final_slot_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hp\\Desktop\\car_parking_slot_occupancy_detection_using_yolov8\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "HOME = os.getcwd()\n",
    "print(HOME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "camera = cv2.VideoCapture(\"parking1.mp4\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "from IPython.display import display, Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=YOLO(r'30epoch_model\\best.pt')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Loading parking coordinates ...\n",
      "[array([[ 53, 438],\n",
      "       [133, 441],\n",
      "       [105, 571],\n",
      "       [ 23, 568]]), array([[109, 443],\n",
      "       [205, 436],\n",
      "       [187, 570],\n",
      "       [ 89, 567]]), array([[181, 429],\n",
      "       [287, 428],\n",
      "       [277, 561],\n",
      "       [167, 569]]), array([[246, 436],\n",
      "       [350, 426],\n",
      "       [365, 557],\n",
      "       [259, 560]]), array([[331, 424],\n",
      "       [424, 419],\n",
      "       [455, 550],\n",
      "       [356, 564]]), array([[406, 420],\n",
      "       [497, 410],\n",
      "       [545, 534],\n",
      "       [446, 548]]), array([[475, 418],\n",
      "       [579, 408],\n",
      "       [640, 524],\n",
      "       [530, 545]]), array([[540, 405],\n",
      "       [646, 395],\n",
      "       [723, 518],\n",
      "       [612, 533]]), array([[605, 394],\n",
      "       [715, 388],\n",
      "       [801, 502],\n",
      "       [691, 522]]), array([[674, 398],\n",
      "       [776, 388],\n",
      "       [877, 487],\n",
      "       [767, 511]]), array([[737, 385],\n",
      "       [831, 374],\n",
      "       [931, 492],\n",
      "       [859, 507]]), array([[ 807,  388],\n",
      "       [ 895,  372],\n",
      "       [1011,  466],\n",
      "       [ 921,  488]])]\n"
     ]
    }
   ],
   "source": [
    "file = open(\"slot_coordinates.txt\")\n",
    "\n",
    "print(\"[INFO] Loading parking coordinates ...\")\n",
    "\n",
    "lines = file.readlines()\n",
    "\n",
    "lines = [line.strip() for line in lines]\n",
    "\n",
    "total_parking_lots = len(lines)\n",
    "\n",
    "parking_lot_coords = list()\n",
    "\n",
    "for i in range(len(lines)):\n",
    "    \n",
    "    coords = lines[i].split()\n",
    "    top_left = (int(coords[0]), int(coords[1]))\n",
    "    top_right = (int(coords[2]), int(coords[3]))\n",
    "    bottom_right = (int(coords[4]), int(coords[5]))\n",
    "    bottom_left = (int(coords[6]), int(coords[7]))\n",
    "    coord = np.array([top_left,top_right, bottom_right,bottom_left])\n",
    "    parking_lot_coords.append(coord)\n",
    "print(parking_lot_coords)"
   ]
  },
  {
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   "execution_count": 7,
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      "0: 320x640 6 cars, 3 frees, 347.8ms\n",
      "Speed: 1.0ms preprocess, 347.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 5 cars, 4 frees, 320.4ms\n",
      "Speed: 2.0ms preprocess, 320.4ms inference, 0.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 5 cars, 4 frees, 323.5ms\n",
      "Speed: 2.4ms preprocess, 323.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 5 cars, 4 frees, 323.4ms\n",
      "Speed: 2.0ms preprocess, 323.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 4 cars, 4 frees, 323.8ms\n",
      "Speed: 2.0ms preprocess, 323.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 4 cars, 4 frees, 333.8ms\n",
      "Speed: 2.0ms preprocess, 333.8ms inference, 0.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 6 frees, 338.1ms\n",
      "Speed: 3.0ms preprocess, 338.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 5 frees, 379.7ms\n",
      "Speed: 3.2ms preprocess, 379.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 5 frees, 368.6ms\n",
      "Speed: 4.0ms preprocess, 368.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 7 frees, 391.6ms\n",
      "Speed: 3.6ms preprocess, 391.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 7 frees, 443.0ms\n",
      "Speed: 4.0ms preprocess, 443.0ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 6 frees, 366.2ms\n",
      "Speed: 4.0ms preprocess, 366.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 6 frees, 374.3ms\n",
      "Speed: 4.1ms preprocess, 374.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 1 car, 6 frees, 369.9ms\n",
      "Speed: 3.0ms preprocess, 369.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 1 car, 6 frees, 394.1ms\n",
      "Speed: 2.0ms preprocess, 394.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 1 car, 6 frees, 368.6ms\n",
      "Speed: 2.0ms preprocess, 368.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 1 car, 8 frees, 377.6ms\n",
      "Speed: 2.0ms preprocess, 377.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 1 car, 8 frees, 391.5ms\n",
      "Speed: 4.3ms preprocess, 391.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 10 frees, 389.9ms\n",
      "Speed: 3.2ms preprocess, 389.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 10 frees, 399.4ms\n",
      "Speed: 3.0ms preprocess, 399.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 9 frees, 373.3ms\n",
      "Speed: 2.2ms preprocess, 373.3ms inference, 0.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 4 frees, 389.8ms\n",
      "Speed: 3.5ms preprocess, 389.8ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 4 frees, 449.9ms\n",
      "Speed: 2.0ms preprocess, 449.9ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 4 frees, 339.3ms\n",
      "Speed: 3.0ms preprocess, 339.3ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 4 frees, 395.8ms\n",
      "Speed: 3.0ms preprocess, 395.8ms inference, 0.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 6 frees, 366.1ms\n",
      "Speed: 2.1ms preprocess, 366.1ms inference, 0.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 6 frees, 372.0ms\n",
      "Speed: 3.4ms preprocess, 372.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 6 frees, 362.1ms\n",
      "Speed: 3.3ms preprocess, 362.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 6 frees, 403.5ms\n",
      "Speed: 4.0ms preprocess, 403.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0: 320x640 6 frees, 405.8ms\n",
      "Speed: 2.4ms preprocess, 405.8ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "[INFO] Camera terminated.\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    available_slot=len(parking_lot_coords)\n",
    "    \n",
    "    ret, frame = camera.read()\n",
    "\n",
    "    if not ret:\n",
    "        \n",
    "        print(\"[ERROR] Failed to initialize camera.\")\n",
    "        cv2.destroyAllWindows()\n",
    "        break\n",
    "\n",
    "    if ret:\n",
    "        frame=cv2.resize(frame,(1300,650))\n",
    "        for i in range(len(parking_lot_coords)):\n",
    "\n",
    "            cv2.polylines(frame,[np.array(parking_lot_coords[i],np.int32)],True,(0,255,0),2)\n",
    "           \n",
    "            \n",
    "        results = model.predict(frame)\n",
    "        result=results[0]\n",
    "        for box in result.boxes:\n",
    "            class_id = box.cls[0].item()\n",
    "            if class_id==0.0:\n",
    "                cord = box.xyxy[0].tolist()\n",
    "                cord = [round(x) for x in cord]\n",
    "                cx=int(cord[0]+cord[2])//2\n",
    "                cy=int(cord[1]+cord[3])//2\n",
    "                point=(cx,cy)\n",
    "                \n",
    "                for arr in parking_lot_coords:\n",
    "                    if cv2.pointPolygonTest(np.array(arr,np.int32),((cx,cy)),False)>=0:\n",
    "                        available_slot-=1   \n",
    "                        \n",
    "                        cv2.polylines(frame,[np.array(arr,np.int32)],True,(0,0,255),2)\n",
    "                        \n",
    "        print(available_slot)\n",
    "        cv2.putText(frame,\"Available Slots:\"+str(available_slot),(50,25),cv2.FONT_HERSHEY_COMPLEX,1,(0,0,255),2)   \n",
    "\n",
    "\n",
    "        cv2.imshow(\"Camera\", frame)\n",
    "\n",
    "        key=cv2.waitKey(1)\n",
    "\n",
    "        if key%256 == 27:\n",
    "            \n",
    "            # ESC\n",
    "            \n",
    "            print(\"[INFO] Camera terminated.\")\n",
    "            cv2.destroyAllWindows()\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "# Open the input video file\n",
    "input_file = 'output_video.mp4'\n",
    "cap = cv2.VideoCapture(input_file)\n",
    "\n",
    "# Get the original video's properties\n",
    "fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "\n",
    "# Define the output video file\n",
    "output_file = 'final_output_video.mp4'\n",
    "output_fps = 10.0\n",
    "\n",
    "# Create a VideoWriter object to save the output video\n",
    "fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec\n",
    "out = cv2.VideoWriter(output_file, fourcc, output_fps, (width, height))\n",
    "\n",
    "# Variables for frame duplication\n",
    "frame_count = 0\n",
    "frame_duplicate = int(fps / output_fps)\n",
    "\n",
    "# Read and write frames until the end of the video\n",
    "while True:\n",
    "    ret, frame = cap.read()\n",
    "\n",
    "    if not ret:\n",
    "        break\n",
    "\n",
    "    frame_count += 1\n",
    "\n",
    "    # Duplicate frames\n",
    "    for _ in range(frame_duplicate):\n",
    "        out.write(frame)\n",
    "\n",
    "# Release the VideoCapture and VideoWriter objects\n",
    "cap.release()\n",
    "out.release()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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