metrike.ipynb

{
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   "source": [
    "Metrike"
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   "source": [
    "import os\n",
    "import xml.etree.ElementTree as ET\n",
    "from PIL import Image\n",
    "import torch\n",
    "import torchvision.transforms as T\n",
    "import torchvision\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from src.customDataset import *\n",
    "from src.modelFunkcije import *\n",
    "from src.metrike import *\n",
    "from src.utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ucitavanje modela i dataseta"
   ]
  },
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      "c:\\Users\\Andjela\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Andjela\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=FasterRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/notebooks/lk-s-2024-detekcija-fudbalera/finetune modeli/frcnn_custom_final.pth'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m model \u001b[38;5;241m=\u001b[39m get_model(num_classes)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# tezine\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m model\u001b[38;5;241m.\u001b[39mload_state_dict(\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/notebooks/lk-s-2024-detekcija-fudbalera/finetune modeli/frcnn_custom_final.pth\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# Prevaluacioni režim\u001b[39;00m\n\u001b[0;32m      8\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n",
      "File \u001b[1;32mc:\\Users\\Andjela\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\serialization.py:986\u001b[0m, in \u001b[0;36mload\u001b[1;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[0;32m    983\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m pickle_load_args\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m    984\u001b[0m     pickle_load_args[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m--> 986\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43m_open_file_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m opened_file:\n\u001b[0;32m    987\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m _is_zipfile(opened_file):\n\u001b[0;32m    988\u001b[0m         \u001b[38;5;66;03m# The zipfile reader is going to advance the current file position.\u001b[39;00m\n\u001b[0;32m    989\u001b[0m         \u001b[38;5;66;03m# If we want to actually tail call to torch.jit.load, we need to\u001b[39;00m\n\u001b[0;32m    990\u001b[0m         \u001b[38;5;66;03m# reset back to the original position.\u001b[39;00m\n\u001b[0;32m    991\u001b[0m         orig_position \u001b[38;5;241m=\u001b[39m opened_file\u001b[38;5;241m.\u001b[39mtell()\n",
      "File \u001b[1;32mc:\\Users\\Andjela\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\serialization.py:435\u001b[0m, in \u001b[0;36m_open_file_like\u001b[1;34m(name_or_buffer, mode)\u001b[0m\n\u001b[0;32m    433\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_open_file_like\u001b[39m(name_or_buffer, mode):\n\u001b[0;32m    434\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m _is_path(name_or_buffer):\n\u001b[1;32m--> 435\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_open_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    436\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    437\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m mode:\n",
      "File \u001b[1;32mc:\\Users\\Andjela\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\serialization.py:416\u001b[0m, in \u001b[0;36m_open_file.__init__\u001b[1;34m(self, name, mode)\u001b[0m\n\u001b[0;32m    415\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, mode):\n\u001b[1;32m--> 416\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/notebooks/lk-s-2024-detekcija-fudbalera/finetune modeli/frcnn_custom_final.pth'"
     ]
    }
   ],
   "source": [
    "## ucitavanje modela\n",
    "model = get_model(num_classes)\n",
    "\n",
    "# tezine\n",
    "model.load_state_dict(torch.load('/notebooks/lk-s-2024-detekcija-fudbalera/finetune modeli/frcnn_custom_final.pth'))\n",
    "\n",
    "# Prevaluacioni režim\n",
    "model.eval()\n",
    "\n",
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "model.to(device)\n",
    "transform = T.Compose([T.ToTensor()])\n",
    "\n",
    "# instanca CustomVOCDataset i DataLoader\n",
    "dataset = CustomVOCDataset(root='/notebooks/lk-s-2024-detekcija-fudbalera/datasetovi/dataset1.zip_sredjen/test', CLASSES=CLASSES, transforms=transform, return_image_id=True)\n",
    "data_loader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detekcija "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# detekcija frcnn na nasem datasetu\n",
    "predictions = {}\n",
    "\n",
    "for images, targets, image_ids in data_loader:\n",
    "    images = list(img.to(device) for img in images)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        prediction = model(images)\n",
    "\n",
    "    for img, pred, image_id in zip(images, prediction, image_ids):\n",
    "        predictions[image_id] = pred\n",
    "\n",
    "### groung truth - prave anotacije\n",
    "annotations = {}\n",
    "for image_id in dataset.image_ids:\n",
    "    ann_path = os.path.join(dataset.annotation_dir, f\"{image_id}.xml\")\n",
    "    boxes, labels = read_voc_annotations(ann_path, CLASSES)\n",
    "    annotations[image_id] = {\n",
    "        'boxes': boxes,\n",
    "        'labels': labels\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Racunanje rezultata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Računanje TP, FP, FN metrika\n",
    "tp, fp, fn = compare_predictions_with_annotations(predictions, annotations, CLASSES, threshold=thrcompare)\n",
    "\n",
    "# Ispis\n",
    "for cls_name in CLASSES:\n",
    "    print(f'Class: {cls_name}')\n",
    "    print(f'TP: {tp[cls_name]}, FP: {fp[cls_name]}, FN: {fn[cls_name]}')\n",
    "    print('----------------------')\n",
    "\n",
    "image_id = dataset.image_ids[0]\n",
    "img_path = os.path.join(dataset.image_dir, f\"{image_id}.jpg\")\n",
    "ann_path = os.path.join(dataset.annotation_dir, f\"{image_id}.xml\")\n",
    "\n",
    "img, _ = load_image_and_annotations(img_path, ann_path, CLASSES)\n",
    "img_tensor = transform(img).unsqueeze(0).to(device)\n",
    "\n",
    "with torch.no_grad():\n",
    "    prediction = model(img_tensor)\n",
    "\n",
    "plot_image_with_boxes(img_tensor.squeeze(0), prediction[0]['boxes'], prediction[0]['labels'], prediction[0]['scores'], CLASSES)"
   ]
  }
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