{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "945c9b80",
   "metadata": {},
   "source": [
    "# Table of contents\n",
    "1. [Introduction](#introduction)\n",
    "2. [Aggregate Model Evaluation](#modelevaluation)\n",
    "    1. [Loading the dataset](#modeload)\n",
    "    2. [Perform detections](#modeldetect)\n",
    "    3. [Evaluate detections](#modeldetectionseval)\n",
    "    4. [Calculate results and plot them](#modelshowresults)\n",
    "    5. [View dataset in fiftyone](#modelfiftyonesession)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01339680",
   "metadata": {},
   "source": [
    "## Introduction <a name=\"introduction\"></a>\n",
    "\n",
    "This notebook loads the test dataset in YOLOv5 format from disk and evaluates the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff25695e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fiftyone as fo\n",
    "from PIL import Image\n",
    "from detection import detect\n",
    "from detection import detect_yolo_only"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86a5e832",
   "metadata": {},
   "source": [
    "## Aggregate Model Evaluation <a name=\"modelevaluation\"></a>\n",
    "\n",
    "First, load the dataset from the directory containing the images and the labels in YOLOv5 format.\n",
    "\n",
    "### Loading the dataset <a name=\"modeload\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bea1038e",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = \"dataset\"\n",
    "dataset_dir = \"dataset\"\n",
    "\n",
    "# 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",
    "dataset.persistent = True\n",
    "classes = dataset.default_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361eeecd",
   "metadata": {},
   "source": [
    "If the dataset already exists because it had been saved under the same name before, load the dataset from fiftyone's folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d479be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = fo.load_dataset('dataset')\n",
    "classes = dataset.default_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4485dce3",
   "metadata": {},
   "source": [
    "### Perform detections <a name=\"modeldetect\"></a>\n",
    "\n",
    "Now we can call the aggregate model to do detections on the images contained in the dataset. The actual detection happens at line 6 where `detect()` is called. This function currently does inference using the GPU via `onnxruntime-gpu`. All detections are saved to the `predictions` keyword of each sample. A sample is one image with potentially multiple detections.\n",
    "\n",
    "> **_NOTE:_** If the dataset already existed beforehand (you used `load_dataset()`), the detections are likely already saved in the dataset and you can skip the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "63f675ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 100% |█████████████████| 640/640 [6.2m elapsed, 0s remaining, 2.1 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": "markdown",
   "id": "10d94167",
   "metadata": {},
   "source": [
    "### Evaluate detections against ground truth <a name=\"modeldetectionseval\"></a>\n",
    "\n",
    "Having saved the predictions, we can now evaluate them by cross-checking with the ground truth labels. If we specify an `eval_key`, true positives, false positives and false negatives will be saved under that key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "68cfdad2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating detections...\n",
      " 100% |█████████████████| 640/640 [2.1s elapsed, 0s remaining, 305.3 samples/s]      \n",
      "Performing IoU sweep...\n",
      " 100% |█████████████████| 640/640 [2.3s elapsed, 0s remaining, 274.2 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": "markdown",
   "id": "94b9751f",
   "metadata": {},
   "source": [
    "### Calculate results and plot them <a name=\"modelshowresults\"></a>\n",
    "\n",
    "Now we have the performance of the model saved in the `results` variable and can extract various metrics from that. Here we print a simple report of all classes and their precision and recall values as well as the mAP with the metric employed by [COCO](https://cocodataset.org/#detection-eval). Next, a confusion matrix is plotted for each class (in our case only one). Finally, we can show the precision vs. recall curve for a specified threshold value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24df35b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "              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",
      "0.6225848121901868\n"
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       "                          0.71641791, 0.71641791, 0.71641791, 0.71641791, 0.71326165, 0.70877193,\n",
       "                          0.70748299, 0.70627063, 0.70418006, 0.69811321, 0.68484848, 0.68144044,\n",
       "                          0.68144044, 0.68144044, 0.68144044, 0.67938931, 0.67938931, 0.67938931,\n",
       "                          0.67938931, 0.6778043 , 0.6778043 , 0.6778043 , 0.6778043 , 0.66590389,\n",
       "                          0.66444444, 0.66444444, 0.65450644, 0.64876033, 0.64876033, 0.64788732,\n",
       "                          0.63547758, 0.63249516, 0.625     , 0.        , 0.        , 0.        ,\n",
       "                          0.        , 0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "                          0.        , 0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "                          0.        , 0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "                          0.        , 0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "                          0.        , 0.        , 0.        , 0.        , 0.        ])}],\n",
       "    'layout': {'margin': {'b': 0, 'l': 0, 'r': 0, 't': 30},\n",
       "               'shapes': [{'line': {'dash': 'dash'}, 'type': 'line', 'x0': 0, 'x1': 1, 'y0': 1, 'y1': 0}],\n",
       "               'template': '...',\n",
       "               'xaxis': {'constrain': 'domain', 'range': [0, 1], 'title': {'text': 'Recall'}},\n",
       "               'yaxis': {'constrain': 'domain',\n",
       "                         'range': [0, 1],\n",
       "                         'scaleanchor': 'x',\n",
       "                         '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, iou_thresh=0.95)\n",
    "pr_curves.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3871c398",
   "metadata": {},
   "source": [
    "### View dataset in fiftyone <a name=\"modelfiftyonesession\"></a>\n",
    "\n",
    "We can launch a fiftyone session in a new tab to explore the dataset and the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfb39b5d",
   "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": 8,
   "id": "e1d00573",
   "metadata": {},
   "outputs": [],
   "source": [
    "session.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53a67321",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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