visualize_video_processing.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize video processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory: c:\\Users\\Thomas\\Pictures\\YOLOv3\n",
      "Python version: 3.10.4 (tags/v3.10.4:9d38120, Mar 23 2022, 23:13:41) [MSC v.1929 64 bit (AMD64)]\n"
     ]
    }
   ],
   "source": [
    "from ultralytics import YOLO\n",
    "import os\n",
    "import pandas as pd\n",
    "import cv2\n",
    "from cv2 import Mat\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "HOME = os.getcwd()\n",
    "print(f\"Directory: {HOME}\")\n",
    "print(f\"Python version: {sys.version}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a pre-trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Model summary: 225 layers, 11136374 parameters, 0 gradients, 28.6 GFLOPs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11136374, 28.6491136)\n"
     ]
    }
   ],
   "source": [
    "train_folders = [folder for folder in os.listdir(os.path.join(HOME, 'runs\\\\detect')) if 'train' in folder]\n",
    "most_recent_train = os.path.join(HOME, 'runs', 'detect',  train_folders[-1], 'weights', 'best.pt')\n",
    "if os.path.exists(most_recent_train):\n",
    "    model = YOLO(model=most_recent_train)\n",
    "    print(model.info())\n",
    "else:\n",
    "    print(\"No pre-trained model found\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to make predictions, configure the confidence threshold and optionally visualize and/or save the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_image(model: YOLO, img: Mat | str, threshold: float = 0.5, save: bool = False, show: bool = False) -> Mat | None:\n",
    "    if isinstance(img, str):\n",
    "        img = cv2.imread(img)\n",
    "\n",
    "    predictions = model.predict(img)\n",
    "    results = predictions[0]\n",
    "    bounding_boxes = results.boxes\n",
    "    height, width, channels = img.shape\n",
    "    img = cv2.resize(img, (width*2, height*2), interpolation=cv2.INTER_CUBIC)\n",
    "    #img = cv2.cvtColor(img, cv2.COLOR_RGB2RGBA)\n",
    "    workers = 0\n",
    "    drones = 0\n",
    "    for box in bounding_boxes:\n",
    "        x1, y1, x2, y2, conf, classid = box.data.tolist()[0]\n",
    "        x1 = int(x1)*2; y1 = int(y1)*2; x2 = int(x2)*2; y2 = int(y2)*2; conf = float(conf); classid = int(classid)\n",
    "        if conf < threshold:\n",
    "            continue\n",
    "        label = f'{int(conf*100)}%'\n",
    "        color = (0, 127, 200)\n",
    "        if classid == 0:\n",
    "            workers += 1\n",
    "            color = (255, 34, 134)\n",
    "        else:\n",
    "            drones += 1\n",
    "        cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)\n",
    "        #fill rectangle with semi transparent color\n",
    "        overlay = img.copy()\n",
    "        cv2.rectangle(overlay, (x1, y1), (x2, y2), (0, 0, 0), -1)\n",
    "        img = cv2.addWeighted(overlay, 0.2, img, 0.8, 0)\n",
    "        \n",
    "        cv2.rectangle(img, (x1, y1-40), (x1+60, y1), color, -1)\n",
    "        cv2.putText(img, label, (x1, y1-12), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)\n",
    "    img = cv2.resize(img, (width, height), interpolation=cv2.INTER_CUBIC)\n",
    "    if save:\n",
    "        cv2.imwrite(\"result.png\", img)\n",
    "    if show:\n",
    "        # downscale image for display\n",
    "        cv2.imshow(\"Result\", img)\n",
    "        cv2.waitKey(0)\n",
    "        cv2.destroyAllWindows()\n",
    "    return (img, workers, drones)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test some images to get general idea of model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = os.path.join(HOME, \"test_images\", \"1.jpg\")\n",
    "test2 = os.path.join(HOME, \"test_images\", \"2.jpg\")\n",
    "test3 = os.path.join(HOME, \"test_images\", \"3.jpg\")\n",
    "test4 = os.path.join(HOME, \"test_images\", \"4.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 640x640 2 Drones, 4 Workers, 140.1ms\n",
      "Speed: 9.0ms preprocess, 140.1ms inference, 16.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 640x640 2 Drones, 24 Workers, 141.2ms\n",
      "Speed: 11.0ms preprocess, 141.2ms inference, 12.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 640x640 3 Drones, 15 Workers, 92.8ms\n",
      "Speed: 8.0ms preprocess, 92.8ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 640x640 4 Workers, 93.1ms\n",
      "Speed: 9.0ms preprocess, 93.1ms inference, 8.1ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "predict_image(model=model, img=test1, threshold=0.5, save=True, show=True)  #This one is saved to the current directory\n",
    "predict_image(model=model, img=test2, threshold=0.5, save=False, show=True)\n",
    "predict_image(model=model, img=test3, threshold=0.5, save=False, show=True)\n",
    "predict_image(model=model, img=test4, threshold=0.5, save=False, show=True)\n",
    "print(\"\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualise a video of bees using YOLO model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_video_with_bounding_boxes(model: YOLO, video_path: str, output_path: str, detection_func, frames_interval: int = 10, threshold: float = 0.5):\n",
    "    df = pd.DataFrame(columns=['workers', 'drones'])\n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "    cap_fps = int(cap.get(cv2.CAP_PROP_FPS))\n",
    "    print(\"Original FPS: \", cap_fps)\n",
    "    out_fps = int(cap_fps / frames_interval)\n",
    "    print(\"Output FPS: \", out_fps)\n",
    "    fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n",
    "    out = cv2.VideoWriter(output_path, fourcc, out_fps, (frame_width, frame_height))\n",
    "    frame_count = 0\n",
    "    modified_frame = None\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "        if frame_count % frames_interval == 0 or modified_frame is None:\n",
    "            modified_frame, workers, drones = detection_func(model=model, img=frame, threshold=threshold, save=False, show=False)\n",
    "            df.loc[len(df)] = [workers, drones]\n",
    "            out.write(modified_frame)\n",
    "        frame_count += 1\n",
    "    cap.release()\n",
    "    out.release()\n",
    "    df.to_csv(\"result.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original FPS:  25\n",
      "Output FPS:  5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 480x640 4 Drones, 28 Workers, 181.2ms\n",
      "Speed: 7.0ms preprocess, 181.2ms inference, 11.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 22 Workers, 75.1ms\n",
      "Speed: 5.0ms preprocess, 75.1ms inference, 6.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 73.5ms\n",
      "Speed: 5.0ms preprocess, 73.5ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 74.2ms\n",
      "Speed: 4.6ms preprocess, 74.2ms inference, 5.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 23 Workers, 75.0ms\n",
      "Speed: 6.0ms preprocess, 75.0ms inference, 7.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 26 Workers, 74.0ms\n",
      "Speed: 5.6ms preprocess, 74.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 24 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 22 Workers, 73.9ms\n",
      "Speed: 5.0ms preprocess, 73.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 24 Workers, 74.4ms\n",
      "Speed: 6.0ms preprocess, 74.4ms inference, 9.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 24 Workers, 74.4ms\n",
      "Speed: 5.0ms preprocess, 74.4ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 24 Workers, 77.5ms\n",
      "Speed: 5.0ms preprocess, 77.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 23 Workers, 73.9ms\n",
      "Speed: 5.0ms preprocess, 73.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 22 Workers, 74.8ms\n",
      "Speed: 5.0ms preprocess, 74.8ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 22 Workers, 73.8ms\n",
      "Speed: 5.0ms preprocess, 73.8ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 25 Workers, 74.2ms\n",
      "Speed: 5.0ms preprocess, 74.2ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 74.8ms\n",
      "Speed: 5.0ms preprocess, 74.8ms inference, 13.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 20 Workers, 78.6ms\n",
      "Speed: 5.0ms preprocess, 78.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 18 Workers, 75.4ms\n",
      "Speed: 6.0ms preprocess, 75.4ms inference, 11.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 22 Workers, 74.4ms\n",
      "Speed: 6.5ms preprocess, 74.4ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 23 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 21 Workers, 74.3ms\n",
      "Speed: 6.0ms preprocess, 74.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 22 Workers, 74.5ms\n",
      "Speed: 6.0ms preprocess, 74.5ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 19 Workers, 74.7ms\n",
      "Speed: 6.0ms preprocess, 74.7ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 18 Workers, 74.5ms\n",
      "Speed: 6.0ms preprocess, 74.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 16 Workers, 75.0ms\n",
      "Speed: 5.0ms preprocess, 75.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 16 Workers, 73.8ms\n",
      "Speed: 5.0ms preprocess, 73.8ms inference, 14.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 18 Workers, 74.3ms\n",
      "Speed: 4.0ms preprocess, 74.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 19 Workers, 74.6ms\n",
      "Speed: 7.1ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 73.7ms\n",
      "Speed: 5.8ms preprocess, 73.7ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 19 Workers, 74.2ms\n",
      "Speed: 5.0ms preprocess, 74.2ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 19 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 7.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 18 Workers, 74.0ms\n",
      "Speed: 4.3ms preprocess, 74.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 73.5ms\n",
      "Speed: 6.0ms preprocess, 73.5ms inference, 13.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 18 Workers, 73.6ms\n",
      "Speed: 4.8ms preprocess, 73.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 16 Workers, 74.2ms\n",
      "Speed: 5.1ms preprocess, 74.2ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 15 Workers, 74.4ms\n",
      "Speed: 7.0ms preprocess, 74.4ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 17 Workers, 77.4ms\n",
      "Speed: 6.0ms preprocess, 77.4ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 18 Workers, 74.3ms\n",
      "Speed: 6.0ms preprocess, 74.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 19 Workers, 74.5ms\n",
      "Speed: 6.0ms preprocess, 74.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 14 Workers, 74.0ms\n",
      "Speed: 5.0ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 14 Workers, 73.0ms\n",
      "Speed: 5.0ms preprocess, 73.0ms inference, 6.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 16 Workers, 73.9ms\n",
      "Speed: 5.0ms preprocess, 73.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 17 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 16 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 74.1ms\n",
      "Speed: 6.3ms preprocess, 74.1ms inference, 14.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 18 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 18 Workers, 73.9ms\n",
      "Speed: 5.0ms preprocess, 73.9ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 21 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 11.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 18 Workers, 74.3ms\n",
      "Speed: 6.6ms preprocess, 74.3ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 74.1ms\n",
      "Speed: 5.0ms preprocess, 74.1ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 18 Workers, 74.3ms\n",
      "Speed: 6.6ms preprocess, 74.3ms inference, 13.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 73.6ms\n",
      "Speed: 4.0ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 74.4ms\n",
      "Speed: 6.1ms preprocess, 74.4ms inference, 6.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 16 Workers, 75.1ms\n",
      "Speed: 5.0ms preprocess, 75.1ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 18 Workers, 75.1ms\n",
      "Speed: 5.0ms preprocess, 75.1ms inference, 10.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 16 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 19 Workers, 73.6ms\n",
      "Speed: 5.9ms preprocess, 73.6ms inference, 11.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 16 Workers, 74.7ms\n",
      "Speed: 5.5ms preprocess, 74.7ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 20 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 19 Workers, 73.5ms\n",
      "Speed: 5.5ms preprocess, 73.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 19 Workers, 73.0ms\n",
      "Speed: 6.3ms preprocess, 73.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 19 Workers, 74.3ms\n",
      "Speed: 5.0ms preprocess, 74.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 19 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 20 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 25 Workers, 73.5ms\n",
      "Speed: 5.9ms preprocess, 73.5ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 24 Workers, 75.1ms\n",
      "Speed: 4.7ms preprocess, 75.1ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 74.0ms\n",
      "Speed: 6.0ms preprocess, 74.0ms inference, 6.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 21 Workers, 74.9ms\n",
      "Speed: 6.0ms preprocess, 74.9ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 75.0ms\n",
      "Speed: 5.0ms preprocess, 75.0ms inference, 14.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 17 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 6.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 16 Workers, 73.0ms\n",
      "Speed: 4.8ms preprocess, 73.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 22 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 22 Workers, 74.0ms\n",
      "Speed: 5.6ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 21 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 21 Workers, 73.7ms\n",
      "Speed: 5.0ms preprocess, 73.7ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 21 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 23 Workers, 76.8ms\n",
      "Speed: 5.0ms preprocess, 76.8ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 22 Workers, 75.1ms\n",
      "Speed: 5.0ms preprocess, 75.1ms inference, 7.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 20 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 21 Workers, 74.0ms\n",
      "Speed: 6.0ms preprocess, 74.0ms inference, 5.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 24 Workers, 73.9ms\n",
      "Speed: 5.5ms preprocess, 73.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 24 Workers, 74.3ms\n",
      "Speed: 6.0ms preprocess, 74.3ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 23 Workers, 74.0ms\n",
      "Speed: 5.6ms preprocess, 74.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 22 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 73.5ms\n",
      "Speed: 5.0ms preprocess, 73.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 23 Workers, 74.3ms\n",
      "Speed: 5.1ms preprocess, 74.3ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 23 Workers, 74.8ms\n",
      "Speed: 5.0ms preprocess, 74.8ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 22 Workers, 73.6ms\n",
      "Speed: 4.9ms preprocess, 73.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 73.7ms\n",
      "Speed: 5.7ms preprocess, 73.7ms inference, 13.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 22 Workers, 73.6ms\n",
      "Speed: 5.2ms preprocess, 73.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 20 Workers, 74.7ms\n",
      "Speed: 5.0ms preprocess, 74.7ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 21 Workers, 74.0ms\n",
      "Speed: 5.0ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 73.7ms\n",
      "Speed: 5.8ms preprocess, 73.7ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 21 Workers, 74.6ms\n",
      "Speed: 5.6ms preprocess, 74.6ms inference, 7.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 22 Workers, 74.8ms\n",
      "Speed: 6.0ms preprocess, 74.8ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 21 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 75.0ms\n",
      "Speed: 6.0ms preprocess, 75.0ms inference, 6.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 19 Workers, 73.6ms\n",
      "Speed: 4.0ms preprocess, 73.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 18 Workers, 74.1ms\n",
      "Speed: 5.0ms preprocess, 74.1ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 18 Workers, 74.7ms\n",
      "Speed: 5.0ms preprocess, 74.7ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 16 Workers, 75.0ms\n",
      "Speed: 5.0ms preprocess, 75.0ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 15 Workers, 74.5ms\n",
      "Speed: 6.0ms preprocess, 74.5ms inference, 5.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 15 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 15 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 17 Workers, 74.5ms\n",
      "Speed: 5.8ms preprocess, 74.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 18 Workers, 75.0ms\n",
      "Speed: 5.0ms preprocess, 75.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 14 Workers, 74.5ms\n",
      "Speed: 4.0ms preprocess, 74.5ms inference, 12.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 15 Workers, 74.1ms\n",
      "Speed: 6.0ms preprocess, 74.1ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 16 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 7.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 5.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 14 Workers, 77.1ms\n",
      "Speed: 6.0ms preprocess, 77.1ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 13 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 13 Workers, 74.3ms\n",
      "Speed: 5.0ms preprocess, 74.3ms inference, 6.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 14 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 13 Workers, 75.1ms\n",
      "Speed: 4.0ms preprocess, 75.1ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 13 Workers, 74.0ms\n",
      "Speed: 5.0ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 12 Workers, 74.3ms\n",
      "Speed: 5.0ms preprocess, 74.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 13 Workers, 73.7ms\n",
      "Speed: 5.4ms preprocess, 73.7ms inference, 5.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 13 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 20 Workers, 74.2ms\n",
      "Speed: 6.0ms preprocess, 74.2ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 15 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 15 Workers, 75.2ms\n",
      "Speed: 5.0ms preprocess, 75.2ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 15 Workers, 74.3ms\n",
      "Speed: 6.0ms preprocess, 74.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 21 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 6.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 20 Workers, 73.4ms\n",
      "Speed: 6.2ms preprocess, 73.4ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 74.0ms\n",
      "Speed: 6.3ms preprocess, 74.0ms inference, 6.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 20 Workers, 74.3ms\n",
      "Speed: 6.0ms preprocess, 74.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 16 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 21 Workers, 74.6ms\n",
      "Speed: 5.6ms preprocess, 74.6ms inference, 5.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 22 Workers, 73.9ms\n",
      "Speed: 5.6ms preprocess, 73.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 25 Workers, 73.7ms\n",
      "Speed: 6.0ms preprocess, 73.7ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 22 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 21 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 20 Workers, 73.5ms\n",
      "Speed: 5.8ms preprocess, 73.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 74.7ms\n",
      "Speed: 4.9ms preprocess, 74.7ms inference, 17.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 23 Workers, 74.8ms\n",
      "Speed: 4.6ms preprocess, 74.8ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 20 Workers, 73.9ms\n",
      "Speed: 8.3ms preprocess, 73.9ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 21 Workers, 73.8ms\n",
      "Speed: 4.9ms preprocess, 73.8ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 26 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 23 Workers, 74.0ms\n",
      "Speed: 5.5ms preprocess, 74.0ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 23 Workers, 73.8ms\n",
      "Speed: 5.1ms preprocess, 73.8ms inference, 7.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 22 Workers, 74.5ms\n",
      "Speed: 4.3ms preprocess, 74.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 20 Workers, 74.8ms\n",
      "Speed: 6.5ms preprocess, 74.8ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 26 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 21 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 8 Drones, 24 Workers, 75.0ms\n",
      "Speed: 8.0ms preprocess, 75.0ms inference, 5.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 20 Workers, 74.2ms\n",
      "Speed: 5.0ms preprocess, 74.2ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 22 Workers, 73.5ms\n",
      "Speed: 6.0ms preprocess, 73.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 22 Workers, 73.5ms\n",
      "Speed: 6.0ms preprocess, 73.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 20 Workers, 74.0ms\n",
      "Speed: 11.2ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 21 Workers, 74.3ms\n",
      "Speed: 6.0ms preprocess, 74.3ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 19 Workers, 74.1ms\n",
      "Speed: 5.0ms preprocess, 74.1ms inference, 8.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 73.8ms\n",
      "Speed: 6.0ms preprocess, 73.8ms inference, 5.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 22 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 18 Workers, 74.5ms\n",
      "Speed: 6.0ms preprocess, 74.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 16 Workers, 73.9ms\n",
      "Speed: 5.0ms preprocess, 73.9ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 16 Workers, 74.7ms\n",
      "Speed: 7.0ms preprocess, 74.7ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 17 Workers, 76.1ms\n",
      "Speed: 7.0ms preprocess, 76.1ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 17 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 74.4ms\n",
      "Speed: 6.0ms preprocess, 74.4ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 21 Workers, 75.0ms\n",
      "Speed: 5.0ms preprocess, 75.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 23 Workers, 74.1ms\n",
      "Speed: 5.0ms preprocess, 74.1ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 26 Workers, 73.5ms\n",
      "Speed: 6.0ms preprocess, 73.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 23 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 24 Workers, 74.4ms\n",
      "Speed: 5.0ms preprocess, 74.4ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 22 Workers, 74.5ms\n",
      "Speed: 6.0ms preprocess, 74.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 24 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 24 Workers, 75.3ms\n",
      "Speed: 5.0ms preprocess, 75.3ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 24 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 5.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 23 Workers, 73.5ms\n",
      "Speed: 6.2ms preprocess, 73.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 21 Workers, 75.2ms\n",
      "Speed: 4.8ms preprocess, 75.2ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 24 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 24 Workers, 75.0ms\n",
      "Speed: 4.5ms preprocess, 75.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 24 Workers, 74.0ms\n",
      "Speed: 5.0ms preprocess, 74.0ms inference, 6.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 74.6ms\n",
      "Speed: 9.0ms preprocess, 74.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 23 Workers, 74.1ms\n",
      "Speed: 5.0ms preprocess, 74.1ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 25 Workers, 74.7ms\n",
      "Speed: 6.0ms preprocess, 74.7ms inference, 11.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 25 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 25 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 24 Workers, 73.5ms\n",
      "Speed: 6.5ms preprocess, 73.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 21 Workers, 74.0ms\n",
      "Speed: 5.1ms preprocess, 74.0ms inference, 5.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 20 Workers, 74.0ms\n",
      "Speed: 6.0ms preprocess, 74.0ms inference, 7.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 74.2ms\n",
      "Speed: 6.0ms preprocess, 74.2ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 20 Workers, 74.0ms\n",
      "Speed: 6.0ms preprocess, 74.0ms inference, 6.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 74.0ms\n",
      "Speed: 5.0ms preprocess, 74.0ms inference, 7.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 17 Workers, 74.0ms\n",
      "Speed: 5.0ms preprocess, 74.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 18 Workers, 74.0ms\n",
      "Speed: 5.0ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 18 Workers, 75.0ms\n",
      "Speed: 4.5ms preprocess, 75.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 74.7ms\n",
      "Speed: 5.0ms preprocess, 74.7ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 74.8ms\n",
      "Speed: 5.4ms preprocess, 74.8ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 19 Workers, 78.1ms\n",
      "Speed: 6.0ms preprocess, 78.1ms inference, 8.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 19 Workers, 75.1ms\n",
      "Speed: 4.0ms preprocess, 75.1ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 17 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 19 Workers, 74.7ms\n",
      "Speed: 5.0ms preprocess, 74.7ms inference, 6.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 22 Workers, 73.7ms\n",
      "Speed: 6.5ms preprocess, 73.7ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 20 Workers, 77.8ms\n",
      "Speed: 5.0ms preprocess, 77.8ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 74.2ms\n",
      "Speed: 6.0ms preprocess, 74.2ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 16 Workers, 73.6ms\n",
      "Speed: 5.3ms preprocess, 73.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 16 Workers, 74.2ms\n",
      "Speed: 8.0ms preprocess, 74.2ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 20 Workers, 75.0ms\n",
      "Speed: 5.0ms preprocess, 75.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 19 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 12.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 19 Workers, 74.0ms\n",
      "Speed: 10.9ms preprocess, 74.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 20 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 20 Workers, 73.6ms\n",
      "Speed: 4.6ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 21 Workers, 74.4ms\n",
      "Speed: 6.0ms preprocess, 74.4ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 22 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 23 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 20 Workers, 74.1ms\n",
      "Speed: 6.0ms preprocess, 74.1ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 20 Workers, 74.4ms\n",
      "Speed: 4.4ms preprocess, 74.4ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 19 Workers, 74.1ms\n",
      "Speed: 6.0ms preprocess, 74.1ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 20 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 74.2ms\n",
      "Speed: 5.0ms preprocess, 74.2ms inference, 8.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 16 Workers, 73.2ms\n",
      "Speed: 4.6ms preprocess, 73.2ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 12 Workers, 73.6ms\n",
      "Speed: 5.1ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 12 Workers, 74.0ms\n",
      "Speed: 5.4ms preprocess, 74.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 15 Workers, 73.8ms\n",
      "Speed: 7.0ms preprocess, 73.8ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 74.7ms\n",
      "Speed: 6.0ms preprocess, 74.7ms inference, 13.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 16 Workers, 74.8ms\n",
      "Speed: 5.5ms preprocess, 74.8ms inference, 6.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 18 Workers, 74.1ms\n",
      "Speed: 6.0ms preprocess, 74.1ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 74.0ms\n",
      "Speed: 7.0ms preprocess, 74.0ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 73.9ms\n",
      "Speed: 6.0ms preprocess, 73.9ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 18 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 21 Workers, 73.5ms\n",
      "Speed: 6.0ms preprocess, 73.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 74.1ms\n",
      "Speed: 6.0ms preprocess, 74.1ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 22 Workers, 74.0ms\n",
      "Speed: 5.6ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 74.0ms\n",
      "Speed: 6.0ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 22 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 18 Workers, 74.5ms\n",
      "Speed: 5.5ms preprocess, 74.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 14 Workers, 74.0ms\n",
      "Speed: 5.0ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 15 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 14 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 14 Workers, 75.0ms\n",
      "Speed: 4.0ms preprocess, 75.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 16 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 15 Workers, 74.2ms\n",
      "Speed: 5.0ms preprocess, 74.2ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 15 Workers, 77.2ms\n",
      "Speed: 6.0ms preprocess, 77.2ms inference, 8.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 17 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 16 Workers, 74.0ms\n",
      "Speed: 5.6ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 1 Drone, 18 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 74.0ms\n",
      "Speed: 6.0ms preprocess, 74.0ms inference, 10.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 73.6ms\n",
      "Speed: 4.1ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 19 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 21 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 18 Workers, 74.1ms\n",
      "Speed: 5.9ms preprocess, 74.1ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 23 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 18 Workers, 74.1ms\n",
      "Speed: 6.0ms preprocess, 74.1ms inference, 7.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 22 Workers, 74.6ms\n",
      "Speed: 4.6ms preprocess, 74.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 20 Workers, 74.4ms\n",
      "Speed: 5.3ms preprocess, 74.4ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 17 Workers, 73.0ms\n",
      "Speed: 7.5ms preprocess, 73.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 74.3ms\n",
      "Speed: 5.0ms preprocess, 74.3ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 18 Workers, 73.5ms\n",
      "Speed: 5.6ms preprocess, 73.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 18 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 19 Workers, 74.3ms\n",
      "Speed: 5.0ms preprocess, 74.3ms inference, 5.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 19 Workers, 73.6ms\n",
      "Speed: 4.9ms preprocess, 73.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 18 Workers, 74.9ms\n",
      "Speed: 5.5ms preprocess, 74.9ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 18 Workers, 75.5ms\n",
      "Speed: 5.0ms preprocess, 75.5ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 18 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 19 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 17.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 73.9ms\n",
      "Speed: 6.0ms preprocess, 73.9ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 18 Workers, 74.2ms\n",
      "Speed: 6.0ms preprocess, 74.2ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 21 Workers, 74.7ms\n",
      "Speed: 5.0ms preprocess, 74.7ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 19 Workers, 74.8ms\n",
      "Speed: 6.0ms preprocess, 74.8ms inference, 10.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 18 Workers, 74.1ms\n",
      "Speed: 8.0ms preprocess, 74.1ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 15 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 14 Workers, 75.0ms\n",
      "Speed: 5.4ms preprocess, 75.0ms inference, 7.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 15 Workers, 74.2ms\n",
      "Speed: 5.8ms preprocess, 74.2ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 15 Workers, 73.5ms\n",
      "Speed: 6.0ms preprocess, 73.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 15 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 15 Workers, 74.0ms\n",
      "Speed: 5.0ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 11 Workers, 73.9ms\n",
      "Speed: 5.0ms preprocess, 73.9ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 13 Workers, 73.6ms\n",
      "Speed: 7.0ms preprocess, 73.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 14 Workers, 74.0ms\n",
      "Speed: 5.0ms preprocess, 74.0ms inference, 6.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 15 Workers, 73.3ms\n",
      "Speed: 5.0ms preprocess, 73.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 15 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 14 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 13.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 18 Workers, 74.1ms\n",
      "Speed: 6.0ms preprocess, 74.1ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 14 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 15 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 15 Workers, 74.1ms\n",
      "Speed: 6.0ms preprocess, 74.1ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 15 Workers, 74.1ms\n",
      "Speed: 6.0ms preprocess, 74.1ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 73.5ms\n",
      "Speed: 5.0ms preprocess, 73.5ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 18 Workers, 74.6ms\n",
      "Speed: 4.5ms preprocess, 74.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 23 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 24 Workers, 74.1ms\n",
      "Speed: 5.0ms preprocess, 74.1ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 26 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 26 Workers, 74.8ms\n",
      "Speed: 6.0ms preprocess, 74.8ms inference, 6.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 25 Workers, 74.5ms\n",
      "Speed: 6.0ms preprocess, 74.5ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 23 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 10.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 20 Workers, 73.9ms\n",
      "Speed: 6.0ms preprocess, 73.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 14 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 74.7ms\n",
      "Speed: 6.0ms preprocess, 74.7ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 20 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 20 Workers, 74.9ms\n",
      "Speed: 10.0ms preprocess, 74.9ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 22 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 23 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 22 Workers, 73.5ms\n",
      "Speed: 6.0ms preprocess, 73.5ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 75.0ms\n",
      "Speed: 5.0ms preprocess, 75.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 73.4ms\n",
      "Speed: 5.0ms preprocess, 73.4ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 74.0ms\n",
      "Speed: 4.0ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 74.4ms\n",
      "Speed: 6.6ms preprocess, 74.4ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 73.6ms\n",
      "Speed: 4.0ms preprocess, 73.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 74.5ms\n",
      "Speed: 6.7ms preprocess, 74.5ms inference, 6.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 16 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 16 Workers, 74.3ms\n",
      "Speed: 5.0ms preprocess, 74.3ms inference, 7.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 18 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 16 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 74.1ms\n",
      "Speed: 5.0ms preprocess, 74.1ms inference, 5.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 20 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 21 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 23 Workers, 73.5ms\n",
      "Speed: 6.0ms preprocess, 73.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 21 Workers, 74.0ms\n",
      "Speed: 6.0ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 17 Workers, 74.5ms\n",
      "Speed: 6.0ms preprocess, 74.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 18 Workers, 74.0ms\n",
      "Speed: 5.0ms preprocess, 74.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 14 Workers, 74.7ms\n",
      "Speed: 5.1ms preprocess, 74.7ms inference, 9.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 14 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 17 Workers, 74.1ms\n",
      "Speed: 5.9ms preprocess, 74.1ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 14 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 9.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 13 Workers, 74.0ms\n",
      "Speed: 6.0ms preprocess, 74.0ms inference, 6.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 13 Workers, 74.3ms\n",
      "Speed: 5.0ms preprocess, 74.3ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 13 Workers, 74.0ms\n",
      "Speed: 7.0ms preprocess, 74.0ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 14 Workers, 74.6ms\n",
      "Speed: 6.0ms preprocess, 74.6ms inference, 13.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 12 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 14 Workers, 73.6ms\n",
      "Speed: 6.0ms preprocess, 73.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 14 Workers, 74.2ms\n",
      "Speed: 5.4ms preprocess, 74.2ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 15 Workers, 75.0ms\n",
      "Speed: 5.6ms preprocess, 75.0ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 16 Workers, 74.6ms\n",
      "Speed: 7.3ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 19 Workers, 74.0ms\n",
      "Speed: 6.0ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 20 Workers, 73.6ms\n",
      "Speed: 5.0ms preprocess, 73.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 19 Workers, 74.5ms\n",
      "Speed: 6.0ms preprocess, 74.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 17 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 16 Workers, 75.0ms\n",
      "Speed: 5.6ms preprocess, 75.0ms inference, 6.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 18 Workers, 74.0ms\n",
      "Speed: 6.0ms preprocess, 74.0ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 18 Workers, 74.9ms\n",
      "Speed: 6.0ms preprocess, 74.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 18 Workers, 74.4ms\n",
      "Speed: 5.0ms preprocess, 74.4ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 17 Workers, 74.6ms\n",
      "Speed: 5.3ms preprocess, 74.6ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 16 Workers, 74.3ms\n",
      "Speed: 6.0ms preprocess, 74.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 6 Drones, 15 Workers, 74.0ms\n",
      "Speed: 5.5ms preprocess, 74.0ms inference, 12.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 15 Workers, 74.6ms\n",
      "Speed: 7.0ms preprocess, 74.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 18 Workers, 74.1ms\n",
      "Speed: 6.0ms preprocess, 74.1ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 15 Workers, 73.9ms\n",
      "Speed: 5.9ms preprocess, 73.9ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 16 Workers, 75.5ms\n",
      "Speed: 5.0ms preprocess, 75.5ms inference, 6.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 14 Workers, 74.9ms\n",
      "Speed: 5.0ms preprocess, 74.9ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 15 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 5.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 14 Workers, 74.6ms\n",
      "Speed: 5.0ms preprocess, 74.6ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 17 Workers, 74.6ms\n",
      "Speed: 4.7ms preprocess, 74.6ms inference, 8.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 18 Workers, 73.5ms\n",
      "Speed: 5.5ms preprocess, 73.5ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 20 Workers, 73.6ms\n",
      "Speed: 9.5ms preprocess, 73.6ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 18 Workers, 74.0ms\n",
      "Speed: 6.0ms preprocess, 74.0ms inference, 6.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 5 Drones, 19 Workers, 74.4ms\n",
      "Speed: 6.0ms preprocess, 74.4ms inference, 15.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 17 Workers, 74.4ms\n",
      "Speed: 6.0ms preprocess, 74.4ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 4 Drones, 17 Workers, 73.8ms\n",
      "Speed: 5.3ms preprocess, 73.8ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 3 Drones, 20 Workers, 74.5ms\n",
      "Speed: 5.0ms preprocess, 74.5ms inference, 9.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 480x640 2 Drones, 19 Workers, 73.9ms\n",
      "Speed: 6.0ms preprocess, 73.9ms inference, 7.0ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    }
   ],
   "source": [
    "video = \"input_video.h264\"\n",
    "process_video_with_bounding_boxes(model=model, video_path=video, output_path=\"output_video.mp4\", threshold=0.5, detection_func=predict_image, frames_interval=5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the latest video results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAOBbSsorddaz87Qru0jXD2+rv57fzeqSEKQcDkMv/rxFz/y4SZI0uG2CXrrqxHN4ftmcqUlvLnbL+S/sm6p/Xtiz+o7HhvhxXbpumrpETRqFaeEDp7vlmAB8z8PT12jaop1qlxit7/44stYt/+pqa2aBxjz3i8oqHXrpqn46t2cLj5wHALxla2aBbp66RFszCxUeate/LuypC/sdf3XH4fPzwkLs+v6uU9TWS4PdyysdGvv8r9qYnq/LBqTpyYt7HfNxh4fqMRGhevayPjqzW3Ov1BgICFCqEKAAAAD4lv/M3KgXf96iFvGR+vGeUYqJCLW6JAS5H9el6+6PVqigtEKpjaP06qT+x1wVVVJeqXOem6cdWUUa26uFRndOqvc5W8RHamj7pm67i7HSYeiUp37W3pxiPXNp7xNeEADgn1bsztGEl+bLMKT3bxqsYe2befR8z/y4Sc/P3qzmcRGadc8oxUaGefR8AOApP21I1x8/WKH80gq1iI/Uq5P6q1fLxrV67paMfNlsNq8PdF+6M1sXvbxQkvTxLUM1qO3RrV6dobrdJjmqrvDffUYn3XlaB9lpj3xSBChVCFAAAAB8x5aMfI357y8qrzT0ysT+OqdHstUlAZLM/zdvmrpU2w8WKjLMricv6qUL+hzZ49rXLyZO+XmL/j1zo/q2aqwvbh9udTkA3Kii0qFxL87Xuv15urBfqp65tI/Hz3l4aHztsDZ6ZFx3j58TANzt5Tlb9dTMDTIMaWCbJnrpqv5KjD3+amNf8sDnq/TB4t3qmBSjGX8YccSqw8ND9anXD9Ks9emaunCnJOns7s319KV9uFHtJGqbG5y8wS8AAADgBoZh6KEv1qi80tDpXZJ0dneWl8N3dEiK1fTJwzW6c6JKyh3644cr9M9v16uy6na+rZkFemXOVknS387v7nPhiSRdOiBNYSE2Ld+VozV7c60uB4Abfb1qn9btz1N8VJge9NKco8iwED0+vqck6X8Ld2jVnhyvnBcA3OW3bVl68nszPJk4pJXeu3GI34QnkvR/53RR0+hwbc4o0FWv/6aM/BJJZqj+4OerZRhmS9hTOiXqsQt66MmLeio8xK6Za9M1Ycp87ThYaPHfIDAQoAAAAMArPl26R79tz1ZUWIgevaA7A2nhc+KjwvTGNQM1eXR7SdJr87bp2rcX61BhmR76YrXKKh0a3TlRY3x05VRibITG9DDnFLy7aKfF1QBwp2lVdxXfOKKtmp1gTpO7jejYTBf0SZFhSA9+sbo6VAYAfzC16vXQxf1b6vHxPRUe6l+Xwhs3CtcLV/RVbGSoluw8pHEvzNfK3Tl6Z8GOmlD9vJpQ/bKBrfThLUOUFBuhzRkFGvfir5qzMcPCv0Fg8K//awAAALxsX04xFwvcILuwTP/8dr0k6a4zOqplk0YWVwQcW4jdpj+d3UVTruynqLAQ/bL5oEY/PUeLtmUrMsyuxy7o4dPh38QhrSVJX67Yp9zicourAeAOa/bmatmuHIWF2HTZoDSvn/8v53VTbGSo1uzN09SFO7x+fgCoj4y8Es1cc0CSdN3wNtYW0wDDOjTTl5OHq0NSjA7kleiSVxfq6R82SZIeGNPlqFC9X6sm+vrOEerbqrHySip0/Tu/65W5WxXgUzw8igAFAADgGMoqHPrL9NUa9q+fdPErC5SeV2J1SX7tX9+t16GicnVJjtX1I9paXQ5wUuf1aqHPbx+mtIQo5RSZQcQfTu+otATfDv8Gtmmizs1jVVxeySoUIEC895v5vXxOjxZKio30+vkTYyP0f+d0kSQ9+f0Gfbd6v9drAIC6+vD33apwGOrfuom6p8RbXU6DtEuM0Re3D9MZXZurrMKh4vJKDWjdRJcOOHao3jwuUh/ePESXDUiTw5D+9d0GPfzlGi9XHTgIUAAAAFxk5pfqqjcW6d1FuyRJy3fl6PwXftWyXYcsrsw/Ld6erY+X7JEk/WNCD4WF8BIU/qFrizh9NXmELuiTonG9U3TTyHZWl3RSNptNt55q1vnCT5u1O7vI4ooANEReSbmmL98nSZo4uJVldVw5qJVO65KkknKHbntvmZ7+YaMcrNAF4KMqKh16/zfzvdykqtW5/i42MkyvTeqvP53dWYPaJujJi3vJbj/+quiI0BD966Ke+vsF3RURatc53Vt4sdrAYjMCfP1OXl6e4uPjlZubq7i4OKvLAQAAPm7VnhzdPHWpDuSVKDYiVA+d11Vvzd+uTekFCg+x6/HxPXTpQO+3z/BXZRUOnfv8L9qSUaArBrXSExf2tLokIOAZhqHLX1uk37Zn67QuSXrzmgE+3XYMwPG9M3+7Hvl6nTo1j9HMu06x9Hu5otKhf323QW/8ul2SdHqXJD17eR/FRYZZVhMAHMvMtQd0y7SlSogO18IHTlNEaIjVJVkqI69ESXHeX8Ho62qbG4R6sSYAAACf9tnSPXrgi9Uqq3CofWK0Xrt6gNonxmhs7xTd+/EKzVybrvs/W6XMglJNHt3B6nL9wjsLtmtLRoGaRofrz1XtPwB4ls1m0z8m9NSY/87TTxsyNHPtAZ3Tg7sOAX9jGIamVbXimzSkteVBaGiIXX8Z203dU+P0589Wa/aGDF300gJNnzxc0RG+dXnJMAy9NGer3vp1u0rKK9123NAQu64c3Er3ntlJoV5eUbs1s0D3fbJS8VFheuriXpa0cwP8hbON6WUD04I+PJFEeNJA9E8AAABBr6LSoce+Xqd7P1mpsgqHzuiapOmTh6t9YowkKSYiVC9f1V93ndFRkvTsj5u0KT3fypL9hrN1131nd1Z8I+5QBbylQ1KMbh3VXpL0yFfrVFBaYXFFAOpq4bYsbc0sVHR4iMb3TbW6nGoT+rbUp7cOU1JshDZnFOi5WZusLukIhaUVuv29Zfr3zI3KKixTYVml2z5yi8v18pytuu6d35VTVOa1v9NPG9I1/sX5Wr4rR3M2ZmrcC/O1cneO184P+JNtmQX6ZfNB2Wxm+0GgoXzrFgEAAAAvO1RYpsnvL9OCrVmSzCHRd53e8ah+sna7TXed0Ulr9+Xpx3XpeuiL1fro5qEn7Dsb7LYfLNSWjAKF2m06tyd3vwPeNnl0B321cp92ZhXp6R826m/nd7e6JAB14LyDenzfVMX6WJusni3j9eTFvXTd27/rrfk7NKFvS3VLsb5t+s6sQt08dak2pucrLMSmv53fXSM7NnPb8ZfvytEDn6/WL5sPatyL8/Xa1f3VJdlzf2/nSpr//LBRhiENaN1EOcXl2pJRoEteXah/Tuipi/u39Nj5AX/0XtXsk9Gdk5SW0MjiahAICFAABAWHw9D/Fu7Q/twS/eH0jorxsSXmAKyxO7tIV7y+SHsOFatReIieubT3SdvcPDquu+ZvOajfdxzSJ0t367KB3NV0PD+uOyBJGtq+qeKjfOvCDxAMIsNC9PcLeujqtxbrfwt26KJ+LdUjNd7qsgAcw8GCUv3z2/XKK65ZLfbzxgxJ0kQfHYA8unOSzuvZQt+s3q8Hv1itz24bphALbyz5ZXOm7nh/uXKLy5UYG6FXJvZX/9ZN3HqO1k2j1Tk5VjdPW6Jd2UW68KUFevqS3hrjgRtFCksr9KdPV+rb1ebrqYlDWumvY7urtKJSd3+0UrPWp+u+T1bqs6V7jmih1iM1Tref2kHhoTSdQfApLqvUJ0t2Swqc4fGwHj9NAQS8gtIK3fruUj369Tq9Nm+bLnxpvnYcLLS6LAA+4NlZm7TnULFaN22kL24fXqsZASmNo3TPmZ0kSU98t0FZBaWeLtNv/bA2XZJ0ZrfmFlcCBK9TOiVqbK8WchjSS3O2WF0OgOP425dr9fmyvZq1Pr36o9JhaHDbBHVtYf3KjuP56/ndFBMRqhW7c/T+4l2W1GAYhl6bt1XXvLVYucXl6pPWWDPuHOH28MSpa4s4fTV5hIZ3aKqiskrd9t4y/WfmRjkchtvOsSurSBe9vEDfrj6gsBCbnriwpx4f31PhoXbFRobptUn99cfTzdayC7dlHfH/zXOzNuvK1xcpI7/EbfUA/uKlOVuUV1KhtIQondIp0epyECBshmG47ye8D8rLy1N8fLxyc3MVF+e7LzoAeMaOg4W6aeoSbc4oUHiIXXFRoTpYUKa4yFC9cGU/jeIXKhC0sgvLNOSJ2SqrcOjLycPVO61xrZ9bUenQ+S/O1/r9ebqwX6qeubSPx+r0VwcLSjXwH7NkGNKCP5+mlMZRVpcEBK2NB/J19nPzFGK3acGfT1NzBokCPuXnjRm67u3fFWK36cFzuyo63Bx4bLfbNLpzkhJjIyyu8MTemb9dj3y9TrGRoZp97yivDjcvLqvUnz9fpS9X7JMkXdK/pf4+vociwzw/NLqi0qF/fbdBb/y6XZJ0epckPXt5H8U1sN3a4StpmsVE6JWJ/TSgTcIxH7tyd47W78+r/nNBaYX+O3uz8ksqlBwXqVcn9a/Ta1zAn23JyNeY//6i8kpDr0zsV6ub4xDcapsb0MMGgN9asiNbK/fkHvfrJeWVenXuVuWVVCgpNkKvTOqvlo2jdMu7S7V8V46ue3uxrh/eVi1OcFEvOS5SY3okM+MACECfLNmtsgqHerWMr/Mby9AQu/45oYcufHmBPl+2Vxf3a6lhHWrXX/vXzQeVHB+pDkkxR33NMAzN3ZSpZjERft9m56f1GTIMqWdqPOEJYLHOybEa1CZBi3dk64PFu3TXGZ2sLglAleKySv31yzWSpOuGtdENI9paXFHdTRraRp8t26vVe3P12Nfr9MIVfWWzef79096cYt08dYnW7stTqN2mv57fTZOGtPbKuSXz9eBfxnZT99Q4/fmz1Zq9IUPjp8zX61cPUPvEo1/nuTIMQz+sS9eeQ8XVn0vPK9Ebv2yTw5B6pzXWqxP7Kzn++IFU77TGR72OPa1Lkm6aukRbMwuZk4KgYRiGHvpijcorDZ3WJUlnd0+2uiQEEAIUAH4pI69EV77+m8oqHSd9bN9W5gvPpKq7LT+8eYj+On2tPlqyu/puoRM5rUuSnnPDnUQAfIfDYejd38zBrPXtK963VRNdNbiV3l20S7dMW6rnLu+j07ueuFXV1yv36c4Plis8xK7HJ/TQpQPSqr9WWlFZ/bPJeQfq9cPbeO0igLv9UDX/hPZdgG+YOLR1dYAyeXQHhYXQzRnwBS/+vFm7s4vVIj5Sd5/pn+FmiN2mf07oqQum/KoZq/arUXiI/j6+hyJCPbcKZNG2LE1+b5myCsuUEB2ul67qpyHtmnrsfCcyoW9LdUg056JsyyzU+Bfnn/R1oet8E1cX92+px+u5kqZdYoymTx5+xJyUtfty9eC5XfnZj4D12bK9+m17tiLD7Hp0XHe/fQ8F30QLLwB+6fnZm/XMj5vUskmU+rU6fm/bDkkxumVUu6NevBuGoekr9mrOxkwd76egwzD047p0lVY41K5ZtF67esAx7xgH4H+crTLiIkP124NnKCq8fm/w80vKdcM7S7R4R7ZsNumeMzrpjtM6HPMFe25xuc54Zq4y82tmplwztLX+MrabsgvLdGvV6rjDXdgvVf+c0NMrbSjcqaisQn0f+1GlFQ59f9dIdUnmNRhgtbIKh4b9a7YOFpTp5av6eWTgMYC62Zyer3OfN9vNvDqpv9/fMf2/BTv06Ndr5TCkPmmN9eqk/m5vGWgYhqYt2qnHvl6nCoeh7ilxenVSf7Vs0sit56mPzPxSTX5v2UlfF+7KKtLN05Zow4F8hYXYdFa3ZIUc1vHglE6JuqhfaoMvADschp6bvVnPz94sSRrarqmmXNVPCdHhDTou4GsOFZbp9GfmKruwTH8e00W3jmpvdUnwE7XNDQhQAPidikqHRjz5sw7klei/l/fRBX1SPXau1Xtydcu0JdqXW6KYiFD9Y0IPdWoeW/31uKgwpdKaBvA7N7zzu2ZvyNANI9rq4bHdGnSssgqH/j5jnaYtMle0jOmRrP9c0lvREUcu9H14+hpNW7RT7ZpF6/zeKfpv1ZvZAa2baFd2kTLyS6vnM23NKNA/vl2vSoehXi3j9crE/n7VBuv7NQd067tLlZYQpXl/Gs0dYICP+M/MjXrx5y0a1r6p3r9pyBFfO1RYpiZcVAO8xjAMXfbqIi3eka0zujbXG9cMsLokt5i3KVN3fmDO70iMjdArE/vXe5h7pcPQ1swCVVYNZzcM6Z0F2/Xxkj2SpAv6pOhfF/aq940wnlBW4dDj36zT1IU1rwvvOK2D7FWvhXZmFerPn69WTtHJ55u4y/drDujej1eosKxSqY2j9ORFvdQ05vg/75vFRPj83J262J9brJyi8uo/h9ptap8YQ5vuAHL/pyv18ZI96tw8VjP+MIKVVqg1ApQqBChA4HFemGsaHa4FD5zm0aXhkjkI+fb3lmnx9uyjvmazSW9cPeCkbXsA+I7d2UU65d8/yzCkn+4dpXa16FFdGx8u3qWHvzT77nZuHqvXru6v1k2jJUkrdudowkvzZRjS+zcN1rD2zfTD2gO6+yPzzawkdWoeo9cmDVCbZuZzFmw5qMnvL9OhonI1iwnXyxP7a6CH32C7y70fr9Rny/bo+uFt9dfzGxZQAXCfvTnFGvnkT3IY0qx7TlGHpFiVlFfqoS/W6LNlezR5dHv96ewuVpcJBLzySvPmi6kLdyoqLESz7h0VUDdl7cwq1E1Tl2hTeoHCQ+z6+/juumxgqzodY29OsW6ZtkRr9uYd9TW7TXpgTFfdOLKtz96kcfjrwmOpzXwTd9qUnq+bpi7Rzqyikz42ItSu6ZOHq2sL/76GVlHp0BPfbdCbx2jb3btlvF6Z1F8t4gPn+y5Yrd6Tq/Nf/FWS9NltQ9W/tX+8X4JvqG1uQCQHwO+8W3WX92UD0zwenkjmHTjv3ThYN41sq+S4SCXGmnfkxEeFyTDMu8oLSys8XgcA93h/8S4ZhjSiQzO3hSeSdPmgVvrw5iFKjI3QxvR8jXtxvn7ZnKmKSoce/Hy1DEO6sG+qhrU3h82f1T1Z0ycP18A2TXRRv5b6/Pbh1eGJJA3r0Exf3TFCXVvE6WBBma54bVH1zz9fVlHp0E8b0iVJZ3UnXAZ8SWrjKJ3Wxfy+fHfRLu3PLdZlry7UZ8vMu7lfnrNVa/bmWlkiEPCyCko18Y3fqlco/PX8bgEVnkhS66bR+vz24Tqne7LKKh36v89W6+Hpa1Rei/mVkjnfZNwLv2rN3jxFhNqr338lxkaoU/MYvXPdIN10SjufDU8k5+vCoeqeEndE/c3jInT10Nb66OYhXgtPJKlT81h9NXmExvZqcUQ9rh+xEaEqrXDowS9Wy+Hw3/utDxWW6eq3FleHJ85VNYmxEYoItWvlnlyd/8J8Ldlx9E2S8C//W7hDkjSudwrhCTyGFSgA/MrWzAKd/vRc2WzSL/ePtrTXbXFZpc56bq52ZxfrppFt9dB53GUN+LrSikoNfeInZReW6ZWJ/XVOD/f3Gj+QW6Jb3l2qlbtzZLdJIzomat6mTMVHhWn2vaPULKZuLRGKyip0/6erNGPVfknSFYNa6ZFx3bwSINfHom1Zuvy1RWrcKExLHjpDoSyhB3zKBvBteQAASnZJREFU3E2ZuuatxYqJCFVkWIgOFpSqcaMwdUqK1eId2erVMl5f3D78iH78ANxjzd5c3TJtqfbmFCsmIlTPXtZHZ3YL3JsNHA5DU37eoqd/3CRJGtQ2QS9d1e+4r4UMw9DUhTv19xm+N98kWBzILdEZz8xVQWmF/jGhh64a3Nrqkups3b483TxtifYcKlaj8BA9fUnvI+Z+7c4u0k1Ta2bQPDKuu1/+PWEGZUOemK3SCoc+u21YvdsFInjVNjcIPe5XAMAHvbdolyTp9C5Jlr+QjgoP0WPjeui6d37XW/N3aELfluqWQlAL+Arnm/BX5m5VcbnZJquy0lB+aYWS4yJ1Rtckj5w3OT5SH908RA9PX6NPlu7RvE2ZkqQHxnSpc3giSY3CQ/XCFX3VPSVeT83coA8W79KOg4WadsMgnwwnflhrrj45vUtzn6wPCHYjOzRT66aNtDOrSAWlFeqSHKvXJg1QZJhdpz89V6v25OrdRTt1zbA2VpcK+A3DMPT+4l2a8tMWFVW95jiWgpIKVTgMtW0Wrdev7q8OSbHHfWwgsNttuvP0juraIk53fbRCi7dna/i/fjruzBKHw1Beibmyf1zvFD15kW/NNwkGyfGRuvesTnr063V68rsNOqtbstfmoRiGoTd/3a43ftmukoqa76NGYSG6fXQHXTW41VGrjqYv36v//LBRBYd1hHB+n7VKaKTXrx6gzslHfp+lJTTS57cP058+WaVvVu/XQ1+s0Zq9eXp0XHeFh/La1Z98unSPSisc6tYiTv1aNba6HAQwfjIA8BvFZZX6dOluSdJVQ3zjDpHRXZJ0bs9kVToMPfjF6uoBhwCsVVJeqfs+WaW/fbVW+3NLlFNUrpyicuVXvbm6cWRbj17cjwwL0VMX99Kj47orLMSmUzol6tIBafU+ns1m022nttfb1w5UTESoFm7Lqh5a70tKKyo1fcVeSfLI6h4ADWe32/SH0zrKZpPO69VCn98+TK2aNlJSXKTuP6ezJOnfMzcqPa/E4koB/1BaUakHv1ith75Yo32HveY41keFw9DozomaPnl4wIcnhzujW3NNnzxc7RKjVVrhOO6/T15JhULsNj14bhf99/I+hCcWuXpoG/VIjVNeSYX+8c06r5yzpLxSd3+0Qo9/s14H8o78PtqXW6K/TF+jBz5frdKqYKWi0qHHZ6zTXR+t0J5DxUd9n43s2Exf3TH8qPDEqVF4qF68sq/uP6ezbDbpg8W7dOXri5SRz+8+f+FwGHr3N/P90KShrX26pR/8Hy28APiNj3/frfs/W6W0hCjNvW+07D7SWiI9r0SnP20uc/77+B6a5CPhDhCs9ucW69ZpS7VyT67sNunPY7rotC41q00iQkPUskmU115kF5ZWKDIsxG3tcN7/bZce/GK1YiJCNeueUV7tn30y05fv1V0frVCL+Ej9cv9oVqAAPqywtELREUc2JKh0GLrw5QVauTtH5/VqoSlX9rOoOsA/ZOSV6NZ3l2rZrhzZbNJ9Z3XW2SeY/xUeEqK0BO+9BvE1FZUO7cgqknT8y1BNGoWraT1W7MK9Vu3J0fgp8+UwpHdvGKwRHZt57Fx7c4p1y7QlWrM3rypA66pRnWrO98O6dP175kYZhtSvVWM9cWEvPTZjreZvyZIkTR7dXhP6plY/vq7fZz9vyNAfPlyu/BJzlfqrk/qrd1pjt/4d4X7zNmXq6rcWKzYiVL89dLoahdNkCXVX29yAAAWAXyircGj8lPlatz9PD4zpoltGtbe6pCO8M3+7Hvl6nWIjQzX73lFKivWdC5pAMFmyI1u3vrusuqf/i1f08+gbPis4HIYufmWBlu3K0ZgeyXp5Yn+rS6p28csLtGTnId17ZifdeXpHq8sBUA9r9uZq3Iu/ymFI71w3UKd29ky7Q8DfLd91SLe+u1TpeaWKiwzV81f05fsFAeWRr9bqnQU71KZpI305eYTiG4W5/Ryr9uTourd/V1ZhmRKiwzXlyn4a2r7pUY+bszFDf/hgeXWLN0lqFB6i/1zSW+ceNt+kvrZlFujmaUu1JaNA4aF2PXtpH53Xq+HHxcntyirSG79u0zk9kjWsfe3ft900dYl+XJeua4e10SPjunuwQgQyApQq1f8Q+/YRoAB+KjO/VHd/tFxLd+YoMsyu2feeqoTocKvLOkKlw9Blry7U2n15uvmUtrr7zM5WlwQEnQ9/36V/frNe5ZWGOjWP0YtX9FNa08AcOrrhQJ4ufnmhKh2GXp7YV6d2tn4A7fr9ebrwpQUKtds0+z6CZMCf/eu79frfgp1qmRCpryaPpI0O4OKzZXv02FfrVFbpUIfEaL1wZT+1aRZtdVmAW+WVlOv8F35RRl6ZWjeN0otX9FOH5u5rPVdSXqkJL83XjoNF6toiVi9c0VepJ5hzuvNgoe78YJk2ZxQqLSFKL1zZV52bu+86X35Juf7vs9X6eUOGGoWHaMadI9SicZTbjo+jzd+SqXs/XqXc4nLZbdK9Z3XSdcPbnnT10L6cYp35zFw5DGnGncPVPohaIsK98vLyFJ+SQoBSHaBIIj4BAAAAAAAAACC45UmKl04aoNCYGgAAAAAAAAAAwEXwTNjZt0+ihRfgFyoqHfr3zI2aunCnJGl0l0T966Jeiot0f89Vd5vy0xa9+PMW9W/dWO/eOMTqcoCAZhiGbvjf71q4NVuxkaH698W9NCrIeo//tD5dk99frlC7TZ/dPkyd3NhWoS7e/22n/j5jvTokRuurO0cE7XBcIJAYhqHJ7y/TzxsyNaB1E/3v+kGy2/neRnBauy9Xf/hgufbllCg6PERPXtRLp3ezvn0m4C3llQ499f0GvbtolyTp8Qk9dFG/lvU6lmEYuvbtxVq8/ZBGdmqmVyf297nXjk//sFFv/LJdLeIj9PWdIxUdEZiXTxduPai7P16p3KJyr5871G7TA+d20RWDWlX/91+8PUt3fbRChwpPXk9ibLhm33uqwkJYG4AGyMuTUlJO+rDA/AlwLNHR5gcAn5ZdWKY7PlqmBVuzpPBI/eG0DrrrjE5+84b94lGd9ezCvfp1f4nW51WqawuCW8BTpi/fo592FymiUSO9d+cIdbQoPLDSaQPa6ZT12Zq5Nl0PzNymT24Z6vWfl4Zh6O0VmSoOj9Qlp3aRLSbGq+cH4Bk2SQ9eMkA/PzNPv+wv1qcbD+nSAWlWlwV43Zcr9ur/PlulknKpbUpTvTapf1C+5kBwC5P00KUDFdu0sZ75cZMe/3mnRvdrW6/ZpF8s26O5e4sVGd1ID186ULYY35tZePt5vfXFphxtO1Ss/y7apwfP7Wp1SW5lGIbemr9D//x2vSodIerYMl4dkrz3Gj481K6rBrfWoLYJR3x+UI9ofZzaTFN+3qqcorLjPt9us+mygWkKi+NnMRqosrJWDwueAAWAz1u3L083T1uiPYeK1Sg8RM9c2lvn9GhhdVl10jwuUmd1b65vVx/Qu4t26h8TelpdEhCQcorK9PiM9ZKkP5zeMagvZPzt/O76dfNBLd15SB8v2a3LB7U64utlFQ7tyi464TGaRoerST3eAEvSb9uztTmjQI3CQzShb2q9jgHAN7Vs0kh3n9lR//x2g574dr3O6Nq8XhfLAH9U6TD01Pcb9Oq8bZKk/2/vvsOjqrY+jv8mlRSSEAIJoSahE2qQIsVCpNgoFkQERa9Is4Fe27UXUF+5NgTRe0WBK9gAGyrSpJfQS+gQSgoB0vvMfv8IjCYECJhkUr6f58nzmHPOnFkj2TknZ+291rXNaum9u9rL16P8r4oHSsvoa8P08/ZYRcel6o2fd+v/7mh70eONMYo5naFca3775ew8q17/6c97+Pr+5S95Ikkebs56tX+4RszYoP+sPKQB7eqqZXDlmByZlWvVs99t13ebj0uSBrWvqzcGtVY1V2cHR5avXg1PTRzEcxSULyRQAJQLP2w9oSe/2aqsXJsa+Hvqk+Ed1SyoYj4QvadLQ/28PU7zNh/X0/2aq3oFKD0GVDRv/hKtU+k5alLbWw/2CHV0OA4V7Oehx29oqtd+2q2JC6MV2TJQAd7ukqQtR5M0elaUYpOzLnoOD1dnLRjX7bJLgMWnZOm1n3ZJkga2r8vvO6ASGtEtRN9tOq7ouFRNXrRHrw3goQYqv6SMHD385Wat2JcoKf+h8RO9m8m5gqyKB0qLq7OTXh/YWrdPW61voo7p9oh66hJas8hjE9OyNXb2Jq07dPq8fU0Dy/89/HXNa6tfeJAW7ojTc/O369tRV1eYyhgXciIpU6NmRWnbsWQ5O1n03I0tNKJbo3JXQg0obygUB8ChrDajN3+J1sNfblZWrk09mgTo+3HdKmzyRJK6htZUWC0vZeRYNe/srA4AJSfqyGl9uf6oJOn1ga3l5sLtzH1XN1LLOj5KzszVG2dn9X298ajunLZGsclZ8nB1lp+na5FfHq7Oysy16rl522WzmWK/56aYM7rlg5XacTxFvh6u5f6PYABXxtXZSS/c3FKS9G3UcaVklX2ddKAs7YlLVf8pq7RiX6I8XJ314d3t9VTf5iRPgLMiGtbQkLMrnv81f4dy8mznHbP9WLJu/WCl1h06LVdnS4F7z7p+Hnrr9rYVonfFi7e0kre7izbHJOnLDTGODudvWX/otG79cKW2HUuWn6ervri/k+7vHkLyBCgGVqAAcJjkzFw9Omezlu05KUl6qGeonuzTTC4V4EbqYiwWi4Z1aaiXftilWWuPaFiXhtyUACUk12rTs9/tkCTd2bHeeXVzqyoXZye9Mai1Bn60St9tPq607Dz9titekhTZIlD/Htz2gqtDjidl6obJy7Xh8Bl9HXVUg69qUORxfzV3Q4yen79TOVabmgZ665PhHdWwJr3mgMqqa1hNNQ301t74NH0XdUz3dQtxdEhAqfhlR6zGf7VVGTlW1avhoenDOlaasj1ASXqqT3P9tjNO+xPSNP2PAxp3fRP7vvmb8/sGZefZFBrgpenDO5Zpf42SFORbTRN6N9XLP+zSmwuj1btlkGpVd3d0WJdt1tojeun7ncqzGTUPqq5Phncst+XTgPLIYowp/lTDCiglJUW+vr5KTk6Wjw83PoCjnErL1gOfb9TxpEz7tozsPKXnWOXu4qS3bm+j/u0qT+38lKxcdX59sTJzrZo7sos6X2BZM4DLM235AU1aGC1/LzctHn/NFfftqKyen79DM9cesX//aK8merRXk0uWG/jkj4N6/efd8vN01eLx16jm2RJg2XlWTfw5Wgt3xOrc4hRjjBLT8ps69mkVqHfubCdvd+bkAJXdzDWH9fyCnQqr5aXfx19TqpNDDiWm64UFOxQa4KWXbm3FRBQH2HE8Wf+av0Otgn30/M0tL1kb/8ipdL30/U6F1vLW82dXLFUkNpvRu7/v1ftL9kuSrg6rqQ/v7kDPH+Ai5m8+rsfmbpGzk8U+VozJL90lSdc1q6V3K0HfIKvNaMCUVdp+PFn92wXrvbvaOzqki/q/X/foq41Hde5h71/v3W9qU0dv395Gnm7cuwNS8fMGFXuaN4AK4/WfdmvL0SSdTM22f6XnWFXXz0Pfjr66UiVPJMmnmqsGnG2m/NeHmQCu3NHTGXr3972SpGdvbEHypAhP9m2mBv6e8nZ30cfDIvT4DU2LVat5RLdGalHHR0kZuXrj52hJ+f1NBn+8VjNWH1Z8yp+/uxPTcmSxSONvaKqpQyNIngBVxID2deXl5qwDJ9O15uCpUnufZXsSdOuHK7ViX6I+X3NEP2yLLbX3QtEWbDmu26au1pajSZq9LkZ3frxGJ/4yCaqwP/ae1C0frNTSPSf1n5WHtPNEchlG+/elZuVq5MyN9uTJA91D9MX9nUieAJfQv12wrm1WS1ab+ct9Yn7yZOx1Yfr03qsqfPJEkpydLHp9YLicLNKCLSe08mxvpPJo7cFT+nDpfiWkFrx3d7JI/+zbTB8OaU/yBLgCrEABUOpWH0jU3Z+sk8UiTbsnQvVr/LlUNKy2l9xdLj6jraLaeSJZN72/Ui5OFq1+5nrVrl7N0SEBFZYxRg98vlFLohPUOcRfc0Z2YUbyBWTk5MnJYrnkbOHCNsec0aCpq2WM9K+bWmj6HweVkJotn2oumnRbGzX6S4muAG831fbhdxpQ1fxr/nbNWhujG1sH6aOhESV6bmOMpi0/qLd+jZYxUk0vN51Kz1GAt7sWT7imUjyEK+/yrDa99eseTf/joCSpS6i/9sSl6kxGrgK83TT1nghd1ejP0pnGGH2y4qAmLYyWzUiuzhblWo2GdGqgiYNaO+pjXJYDJ9M08ouNOnAyXW4uTpo4sLVui6jn6LCACiMnz6aDiWmy/aUNir+Xm4J8K9994kvf79SM1YcVEuClhY/2uOx77dKWk2dTv/f+0IGT6bo9op7u/0u5zYDqbjyPAIpQ3LwBCRQApSo7z6p+767QwcR0DevSUK8OCHd0SGVq0EertCkmSRNuaKqHezW59AtQwIItx7UkOqHAtno1PDTm2sbyYtZ7lfLLjliNmrVJrs4WLXy0Z4Wto1zenXs4ek7TQG9NH9ZRjQLobwJAio5LUd93V8jZyaLVT1+vwCtMpKZl52nqsv06dubPVQ1xyVlad+i0JGlIp/p67qaWuvXDlTp4Ml33dGmg1wZUjAfy5VVCSpamLj+g0+k5Fzzm8KkMbT2aJEkac22YJvRuphNJmXrwi42KjkuVi5NFfVoFycU5fwJDfEqW1h7M/ze7s2M93dI2WMP+s16ebs5a+2wv+Vyg91Z5sebAKY38YqNSs/NUx7eaPh4WoTb1/BwdFoByKjUrV73eWa6E1Gx1CvFXnYskibqE1tRdV9Uv0wlfHy7Zp//7ba8CvN20ePy18vUs37+DgfKguHkDnj4BKFXTlh3UwcR01arurif7NnN0OGVuWNeG2hSTpP+tj9Hoa8Pk4kzlxOLaHZui8V9tldV2fp7/910Jmj48gqbVVURadp5e+n6XJGnUNWEkT0rRk32a69ed8TqZmk1/EwDnaR7ko6sa1dCGw2c0Z/1RPRp5+ZNDDiem68EvNmpfQtp5+1ycLHrp1lYa2rmBLBaLXhsQrrs/WafZ62J0W4d6at+gRkl8jCpnc8wZjZoVpfiU7Ese6+HqrLfvaKOb2wRLkur7e+q7MVfryW+26adtsfppe8GSai5OFr1wS0sN69JQUn7ifW98muZtOq57r25U4p+lpBhj9Ny87UrNztNVjWroo6ERFbIxNICyU72aq166tZXGzN6k9WcT/heyYMsJrdyfWGb9Ro6cStcHZ8sQPn9zS5InQAljBQqAUnMoMV193v1DOXk2vT+kvW5tG+zokMpcVq5VV09aotPpOZo+LEK9WwU5OqQKwWYzum3aam2OSVLX0JqKbBkoSbLabPp0xSElpGbL18NVHwxpr55Nazk4WpS2l3/Yqc9WHVbDmp769bGe5W65fGVz9HSG9iWk6tqmtYvVPwVA1bJgy3E9OmeLAn3ctfKp6+V6GZNDlu89qYf/t0kpWXkK9HHXA91D5Oz05+uvDqupFnUK/s02/qst+m7TcbWo46MfxnVjMspl+mrjUf1r3g7lWG1qUttbgy8yI9rJIl3brLZCilh1aIzRol3xOnqmYC+ULqH+ahXsa//+izWH9cKCnWpc21uLHu9ZbsttrtqfqKGfrpO3u4vWPtuLyQIAiu3XnXEFVlAWdiotW9P/OKg8m1HzoOr6ZHhH1ff3vODxf5cxRsP/u14r9iWqe+MAzXygU7n93QuUN6xAAeBQxhg9P3+HcvJs6tEkQLe0qePokByimquz7uxYX9OWH9DMtUdIoBTTlxtitDkmSV5uzvr34HYFauj2b1dXD82M0pajSbrvs/V6ul9zjewZ5sBoUZp2HE/W56sPS5Je7R9O8qQM1Pf3LNU/8gBUbH3DgxTg7ab4lGwt3h2vvuGXvsczxujjPw7qrV/ye2V0aOCnafdEFKuX0nM3ttCS6ATtjk3Ri9/vVOu6fz6sb13Pt8DD+7/aHZtiL0d1Tlht7wI9PMqT1KxcLd6doKxcq32bh5uzIlsEFlm2NCvXqhX7EtUpxL/I/jC5Vpte/2m3Zpy9hvZuGajJg698VaHFYinWfezA9nU1aWG09iekae3B0+oaVvOK3q+0zVxzRJI0qENdkicALkufYvwuvK55bY2eFaXouFTd8uFKPXJ9E3m6/fl3THhdX4XXLfr6dbl+3BarFfsS5ebipFcHhJM8AUoBdwoASlxmjlX//HabVu7Pv4i/VsUv4kM7N9DHfxzQin2JOpSYXuSsPvzpZGq23lwYLUma0LvZeQ0IA32qae5DXfT8/B36auMxvfFztOr4euiWKrjCqbKz2oyenbddNiPd0jaY1UYAUA64uzhr8FX1NWXpAf1r/g75e7mrU8iFkxLn7gt/2HpCknTXVfX1cv9WcncpXkK8pre7nunXXE99u12z18UU2OdkkZ7u11wP9gi132saY/SflYf0xs+7VbgKqMUifTPqakU0LF+lwPbGp2rkFxt1+FTGefua1PbWJ8ML9qI6kZSpUbOitO1Ysur7e2j6sI4FVu6cSsvW2P9tsvcneTyyqR6+vnGZrCqsXs1VA9vX1ex1MZq19ki5TKDEJmdq0e54SdI9Z0uPAUBJuqqRv74f110PzYzS9uPJeuXHXQX2WyzSk32aafQ1YX/rWcn3W0/on99slSSNvbYxzxqAUkIJLwAl6ujpDD00M0q7YlPk4mTRpNva6PaIeo4Oy+Hun7FBS6IT9I/uIfrXzS0dHU659ticzZq/5YTC6/powdjucr7AH/vGGL35yx5NW35Ataq7a/GEa8p9s1Jcns9XH9aL3+9U9WouWjzhGtWufmXNigEAJetMeo6GfLLW3lj8pVtbFfkguvB94Yu3ttI9Z/ubXA6bzeidRXu0Jy7Vvi05M1cbDp+RJPVvF6xJg9rIYpGe+W675m0+Lknq2LCG/M7WgT92JlPRcalqHlRdPzzc/bJKj5WmX3fGafzcLUrPsSrIp5rC6/75N+uWo8lKTMuWTzUXfXB3B13TtJY2HD6t0bOilJj2ZzN4D1dnvXNnW93Yuo52HE/WQzOjdDwp076St6xXQO+OTVG/91bIxcmiVU9fr8BirDQqS5MX7dX7i/epc4i/5j7U1dHhAKjEsnKt+mDJvgLXr5TMPK0/nJ/gvqlNnSvqk2K1Gb31a7Q+Xn5QknRN01qaPjyi2JMTAOQrbt6ABAqAErP6QKLGzt6kMxm5qunlpo+GdlDn0PI368wRlkTH6/4ZG+Xr4ap1z/aiDNFZuVabYpOy7N/vPJGs0bM3yWKRFoztpjb1/C76+uw8q/q9u0IHE9M1vGtDvdI/vJQjRlmJT8lSr3eWKy07T6/2b6VhXRs5OiQAwF9k5OTZG4tL0pBO9TXqmjBZlJ8c2X8yVU98vU2n03NK5b7QGKOZa4/olR92Kc9m1CrYR04Wi7YfT5azk0X/uqmF7ru6kT1Zczo9R73eWaYzGbl69kbHl/+02YzeW7xP7y3eJym/l8iUuzuopvefjczjU7I0alaUNsckycmSX8b0h60nlGczalHHR2/d1kZv/hKtlfsTJeUnkn7dGaesXJtCArw0fViEmgRWd8jnu2Paam04fEaPRzbVo5FNHBJDUXKtNnWbtEQJqdn68O72urkNK5gBlC1jjGavi9FL3++8oj4pyRm5enjOZv2x96QkadQ1YXqyT7MLTjwEcGEkUM4igQKUPmOMZqw+rNd+2i2rzah1XV99PCxCwX4ejg6t3LDajK55e6mOncnU0M4N9PKtrap8E9QtR5M0ZlaUTiRnnbfv3q4N9XIxkyGr9yfq7k/XyWKR5o3ppnb1/Uo4UjjC2Nmb9NP2WLWt76fvRl/NHwQAUA4ZYzRt+UG99Wu0LvRXZXhdH00f1rHU7gvXHjylsbM36VR6/oqMGp6umjK0g64OCzjv2K82HtU/v9kmD1dnLRrfU/VqOKbfU2pWrsZ/tVWLduWXkbrv6kZ67qYWRa6Kyc6z6oX5OzV341H7tpvb1NFbZ2cs51ltevOXaH2y4pB9/7XNaum9u9oX2RulrCzYclyPztmiGp6u+mxEp3Jzf/bz9liNmb1JAd7uWv309XJzqdr34wAcZ/2h0xozO39FYQ1PV025u4Oubnz+teuv9san6sEvNurIqQxVc3XSW7e31a2UsgauGAmUs0igAKUrK9eq5+bt0LebjknKbxw5cVBrVlgU4ZuoY3ri6/z6pFeH1dSHd3eQv5ebg6NyjK83HtVz83coJ88mN2cnuTr/+XC8cW1vzfxH58sqx/X43C2at/m4Wtbx0ffjulX55FRFt3RPgkZ8tkFOFumHh7tfsEEwAKB8WLonQS8u2KlTadn2bU4Wi25qU0cv3dqq1O8Ljydl6omvtspqjN65o+0FZ/EaYzR4+lqtP3RakS1q65PhHcu8T9+hxHQ9+MVG7U9Ik5uzk14bGK47O9a/6GuMMZq19og+XXlIQzo10EM9Q8+Le/7m43r39726pW2wHots6vCJB9l5Vg2Yslq7Y1Pk5uKkNwa2LhdlfYdMX6s1B0/p4esba0LvZo4OB0AVdyIp094nxdnJomdvbKH7uzUq8tr015KPdf08NH14BH8nAX8TCZSzSKAApSc2OVOjZkZp67FkOVmkZ29soQe6h1TphvGX8suOWI3/aqsycqyqVyO/6WfL4KrzuynXatPrP+3WjNWHJUmRLQL178FtVf1v9i5JTMtWr3eWKzkzV/+6qYX+0SO0BKKFI2TmWNX73eU6ejpTD3QP0fP0DAIAlKB98am68f0VyrUaTbsnQn3Dy64/yNI9CXrky81KzcpToI+7pt0TofYNyldD+5KUmpWrx+du1e+7L73SpizsT0hV5OQ/5GSRVj51PavlAZQLWblWPfvddn13tn/XoA519cbAPyel2mxG7y7ep/fPlnzsGlpTU4ZW3cmYQEkigXIWCRSgdMScytCgqauVmJYtP09XfTikg7o3ufhyU+TbE5eqkTP/XHb79u1tdUslWHYbm5yp5+btKNAgr7CsXKu9xMajvZro0V5N5FRCMyS/XB+jZ77bLk83Z80d2VWt6zEbpyJ657c9+mDJftXxrabfx18jL/fLa6gIAMClvP1rtKYsPaAgn2qa+1AXNazpVerv+U3UMT35zVYZI3Vo4Kdp90Sodjlrrl4aCvd6KcsHf1OXHdCstUfs36fn5CkpI1e9WwZq+vCOpf7+AFBcxhj9Z+UhvfHzbtlMfjnKc43ls/NsSjy7wnNEt0Z69kbHJaKByoYEylkkUICSZ4zRsP+s18r9iWoWmN/wrEFNx9SQrqiSM3I17stNWrEvv+nn6GvD9ETvitv4bePh0xo1a5P9xu5ivNyc9c6d7Up8xqfNZnTX9LVaf/i03F2cNOm21hrY3vGlIlB8GTl56vzGYqVm5emjoR10Y+s6jg4JAFAJZeVa1e+9FTqUmC5fD1d9eHd79WhSq9TeLzY5U5HvLFd6jlWDO9bXKwNayd2lapW7LevSM9uOJan/lFXn9eaxWKQ5D3ZR59CapfbeAHClVu5L1LgvNykpI7fAdjcXJ70+IFx3XKLkI4DLQwLlLBIoQMk71xTSzcVJvz3WU40CSn/WXmVktRm99Uu0Pv7joKTy0fDzSsxed0Qvfb9TuVaj5kHV9dKtreRxkVrnjWp6ydezdD5jSlauHpuzRUuiEyRJ/+geoqf7NacnSgUxd0OMnvp2uxr4e2rZE9eW2OokAAAKi0vO0kOzorT1aJKcLNLT/ZrrwR7n9xYpCaNnRWnhjjh1aOCnb0ZdXWWvb3vjUzXyi406XMrNj602o/5TVmrH8RT1Cw/SqGvC7Pv8vdwu2CMHAMqDtOw8HUhIK7CtXg0P1fR2d1BEQOVFAuUsEiiQpJw8mz5bdUj7Cl2EOjXy151XkcG/HMmZuer1znIlpmVrwg1N9XCvJo4OqcJbsOW4nvp2m7JybWpY01NXNfK373N3cdLInqFlUloiz2rT+4v36URyVrFfk5iWrWV7TkqSbmpdR2/f0ca+1NhRrDajfy/aqw+X7pcktavvp8a1vS94vJ+Hqx7sGarAKlBGozwzxujmD1Zq54kUPdOvuR76y8MOAABKQ1auVc/P36Gvo45Jkvq3C9akQW3k4Xb+RJAl0fHaEpOkf/QMlc9l9G5bEh2v+2dslLOTRT8+3F0t6lTtv0mTM3L1yJzNWr43//6xd8tA+Vxk8lBYLW890D1Ebi7Fnwzz2apDevmHXapezUWLJ1yj2tW5xwMAAOcjgXIWCRQkpmVrzKxNWn/4dJH7v3qoqzqF+Be5D+d7bt52zV4Xo7BaXvr50R5VrvxAadlxPFkPzYzS8aTM8/ZFtqitT++9qtRj+DbqmCZ8vfWyX2exSE/0bqYx14aVyqzNK/Xz9lg98fVWZeRYL3ls7erumjYsQh0qcSPX8m5zzBkN/Gi13FyctPaZXjRFBACUCWOMvlhzRK/8uEtWm1GrYB9NH95Rdc82GLfajN75bY8+WnZAkhRay0ufDO+osFoXnpxxTkZOnm6Y/IeOJ2XqoZ6heubGFqX6WSoKq83o7V/3aNryA8U6vmPDGvrong7FSoTEJWcpcvJypWXn6bUB4bqnS8O/Gy4AAKikSKCcRQKlatt+LFkjZ25UbHKWqru76IEeIap2trTQuoOntHTPSTWp7a2fHulxWbOaqqpNMWd029TVMkb68sEu6hpG7eCSdDo9Rz9uO2F/4J+Va9W7v++Tk0X645/XqV6N0i03MGDKKm05mqQbWwepTT2/Yr+uU4h/uU08HEpM1++74mW9wKXOGGn+5uPaE58qN2cnvTqglQZf1aCMo4QkTfhqq77ddEyDOtTV5DvbOTocAEAVs+bAKY393yadTs9RTS83TRnaQS3q+OjROZvtq219PVyVnJmr6u4uem9IO13fPPCi55y0MFrTlh9QXT8PLRrf0+GrdMubNQdOaeuxpAvuz8616dOVB5Walacgn2r6eFiE2tb3u+g5x8yO0s/b49S+gZ++rcLl0gAAwKWRQDmLBErVNW/zMT397XZl59kUWstL04d1LFDGJykjR73eWa5T6Tl6sk8zjb2usQOjLX1ZuVZFHTmjTiH+cr2CfhB5Vptu+XCVdsem6LYO9fTOnW1LIUoUdvcna7X6wCmNvS5MT/ZpXmrvs+N4sm7+YKVcnS1a80wvBVSh+qrp2Xma8NVW/bIzTpI0rEtDvXBLyysaJ7gyZ9Jz1HniYuXk2TRvzNVqX04TcgCAyu3YmQw9NDNKO0+kyMXJotrV3XUiOUvuLk5687Y26tY4QGNmR2nD4TOyWKQJNzTV2OsaF7kCd09cqm56f4XybEafDu+oyJYXT7agaAdPpmnkzCjtT0iTm4uT3hjYWrdH1Cvy2KXRCRoxYwPl0gAAQLEUN2/A0yFUOnlWm177cZcen7tV2Xk2Xd+8tuaP7XZeDwQ/Tzf96+b8ZfTvL96nmFMZjgi3TBw7k6FBH63W0E/X6alvtl3ROT5bdVi7Y1Pk5+mq526i/EBZGXa27MDcDUeVk2crtfeZtfaIJKlfeJ0qlTyRJC93F300tIMm3NBUkjRz7RG9v3ifg6OqWr6Oyv/5bhXso3aXmFkKAEBpqVfDU9+Mulq3tg1Wns3oRHKWgn2r6dvRV2tA+7qqVd1ds//RRfd0aSBjpP/7ba/G/m+T0rPzCpxn69Ek3fvf9cqzGfVpFUjy5G8IreWteWOuVmSLQOXk2fTE11v18g87lWcteF+8eHe8HvlysyTpge4hJE8AAECJIYGCSuVMeo7u+2yDPl15SJI07rrG+nR4xws2ehzQrq6uDqup7Dybnl+wQ5VxQdaaA6d064ertCs2RZL03ebjWrU/8bLOcTwpU5MX7ZUkPduvBb0JylBky0AF+rgrMS3HvkKipCVn5mr+luOSpGFdq2adaCcnix7u1USTz66smrb8gPYnpDo4qqrBZjOavS5GUn7CsDz10QEAVD0ebs567652em1AuO7u3EDfP9xd4XV97fvdXJz02oDWmjiotVydLfp5e5xum7raPhnrm6hjuuPjNYpLyVJYLS+90j/cUR+l0qhezVXTh0Xo0V5NJOVP7Br+3/U6nZ4jY4w+XLJP//hio1Kz89Spkb8ei2zi4IgBAEBlQgkv2CWmZSv3LzN5PF1d5OtZdOKhPNodm6KRMzfq6OlMebo56//uaKsbW9e55OsOnExTv3dXKMdq09u3t1H3JgH2fTW93CtsbxRjjD5ffViv/rTb3hCzcW1vLdhyQiEBXlr4aA97P5hLefCLjVq0K15XNaqhuSO7Uku4jL37+169+/s+dWrkr69GdS3x8/935SG98uMuNQ+qroWP9qjSD7CNMfrH5xu1ODpBnUP8NWdklyr9/6M4bDaj+NSsK3591JEzGve/zapezUXrnu1FfXgAQIURdeS0Rs3apJOp2fL1cNV1zWpp/pYTkqTIFoH69+C2qn6BiVy4Mr/siNOEr7YoPcequn4ealGnun7fnSApfyLG8ze3rLB/vwEAgLJV3LwBTymgtOw8Pfn1Vi3cUXB2u8Ui/aN7iJ7q21wu5bwXwM/bYzXhq63KzLWqgb+npg+PUPOg4iXMwmp5a/S1YXpv8T49Wai8Va3q7vpgSHt1Ca1YzdKzcq16fv4OfR11TJLUv12wJg1qo1ybTWsOnNKhxHRNW35Aj0U2veS5ftsZp0W74uXiZNHrA1uTPHGAIZ0a6IMl+7X+8GlFx6UU+2e7OIwx9vJd9zD7XxaLRS/d2kqrDiRq3aHT+nbT8QvW2Ya0Nz5Vo2ZF6eDJ9L99rtsj6pE8AQBUKBEN/fXDuO56aFaUth5NsidPHu3VRI/2asJ9cynoGx6k0FrdNPKLjTp8KkPHkzLl6mzRq/3DdVenBo4ODwAAVELl+6k4St3hxHQNnLLKnjxxc3ayfxkjfbLikEbM2KCkjBwHR1o0q83o7V+jNWb2JmXmWtWjSYC+H9ftsh8wj742TJ0a+Rf4/M5OFp1MzdY9n67TF2sOV5jyXnHJWRo8fa2+jjomJ4v03I0t9O7gdvJwc5ZPNVe9cEtLSdJHSw/o4Mm0i54rPTtPL32/U5I0smeomgZWL/X4cb5An2rq0yq/dva5ZEdJWX3glA4mpsvb3UUD2tct0XNXVPX9Pe3JxTd+3q0z6eXz95+j/bozTgOnrNLBk+lyshS8flzuV10/D93fLcTRHwkAgMsW5FtNc0d20dDODVTXz0PT7onQ4zc0JXlSipoGVteCsd3Vt1WQwmp5ac7IriRPAABAqaGEVxW2bE+CHvlys1Ky8hTo465p90SofYMa9v1/Z1VHWUjJytVjc7ZoSXT+ku0He5TsapnMHKue+nabvt+aP5Pszo719OqAcLm7FK/slSMULiPw4d3t1aNJrQLHGGN032cbtHzvSV0dVlOz/9H5gqsOXvtxlz5deUj1/T3022PXyMOt/H72ym71gUTd/ck6ebk5a91zkfJ2L5mZ+qNnRWnhjjgN79qQGt1/kWu16eb3V2pPfKru7FhPb93e1tEhlRs2m9F7i/fpvcX7JEldQ2tqytAO9EYCAAAAAAAVRnHzBiRQqiBjjKYtP6i3fo2WMVKHBn6adk+EavtUO+/Ywn1F3rmjrfoVo69IadufkKaRX2zUwcR0ubs46c3b2pTK7HljjD5ZcVCTFkbLZqQAb3d5u/+ZRAit5a1XB4Srrp9Hgddl51n11i97tCQ6ocDKlSDfanr51nA1Cyr5lRxfro/RCwt2KNdq1CywuqYPj1DDml5FHhtzKkM3/Hu5svNsqlfDQy4XmCEXczpDNiN9NuIqXdesdonHjOIzxihy8nIdOJmuIJ9qquZadKLQxdlJd11VXw90DzkvMbb1aJJe/XGXEtOy7dvO/Rv/9nhPVhgVEnXktG6bukaS1LCmp87936zm6qzXBoSrYyN/xwXnIKlZuXp87lb9vjtekjSiWyM9e2MLuZbzMo8AAAAAAAB/RQLlLBIo57PajO77bL1W7EvUkE719dKtrS66quJMeo7GfblJq/afkiSNu66xxjtwWfrvu+L12NwtSsvOU7BvNU0f3lHhdX1L9T3/2HtSD3+5WcmZueftq+nlpo+GdlDns31SElKyNGpWlDbFJBV5Lk83Z02+s636hpdMIionz6ZXftypWWtjJEn9woP0f3e0ldclVihMW35AkxZGX/L8t7QN1gdD2pdIrPh7vlwfo2e+216sY29uU0dv3d7G3lPi26hjembeduXk2c47tkeTAM18oHOJxlpZ/Gv+dvvY+quGNT3162M9Vc216qzKOngyTSNnRml/QprcXJz0+oBw3dGxvqPDAgAAAAAAuGwkUM4igVK05IxcLdodr9s61C1W0+g8q02TFkbr05WHJEm9mtfWv+9qJ59qrqUdqp3NZvTh0v2avGivJKlTiL8+GtpBAd7uZfL+yZm52hefav8+O8+m13/arV2xKXJxsuiFW1qqdV1fPTQzSgmp2fKp5qJX+oerXo381SnWs2VvVh/IT0Q9cn1jPRb59xJRJ1OzNXb2Jq0/fFoWizThhqYae13jYv2bGmO080SKsnKtFzzG2cmiVsG+cnNhdnl5YIzR7thUZeTkXfCYLUeTNGlhtPJsRi3q+Gjq0A76Ys0R/XfVn2N31LVh9tUUFotFLev4UJ7tAvKsNu08kaJca37iyWozemTOZsWnZOvh6xtrQu9mDo6wbCyNTtAjczYrNStPQT7VNG1YhNrV93N0WAAAAAAAAFeEBMpZJFBK1rzNx/T0t9uVnWdTSICXupxddVEcbs4W3R5RX63rnb9aJCkjR5+uOKRTF2nWfDgxXWsO5icfhndtqOdvbunwsjGF+6Q4WSSbkZrU9tb04R0VElCwhFae1abXf96tz1YdliRFtqitfw9up+pXkIjadixJD82MUmxylqq7u+jdu9qpV4vAv/2ZUPGtO3hKY2Zv0qn0HPvPpFQySTtIC7fHavTsTXJ1tmjhoz3VuLa3o0MqtjyrTV+sOaJ9CWnFfk1GTp6+33pCxkgRDWto6j0dVLv6+SUfAQAAAAAAKgoSKGeRQCl5248la+TMjYpNzrrs17o5O+m1geG68y9lX6LjUjTyiyjFnM4o1utfHdBKg69qcNnvXVoK90np3TJQkwe3u2iT72+ijunZs+WUwmp5afrwjgqrVfyHsN9tOqZnvstPZIXW8tL0YR0r1ENclL4TSZl6aGaUth9PLvGycVWdMUb3z9igpXtOqnOIv+aM7FKsVV+Odjo9R+P+t8m+Cu5yDenUQC/f2ooVaQAAAAAAoMIjgXIWCZTScSotW/M2H1dmzoXLPxW2KeaMlu45KUm6t2tD/evmllq0K15PfL1VGTlW1ff30B0R9XWhx5BOThZd37y2WtQpn/+Om2PO6OiZTN3cuk6xZvhvPZq/giQuJX8FyXt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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"result.csv\")\n",
    "\n",
    "#add 5ma\n",
    "df['workers_10ma'] = df['workers'].rolling(window=10).mean()\n",
    "df['drones_10ma'] = df['drones'].rolling(window=10).mean()\n",
    "#shift back 10\n",
    "df['workers_10ma'] = df['workers_10ma'].shift(-5)\n",
    "df['drones_10ma'] = df['drones_10ma'].shift(-5)\n",
    "\n",
    "#plot workers and workers 5ma\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(df['workers_10ma'], label='drones')\n",
    "plt.plot(df['drones_10ma'], label='workers')\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('Frame')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Drone and Worker Count')\n",
    "#draw line at average\n",
    "plt.axhline(y=df['workers_10ma'].mean(), color='r', linestyle='-', label='workers average')\n",
    "plt.axhline(y=df['drones_10ma'].mean(), color='b', linestyle='-', label='drones average')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}