{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3fe8177c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fiftyone as fo\n",
    "from PIL import Image\n",
    "from detection import detect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32f0f8ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = \"dataset\"\n",
    "dataset_dir = \"dataset\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6343aa55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 100% |█████████████████| 640/640 [716.7ms elapsed, 0s remaining, 894.6 samples/s]      \n"
     ]
    }
   ],
   "source": [
    "# The splits to load\n",
    "splits = [\"val\"]\n",
    "\n",
    "# Load the dataset, using tags to mark the samples in each split\n",
    "dataset = fo.Dataset(name)\n",
    "for split in splits:\n",
    "    dataset.add_dir(\n",
    "        dataset_dir=dataset_dir,\n",
    "        dataset_type=fo.types.YOLOv5Dataset,\n",
    "        split=split,\n",
    "        tags=split,\n",
    "    )\n",
    "\n",
    "classes = dataset.default_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "29827e3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 100% |█████████████████| 640/640 [8.8m elapsed, 0s remaining, 1.5 samples/s]      \n"
     ]
    }
   ],
   "source": [
    "# Do detections with model and save bounding boxes\n",
    "with fo.ProgressBar() as pb:\n",
    "    for sample in pb(dataset.view()):\n",
    "        image = Image.open(sample.filepath)\n",
    "        w, h = image.size\n",
    "        pred = detect(sample.filepath, '../weights/yolo.onnx', '../weights/resnet.onnx')\n",
    "\n",
    "        detections = []\n",
    "        for _, row in pred.iterrows():\n",
    "            xmin, xmax = int(row['xmin']), int(row['xmax'])\n",
    "            ymin, ymax = int(row['ymin']), int(row['ymax'])\n",
    "            rel_box = [\n",
    "                xmin / w, ymin / h, (xmax - xmin) / w, (ymax - ymin) / h\n",
    "            ]\n",
    "            detections.append(\n",
    "                fo.Detection(label=classes[int(row['cls'])],\n",
    "                             bounding_box=rel_box,\n",
    "                             confidence=int(row['cls_conf'])))\n",
    "\n",
    "        sample[\"predictions\"] = fo.Detections(detections=detections)\n",
    "        sample.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "06e1b4c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.persistent = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ad67806",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating detections...\n",
      " 100% |█████████████████| 640/640 [2.0s elapsed, 0s remaining, 319.5 samples/s]         \n",
      "Performing IoU sweep...\n",
      " 100% |█████████████████| 640/640 [2.1s elapsed, 0s remaining, 297.3 samples/s]      \n"
     ]
    }
   ],
   "source": [
    "results = dataset.view().evaluate_detections(\n",
    "    \"predictions\",\n",
    "    gt_field=\"ground_truth\",\n",
    "    eval_key=\"eval\",\n",
    "    compute_mAP=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b180420b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Healthy       0.82      0.74      0.78       662\n",
      "    Stressed       0.71      0.78      0.74       488\n",
      "\n",
      "   micro avg       0.77      0.76      0.76      1150\n",
      "   macro avg       0.77      0.76      0.76      1150\n",
      "weighted avg       0.77      0.76      0.77      1150\n",
      "\n",
      "0.6225828327927432\n"
     ]
    },
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       "                         'scaleratio': 1,\n",
       "                         'title': {'text': 'Precision'}}}\n",
       "})"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print a classification report for all classes\n",
    "results.print_report()\n",
    "\n",
    "print(results.mAP())\n",
    "\n",
    "# Plot confusion matrix\n",
    "matrix = results.plot_confusion_matrix(classes=classes)\n",
    "matrix.show()\n",
    "\n",
    "pr_curves = results.plot_pr_curves(classes=classes)\n",
    "pr_curves.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d1137788",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session launched. Run `session.show()` to open the App in a cell output.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "window.open('http://localhost:5151/');"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "session = fo.launch_app(dataset, auto=False)\n",
    "session.view = dataset.view()\n",
    "session.plots.attach(matrix)\n",
    "session.open_tab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6eed9a86",
   "metadata": {},
   "outputs": [],
   "source": [
    "def export_dataset(dataset, export_dir):\n",
    "    label_field = \"ground_truth\"\n",
    "\n",
    "    # The splits to export\n",
    "    splits = [\"val\"]\n",
    "\n",
    "    classes = [\"Healthy\", \"Stressed\"]\n",
    "\n",
    "    # Export the splits\n",
    "    for split in splits:\n",
    "        split_view = dataset.match_tags(split)\n",
    "        split_view.export(\n",
    "            export_dir=export_dir,\n",
    "            dataset_type=fo.types.YOLOv5Dataset,\n",
    "            label_field=label_field,\n",
    "            split=split,\n",
    "            classes=classes,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19c5b271",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebdde519",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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