 684]], [[759, 759, 760, 759, 760, 760, 759, 759, 759, 759, 759, 759], [693, 693, 693, 695, 695, 697, 697, 695, 696, 694, 696, 696]], [[1307, 1306, 1306, 1307, 1307, 1307, 1307, 1307, 1307, 1307, 1307, 1307], [719, 718, 718, 719, 719, 719, 720, 719, 720, 720, 720, 720]], [[1891, 1884, 1886, 1880, 1874, 1871, 1865, 1861], [1034, 1039, 1031, 1027, 1034, 1039, 1042, 1042]], [[1557, 1563, 1570, 1578, 1588, 1593, 1600, 1608, 1614, 1624, 1630, 1630], [650, 649, 650, 648, 647, 648, 645, 643, 643, 636, 642, 642]], [[1647, 1654, 1658, 1665, 1671, 1674, 1683], [632, 634, 633, 631, 629, 627, 623]]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpolated_tracklets3.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ef752edb-1a47-48c1-bd46-089b3493fec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([22, 23, 24, 25, 27, 21, 26])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpolated_tracklets3.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b4e33106-833e-4d48-b229-62d7fe7820fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "manipulated_tracklets = list(interpolated_tracklets3.values())\n",
    "manipulated_tracklets[-2] = [[1557, 1563, 1570, 1578, 1588, 1593, 1600, 1608, 1614, 1624, 1630], \n",
    "                             [650, 649, 650, 648, 647, 648, 645, 644, 642, 641, 639]]\n",
    "\n",
    "manipulated_tracklets[-1] = [[1647, 1654, 1658, 1665, 1671, 1674, 1678, 1683],\n",
    "                             [632, 634, 633, 631, 629, 627, 625, 623]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "28b4d1b3-ee20-4010-ad44-6c99c41569bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 12)\n",
      "(2, 12)\n",
      "(2, 12)\n",
      "(2, 12)\n",
      "(2, 8)\n",
      "(2, 11)\n",
      "(2, 8)\n"
     ]
    }
   ],
   "source": [
    "filtered_track_id = []\n",
    "filtered_traj = []\n",
    "\n",
    "# for idx, traj in enumerate(interpolated_tracklets3.values()):\n",
    "#     print(np.array(traj).shape)\n",
    "#     if np.array(traj).shape[-1] >= 8:\n",
    "#         filtered_track_id.append(list(interpolated_tracklets3.keys())[idx])\n",
    "#         filtered_traj.append(np.array(traj)[:,-8:])\n",
    "\n",
    "for idx, traj in enumerate(manipulated_tracklets):\n",
    "    print(np.array(traj).shape)\n",
    "    if np.array(traj).shape[-1] >= 8:\n",
    "        filtered_track_id.append(list(interpolated_tracklets3.keys())[idx])\n",
    "        filtered_traj.append(np.array(traj)[:,-8:])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "637c2949-cc3e-4527-be49-b8e2d84c28fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[22, 23, 24, 25, 27, 21, 26]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_track_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "52b1bccb-30d0-4e89-91d8-634a29c4a65c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[694, 694, 695, 694, 695, 696, 695, 695],\n",
       "        [707, 708, 707, 707, 707, 707, 707, 707]]),\n",
       " array([[923, 922, 922, 925, 925, 925, 926, 926],\n",
       "        [683, 684, 684, 683, 684, 684, 684, 684]]),\n",
       " array([[760, 760, 759, 759, 759, 759, 759, 759],\n",
       "        [695, 697, 697, 695, 696, 694, 696, 696]]),\n",
       " array([[1307, 1307, 1307, 1307, 1307, 1307, 1307, 1307],\n",
       "        [ 719,  719,  720,  719,  720,  720,  720,  720]]),\n",
       " array([[1891, 1884, 1886, 1880, 1874, 1871, 1865, 1861],\n",
       "        [1034, 1039, 1031, 1027, 1034, 1039, 1042, 1042]]),\n",
       " array([[1578, 1588, 1593, 1600, 1608, 1614, 1624, 1630],\n",
       "        [ 648,  647,  648,  645,  644,  642,  641,  639]]),\n",
       " array([[1647, 1654, 1658, 1665, 1671, 1674, 1678, 1683],\n",
       "        [ 632,  634,  633,  631,  629,  627,  625,  623]])]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_traj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "87b6cfc8-9bbd-4167-85a9-9a302bac6bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_traj_temp = filtered_traj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a347fb6-432e-4c16-aec8-985976c08b27",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c991318c-79c7-449d-a9de-d58a48bc1e87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_572192/3079637916.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  obs_traj = torch.tensor(obs_traj, device='cuda:0', dtype=torch.float64)\n",
      "/tmp/ipykernel_572192/3079637916.py:10: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  V_obs = torch.tensor(V_obs, device='cuda:0', dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "from yolox.tracker.tp_tracker_interpolation import calculate_v_obs, seq_to_nodes, nodes_rel_to_nodes_abs, split_array\n",
    "\n",
    "KSTEPS = 20\n",
    "\n",
    "# create the tensor\n",
    "obs_traj = torch.tensor(obj_traj_temp).unsqueeze(0)\n",
    "V_obs = calculate_v_obs(obs_traj)\n",
    "\n",
    "obs_traj = torch.tensor(obs_traj, device='cuda:0', dtype=torch.float64)\n",
    "V_obs = torch.tensor(V_obs, device='cuda:0', dtype=torch.float64)\n",
    "\n",
    "V_obs_tmp = V_obs.permute(0, 3, 1, 2)\n",
    "V_predx = tp_model(V_obs_tmp, obs_traj, KSTEPS=KSTEPS)\n",
    "V_x = seq_to_nodes(obs_traj.data.cpu().numpy())\n",
    "\n",
    "num_of_objs = V_obs.shape[-2]\n",
    "pred_result = []\n",
    "\n",
    "for k in range(KSTEPS):\n",
    "    V_pred = V_predx[k:k + 1, ...]\n",
    "    \n",
    "    V_pred = V_pred.permute(0, 2, 3, 1)\n",
    "    V_pred = V_pred[0]\n",
    "    final_V_pred = V_pred.data.cpu().numpy()\n",
    "    V_pred_rel_to_abs = nodes_rel_to_nodes_abs(\n",
    "        final_V_pred, V_x[-1, :, :].copy())\n",
    "\n",
    "    for n in range(num_of_objs):\n",
    "        pred_result.append(V_pred_rel_to_abs[:, n:n + 1, :])\n",
    "\n",
    "sample_traj_per_object = split_array(np.array(pred_result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "a11a8164-8dbe-43e5-97ea-55c39af795a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 20, 12, 1, 2)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(sample_traj_per_object).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b56e7990-4582-4fdc-bc9c-8f7a469b9559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[22, 23, 24, 25, 27, 21, 26]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_track_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d3b83cec-7b61-4cf2-b802-dbb0c6caa9ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_predictions(img, sample_traj_per_object):\n",
    "    for object_trajectories in sample_traj_per_object:\n",
    "        all_trajectories = [trajectory[:, 0] for trajectory in object_trajectories]\n",
    "        \n",
    "        # Draw averaged trajectory\n",
    "        img = draw_avg_trajectory(img, all_trajectories)\n",
    "\n",
    "    return img\n",
    "\n",
    "def draw_avg_trajectory(img, trajectories):\n",
    "    avg_trajectory = np.mean(trajectories, axis=0)\n",
    "    color = (0, 0, 255)  # Green color for average prediction\n",
    "    for i in range(len(avg_trajectory) - 1):\n",
    "        img = cv2.line(img, (int(avg_trajectory[i][0]), int(avg_trajectory[i][1])), \n",
    "                       (int(avg_trajectory[i+1][0]), int(avg_trajectory[i+1][1])), color, 3)\n",
    "        \n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8fbcea2-1b72-4c5f-b5a9-587a0c055175",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1359db42-f01d-4a80-bae6-57514e2a857f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3a555962-83dc-4de6-872d-e6418a24e87e",
   "metadata": {},
   "source": [
    "### Vis for cam#3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "a63800c2-bbc1-4176-9887-704b80773497",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Usage\n",
    "cam3_final_result_img = draw_predictions(cam3_result_img.copy(), sample_traj_per_object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "98fa86bd-d639-4541-927b-5b0c88e76873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cv2.cvtColor(cam3_final_result_img, cv2.COLOR_BGR2RGB))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "0cb6b93c-589b-4490-85ae-83bb88e96a8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('final_result3.png',cam3_final_result_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3f04671-9c96-45b2-b41e-c00e43a80481",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cdab55a1-5461-495d-b0fe-6a0646fed5d1",
   "metadata": {},
   "source": [
    "# 3. Multi-Cam Association "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c66d60b6-95af-4698-9d64-dc7631d1ab89",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ WARN:0@40.796] global loadsave.cpp:244 findDecoder imread_('./test-site/map.png'): can't open/read file: check file path/integrity\n"
     ]
    }
   ],
   "source": [
    "cam1_tracklet = np.array(cam1_tracklet)\n",
    "cam2_tracklet = np.array(cam2_tracklet)\n",
    "\n",
    "map_img=cv2.imread('./test-site/map.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e185bd2-9daf-40ff-a565-8f4ee2e6dccc",
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(4.7.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(\u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcvtColor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmap_img\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCOLOR_BGR2RGB\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "\u001b[0;31merror\u001b[0m: OpenCV(4.7.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n"
     ]
    }
   ],
   "source": [
    "plt.imshow(cv2.cvtColor(map_img, cv2.COLOR_BGR2RGB))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "652a1d07-246a-4e10-89bb-800fd09dade1",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Camera Calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c62700e1-dad9-4811-8b83-e8bad101ddba",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pts1 = np.array([(1753, 515), (1348, 411),(869, 289),(476, 231),(624, 227),(1093, 166)])\n",
    "pts2 = np.array([(1065, 197), (1065, 342),(1065, 562),(1108, 753),(1065, 709),(858, 562)])\n",
    "H_c124, _ = cv2.findHomography(pts1, pts2, cv2.RANSAC)\n",
    "\n",
    "pts1 = np.array([(176,447),(692,251),(1074,156),(400,147),(635,80),(929,161)])\n",
    "pts2 = np.array([(1065,562),(1065,342),(1108,153),(858,342),(861,197),(1065,197)])\n",
    "H_c125, _ = cv2.findHomography(pts1, pts2, cv2.RANSAC)\n",
    "\n",
    "pts1 = np.array([(566, 488), (968, 548), (718,487), (1495,674), (1210, 449),(1413,477),(1305,320),(175,898)])\n",
    "pts2 = np.array([(817, 153), (861, 342), (861,197), (861,562), (1065, 342),(1040,452),(1400,200),(520,350)])\n",
    "H_c126, _ = cv2.findHomography(pts1, pts2, cv2.RANSAC,5.0)\n",
    "\n",
    "pts1 = np.array([(508,388),(220,605),(760,471),(1007,410),(1151,410),(323,233),(408,233)])\n",
    "pts2 = np.array([(1065,562),(858,342),(858,562),(858,709),(817,753),(1598,709),(1554,753)])\n",
    "H_c127, _ = cv2.findHomography(pts1, pts2, cv2.RANSAC)\n",
    "\n",
    "pts1 = np.array([(117, 858), (66, 588), (198, 488),(1219, 251),(1361, 265)])\n",
    "pts2 = np.array([(858, 562), (858, 709),(817, 753),(369, 753),(325, 709)])\n",
    "H_c128, _ = cv2.findHomography(pts1, pts2, cv2.RANSAC)\n",
    "\n",
    "pts1 = np.array([(30,756),(601,697),(1284,623),(1638,584),(1033,505),(926,350),(1006,355), (1854,604)])\n",
    "pts2 = np.array([(1065,709),(1065,562),(1065,342),(1065,197),(858,342),(325,197),(367,153), (1108,153)])\n",
    "H_c129, _ = cv2.findHomography(pts1, pts2, cv2.RANSAC)\n",
    "\n",
    "H_list = [H_c124,H_c125,H_c126,H_c127,H_c128,H_c129]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "744b43e6-2fa6-42ab-8464-ff5ac84bfea0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "H_list[1] # connected to cam1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1036b1df-2ad7-4f16-8df6-9ad82ac039ce",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "H_list[0] # connected to cam2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f281d5ec-900c-4226-9fa6-b70818b023aa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def transform_tracklets(tracklets, H):\n",
    "    for idx,obj in enumerate(tracklets):\n",
    "        for jdx,sample in enumerate(obj):\n",
    "            x_sequence = sample[0]\n",
    "            y_sequence = sample[1]\n",
    "            \n",
    "            x_global_sequence = []\n",
    "            y_global_sequence = []\n",
    "            \n",
    "            for kdx in range(len(x_sequence)):\n",
    "                c_x = x_sequence[kdx]\n",
    "                bottom_y = y_sequence[kdx]\n",
    "                \n",
    "                point1 = np.array([[c_x, bottom_y, 1]])\n",
    "                point2 = np.dot(H, point1.T)\n",
    "                point2 /= point2[2]\n",
    "                \n",
    "                x_global_sequence.append(point2[0])\n",
    "                y_global_sequence.append(point2[1])\n",
    "            \n",
    "            tracklets[idx][jdx][0] = x_global_sequence\n",
    "            tracklets[idx][jdx][1] = y_global_sequence\n",
    "\n",
    "    return tracklets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1176416c-319e-4695-bad4-e9a9cc3b5a49",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cam1_global_tracklets = transform_tracklets(cam1_tracklet, H_list[1])\n",
    "cam2_global_tracklets = transform_tracklets(cam2_tracklet, H_list[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fba17015-a0e4-4f75-8065-428db05b04cb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt.imshow(cv2.cvtColor(map_img, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "for idx,tracklet_samples in enumerate(cam1_global_tracklets):   \n",
    "    sns.kdeplot(x = tracklet_samples[:, 0].reshape(-1),\n",
    "                y = tracklet_samples[:, 1].reshape(-1),\n",
    "                shade=True,\n",
    "                shade_lowest=False,\n",
    "                color=cmap[idx],\n",
    "                alpha=0.8)\n",
    "    \n",
    "for idx,tracklet_samples in enumerate(cam2_global_tracklets):   \n",
    "    sns.kdeplot(x = tracklet_samples[:, 0].reshape(-1),\n",
    "                y = tracklet_samples[:, 1].reshape(-1),\n",
    "                shade=True,\n",
    "                shade_lowest=False,\n",
    "                color=cmap[idx],\n",
    "                alpha=0.8)\n",
    "        \n",
    "# plt.xlim([0, 1920]) # Setting x limit\n",
    "# plt.ylim([0, 1080]) # Setting y limit\n",
    "\n",
    "clear_output(wait=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34f41b1e-d1b3-4ef2-aaca-7f3b128bafe0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "channel_result = [cam1_global_tracklets, cam2_global_tracklets]\n",
    "channel_id_position = [] # give ID based on order\n",
    "for result in channel_result:\n",
    "    temp_dic = {}\n",
    "    for idx, obj in enumerate(result):\n",
    "        x = obj[0][0][0] # smaples, x, t=1\n",
    "        y = obj[0][1][0] # smaples, y, t=1\n",
    "        temp_dic[idx] = (x,y)\n",
    "    channel_id_position.append(temp_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c779cbfd-0cde-4cf3-89c3-97e3a4a1f5c7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "channel_id_position"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "170979da-7f03-42c1-96b8-4a134c0ee48d",
   "metadata": {},
   "source": [
    "### Only for No-Occlusion, Seen-Foot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29cbc99d-c9b9-40eb-b166-57cee201d019",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def euclidean_distance_2d(p1, p2):\n",
    "    return int(np.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2))\n",
    "\n",
    "# data = channel_position\n",
    "def find_n_for_clustering(data):\n",
    "    err_dist = 60\n",
    "    tracklets = []\n",
    "    distance_valid_bit = True\n",
    "    for camera in data:\n",
    "        camera_tracklets = {}\n",
    "        for track_id, coords in camera.items():\n",
    "            camera_tracklets[track_id] = coords\n",
    "        tracklets.append(camera_tracklets)\n",
    "    \n",
    "    clusters = defaultdict(set)\n",
    "    cluster_id = 1\n",
    "    for i in range(len(tracklets)): # channel\n",
    "        camera_i = tracklets[i]\n",
    "        for track_id_i, coord_i in camera_i.items(): # obj in channel\n",
    "            for j in range(len(tracklets)): # channel\n",
    "                camera_j = tracklets[j]\n",
    "                for track_id_j, coord_j in camera_j.items(): # obj in channel\n",
    "                    distance = euclidean_distance_2d(coord_i, coord_j)\n",
    "                    if i == j and distance < err_dist*1.1:\n",
    "                        distance_valid_bit = False\n",
    "                    elif i != j and distance < err_dist:\n",
    "                        clusters[cluster_id].add((i+1, track_id_i))\n",
    "                        clusters[cluster_id].add((j+1, track_id_j))\n",
    "                        cluster_id += 1\n",
    "                        \n",
    "    # Merge clusters with common elements\n",
    "    merged_clusters = []\n",
    "    for key, value in clusters.items():\n",
    "        for cluster in merged_clusters:\n",
    "            if value.intersection(cluster):\n",
    "                cluster.update(value)\n",
    "                break\n",
    "        else:\n",
    "            merged_clusters.append(value)\n",
    "\n",
    "    return len(merged_clusters), distance_valid_bit, merged_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d9c3350-b3a0-40d4-9aef-84f8edb5fe1e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "N, distance_valid_bit, merged_clusters = find_n_for_clustering(channel_id_position)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d2d1252-d886-4480-bdf9-b2df4ebd73e7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "merged_clusters \n",
    "\n",
    "# 해석: N개의(len) 클러스터가 생성이 되었고 각 클러스터 마다 (채널 아이디 1,2 가리킴, 채널 내 객체 아이디 1,2,3 가리킴) 이 존재함"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6405fae-1319-46fd-a41d-763a2802b150",
   "metadata": {},
   "source": [
    "### Tracklet Averaging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0985b1d-2982-497a-bb54-8edff72ccaad",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "associated_cam_tracklet = []\n",
    "\n",
    "for cluster in merged_clusters:\n",
    "    array_list = []\n",
    "    for item in cluster:\n",
    "        channel_idx = item[0] - 1\n",
    "        track_id = item[1]\n",
    "        \n",
    "        for c_id, trackelts in enumerate(channel_result):\n",
    "            for t_id, tracklet in enumerate(trackelts): # tracklet -> (20,2,12)\n",
    "                if c_id == channel_idx and t_id == track_id:\n",
    "                    array_list.append(tracklet)\n",
    "    # Compute the sum\n",
    "    arrays_sum = np.sum(array_list, axis=0)\n",
    "    # Compute the average\n",
    "    average_array = arrays_sum / len(array_list)\n",
    "    \n",
    "    associated_cam_tracklet.append(average_array)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2248c4e1-9500-4ab0-a00c-6dbe6021c50f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.array(associated_cam_tracklet).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93f35359-2540-46dc-ba5e-2669908124d3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt.imshow(cv2.cvtColor(map_img, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "for idx,tracklet_samples in enumerate(associated_cam_tracklet):   \n",
    "    sns.kdeplot(x = tracklet_samples[:, 0].reshape(-1),\n",
    "                y = tracklet_samples[:, 1].reshape(-1),\n",
    "                shade=True,\n",
    "                shade_lowest=False,\n",
    "                color=cmap[idx],\n",
    "                alpha=0.8)\n",
    "    \n",
    "\n",
    "clear_output(wait=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac90e99d-fd10-4e58-9f03-93704fbdc505",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "895eb92d-79c3-46c0-a4ce-75cd080c5e81",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cd956883-375a-4142-a961-2868f8335cec",
   "metadata": {},
   "source": [
    "# 3. Prepare Vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5472a6a1-a102-4551-a051-622309868013",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 1. 처음 5초동안 이동경로 그려줌 - Complete\n",
    "# 2. 이후 5~8초동안 5초 프레임에다가 + 이동경로 그린상태에서 + 예측경로 그려줌 (3초동안) - Complete\n",
    "# 3. 글로벌 맵으로 이동경로 및 예측경로 그려놓음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58811fee-1008-4902-8fe8-f3e46dc51a8d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# homography -> averaging -> vis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9ade3e1-98b1-4891-8c09-7a705e96777a",
   "metadata": {},
   "source": [
    "### homography"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e38505c0-6213-4558-b578-77578a552a9e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "total_traj_result = []\n",
    "\n",
    "for channel_traj in traj_list:\n",
    "    channel_traj_result = []\n",
    "    for idx, obj_traj in enumerate(channel_traj):\n",
    "        if (obj_traj.shape[0]) == 48:\n",
    "            coords = []\n",
    "            for item in obj_traj:\n",
    "                x, y, w, h = item[0],item[1],item[2],item[3]\n",
    "                coords.append([int(x+(w/2)),int(y+h)])\n",
    "            channel_traj_result.append(coords)\n",
    "    \n",
    "    total_traj_result.append(channel_traj_result)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4669299f-bb10-44ef-bfc5-1082e3908f04",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def transform_tracklets2(tracklets, H):\n",
    "    for idx,obj in enumerate(tracklets):\n",
    "        for jdx, coords in enumerate(obj):\n",
    "            c_x = coords[0]\n",
    "            bottom_y = coords[1]\n",
    "\n",
    "            point1 = np.array([[c_x, bottom_y, 1]])\n",
    "            point2 = np.dot(H, point1.T)\n",
    "            point2 /= point2[2]\n",
    "\n",
    "            tracklets[idx][jdx][0] = point2[0]\n",
    "            tracklets[idx][jdx][1] = point2[1]\n",
    "\n",
    "    return tracklets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46f9a29c-fe25-4451-98f3-25e8ae034c11",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "global_cam1_taj = transform_tracklets2(total_traj_result[0], H_list[1])\n",
    "global_cam2_taj = transform_tracklets2(total_traj_result[1], H_list[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cf1b46b-7af7-475c-a870-c2ec17b9498a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(merged_clusters )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "039ade38-d395-4e22-aa82-bcd2eaa10ca5",
   "metadata": {},
   "source": [
    "### Previous Tracklets Averaging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdaea48d-61a9-4f8f-908e-970b25db672b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "channel_traj = [global_cam1_taj, global_cam2_taj]\n",
    "associated_cam_tracklet2 = []\n",
    "\n",
    "for cluster in merged_clusters:\n",
    "    array_list = []\n",
    "    for item in cluster:\n",
    "        channel_idx = item[0] - 1\n",
    "        track_id = item[1]\n",
    "        \n",
    "        for c_id, trackelts in enumerate(channel_traj):\n",
    "            for t_id, tracklet in enumerate(trackelts): # tracklet -> (20,2,12)\n",
    "                if c_id == channel_idx and t_id == track_id:\n",
    "                    array_list.append(tracklet)\n",
    "    # Compute the sum\n",
    "    arrays_sum = np.sum(array_list, axis=0)\n",
    "    # Compute the average\n",
    "    average_array = arrays_sum / len(array_list)\n",
    "    \n",
    "    associated_cam_tracklet2.append(average_array)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b352b7e-a551-43fb-a984-395afb3bf6d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a5ddfc2-0188-4610-b4fa-19559a892a1b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.array(associated_cam_tracklet2).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "124daab7-239e-42f5-80bc-9e461371c638",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "color_list = [(0,0,255),(0,255,0),(255,0,0)]\n",
    "\n",
    "map_img_copy = map_img.copy()\n",
    "\n",
    "for idx, obj_traj in enumerate(associated_cam_tracklet2):\n",
    "    if (obj_traj.shape[0]) == 48:\n",
    "        x_coords = []\n",
    "        y_coords = []\n",
    "        for item in obj_traj:\n",
    "            c_x, b_y= item[0],item[1]\n",
    "            x_coords.append(int(c_x))\n",
    "            y_coords.append(int(b_y))\n",
    "\n",
    "        # color = (255,0,0)\n",
    "        \n",
    "        # Draw the points and connect them with a line\n",
    "        for i in range(len(x_coords)-1):\n",
    "            # Draw point\n",
    "            map_img_copy = cv2.circle(map_img_copy, (x_coords[i], y_coords[i]), 2, color_list[idx%3], -1)\n",
    "            # Draw line\n",
    "            map_img_copy = cv2.line(map_img_copy, (x_coords[i], y_coords[i]), (x_coords[i+1], y_coords[i+1]), color_list[idx%3], 1)\n",
    "        # Draw the last point\n",
    "        map_img_copy = cv2.circle(map_img_copy, (x_coords[-1], y_coords[-1]), 2, color_list[idx%3], -1)\n",
    "\n",
    "plt.imshow(cv2.cvtColor(map_img_copy, cv2.COLOR_BGR2RGB))\n",
    "clear_output(wait=False)\n",
    "# plt.savefig('traj_map.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b74045ee-15df-4f31-be4b-222e3994b491",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec21fb33-1262-4982-9759-ca826f66b508",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0272485f-3b36-49d1-aa3c-b544d6d98132",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "map_img=cv2.imread('./test-site/map.png')\n",
    "\n",
    "for idx, obj_traj in enumerate(associated_cam_tracklet2):\n",
    "    if (obj_traj.shape[0]) == 48:\n",
    "        x_coords = []\n",
    "        y_coords = []\n",
    "        for item in obj_traj:\n",
    "            c_x, b_y= item[0],item[1]\n",
    "            x_coords.append(int(c_x))\n",
    "            y_coords.append(int(b_y))\n",
    "\n",
    "        color = (255,0,0)\n",
    "        \n",
    "        # Draw the points and connect them with a line\n",
    "        for i in range(len(x_coords)-1):\n",
    "            # Draw point\n",
    "            map_img = cv2.circle(map_img, (x_coords[i], y_coords[i]), 2, color, -1)\n",
    "            # Draw line\n",
    "            map_img = cv2.line(map_img, (x_coords[i], y_coords[i]), (x_coords[i+1], y_coords[i+1]), color, 1)\n",
    "        # Draw the last point\n",
    "        map_img = cv2.circle(map_img, (x_coords[-1], y_coords[-1]), 2, color, -1)\n",
    "\n",
    "plt.imshow(cv2.cvtColor(map_img, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "for idx,tracklet_samples in enumerate(associated_cam_tracklet):   \n",
    "    sns.kdeplot(x = tracklet_samples[:, 0].reshape(-1),\n",
    "                y = tracklet_samples[:, 1].reshape(-1),\n",
    "                shade=True,\n",
    "                shade_lowest=False,\n",
    "                # color=cmap[idx],\n",
    "                color = 'blue',\n",
    "                alpha=0.8)\n",
    "        \n",
    "clear_output(wait=False)\n",
    "plt.savefig('traj_map.png', dpi=300, bbox_inches='tight', pad_inches=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5370758c-4e14-4490-ab04-94348b480aeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The list to store the images for each tracklet\n",
    "tracklet_images = []\n",
    "\n",
    "# Loop over each tracklet\n",
    "for idx, obj_traj in enumerate(associated_cam_tracklet2):\n",
    "    if (obj_traj.shape[0]) == 48:\n",
    "        x_coords = []\n",
    "        y_coords = []\n",
    "        for item in obj_traj:\n",
    "            c_x, b_y= item[0],item[1]\n",
    "            x_coords.append(int(c_x))\n",
    "            y_coords.append(int(b_y))\n",
    "\n",
    "        # Create a white image with the same size as the original map\n",
    "        white_img = 255 * np.ones_like(map_img)\n",
    "        \n",
    "        color = (255,0,0)\n",
    "        \n",
    "        # Draw the points and connect them with a line\n",
    "        for i in range(len(x_coords)-1):\n",
    "            # Draw point\n",
    "            white_img = cv2.circle(white_img, (x_coords[i], y_coords[i]), 2, color, -1)\n",
    "            # Draw line\n",
    "            white_img = cv2.line(white_img, (x_coords[i], y_coords[i]), (x_coords[i+1], y_coords[i+1]), color, 1)\n",
    "        # Draw the last point\n",
    "        white_img = cv2.circle(white_img, (x_coords[-1], y_coords[-1]), 2, color, -1)\n",
    "\n",
    "        # Add the image to the list\n",
    "        tracklet_images.append(white_img)\n",
    "\n",
    "# Now, you can display or save each image separately\n",
    "for idx, track_img in enumerate(tracklet_images):\n",
    "    plt.imshow(cv2.cvtColor(track_img, cv2.COLOR_BGR2RGB))\n",
    "    plt.savefig(f'tracklet_{idx}.png', dpi=300, bbox_inches='tight', pad_inches=0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de012a3d-cc0e-4859-8e51-1b59d5798e89",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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