{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a619e638",
   "metadata": {},
   "source": [
    "## Question 7 - YOLOv8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb626037",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import torch\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ultralytics import YOLO\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "39beaeb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_NAME = \"data/yolov8.pt\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78f8f8d3",
   "metadata": {},
   "source": [
    "## Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f920de25",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_labels = \"./data/MaskedFace/val/labels\"\n",
    "val_imgs = \"./data/MaskedFace/val/images\"\n",
    "\n",
    "y_true = glob.glob(os.path.join(val_labels,\"*.txt\"))\n",
    "y_true.sort()\n",
    "\n",
    "images = glob.glob(os.path.join(val_imgs,\"*.png\"))\n",
    "images.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78f3faca",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = {\n",
    "    'images': images,  # list of image paths\n",
    "    'y_true': y_true,  # list of label paths\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dace1605",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_obj(txt_file, n_class):\n",
    "    with open(txt_file, 'r') as file:\n",
    "        lines = file.readlines()\n",
    "    # Extracting the class identifiers from each line\n",
    "    class_ids = [int(line.split()[0]) for line in lines]\n",
    "\n",
    "    # Counting the occurrences of each class\n",
    "    class_counts = Counter(class_ids)\n",
    "\n",
    "    # Sorting the dictionary by class id and converting it to a list of counts\n",
    "    sorted_counts = [class_counts[i] if i in class_counts else 0 for i in range(n_class)]\n",
    "    return sorted_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bfc50534",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "85it [00:00, 7354.03it/s]\n"
     ]
    }
   ],
   "source": [
    "gt_counts = []\n",
    "for idx , (img , txt) in enumerate(tqdm(zip(test_dataset['images'], test_dataset['y_true']))):\n",
    "    # get ground truth\n",
    "    obj_count = count_obj(txt, 3)\n",
    "    gt_counts.append(obj_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44602de6",
   "metadata": {},
   "source": [
    "## Load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5ff04e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = YOLO(MODEL_NAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ea8aa59",
   "metadata": {},
   "source": [
    "## Test on the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d15ae87",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def calculate_mape(actual, forecast):\n",
    "    if len(actual) != len(forecast):\n",
    "        raise ValueError(\"The length of actual and forecast arrays must be the same.\")\n",
    "    \n",
    "    n = len(actual)\n",
    "    sum_error = 0\n",
    "    \n",
    "    for a, f in zip(actual, forecast):\n",
    "        sum_error += abs(a - f) / max(a, 1)\n",
    "    \n",
    "    mape_value = (sum_error / n) * 100\n",
    "    return mape_value\n",
    "\n",
    "def count_masks(model, dataset):\n",
    "    n_class = 3\n",
    "    mape_scores = []\n",
    "    all_pred_counts = []\n",
    "    all_obj_counts = []\n",
    "    for idx , (img , txt) in enumerate(tqdm(zip(dataset['images'],dataset['y_true']))):\n",
    "        # get predicted list\n",
    "        preds = model.predict(img)\n",
    "        pred = preds[0]\n",
    "        predict_list = [ box.cls[0].item() for box in pred.boxes]\n",
    "        count = Counter(predict_list)\n",
    "        predict_count = [count[i] if i in count else 0 for i in range(n_class)]\n",
    "        # get ground truth\n",
    "        obj_count = count_obj(txt, n_class)\n",
    "        all_obj_counts.append(obj_count)\n",
    "        all_pred_counts.append(predict_count)\n",
    "\n",
    "    '''\n",
    "    After the model was trained, I just found that I defined the format class in data.yaml is [without_mask, with_mask, mask_weared_incorrect] which is wrong in order. \n",
    "    Therefore, I will swap the true label and predicted label to [with_mask, without_mask, mask_weared_incorrect] in the count_masks function to return the values should respectively indicate the number of faces wearing mask, without mask and incorrectly wearing mask.\n",
    "    The reason why I did not correct the data.yaml and train the model again because of the limitation of time.\n",
    "    '''\n",
    "    all_pred_counts = np.array(all_pred_counts)\n",
    "    all_obj_counts = np.array(all_obj_counts)\n",
    "\n",
    "#     all_pred_counts[:, [0, 1]] = all_pred_counts[:, [1, 0]]\n",
    "#     all_obj_counts[:, [0, 1]] = all_obj_counts[:, [1, 0]]\n",
    "\n",
    "    mape_scores = [calculate_mape(a, p) for a, p in zip(all_obj_counts, all_pred_counts)]\n",
    "\n",
    "    # Convert all_pred_counts to int64 before returning\n",
    "    all_pred_counts = all_pred_counts.astype(np.int64)\n",
    "    \n",
    "    return np.array(all_pred_counts), np.mean(mape_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1428b97d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-023.png: 480x640 1 with_mask, 4.3ms\n",
      "Speed: 1.5ms preprocess, 4.3ms inference, 0.8ms postprocess per image at shape (1, 3, 480, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "1it [00:01,  1.65s/it]"
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     "text": [
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-819.png: 384x640 1 with_mask, 4.4ms\n",
      "Speed: 0.8ms preprocess, 4.4ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-131.png: 448x640 4 with_masks, 2 without_masks, 4.2ms\n",
      "Speed: 0.8ms preprocess, 4.2ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
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      "Speed: 0.8ms preprocess, 4.0ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
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      "Speed: 0.8ms preprocess, 4.0ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
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      "Speed: 0.7ms preprocess, 4.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
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      "Speed: 0.7ms preprocess, 4.3ms inference, 0.7ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
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      "Speed: 0.9ms preprocess, 4.4ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 512)\n",
      "\n",
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      "Speed: 1.1ms preprocess, 4.3ms inference, 0.7ms postprocess per image at shape (1, 3, 608, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-383.png: 640x512 1 with_mask, 4.0ms\n",
      "Speed: 1.0ms preprocess, 4.0ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 512)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "10it [00:01,  7.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-307.png: 384x640 2 with_masks, 1 mask_weared_incorrect, 4.2ms\n",
      "Speed: 0.7ms preprocess, 4.2ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-011.png: 448x640 26 with_masks, 1 mask_weared_incorrect, 4.0ms\n",
      "Speed: 0.9ms preprocess, 4.0ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
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      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-098.png: 448x640 4 with_masks, 1 without_mask, 4.0ms\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Speed: 0.7ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
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      "\n",
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      "Speed: 0.8ms preprocess, 4.3ms inference, 0.7ms postprocess per image at shape (1, 3, 416, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     "text": [
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-450.png: 480x640 4 with_masks, 4.0ms\n",
      "Speed: 0.9ms preprocess, 4.0ms inference, 0.7ms postprocess per image at shape (1, 3, 480, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-095.png: 640x512 1 without_mask, 4.0ms\n",
      "Speed: 0.9ms preprocess, 4.0ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 512)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-269.png: 480x640 2 with_masks, 4.0ms\n",
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      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-555.png: 384x640 4 with_masks, 4.1ms\n",
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      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-139.png: 384x640 17 with_masks, 2 without_masks, 2 mask_weared_incorrects, 3.8ms\n",
      "Speed: 0.9ms preprocess, 3.8ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
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      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-371.png: 384x640 1 with_mask, 1 mask_weared_incorrect, 3.8ms\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "30it [00:01, 27.19it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-019.png: 384x640 3 with_masks, 2 without_masks, 1 mask_weared_incorrect, 4.1ms\n",
      "Speed: 0.8ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-719.png: 384x640 4 with_masks, 1 without_mask, 3.8ms\n",
      "Speed: 0.7ms preprocess, 3.8ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-410.png: 448x640 18 with_masks, 2 without_masks, 1 mask_weared_incorrect, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
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      "Speed: 0.9ms preprocess, 3.7ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-765.png: 640x512 1 with_mask, 4.2ms\n",
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      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-305.png: 480x640 6 with_masks, 9 without_masks, 4 mask_weared_incorrects, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 480, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-528.png: 640x512 1 without_mask, 4.1ms\n",
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      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-209.png: 352x640 4 with_masks, 13 without_masks, 4.1ms\n",
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      "Speed: 1.0ms preprocess, 4.0ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 512)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     "text": [
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-833.png: 384x640 2 with_masks, 4.1ms\n",
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      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-512.png: 448x640 8 with_masks, 4.0ms\n",
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      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-286.png: 480x640 3 with_masks, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 480, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-370.png: 640x512 1 without_mask, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 512)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-123.png: 512x640 3 with_masks, 4.6ms\n",
      "Speed: 1.0ms preprocess, 4.6ms inference, 0.7ms postprocess per image at shape (1, 3, 512, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-829.png: 320x640 6 with_masks, 1 without_mask, 4.3ms\n",
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      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-197.png: 384x640 1 with_mask, 4.2ms\n",
      "Speed: 0.8ms preprocess, 4.2ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-793.png: 640x512 1 with_mask, 4.0ms\n",
      "Speed: 1.0ms preprocess, 4.0ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 512)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-562.png: 352x640 1 with_mask, 1 mask_weared_incorrect, 4.1ms\n",
      "Speed: 0.7ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
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      "Speed: 0.9ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "49it [00:02, 47.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-149.png: 480x640 7 with_masks, 1 without_mask, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 480, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-560.png: 448x640 1 with_mask, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-373.png: 448x640 13 with_masks, 2 without_masks, 3.8ms\n",
      "Speed: 0.9ms preprocess, 3.8ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-248.png: 640x512 1 without_mask, 4.2ms\n",
      "Speed: 0.9ms preprocess, 4.2ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 512)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-292.png: 480x640 1 with_mask, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.6ms postprocess per image at shape (1, 3, 480, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-041.png: 480x640 8 with_masks, 3.9ms\n",
      "Speed: 1.0ms preprocess, 3.9ms inference, 0.7ms postprocess per image at shape (1, 3, 480, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-515.png: 640x512 1 with_mask, 4.1ms\n",
      "Speed: 1.0ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 512)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-148.png: 608x640 2 with_masks, 3 without_masks, 4.2ms\n",
      "Speed: 1.2ms preprocess, 4.2ms inference, 0.7ms postprocess per image at shape (1, 3, 608, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-036.png: 384x640 6 with_masks, 1 without_mask, 4.1ms\n",
      "Speed: 0.7ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "58it [00:02, 55.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-275.png: 640x544 1 with_mask, 4.5ms\n",
      "Speed: 1.1ms preprocess, 4.5ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 544)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-690.png: 416x640 19 with_masks, 1 without_mask, 4.2ms\n",
      "Speed: 0.8ms preprocess, 4.2ms inference, 0.7ms postprocess per image at shape (1, 3, 416, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-581.png: 640x512 1 with_mask, 4.3ms\n",
      "Speed: 0.9ms preprocess, 4.3ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 512)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-607.png: 448x640 2 with_masks, 1 without_mask, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-227.png: 448x640 11 with_masks, 3.8ms\n",
      "Speed: 0.9ms preprocess, 3.8ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-184.png: 352x640 16 with_masks, 4.1ms\n",
      "Speed: 0.7ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-387.png: 384x640 5 with_masks, 1 mask_weared_incorrect, 4.1ms\n",
      "Speed: 0.8ms preprocess, 4.1ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-169.png: 640x512 1 with_mask, 4.1ms\n",
      "Speed: 1.0ms preprocess, 4.1ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 512)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-411.png: 448x640 7 with_masks, 1 without_mask, 4.1ms\n",
      "Speed: 0.8ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "67it [00:02, 62.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-742.png: 640x512 1 with_mask, 4.2ms\n",
      "Speed: 0.9ms preprocess, 4.2ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 512)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-621.png: 384x640 3 with_masks, 2 without_masks, 4.1ms\n",
      "Speed: 0.8ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-280.png: 448x640 14 with_masks, 7 without_masks, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-637.png: 384x640 6 with_masks, 4.1ms\n",
      "Speed: 0.7ms preprocess, 4.1ms inference, 0.9ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-745.png: 640x512 1 with_mask, 4.1ms\n",
      "Speed: 1.0ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 512)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-606.png: 448x640 4 with_masks, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-152.png: 480x640 8 with_masks, 4.1ms\n",
      "Speed: 1.0ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 480, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-296.png: 384x640 27 with_masks, 15 without_masks, 4.1ms\n",
      "Speed: 0.8ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-699.png: 448x640 1 with_mask, 6 without_masks, 4.0ms\n",
      "Speed: 0.9ms preprocess, 4.0ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "76it [00:02, 69.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-094.png: 384x640 5 with_masks, 1 without_mask, 1 mask_weared_incorrect, 4.0ms\n",
      "Speed: 0.8ms preprocess, 4.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-130.png: 640x544 2 with_masks, 2 without_masks, 4.0ms\n",
      "Speed: 1.0ms preprocess, 4.0ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 544)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-086.png: 480x640 3 with_masks, 1 without_mask, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.6ms postprocess per image at shape (1, 3, 480, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-589.png: 640x448 1 with_mask, 1 mask_weared_incorrect, 4.2ms\n",
      "Speed: 0.9ms preprocess, 4.2ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 448)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-058.png: 448x640 13 with_masks, 4.0ms\n",
      "Speed: 0.9ms preprocess, 4.0ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-377.png: 480x640 1 with_mask, 4.1ms\n",
      "Speed: 0.9ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 480, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-260.png: 480x640 50 with_masks, 3.8ms\n",
      "Speed: 0.9ms preprocess, 3.8ms inference, 0.7ms postprocess per image at shape (1, 3, 480, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-594.png: 448x640 8 with_masks, 4.1ms\n",
      "Speed: 0.8ms preprocess, 4.1ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)\n",
      "\n",
      "image 1/1 /home/wuhanstudio/Documents/Marking/Template/data/MaskedFace/val/images/mask-598.png: 640x512 1 with_mask, 3.9ms\n",
      "Speed: 1.0ms preprocess, 3.9ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 512)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "85it [00:02, 32.88it/s]\n"
     ]
    }
   ],
   "source": [
    "predicted_counts, mape_score = count_masks(model, test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67dda1aa",
   "metadata": {},
   "source": [
    "## MAPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e7624ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_mape(prediction, truth):\n",
    "    mape = np.mean( np.abs(truth - prediction) / np.maximum(truth, np.ones_like(truth)) ) * 100\n",
    "    return mape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fbb7aa74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X2d0f9f39\n",
    "# predicted_counts[:, [0, 1]] = predicted_counts[:, [1, 0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "028f3e71",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_counts[:, [1, 2]] = predicted_counts[:, [2, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c9176cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "MAPE = compute_mape(predicted_counts, gt_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "828484ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133.83205417471694\n"
     ]
    }
   ],
   "source": [
    "print(MAPE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b29e3ba9",
   "metadata": {},
   "source": [
    "## Final Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9b170114",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score:  0\n"
     ]
    }
   ],
   "source": [
    "if MAPE <= 10:\n",
    "    print(\"Score: \", 25*1.0)\n",
    "elif MAPE <= 15:\n",
    "    print(\"Score: \", 25*0.875)\n",
    "elif MAPE <= 20:\n",
    "    print(\"Score: \", 25*0.75)\n",
    "elif MAPE <= 25:\n",
    "    print(\"Score: \", 25*0.625)\n",
    "elif MAPE <= 30:\n",
    "    print(\"Score: \", 25*0.5)\n",
    "else:\n",
    "    print(\"Score: \", 0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "258ec405",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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