position centrale des coordonnées pour chaque boîte englobante des objets détectés par YOLOv8 qui ont une confiance supérieure ou égale au seuil défini..ipynb

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   "id": "2b51b916",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\orchi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\orchi\\anaconda3\\lib\\site-packages)\n",
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      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\orchi\\anaconda3\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!pip install ultralytics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81a54c6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\orchi\\anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "C:\\Users\\orchi\\anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "C:\\Users\\orchi\\anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
    }
   ],
   "source": [
    "from ultralytics import YOLO\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f266dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = YOLO('yolov8n.pt')  # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee3fe81a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.0.6  Python-3.9.13 torch-1.13.1 CUDA:0 (NVIDIA GeForce RTX 3080 Laptop GPU, 8192MiB)\n",
      "Fusing layers... \n",
      "YOLOv8n summary: 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
      "image 1/1 C:\\Users\\orchi\\datasets\\roboflow\\test\\images\\IC1-08nov-TGI-Hazel-Jennie-Spring_mp4-0177_jpg.rf.c69514026d011246cdde8076972efe84.jpg: 384x640 11 cows, 51.8ms\n",
      "Speed: 2.0ms pre-process, 51.8ms inference, 40.2ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    }
   ],
   "source": [
    " #Chemin vers l'image de test\n",
    "image_path = \"C:\\\\Users\\\\orchi\\\\datasets\\\\roboflow\\\\test\\\\images\\\\IC1-08nov-TGI-Hazel-Jennie-Spring_mp4-0177_jpg.rf.c69514026d011246cdde8076972efe84.jpg\"\n",
    "\n",
    "# Effectuer l'inférence sur l'image\n",
    "results = model(image_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4439c886",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.0.6  Python-3.9.13 torch-1.13.1 CUDA:0 (NVIDIA GeForce RTX 3080 Laptop GPU, 8192MiB)\n",
      "Fusing layers... \n",
      "YOLOv8n summary: 168 layers, 3151904 parameters, 0 gradients\n",
      "image 1/1 C:\\Users\\orchi\\datasets\\roboflow\\test\\images\\IC1-08nov-TGI-Hazel-Jennie-Spring_mp4-0177_jpg.rf.c69514026d011246cdde8076972efe84.jpg: 384x640 11 cows, 12.6ms\n",
      "Speed: 1.4ms pre-process, 12.6ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    }
   ],
   "source": [
    "# Effectuer l'inférence sur l'image\n",
    "results = model(image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9da6d46",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.0.6  Python-3.9.13 torch-1.13.1 CUDA:0 (NVIDIA GeForce RTX 3080 Laptop GPU, 8192MiB)\n",
      "Fusing layers... \n",
      "YOLOv8n summary: 168 layers, 3151904 parameters, 0 gradients\n",
      "image 1/1 C:\\Users\\orchi\\datasets\\roboflow\\test\\images\\IC1-08nov-TGI-Hazel-Jennie-Spring_mp4-0177_jpg.rf.c69514026d011246cdde8076972efe84.jpg: 384x640 11 cows, 12.9ms\n",
      "Speed: 2.3ms pre-process, 12.9ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Une erreur est survenue : 'NoneType' object has no attribute 'show'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    results = model(image_path)\n",
    "    results.show()\n",
    "except Exception as e:\n",
    "    print(f\"Une erreur est survenue : {e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66f24798",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# position centrale des coordonnées pour chaque boîte englobante des objets détectés par YOLOv8 qui ont une confiance supérieure ou égale au seuil défini."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6157f77c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import torch\n",
    "\n",
    "# Définir un seuil de confiance\n",
    "SEUIL_CONF = 0.6  # Vous pouvez ajuster cette valeur\n",
    "\n",
    "# Charger le modèle YOLOv8 pré-entraîné\n",
    "model = torch.hub.load('ultralytics/yolov8', 'yolov8')  # Assurez-vous d'avoir le bon nom de modèle\n",
    "\n",
    "# Charger une image\n",
    "img = cv2.imread('path/to/your/image.jpg')\n",
    "\n",
    "# Effectuer la détection\n",
    "results = model(img)\n",
    "\n",
    "# Récupérer les prédictions\n",
    "predictions = results.pred[0]\n",
    "\n",
    "# Filtrer les prédictions avec le seuil de confiance et appliquer NMS\n",
    "for *box, conf, cls in predictions:\n",
    "    if conf >= SEUIL_CONF:  # Appliquer le seuil de confiance\n",
    "        # Calculer les coordonnées du centre (x, y)\n",
    "        x_center = (box[0] + box[2]) / 2\n",
    "        y_center = (box[1] + box[3]) / 2\n",
    "        \n",
    "        print(f\"Objet détecté de classe {cls} avec confiance {conf:.2f} au centre ({x_center:.2f}, {y_center:.2f})\")\n"
   ]
  }
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