{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "419e9476",
   "metadata": {},
   "source": [
    "# 下載課程所需檔案 (YOLOv7, Dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89a8e830",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://github.com/TA-aiacademy/course_3.0/releases/download/CVCNN_Data/dog_cat_for_YOLO.zip\n",
    "!unzip dog_cat_for_YOLO.zip\n",
    "!wget https://github.com/TA-aiacademy/course_3.0/releases/download/CVCNN_Data/yolov7.zip\n",
    "!unzip yolov7.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b363a9b",
   "metadata": {},
   "source": [
    "# YOLOv7 實作\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dce3ccb",
   "metadata": {},
   "source": [
    "## [貓狗公開資料集](https://public.roboflow.com/object-detection/oxford-pets/2/images/fc82071578629d4d44696cb666898d45)\n",
    "![](https://i.imgur.com/VnNscKi.png)\n",
    "這個貓狗公開資料集提供了 3680 張影像，為了訓練快一點，這邊只取了 250 張影像來訓練，檔案放在 datasets/pet.zip 中"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68b2961",
   "metadata": {},
   "source": [
    "## 1. 準備資料集\n",
    "    改變標籤格式，從Pascal_voc->Yolo\n",
    "    \n",
    "![](https://i.imgur.com/eNWUWGQ.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba0d8e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import random\n",
    "import shutil\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "#讀取資料夾的圖片名稱\n",
    "def getImagesInDir(dir_path):\n",
    "    img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng']\n",
    "    image_list = []\n",
    "    for img_format in img_formats:\n",
    "        for filename in glob.glob(dir_path + f'/*.{img_format}'):\n",
    "            image_list.append(filename)\n",
    "\n",
    "    return image_list\n",
    "\n",
    "# 座標轉換\n",
    "def convert(size, box):\n",
    "    dw = 1./(size[0])\n",
    "    dh = 1./(size[1])\n",
    "    x = (box[0] + box[1])/2.0 - 1\n",
    "    y = (box[2] + box[3])/2.0 - 1\n",
    "    w = box[1] - box[0]\n",
    "    h = box[3] - box[2]\n",
    "    x = x*dw\n",
    "    w = w*dw\n",
    "    y = y*dh\n",
    "    h = h*dh\n",
    "    return (x, y, w, h)\n",
    "\n",
    "# 讀取 annotation 檔案內容並轉換\n",
    "def convert_annotation(img_path, ann_dir,\n",
    "                       output_image_path, output_label_path):\n",
    "    basename = os.path.basename(img_path)\n",
    "    basename_no_ext = os.path.splitext(basename)[0]\n",
    "\n",
    "    # copy image\n",
    "    shutil.copy(img_path, os.path.join(output_image_path, basename))\n",
    "\n",
    "    in_file = open(ann_dir + '/' + basename_no_ext + '.xml')\n",
    "    out_file = open(output_label_path + basename_no_ext + '.txt', 'w')\n",
    "    tree = ET.parse(in_file)\n",
    "    root = tree.getroot()\n",
    "    size = root.find('size')\n",
    "    w = int(size.find('width').text)\n",
    "    h = int(size.find('height').text)\n",
    "\n",
    "    for obj in root.iter('object'):\n",
    "        difficult = obj.find('difficult').text\n",
    "        cls = obj.find('name').text\n",
    "        if cls not in classes or int(difficult) == 1:\n",
    "            continue\n",
    "        cls_id = classes.index(cls)\n",
    "        xmlbox = obj.find('bndbox')\n",
    "        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text),\n",
    "             float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))\n",
    "        bb = convert((w, h), b)\n",
    "        out_file.write(str(cls_id) + \" \" + \" \".join(\n",
    "                        [str(a) for a in bb]) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acebedea",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "name = 'pet'  # 資料集名稱\n",
    "classes = ['cat', 'dog']  # 修改自己的類別\n",
    "train_test_split_rate = 0.2\n",
    "\n",
    "img_dir = 'datasets/JPEGImages/'  # 照片存放路徑\n",
    "ann_dir = 'datasets/Annotations/'  # 標籤存放路徑\n",
    "image_paths = getImagesInDir(img_dir)\n",
    "random.seed(2022)\n",
    "random.shuffle(image_paths)\n",
    "\n",
    "train_image_path = f'datasets/{name}/train/images/'\n",
    "train_label_path = f'datasets/{name}/train/labels/'\n",
    "valid_image_path = f'datasets/{name}/valid/images/'\n",
    "valid_label_path = f'datasets/{name}/valid/labels/'\n",
    "\n",
    "if not os.path.exists(train_image_path):\n",
    "    os.makedirs(train_image_path)\n",
    "if not os.path.exists(train_label_path):\n",
    "    os.makedirs(train_label_path)\n",
    "if not os.path.exists(valid_image_path):\n",
    "    os.makedirs(valid_image_path)\n",
    "if not os.path.exists(valid_label_path):\n",
    "    os.makedirs(valid_label_path)\n",
    "\n",
    "train_test_split = len(image_paths)*train_test_split_rate\n",
    "\n",
    "for i, img_path in enumerate(image_paths):\n",
    "    if i >= train_test_split:\n",
    "        # train\n",
    "        convert_annotation(img_path, ann_dir,\n",
    "                           train_image_path, train_label_path)\n",
    "    else:\n",
    "        # valid\n",
    "        convert_annotation(img_path, ann_dir,\n",
    "                           valid_image_path, valid_label_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd2d1f3",
   "metadata": {},
   "source": [
    "## 2. 更改設定檔案\n",
    "- 修改 cfg/training/yolov7.yaml\n",
    "- 修改 data/coco.yaml 製作一個自己資料集的 yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3beea9c9",
   "metadata": {},
   "source": [
    "將yolov7.yaml 設定檔複製一份\n",
    " \n",
    "!cp 要複製的檔案 新檔案名稱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1da4d0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "!cp cfg/training/yolov7.yaml cfg/training/yolov7-pet.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eecaf7e9",
   "metadata": {},
   "source": [
    "將class的地方改成自己的class數量\n",
    "\n",
    "!sed -n -e (顯示) 第幾行 檔案名稱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d0c055f",
   "metadata": {},
   "outputs": [],
   "source": [
    "!sed -n -e 2p cfg/training/yolov7-pet.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a4fc1a",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "!sed -i (修改) 第幾行/欲修改的字/目標字/ 檔案名稱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "034cd458",
   "metadata": {},
   "outputs": [],
   "source": [
    "!sed -i '2s/80/2/' cfg/training/yolov7-pet.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dadf99a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "!sed -n -e 2p cfg/training/yolov7-pet.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939075e6",
   "metadata": {},
   "source": [
    "![](https://i.imgur.com/ZmNHP9h.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e1a0f59",
   "metadata": {},
   "source": [
    "參考data/coco.yaml 製作一個自己資料集的yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61c7bdde",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \\\n",
    "    \"\"\"\n",
    "    train: ./datasets/pet/train # 訓練資料夾位置\n",
    "    val: ./datasets/pet/valid # 驗證資料夾位置\n",
    "\n",
    "    # number of classes\n",
    "    nc: 2 # <-需修改乘自己的類別數量\n",
    "\n",
    "    # class names\n",
    "    names: [ 'cat','dog' ]\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "532bbd90",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f'data/{name}.yaml', 'w') as file:\n",
    "    file.write(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d23554f",
   "metadata": {},
   "source": [
    "![](https://i.imgur.com/DKR925y.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8782513",
   "metadata": {},
   "source": [
    "下載預訓練權重檔案\n",
    "https://github.com/WongKinYiu/yolov7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f97dea5",
   "metadata": {},
   "source": [
    "![](https://i.imgur.com/5jsscuE.png)放置於weights/資料夾底下"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4854c4d3",
   "metadata": {},
   "source": [
    "執行訓練，訓練參數介紹：\n",
    "- --weights : 預先訓練的權重路徑(weights/yolov7_training.pt)\n",
    "- --cfg：模型設定檔案路徑(cfg/training/yolov7-pet.yaml)\n",
    "- --data：資料集設定檔案路徑(data/pet.yaml)\n",
    "- --device：GPU設定\n",
    "- --batch-size：一次訓練照片張數\n",
    "- --epoch： 訓練圈數\n",
    "\n",
    "其他可調控參數可置train.py中察看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcb164a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "!python train.py --weights weights/yolov7_training.pt --cfg cfg/training/yolov7-pet.yaml --data data/pet.yaml --device 0 --batch-size 16 --epoch 50\n"
   ]
  }
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