statis_gts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基本信息\n",
    "在训练集或者验证集中标注了目标信息，可以计算\n",
    "\n",
    "1、目标的类别\n",
    "\n",
    "2、目标的尺寸位置(y1,x1,y2,x2)\n",
    "\n",
    "3、目标的面积\n",
    "\n",
    "4、宽高比\n",
    "\n",
    "5、目标的多边形面积\n",
    "\n",
    "6、面积比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\ps\\anaconda3\\envs\\mask_rcnn\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "c:\\users\\ps\\anaconda3\\envs\\mask_rcnn\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "c:\\users\\ps\\anaconda3\\envs\\mask_rcnn\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "c:\\users\\ps\\anaconda3\\envs\\mask_rcnn\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "c:\\users\\ps\\anaconda3\\envs\\mask_rcnn\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "c:\\users\\ps\\anaconda3\\envs\\mask_rcnn\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import Mask RCNN\n",
    "# sys.path.append(ROOT_DIR)  # To find local version of the library\n",
    "import os, json\n",
    "import numpy as np\n",
    "import skimage.draw\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from mrcnn.config import Config\n",
    "from mrcnn import model as modellib, utils\n",
    "from mrcnn import visualize\n",
    "\n",
    "# Path to data set\n",
    "DATASET = \"D:\\Projects\\Mask_RCNN\\my_mask_rcnn\\dataset_2021_1113\\\\\"\n",
    "\n",
    "class MyConfig(Config):\n",
    "    \"\"\"Configuration for training on the toy  dataset.\n",
    "    Derives from the base Config class and overrides some values.\n",
    "    \"\"\"\n",
    "    # Give the configuration a recognizable name\n",
    "    NAME = \"mask\"\n",
    "\n",
    "    # We use a GPU with 12GB memory, which can fit two images.\n",
    "    # Adjust down if you use a smaller GPU.\n",
    "    IMAGES_PER_GPU = 1\n",
    "\n",
    "    # Number of classes (including background)\n",
    "    NUM_CLASSES = 1 + 3  # Background + my\n",
    "\n",
    "    # Number of training steps per epoch\n",
    "    STEPS_PER_EPOCH = 100\n",
    "\n",
    "    # Skip detections with < 90% confidence\n",
    "    DETECTION_MIN_CONFIDENCE = 0.9\n",
    "    BACKBONE = \"resnet50\"\n",
    "    IMAGE_RESIZE_MODE = \"square\"\n",
    "    IMAGE_MIN_DIM = 800  \n",
    "    IMAGE_MAX_DIM = 1024\n",
    "    IMAGE_MIN_SCALE = 0\n",
    "\n",
    "    # BACKBONE = \"resnet50\"\n",
    "    # BACKBONE_STRIDES = [4, 8, 16, 32, 64]\n",
    "   # # BACKBONE_STRIDES = [2, 4, 8, 16, 32]\n",
    "    # RPN_ANCHOR_SCALES = (10, 32, 64, 128, 256)\n",
    "    # RPN_ANCHOR_STRIDE = 2\n",
    "    # RPN_NMS_THRESHOLD = 0.9\n",
    "    # RPN_TRAIN_ANCHORS_PER_IMAGE = 512\n",
    "    # TRAIN_ROIS_PER_IMAGE = 512\n",
    "\n",
    "############################################################\n",
    "#  Dataset\n",
    "############################################################\n",
    "\n",
    "class MyDataset(utils.Dataset):\n",
    "\n",
    "    def print_size(self, poly):\n",
    "        for p in poly:\n",
    "            a = np.array(p['all_points_y'])\n",
    "            height = a.max() - a.min()\n",
    "            a = np.array(p['all_points_x'])\n",
    "            width = a.max() - a.min()\n",
    "            self.areas.append(height * width)\n",
    "            #if height * width < 4096:\n",
    "            #    print(width, height)\n",
    "\n",
    "    def load_my(self, dataset_dir, subset, class_dict):\n",
    "        \"\"\"Load a subset of the My dataset.\n",
    "        dataset_dir: Root directory of the dataset.\n",
    "        subset: Subset to load: train or val\n",
    "        \"\"\"\n",
    "        self.areas = []\n",
    "        # Add classes. We have only one class to add.\n",
    "        for (k, v) in class_dict.items():\n",
    "            self.add_class(\"my\", v, k)\n",
    "\n",
    "        # Train or validation dataset?\n",
    "        assert subset in [\"train\", \"val\"]\n",
    "        dataset_dir = os.path.join(dataset_dir, subset)\n",
    "\n",
    "        # Load annotations\n",
    "        # VGG Image Annotator (up to version 1.6) saves each image in the form:\n",
    "        # { 'filename': '28503151_5b5b7ec140_b.jpg',\n",
    "        #   'regions': {\n",
    "        #       '0': {\n",
    "        #           'region_attributes': {},\n",
    "        #           'shape_attributes': {\n",
    "        #               'all_points_x': [...],\n",
    "        #               'all_points_y': [...],\n",
    "        #               'name': 'polygon'}},\n",
    "        #       ... more regions ...\n",
    "        #   },\n",
    "        #   'size': 100202\n",
    "        # }\n",
    "        # We mostly care about the x and y coordinates of each region\n",
    "        # Note: In VIA 2.0, regions was changed from a dict to a list.\n",
    "        annotations = json.load(open(os.path.join(dataset_dir, \"via_region_data.json\")))\n",
    "        annotations = list(annotations.values())  # don't need the dict keys\n",
    "\n",
    "        # The VIA tool saves images in the JSON even if they don't have any\n",
    "        # annotations. Skip unannotated images.\n",
    "        annotations = [a for a in annotations if a['regions']]\n",
    "\n",
    "        # Add images\n",
    "        for a in annotations:\n",
    "            # Get the x, y coordinaets of points of the polygons that make up\n",
    "            # the outline of each object instance. These are stores in the\n",
    "            # shape_attributes (see json format above)\n",
    "            # The if condition is needed to support VIA versions 1.x and 2.x.\n",
    "            # print(a['regions'])\n",
    "            # print(a['filename'])\n",
    "            if type(a['regions']) is dict:\n",
    "                polygons = [r['shape_attributes'] for r in a['regions'].values()]\n",
    "                class_ids = [class_dict[r['region_attributes']['type']] for r in a['regions'].values()]\n",
    "            else:\n",
    "                polygons = [r['shape_attributes'] for r in a['regions']]\n",
    "                class_ids = [class_dict[r['region_attributes']['type']] for r in a['regions']]\n",
    "            self.print_size(polygons)\n",
    "                    # print(class_ids)\n",
    "                        \n",
    "\n",
    "            # load_mask() needs the image size to convert polygons to masks.\n",
    "            # Unfortunately, VIA doesn't include it in JSON, so we must read\n",
    "            # the image. This is only managable since the dataset is tiny.\n",
    "            image_path = os.path.join(dataset_dir, a['filename'])\n",
    "            image = skimage.io.imread(image_path)\n",
    "            height, width = image.shape[:2]\n",
    "\n",
    "            self.add_image(\n",
    "                \"my\",\n",
    "                image_id=a['filename'],  # use file name as a unique image id\n",
    "                path=image_path,\n",
    "                width=width, height=height,\n",
    "                polygons=polygons,\n",
    "                class_ids=class_ids)\n",
    "        self.areas.sort()\n",
    "        print(np.unique(np.round(np.sqrt(self.areas))))\n",
    "\n",
    "    def load_mask(self, image_id):\n",
    "        \"\"\"Generate instance masks for an image.\n",
    "       Returns:\n",
    "        masks: A bool array of shape [height, width, instance count] with\n",
    "            one mask per instance.\n",
    "        class_ids: a 1D array of class IDs of the instance masks.\n",
    "        \"\"\"\n",
    "        # If not a my dataset image, delegate to parent class.\n",
    "        image_info = self.image_info[image_id]\n",
    "        if image_info[\"source\"] != \"my\":\n",
    "            return super(self.__class__, self).load_mask(image_id)\n",
    "\n",
    "        # Convert polygons to a bitmap mask of shape\n",
    "        # [height, width, instance_count]\n",
    "        info = self.image_info[image_id]\n",
    "        mask = np.zeros([info[\"height\"], info[\"width\"], len(info[\"polygons\"])],\n",
    "                        dtype=np.uint8)\n",
    "        for i, p in enumerate(info[\"polygons\"]):\n",
    "            # Get indexes of pixels inside the polygon and set them to 1\n",
    "            rr, cc = skimage.draw.polygon(p['all_points_y'], p['all_points_x'])\n",
    "            mask[rr, cc, i] = 1\n",
    "        \n",
    "        class_ids = np.array(info['class_ids'])\n",
    "        # Return mask, and array of class IDs of each instance. Since we have\n",
    "        # one class ID only, we return an array of 1s\n",
    "        return mask.astype(np.bool), class_ids.astype(np.int32)\n",
    "\n",
    "    def image_reference(self, image_id):\n",
    "        \"\"\"Return the path of the image.\"\"\"\n",
    "        info = self.image_info[image_id]\n",
    "        if info[\"source\"] == \"my\":\n",
    "            return info[\"path\"]\n",
    "        else:\n",
    "            super(self.__class__, self).image_reference(image_id)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标注目标的正方形尺寸\n",
    "\n",
    "上面已知标注的目标尺寸与宽高比，可以计算目标的正方形尺寸\n",
    "\n",
    "$$Size = \\lceil \\frac {width}{ \\sqrt {wh\\_ratio}} \\rceil$$\n",
    "\n",
    "尺寸$Size$向上取整。\n",
    "\n",
    "宽高比$wh\\_ratio$设定为小数点后一位，进位后舍弃后面的小数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def toSquareBox(bbox):\n",
    "    \"\"\"bbox:[y1, x1, y2, x2]\n",
    "    将它按照宽高比转换为正方形\n",
    "    并调整左上和右下的坐标\n",
    "    \n",
    "    正方形的坐标 [y1, x1, y2, x2]\n",
    "    \"\"\"\n",
    "    box_height = bbox[2] - bbox[0]\n",
    "    box_width = bbox[3] - bbox[1]\n",
    "    wh_ratio = box_width / box_height\n",
    "    box_size = box_width / math.sqrt(wh_ratio)\n",
    "    y1 = int(bbox[0] + box_height / 2 - box_size / 2)\n",
    "    y2 = int(y1 + box_size)\n",
    "    x1 = int(bbox[1] + box_width / 2 - box_size / 2)\n",
    "    x2 = int(x1 + box_size)\n",
    "    \n",
    "    return wh_ratio, box_size, box_height * box_width, [y1, x1, y2, x2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 40.  41.  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.\n",
      "  54.  55.  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.\n",
      "  68.  69.  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.\n",
      "  82.  83.  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.\n",
      "  96.  97.  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109.\n",
      " 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123.\n",
      " 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137.\n",
      " 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151.\n",
      " 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 164. 165. 166.\n",
      " 167. 168. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.\n",
      " 184. 185. 186. 191. 192. 200.]\n"
     ]
    }
   ],
   "source": [
    "target = {\n",
    "    'file' : '',\n",
    "    'gts' : [],\n",
    "    'objs' : []\n",
    "}\n",
    "obj = {\n",
    "    'class_id' : 1,\n",
    "    'bbox' : [],\n",
    "    'bbox_area' : 123,\n",
    "    'wh_ratio' : 0.34,\n",
    "    'mask_area' : 123,\n",
    "    'mask_ratio' : 0.1,\n",
    "    'score' : 1\n",
    "}\n",
    "config = MyConfig()\n",
    "class_dict = {}\n",
    "label_file = open(DATASET+'label.txt')\n",
    "label_lines = label_file.readlines()\n",
    "label_id = 1\n",
    "for label_line in label_lines:\n",
    "    label_line = label_line.replace('\\n', '')\n",
    "    class_dict[label_line] = label_id\n",
    "    label_id = label_id + 1\n",
    "# Validation dataset\n",
    "dataset_val = MyDataset()\n",
    "dataset_val.load_my(DATASET, \"train\", class_dict)\n",
    "dataset_val.prepare()\n",
    "gt_targets = []\n",
    "for image_id in dataset_val.image_ids:\n",
    "    image, image_meta, gt_class_id, gt_box, gt_mask = modellib.load_image_gt(dataset_val, config, image_id, use_mini_mask=False)\n",
    "    #print(dataset_val.image_reference(image_id))\n",
    "    # 针对一个样本的\n",
    "    target = {\n",
    "        'file' : dataset_val.image_reference(image_id),\n",
    "        'gts' : [],\n",
    "        'objs' : []\n",
    "    }\n",
    "    # 处理每个标注目标\n",
    "    for i in range(0,len(gt_class_id)):\n",
    "        wh_ratio, box_size, box_area, square_box = toSquareBox(gt_box[i])\n",
    "        mask_area = np.sum(gt_mask[:,:,i]==True)\n",
    "        mask_ratio = mask_area / box_area\n",
    "        obj = {}\n",
    "        obj['class_id'] = gt_class_id[i]\n",
    "        obj['bbox'] = gt_box[i]\n",
    "        obj['bbox_area'] = box_area\n",
    "        obj['wh_ratio'] = wh_ratio\n",
    "        obj['mask_area'] = mask_area\n",
    "        obj['mask_ratio'] = mask_ratio\n",
    "        obj['size'] = box_size\n",
    "        target['gts'].append(obj)\n",
    "        # print(gt_class_id[i], gt_box[i], box_area, box_ratio, mask_area, mask_ratio)\n",
    "    gt_targets.append(target)\n",
    "# 按照类别再把尺寸和宽高比分组\n",
    "for target in gt_targets:\n",
    "    target['gt_wh_ratios'] = []\n",
    "    target['gt_sizes'] = []\n",
    "    target['gt_mask_area'] = []\n",
    "    target['gt_mask_ratio'] = []\n",
    "    for i in range(0, 8):\n",
    "        #target['obj_sizes'] = [obj['size'] for obj in target['objs'] if obj['class_id'] == 7]\n",
    "        #target['obj_wh_ratios'] = [obj['wh_ratio'] for obj in target['objs'] if obj['class_id'] == 7]\n",
    "        target['gt_wh_ratios'].append([gt['wh_ratio'] for gt in target['gts'] if gt['class_id'] == i])#gt_wh_ratios\n",
    "        target['gt_sizes'].append([gt['size'] for gt in target['gts'] if gt['class_id'] == i])#gt_wh_ratiosgt_sizes\n",
    "        target['gt_mask_area'].append([gt['mask_area'] for gt in target['gts'] if gt['class_id'] == i])\n",
    "        target['gt_mask_ratio'].append([gt['mask_ratio'] for gt in target['gts'] if gt['class_id'] == i])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 尺寸与宽高比分布\n",
    "\n",
    "横坐标为尺寸，纵坐标为宽高比，每个类别绘制一个坐标图\n",
    "\n",
    "主要查看目标的分布情况，同类目标应该在坐标图上聚集一起。\n",
    "\n",
    "如果存在散漫的点，估计是标注错误。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1128\n"
     ]
    },
    {
     "data": {
      "image/png": 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v9C7F/WKSv2Fy1jcHzFXVl/rbH6f3L4FJWd8vAfdX1XxV/RC4FXgZk7O+U5Zaz8T0JslbgNcCb6hHP2B1xusbR/SHua3DmpEk9K4H31NVfzFwaD/wlv7PbwE+fbbn1oWqeldVbayqZ9P7s/r7qnojk7O+/wCOJjl158JXAV9jQtZH77LOlUme2P+7+ip6v3ealPWdstR69gM7kjw+ySZ6/8+Pfx7D/EaSZCvwTuB1VfX9gUNnvr6qOutf9G7ZcC/wb8C7xzGHDtfycnr/OXUI+HL/62rgafTeRfCN/venjnuuHaz1KuAz/Z8nZn3AC4GZ/p/hp4CnTNj6/hj4OnA38FHg8Wt5fcAt9H4/8UN6Z7pvPd16gHf3W3ME2Dbu+a9wfbP0rt2faszula7P2zBIUkP8RK4kNcToS1JDjL4kNcToS1JDjL4kNcToS1JDjL4kNeR/ATxV3cL58AomAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.cla()\n",
    "plt.clf()\n",
    "plt.close()\n",
    "print(len(gt_targets))\n",
    "for i in range(0, 4):\n",
    "    for target in gt_targets:\n",
    "        # plt.title(target['file'])\n",
    "        #print('标注目标尺寸：', np.sort(target['obj_sizes']))\n",
    "        #print('标注目标宽高比：', np.sort(target['obj_wh_ratios']))\n",
    "        plt.plot(target['gt_sizes'][i], target['gt_wh_ratios'][i], 'ro')\n",
    "        #plt.plot(target['obj_sizes'], target['obj_wh_ratios'], 'go')\n",
    "        plt.axis([0, 120, 0, 1.2])\n",
    "    plt.title(i)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标注像素面积与宽高比的分布\n",
    "\n",
    "横坐标为标注目标的像素个数和，纵坐标为宽高比，每个类别绘制一个坐标图。\n",
    "\n",
    "主要查看目标的Mask情况，同类目标应该在坐标图上聚集一起。\n",
    "\n",
    "如果存在散漫的点，估计是标注错误。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5nU1V9Z6q2lRVL6T/d+Dvq+otNLYOAFX1H8CxJE98euLrgftocC3oX9a5MsnTBz8rr6f/e68W12LYUs//ILAryVOTbKH//zT559PeU1Wd9S/6H9nwAPBvwHsnMYdJfQGvof/Pr3uALw6+rgaeQ/+38l8d/PnsSc/1LK7JVcBnBt83uQ7Ay4GZwd+LTwHPangt/gD4CnAv8HHgqS2tBXAr/d9nfI/+mfzbT/f8gfcOWnoU2LHc/fsxDJLUEN+RK0kNMfqS1BCjL0kNMfqS1BCjL0kNMfqS1BCjL0kN+V9Qh7xwujPXGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0, 4):\n",
    "    for target in gt_targets:\n",
    "        # plt.title(target['file'])\n",
    "        #print('标注目标尺寸：', np.sort(target['obj_sizes']))\n",
    "        #print('标注目标宽高比：', np.sort(target['obj_wh_ratios']))\n",
    "        plt.plot(np.array(target['gt_mask_area'][i])/100, target['gt_wh_ratios'][i], 'ro')\n",
    "        #plt.plot(target['obj_sizes'], target['obj_wh_ratios'], 'go')\n",
    "        plt.axis([0, 100, 0, 1.2])\n",
    "    plt.title(i)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标注像素面积占比与宽高比的分布\n",
    "\n",
    "横坐标为标注目标的像素占比（标注的像素面积与矩形包围框面积的比例），纵坐标为宽高比，每个类别绘制一个坐标图。\n",
    "\n",
    "主要查看目标的分布情况，同类目标应该在坐标图上聚集一起。\n",
    "\n",
    "如果面积占比很小，表示标注目标有效内容很少，大部分都是背景信息，不利于训练模型。可以考虑重新标注的方式。\n",
    "\n",
    "如果存在散漫的点，估计是标注错误。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wgzcvJn1wZM5n+M0LMz+Z4QWOLnm3078j9uOzyrnazCPz72f2F0W7fM4fAP55MPddwFPAh9Zx3m8Bfz34/VLg58CWdfD38X6Wv8i4btZex7zrau11yTwyb83X3poeodfaPTZglnnvBC4B7h38r3u2ZvgUuI6Z15Uumavqp0l+ABwHXgfuq6qZPG6542d8F3B/kifpF+TtVTXTx70meQC4DtiSZBH4GvAOWH9rDzrlXVdrDzplnm6ewf8ckqQNzjtFJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqxP8CVVJ4kaaWvaIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0, 4):\n",
    "    for target in gt_targets:\n",
    "        # plt.title(target['file'])\n",
    "        #print('标注目标尺寸：', np.sort(target['obj_sizes']))\n",
    "        #print('标注目标宽高比：', np.sort(target['obj_wh_ratios']))\n",
    "        plt.plot(target['gt_mask_ratio'][i], target['gt_wh_ratios'][i], 'ro')\n",
    "        #plt.plot(target['obj_sizes'], target['obj_wh_ratios'], 'go')\n",
    "        plt.axis([0, 1.5, 0, 1.2])\n",
    "    plt.title(i)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 目标检测数据\n",
    "\n",
    "对数据集，如验证集（val）、测试集（test）等都可以，每张图片检测后把结果收集。\n",
    "\n",
    "检测结果：类别、ROIS、目标框面积、宽高比、掩码面积、面积比、得分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 目标框\n",
    "\n",
    "计算GT的bbox与检测的bbox的IoU。\n",
    "\n",
    "2个目标怎么匹配？IoU最大的那个"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 覆盖测试\n",
    "\n",
    "人工不可能把所有目标的尺寸和宽高比逐个设置。现实做法是设置几个代表性的尺寸，如32、64、128。代表性的宽高比，如0.5、1、2\n",
    "\n",
    "代表性数据不可能把所有目标都覆盖（IOU）\n",
    "\n",
    "根据上述坐标图的数据分布情况，设置代表性尺寸、宽高比，在每个目标上标识最大的交并比（IOU）。\n",
    "\n",
    "最大交并比（IOU）：理想值就是目标本身，但是以理想值为代表值，必然造成其它目标的交并比（IOU）下降。\n",
    "\n",
    "三维坐标图：尺寸、宽高比、IOU\n",
    "\n",
    "尺寸：锚框的正方形尺寸，应该从GT目标中挑选，把尺寸差值小于阈值的目标归为一组，将平均值作为锚框尺寸。目前网络设计，最多只有5个尺寸设置\n",
    "\n",
    "宽高比：锚框的，也应该从GT目标中挑选。IoU：对所有目标的平均IoU，\n",
    "\n",
    "理想状态是IOU值足够高（0~1），足以用于目标的过滤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从小到大排序GT目标框尺寸\n",
    "\n",
    "# 查找GT目标框尺寸的分组位置，即组内尺寸接近"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 代表性的尺寸、宽高比挑选（实验性）\n",
    "\n",
    "人工设置代表值达到IOU理想状态也不现实。。。\n",
    "\n",
    "怎么让计算机挑选代表值？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}