evaluation notebook: yolov7+OCR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import cv2\n",
    "import glob\n",
    "import json\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import time\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import cv2\n",
    "import numpy as np\n",
    "import sys\n",
    "# sys.path.append(\"/kaggle/input/ultralytics-package-for-yolo-v8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fusing layers... \n",
      "IDetect.fuse\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.path.append(\"./yolov7\")\n",
    "from models.experimental import attempt_load\n",
    "from utils.datasets import LoadStreams, LoadImages\n",
    "from utils.general import check_file, check_img_size, check_requirements, \\\n",
    "    box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr\n",
    "\n",
    "device=\"cuda:7\"\n",
    "half = device != 'cpu'  # half precision only supported on CUDA\n",
    "yolo_model = attempt_load(\"yolov7_best.pt\", map_location=device)  # load FP32 model\n",
    "yolo_model.to(device)\n",
    "stride = int(yolo_model.stride.max())  # model stride\n",
    "imgsz = check_img_size(640, s=stride)  # check img_size\n",
    "yolo_model.eval()\n",
    "1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Configs:\n",
    "    conf_thres=0.3\n",
    "    iou_thres=0.65\n",
    "    classes=[0,1,2,3,4,5,6,7,8]\n",
    "    agnostic_nms=False\n",
    "    augment=False\n",
    "optyolo = Configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "def detect_yolo(imgpth):\n",
    "    dataset = LoadImages(imgpth, img_size=imgsz, stride=stride)\n",
    "    old_img_w = old_img_h = imgsz\n",
    "    old_img_b = 1\n",
    "    for path, img, im0, vid_cap in dataset:\n",
    "        img = torch.from_numpy(img)\n",
    "        img = img.float()  # uint8 to fp16/32\n",
    "        img /= 255.0  # 0 - 255 to 0.0 - 1.0\n",
    "        if img.ndimension() == 3:\n",
    "            img = img.unsqueeze(0)\n",
    "        with torch.no_grad():   # Calculating gradients would cause a GPU memory leak\n",
    "            pred = yolo_model(img.to(\"cuda:7\"), augment=optyolo.augment)[0]\n",
    "        pred = non_max_suppression(pred, optyolo.conf_thres, optyolo.iou_thres, classes=optyolo.classes, agnostic=optyolo.agnostic_nms)\n",
    "        for i, det in enumerate(pred):  # detections per image\n",
    "            if len(det):\n",
    "            # Rescale boxes from img_size to im0 size\n",
    "                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()\n",
    "                det[:,2] = det[:,2] - det[:,0]\n",
    "                det[:,3] = det[:,3] - det[:,1]\n",
    "                det[:,0] = det[:,0] + det[:,2]/2\n",
    "                det[:,1] = det[:,1] + det[:,3]/2\n",
    "\n",
    "        return pred[0].detach().cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "NAMES = [\"x-label\",\"y-label\",\"diagram-tick\",'line','dot','vertical_bar','scatter','horizontal_bar']\n",
    "def divide_by_class(tensor,classes,confid):\n",
    "    bbox_by_class = {i:[] for i in range(len(NAMES))}\n",
    "    for i,c,confid in zip(range(tensor.shape[0]),classes,confid):\n",
    "        bbox_by_class[int(c.item())].append(tensor[i].tolist()+[confid.item()])\n",
    "    return bbox_by_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_imgs = [os.path.join(\"../datasets/dataset/keypoint/images/valid\",x.strip()) for x in open(\"../valid_names.txt\").readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../OCR/EasyOCR/trainer\")\n",
    "sys.path.append(\"../OCR/EasyOCR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model import Model\n",
    "from trainer.utils import AttrDict\n",
    "import yaml\n",
    "from trainer.utils import CTCLabelConverter, AttnLabelConverter, Averager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_config(file_path):\n",
    "    with open(file_path, 'r', encoding=\"utf8\") as stream:\n",
    "        opt = yaml.safe_load(stream)\n",
    "    opt = AttrDict(opt)\n",
    "    if opt.lang_char == 'None':\n",
    "        characters = ''\n",
    "        for data in opt['select_data'].split('-'):\n",
    "            csv_path = os.path.join(opt['train_data'], data, 'labels.csv')\n",
    "            df = pd.read_csv(csv_path, sep='^([^,]+),', engine='python', usecols=['filename', 'words'], keep_default_na=False)\n",
    "            all_char = ''.join(df['words'])\n",
    "            characters += ''.join(set(all_char))\n",
    "        characters = sorted(set(characters))\n",
    "        opt.character= ''.join(characters)\n",
    "    else:\n",
    "        opt.character = opt.number + opt.symbol + opt.lang_char\n",
    "    os.makedirs(f'./saved_models/{opt.experiment_name}', exist_ok=True)\n",
    "    return opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = get_config(\"../OCR/config_files/en_filtered_config.yaml\")\n",
    "device = torch.device('cpu')\n",
    "if 'CTC' in opt.Prediction:\n",
    "    converter = CTCLabelConverter(opt.character)\n",
    "else:\n",
    "    converter = AttnLabelConverter(opt.character)\n",
    "opt.num_class = len(converter.character)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_id(texts,i):\n",
    "    for k in texts:\n",
    "        if i==k[\"id\"]:\n",
    "            return k\n",
    "def get_bounding_box(points):\n",
    "    xs = [p[0] for p in points]\n",
    "    ys = [p[1] for p in points]\n",
    "    x_min = min(xs)\n",
    "    y_min = min(ys)\n",
    "    x_max = max(xs)\n",
    "    y_max = max(ys)\n",
    "    return [x_min, y_min, x_max - x_min, y_max - y_min]\n",
    "def create_square(point, k=5):\n",
    "    if type(point)==dict:\n",
    "        x,y = point[\"x\"],point[\"y\"]\n",
    "    else:\n",
    "        x,y =point\n",
    "    half_k = k / 2.0\n",
    "\n",
    "    # Define the corners of the square\n",
    "    corners = [x - half_k, y - half_k,x + half_k, y - half_k,x + half_k, y + half_k,x - half_k, y + half_k]\n",
    "    return corners\n",
    "def polygon_to_bbox(polygon):\n",
    "    # Create list of x and y coordinates\n",
    "    x_coords = polygon[0::2]\n",
    "    y_coords = polygon[1::2]\n",
    "\n",
    "    # Compute minimum and maximum coordinates\n",
    "    x_min, x_max = min(x_coords), max(x_coords)\n",
    "    y_min, y_max = min(y_coords), max(y_coords)\n",
    "\n",
    "    # Calculate the center of the bounding box\n",
    "    x_center = (x_min + x_max) / 2.0\n",
    "    y_center = (y_min + y_max) / 2.0\n",
    "\n",
    "    # Calculate the width and height of the bounding box\n",
    "    width = x_max - x_min\n",
    "    height = y_max - y_min\n",
    "\n",
    "    # Return the bounding box in the format [x, y, width, height]\n",
    "    return [x_center, y_center, width, height]\n",
    "def get_line_bboxes(v,ctype=None):\n",
    "    texts = v[\"text\"]\n",
    "    polygon_info = []\n",
    "    point_info = []\n",
    "    for tinf in v[\"axes\"][\"x-axis\"][\"ticks\"]:\n",
    "        bbox_label = get_id(texts,tinf[\"id\"])\n",
    "        polygon_info.append(bbox_label)\n",
    "\n",
    "    for tinf in v[\"axes\"][\"y-axis\"][\"ticks\"]:\n",
    "        bbox_label = get_id(texts,tinf[\"id\"])\n",
    "        polygon_info.append(bbox_label)\n",
    "    return polygon_info\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Transformation module specified\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocr_model = Model(opt)\n",
    "statedict = torch.load(\"../OCR/saved_models_best64/best_accuracy.pth\",map_location=device)\n",
    "newstatedict = {}\n",
    "for k in statedict:\n",
    "    newk = k[7:]\n",
    "    newstatedict[newk] =statedict[k]\n",
    "ocr_model.load_state_dict(newstatedict)\n",
    "ocr_model.eval()\n",
    "1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "class NormalizePAD(object):\n",
    "\n",
    "    def __init__(self, max_size, PAD_type='right'):\n",
    "        self.toTensor = transforms.ToTensor()\n",
    "        self.max_size = max_size\n",
    "        self.max_width_half = math.floor(max_size[2] / 2)\n",
    "        self.PAD_type = PAD_type\n",
    "\n",
    "    def __call__(self, img):\n",
    "        img = self.toTensor(img)\n",
    "        img.sub_(0.5).div_(0.5)\n",
    "        c, h, w = img.size()\n",
    "        Pad_img = torch.FloatTensor(*self.max_size).fill_(0)\n",
    "        Pad_img[:, :, :w] = img  # right pad\n",
    "        if self.max_size[2] != w:  # add border Pad\n",
    "            Pad_img[:, :, w:] = img[:, :, w - 1].unsqueeze(2).expand(c, h, self.max_size[2] - w)\n",
    "\n",
    "        return Pad_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "class AlignCollate(object):\n",
    "\n",
    "    def __init__(self, imgH=64, imgW=64, keep_ratio_with_pad=False, contrast_adjust = 0.):\n",
    "        self.imgH = imgH\n",
    "        self.imgW = imgW\n",
    "        self.keep_ratio_with_pad = keep_ratio_with_pad\n",
    "        self.contrast_adjust = contrast_adjust\n",
    "\n",
    "    def __call__(self, images):\n",
    "\n",
    "        if self.keep_ratio_with_pad:  # same concept with 'Rosetta' paper\n",
    "            resized_max_w = self.imgW\n",
    "            input_channel = 3 if images[0].mode == 'RGB' else 1\n",
    "            transform = NormalizePAD((input_channel, self.imgH, resized_max_w))\n",
    "\n",
    "            resized_images = []\n",
    "            for image in images:\n",
    "                w, h = image.size\n",
    "\n",
    "                #### augmentation here - change contrast\n",
    "                if self.contrast_adjust > 0:\n",
    "                    image = np.array(image.convert(\"L\"))\n",
    "                    image = adjust_contrast_grey(image, target = self.contrast_adjust)\n",
    "                    image = Image.fromarray(image, 'L')\n",
    "\n",
    "                ratio = w / float(h)\n",
    "                if math.ceil(self.imgH * ratio) > self.imgW:\n",
    "                    resized_w = self.imgW\n",
    "                else:\n",
    "                    resized_w = math.ceil(self.imgH * ratio)\n",
    "\n",
    "                resized_image = image.resize((resized_w, self.imgH), Image.BICUBIC)\n",
    "                resized_images.append(transform(resized_image))\n",
    "                # resized_image.save('./image_test/%d_test.jpg' % w)\n",
    "\n",
    "            image_tensors = torch.cat([t.unsqueeze(0) for t in resized_images], 0)\n",
    "\n",
    "        else:\n",
    "            transform = ResizeNormalize((self.imgW, self.imgH))\n",
    "            image_tensors = [transform(image) for image in images]\n",
    "            image_tensors = torch.cat([t.unsqueeze(0) for t in image_tensors], 0)\n",
    "\n",
    "        return image_tensors\n",
    "    \n",
    "AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, contrast_adjust=opt.contrast_adjust)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_prediction(image):\n",
    "    timg = AlignCollate_valid([image.convert(\"L\")])\n",
    "    preds = ocr_model(timg,\"\")\n",
    "    preds_size = torch.IntTensor([preds.size(1)] * 1)\n",
    "    _, preds_index = preds.max(2)\n",
    "    preds_index = preds_index.view(-1)\n",
    "    preds_str = converter.decode_greedy(preds_index.data, preds_size.data)\n",
    "    return preds_str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def show_bboxes_get_strings(image_path, bboxes,show=True):\n",
    "    img = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)\n",
    "    if show:\n",
    "        # Create figure and axes\n",
    "        fig, ax = plt.subplots(1, figsize=(12, 9))\n",
    "\n",
    "        # Display the image\n",
    "        ax.imshow(img)\n",
    "    strings = []\n",
    "    # Loop over tensor and add boxes\n",
    "    for box in bboxes:\n",
    "        # Bounding box data\n",
    "        x, y, w, h= box\n",
    "        x = x - w / 2\n",
    "        y = y - h / 2\n",
    "        if show:\n",
    "            # Create a Rectangle patch\n",
    "            rect = patches.Rectangle((x, y), w, h, linewidth=1, edgecolor=\"r\", facecolor='none')\n",
    "            # Add the patch to the Axes\n",
    "            ax.add_patch(rect)\n",
    "        cropbox = img[int(y):int(y+h),int(x):int(x+w)]\n",
    "        result = reader.recognize(cropbox)\n",
    "        strings.append(result)\n",
    "    if show:\n",
    "        plt.show()\n",
    "    return [x[0][1] for x in strings]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_line_bboxes(v,ctype=None):\n",
    "    texts = v[\"text\"]\n",
    "    polygon_info = []\n",
    "    point_info = []\n",
    "    for tinf in v[\"axes\"][\"x-axis\"][\"ticks\"]:\n",
    "        bbox_label = get_id(texts,tinf[\"id\"])\n",
    "        polygon_info.append(bbox_label)\n",
    "\n",
    "    for tinf in v[\"axes\"][\"y-axis\"][\"ticks\"]:\n",
    "        bbox_label = get_id(texts,tinf[\"id\"])\n",
    "        polygon_info.append(bbox_label)\n",
    "    return polygon_info\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bounding_boxes(bounding_boxes,inter_limit=0.5):\n",
    "    pruned_boxes = []\n",
    "    to_remove = set()\n",
    "\n",
    "    for i, big_box in enumerate(bounding_boxes):\n",
    "        x1, y1, w1, h1,_ = big_box\n",
    "        area1 = w1 * h1\n",
    "        for j, small_box in enumerate(bounding_boxes):\n",
    "            if i != j and j not in to_remove:\n",
    "                x2, y2, w2, h2,_ = small_box\n",
    "                area2 = w2 * h2\n",
    "\n",
    "                # calculate the intersection rectangle\n",
    "                x_int = max(x1 - w1/2, x2 - w2/2)\n",
    "                y_int = max(y1 - h1/2, y2 - h2/2)\n",
    "                w_int = min(x1 + w1/2, x2 + w2/2) - x_int\n",
    "                h_int = min(y1 + h1/2, y2 + h2/2) - y_int\n",
    "\n",
    "                if w_int < 0 or h_int < 0:\n",
    "                    # no overlap\n",
    "                    continue\n",
    "\n",
    "                intersection = w_int * h_int\n",
    "                iou = intersection / area2  # change here to check how much of the smaller box is in the larger one\n",
    "\n",
    "                # if smaller box is contained in larger box 80% or more\n",
    "                if iou >= inter_limit:\n",
    "                    to_remove.add(j)\n",
    "\n",
    "    for k, box in enumerate(bounding_boxes):\n",
    "        if k not in to_remove:\n",
    "            pruned_boxes.append(box)\n",
    "\n",
    "    return pruned_boxes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def iou(box1, box2):\n",
    "    \"\"\"Calculate Intersection Over Union (IOU) of two bounding boxes.\"\"\"\n",
    "    x1, y1, w1, h1, _ = box1\n",
    "    x2, y2, w2, h2, _ = box2\n",
    "    \n",
    "    xi1 = max(x1, x2)\n",
    "    yi1 = max(y1, y2)\n",
    "    xi2 = min(x1+w1, x2+w2)\n",
    "    yi2 = min(y1+h1, y2+h2)\n",
    "    inter_area = max(xi2 - xi1, 0) * max(yi2 - yi1, 0)\n",
    "\n",
    "    box1_area = w1 * h1\n",
    "    box2_area = w2 * h2\n",
    "    union_area = box1_area + box2_area - inter_area\n",
    "    \n",
    "    iou = inter_area / union_area\n",
    "    return iou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 223/223 [00:00<00:00, 3543.31it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(223, 0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_images = []\n",
    "generated_images = []\n",
    "for pth in tqdm(all_imgs):\n",
    "    jsonname = os.path.split(pth)[-1].replace(\".jpg\",\".json\")\n",
    "    jsonname = os.path.join(\"../datasets/dataset/train/annotations\",jsonname)\n",
    "    annot = json.load(open(jsonname))\n",
    "    if annot[\"source\"]==\"generated\":\n",
    "        generated_images.append(pth)\n",
    "    else:\n",
    "        extracted_images.append(pth)\n",
    "len(extracted_images),len(generated_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_boxes(image_path, tensor,classes,confid):\n",
    "    # Load image\n",
    "    img = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)\n",
    "    print(img.shape)\n",
    "    # Colors for different classes\n",
    "    colors = ['r', 'g', 'b', 'y', 'm', 'c', 'k', 'lime', 'teal', 'purple']\n",
    "\n",
    "    # Create figure and axes\n",
    "    fig, ax = plt.subplots(1, figsize=(12, 9))\n",
    "\n",
    "    # Display the image\n",
    "    ax.imshow(img)\n",
    "\n",
    "    # Loop over tensor and add boxes\n",
    "    for i in range(tensor.shape[0]):\n",
    "        # Bounding box data\n",
    "        x, y, w, h=[int(x) for x in tensor[i]]\n",
    "        print(x,y,w,h)\n",
    "        x = x - w / 2\n",
    "        y = y - h / 2\n",
    "        conf, cls =confid[i],int(classes[i])\n",
    "        # Create a Rectangle patch\n",
    "        rect = patches.Rectangle((x, y), w, h, linewidth=1, edgecolor=colors[int(cls)], facecolor='none')\n",
    "        # Add the patch to the Axes\n",
    "        ax.add_patch(rect)\n",
    "        # Add text (confidence score)\n",
    "        plt.text(x, y, str(round(conf.item(), 2)), color=colors[int(cls)], fontsize=10, \n",
    "                 horizontalalignment='left', verticalalignment='top', \n",
    "                 bbox={'facecolor': colors[int(cls)], 'alpha': 0.4, 'pad': 1})\n",
    "    plt.savefig(\"output.png\", bbox_inches='tight', pad_inches=0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLASS_NAMES = [\"x-label\",\"y-label\",\"diagram-tick\",'line','dot','vertical_bar','scatter','horizontal_bar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclidean_distance(p1,p2):\n",
    "    x1,y1 = p1\n",
    "    x2,y2 = p2\n",
    "    return np.sqrt((x1-x2)**2+(y1-y2)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def is_number(s: str):\n",
    "    s = re.sub('[,$% ]', '', s)\n",
    "    try:\n",
    "        float(s)\n",
    "        return True\n",
    "    except ValueError:\n",
    "        return False\n",
    "def get_number(s):\n",
    "    if type(s)==str:\n",
    "        return float(re.sub('[,$% ]', '', s))\n",
    "    else:\n",
    "        return float(s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def scatter_inverse(result, x_tickinfo, y_tickinfo,charttype):\n",
    "    x_labels_numerical = all(is_number(label) for label in x_tickinfo.keys())\n",
    "    y_labels_numerical = all(is_number(label) for label in y_tickinfo.keys())\n",
    "\n",
    "    if x_labels_numerical:\n",
    "        x_ticks = {get_number(k): v for k, v in x_tickinfo.items()}\n",
    "        x_values = list(x_ticks.keys())\n",
    "        x_coords = list(x_ticks.values())\n",
    "    else:\n",
    "        x_mapping = {info['x']: label for label, info in x_tickinfo.items()}\n",
    "\n",
    "    if y_labels_numerical:\n",
    "        y_ticks = {get_number(k): v for k, v in y_tickinfo.items()}\n",
    "        y_values = list(y_ticks.keys())\n",
    "        y_coords = list(y_ticks.values())\n",
    "    else:\n",
    "        y_mapping = {info['y']: label for label, info in y_tickinfo.items()}\n",
    "\n",
    "    dataseries = []\n",
    "    for point in result:\n",
    "        x_coord, y_coord = point\n",
    "\n",
    "        if x_labels_numerical:\n",
    "            for i in range(len(x_values) - 1):\n",
    "                if x_coords[i+1]['x'] <= x_coord <= x_coords[i]['x']:\n",
    "                    ratio = (x_coord - x_coords[i]['x']) / (x_coords[i + 1]['x'] - x_coords[i]['x'])\n",
    "                    x_val = x_values[i] + ratio * (x_values[i + 1] - x_values[i])\n",
    "                    break\n",
    "            else:\n",
    "                ratio = (x_coord - x_coords[0]['x']) / (x_coords[-1]['x'] - x_coords[0]['x'])\n",
    "                x_val = x_values[0] + ratio * (x_values[-1] - x_values[0])\n",
    "        else:\n",
    "            x_val = x_mapping[min(x_mapping.keys(), key=lambda x: abs(x - x_coord))]\n",
    "\n",
    "\n",
    "        if y_labels_numerical:\n",
    "            for i in range(len(y_values) - 1):\n",
    "                if y_coords[i+1]['y'] <= y_coord <= y_coords[i]['y']:\n",
    "                    ratio = (y_coord - y_coords[i]['y']) / (y_coords[i + 1]['y'] - y_coords[i]['y'])\n",
    "                    y_val = y_values[i] + ratio * (y_values[i + 1] - y_values[i])\n",
    "                    break\n",
    "            else:\n",
    "                ratio = (y_coord - y_coords[0]['y']) / (y_coords[-1]['y'] - y_coords[0]['y'])\n",
    "                y_val = y_values[0] + ratio * (y_values[-1] - y_values[0])\n",
    "        else:\n",
    "            y_val = y_mapping[min(y_mapping.keys(), key=lambda x: abs(x - y_coord))]\n",
    "            \n",
    "\n",
    "        dataseries.append({'x': x_val, 'y': y_val})\n",
    "\n",
    "    return dataseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def other_inverse(result, x_tickinfo, y_tickinfo,charttype):\n",
    "    y_labels_numerical = all(is_number(label) for label in y_tickinfo.keys())\n",
    "    \n",
    "    x_mapping = {info['x']: label for label, info in x_tickinfo.items()}\n",
    "\n",
    "    if y_labels_numerical:\n",
    "        y_ticks = {get_number(k): v for k, v in y_tickinfo.items()}\n",
    "        y_values = list(y_ticks.keys())\n",
    "        y_coords = list(y_ticks.values())\n",
    "    else:\n",
    "        y_mapping = {info['y']: label for label, info in y_tickinfo.items()}\n",
    "\n",
    "    dataseries = []\n",
    "    for x_val,tick in x_tickinfo.items():\n",
    "        x_coord, y_coord = min(result,key=lambda x: abs(x[0]-tick[\"x\"]))\n",
    "        if y_labels_numerical:\n",
    "            for i in range(len(y_values) - 1):\n",
    "                if y_coords[i+1]['y'] <= y_coord <= y_coords[i]['y']:\n",
    "                    ratio = (y_coord - y_coords[i]['y']) / (y_coords[i + 1]['y'] - y_coords[i]['y'])\n",
    "                    y_val = y_values[i] + ratio * (y_values[i + 1] - y_values[i])\n",
    "                    break\n",
    "            else:\n",
    "                ratio = (y_coord - y_coords[0]['y']) / (y_coords[-1]['y'] - y_coords[0]['y'])\n",
    "                y_val = y_values[0] + ratio * (y_values[-1] - y_values[0])\n",
    "        else:\n",
    "            y_val = y_mapping[min(y_mapping.keys(), key=lambda x: abs(x - y_coord))]\n",
    "            \n",
    "        dataseries.append({'x': x_val, 'y': y_val})\n",
    "    return dataseries\n",
    "\n",
    "def horizontalbar_inverse(result, x_tickinfo, y_tickinfo,charttype):\n",
    "    y_labels_numerical = all(is_number(label) for label in y_tickinfo.keys())\n",
    "    \n",
    "    x_mapping = {info['x']: label for label, info in x_tickinfo.items()}\n",
    "\n",
    "    if y_labels_numerical:\n",
    "        y_ticks = {get_number(k): v for k, v in y_tickinfo.items()}\n",
    "        y_values = list(y_ticks.keys())\n",
    "        y_coords = list(y_ticks.values())\n",
    "    else:\n",
    "        y_mapping = {info['y']: label for label, info in y_tickinfo.items()}\n",
    "\n",
    "    dataseries = []\n",
    "    for x_val,tick in x_tickinfo.items():\n",
    "        y_coord, x_coord = min(result,key=lambda x: abs(x[1]-tick[\"y\"]))\n",
    "        if y_labels_numerical:\n",
    "            for i in range(len(y_values) - 1):\n",
    "                if y_coords[i]['x'] <= y_coord <= y_coords[i+1]['x']:\n",
    "                    ratio = (y_coord - y_coords[i]['x']) / (y_coords[i + 1]['x'] - y_coords[i]['x'])\n",
    "                    y_val = y_values[i] + ratio * (y_values[i + 1] - y_values[i])\n",
    "                    break\n",
    "            else:\n",
    "                ratio = (y_coord - y_coords[0]['x']) / (y_coords[-1]['x'] - y_coords[0]['x'])\n",
    "                y_val = y_values[0] + ratio * (y_values[-1] - y_values[0])\n",
    "        else:\n",
    "            y_val = y_mapping[min(y_mapping.keys(), key=lambda x: abs(x - y_coord))]\n",
    "            \n",
    "        dataseries.append({'x': x_val, 'y': y_val})\n",
    "    return dataseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_paired_bbox_ticks(bboxes,bbox,idx):\n",
    "    x,y,w,h = bbox\n",
    "    tickbox = {}\n",
    "    tcount = {}\n",
    "    for x1,y1,text in bboxes:\n",
    "        if text in tickbox:\n",
    "            tcount[text] +=1\n",
    "            text = text+str(tcount[text])\n",
    "        if idx==0:\n",
    "            tickbox[text] = {\"x\":x1,\"y\":y+h/2}\n",
    "        else:\n",
    "            tickbox[text] = {\"x\":x-w/2,\"y\":y1}\n",
    "        if text not in tcount:\n",
    "            tcount[text] = 0\n",
    "    return tickbox\n",
    "    \n",
    "def get_predictions_others(classwise_preds,img,charttype,bbox):\n",
    "    x_bboxes = []\n",
    "    y_bboxes = []\n",
    "    for x,y,w,h,_ in classwise_preds[0]:\n",
    "        x1 = x - w / 2\n",
    "        y1 = y - h / 2\n",
    "        cropbox = img[int(y1):int(y1+h),int(x1):int(x1+w)]\n",
    "        result = get_prediction(Image.fromarray(cropbox))\n",
    "        x_bboxes.append([x,y,result])\n",
    "    for x,y,w,h,_ in classwise_preds[1]:\n",
    "        x1 = x - w / 2\n",
    "        y1 = y - h / 2\n",
    "        cropbox = img[int(y1):int(y1+h),int(x1):int(x1+w)]\n",
    "        result = get_prediction(Image.fromarray(cropbox))\n",
    "        y_bboxes.append([x,y,result])\n",
    "    if charttype==\"horizontal_bar\":\n",
    "        xidx,yidx=1,0\n",
    "    else:\n",
    "        xidx,yidx=0,1\n",
    "    x_tickinfo = get_paired_bbox_ticks(x_bboxes,bbox,xidx)\n",
    "    y_tickinfo = get_paired_bbox_ticks(y_bboxes,bbox,yidx)\n",
    "    print(\"xtick:\",len(x_tickinfo))\n",
    "    print(\"ytick:\",len(y_tickinfo))\n",
    "    \n",
    "    results = [tmp[:2] for tmp in classwise_preds[2]]\n",
    "    \n",
    "    if charttype==\"horizontal_bar\":\n",
    "        results = sorted(results,key=lambda ttmp: ttmp[1])\n",
    "        y_tickinfo = {k: v for k, v in sorted(y_tickinfo.items(), key=lambda item: item[1][\"x\"])}\n",
    "        x_tickinfo = {k: v for k, v in sorted(x_tickinfo.items(), key=lambda item: item[1][\"y\"])}        \n",
    "    else:\n",
    "        results = sorted(results,key=lambda ttmp: ttmp[0])\n",
    "        x_tickinfo = {k: v for k, v in sorted(x_tickinfo.items(), key=lambda item: item[1][\"x\"])}\n",
    "        y_tickinfo = {k: v for k, v in sorted(y_tickinfo.items(),reverse=True, key=lambda item: item[1][\"y\"])}\n",
    "#     print(x_tickinfo)\n",
    "    if charttype==\"scatter\":\n",
    "        answer = scatter_inverse(results,x_tickinfo,y_tickinfo,charttype)\n",
    "    elif charttype==\"horizontal_bar\":\n",
    "        answer = horizontalbar_inverse(results,x_tickinfo,y_tickinfo,charttype)\n",
    "    else:\n",
    "        answer = other_inverse(results,x_tickinfo,y_tickinfo,charttype)\n",
    "    return answer,charttype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_contained_boxes(boxes,THRES=0.95):\n",
    "    to_remove = []\n",
    "    for i in range(len(boxes)):\n",
    "        for j in range(i + 1, len(boxes)):\n",
    "            box1 = boxes[i]\n",
    "            box2 = boxes[j]\n",
    "\n",
    "            # Calculate the top-left and bottom-right coordinates of each box\n",
    "            box1_coords = [(box1[0] - box1[2]/2, box1[1] - box1[3]/2), \n",
    "                           (box1[0] + box1[2]/2, box1[1] + box1[3]/2)]\n",
    "            box2_coords = [(box2[0] - box2[2]/2, box2[1] - box2[3]/2), \n",
    "                           (box2[0] + box2[2]/2, box2[1] + box2[3]/2)]\n",
    "\n",
    "            # Calculate the overlap of the two boxes\n",
    "            dx = min(box1_coords[1][0], box2_coords[1][0]) - max(box1_coords[0][0], box2_coords[0][0])\n",
    "            dy = min(box1_coords[1][1], box2_coords[1][1]) - max(box1_coords[0][1], box2_coords[0][1])\n",
    "            if (dx>=0) and (dy>=0):\n",
    "                overlap_area = dx*dy\n",
    "                box1_area = box1[2]*box1[3]\n",
    "                box2_area = box2[2]*box2[3]\n",
    "                \n",
    "                # If box1 is contained within box2\n",
    "                if overlap_area >= THRES * box1_area:\n",
    "                    to_remove.append(i)\n",
    "                \n",
    "                # If box2 is contained within box1\n",
    "                elif overlap_area >= THRES * box2_area:\n",
    "                    to_remove.append(j)\n",
    "\n",
    "    # Remove the boxes that are contained within others\n",
    "    return [box for i, box in enumerate(boxes) if i not in to_remove]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_predictions(classwise_preds,img):\n",
    "    scores = []\n",
    "    bbox = []\n",
    "    for i in range(3,8):\n",
    "        if len(classwise_preds[i])>0:\n",
    "            mostconf = max(classwise_preds[i],key = lambda x: x[-1])\n",
    "            scores.append(mostconf[-1])\n",
    "            bbox.append(mostconf[:4])\n",
    "        else:\n",
    "            scores.append(0)\n",
    "            bbox.append([])\n",
    "            \n",
    "    amx = np.argmax(scores)\n",
    "    if scores[amx]==0:\n",
    "        return None\n",
    "    charttype = CLASS_NAMES[3+amx]\n",
    "    bounding_box = bbox[amx]\n",
    "    x,y,w,h = bounding_box\n",
    "    xe = x+w/2\n",
    "    ye = y+h/2\n",
    "    x = x-w/2\n",
    "    y= y-h/2\n",
    "    yaxisidx,xaxisidx=1,0\n",
    "    if charttype==\"horizontal_bar\":\n",
    "        yaxisidx,xaxisidx=0,1\n",
    "    # Check1: Only include diagram ticks that are inside the bounding box\n",
    "    filtered = []\n",
    "    for idx, (x1, y1, w1, h1,conf) in enumerate(classwise_preds[2]):\n",
    "        if not ( x>(x1+w1/2) or y>(y1+h1/2) or xe<(x1-w1/2) or ye<(y1-h1/2)):\n",
    "            filtered.append([x1, y1, w1, h1,conf])\n",
    "    print(len(classwise_preds[2]),len(filtered))\n",
    "    classwise_preds[2] = remove_contained_boxes(filtered)\n",
    "    # CHeck2: Only keep yaxis labels on the left side of bounding box\n",
    "    filtered = []\n",
    "    for idx,(x1,y1,w1,h1,conf) in enumerate(classwise_preds[yaxisidx]):\n",
    "        if (x1-w1/2)<x and (y1-h1/2)<ye and (y1+h1/2)>y:\n",
    "            filtered.append([x1, y1, w1, h1,conf])\n",
    "    print(len(classwise_preds[yaxisidx]),len(filtered))\n",
    "    classwise_preds[yaxisidx] = remove_contained_boxes(filtered)\n",
    "    print(\"removed contained:\",len(classwise_preds[yaxisidx]),len(filtered))\n",
    "    \n",
    "    # Check 3: Only keep xaxis labels on the bottom side of bounding box\n",
    "    filtered = []\n",
    "    for idx,(x1,y1,w1,h1,conf) in enumerate(classwise_preds[xaxisidx]):\n",
    "        if (y1+h1/2)>ye and (x1-w1/2)<xe and (x1+w1/2)>x:\n",
    "            filtered.append([x1, y1, w1, h1,conf])\n",
    "    print(len(classwise_preds[xaxisidx]),len(filtered))\n",
    "    classwise_preds[xaxisidx] = remove_contained_boxes(filtered)\n",
    "    print(\"removed contained:\",len(classwise_preds[xaxisidx]),len(filtered))\n",
    "#     print(classwise_preds)\n",
    "    return get_predictions_others(classwise_preds,img,charttype,bounding_box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from rapidfuzz.distance.Levenshtein import distance as levenshtein\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 2 - 2 / (1 + np.exp(-x))\n",
    "\n",
    "def r2_score(y_true, y_pred):\n",
    "    ss_res = np.sum(np.square(y_true - y_pred))\n",
    "    ss_tot = np.sum(np.square(y_true - np.mean(y_true)))\n",
    "    r2 = 1 - (ss_res / ss_tot)\n",
    "    return r2\n",
    "\n",
    "def normalized_rmse(y_true, y_pred):\n",
    "    # The argument to the sigmoid transform is equal to \n",
    "    # rmse(y_true, y_pred) / rmse(y_true, np.mean(y_true))\n",
    "    return sigmoid((1 - r2_score(np.array(y_true), np.array(y_pred))) ** 0.5)\n",
    "\n",
    "\n",
    "def normalized_levenshtein_score(y_true, y_pred):\n",
    "    total_distance = np.sum([levenshtein(yt, yp) for yt, yp in zip(y_true, y_pred)])\n",
    "    length_sum = np.sum([len(yt) for yt in y_true])\n",
    "    return sigmoid(total_distance / length_sum)\n",
    "\n",
    "\n",
    "def score_series(y_true, y_pred):\n",
    "    y_pred = y_pred.split(\";\")\n",
    "    if len(y_true) != len(y_pred):\n",
    "        # print(\"unequal\")\n",
    "        return 0.0\n",
    "    if isinstance(y_true[0], str):\n",
    "        return normalized_levenshtein_score(y_true, y_pred)\n",
    "    else:\n",
    "        try:\n",
    "            y_pred = [float(x) for x in y_pred]\n",
    "        except:\n",
    "            # \"Error\"\n",
    "            return 0\n",
    "        return normalized_rmse(y_true, y_pred)\n",
    "\n",
    "\n",
    "def benetech_score(ground_truth: pd.DataFrame, predictions: pd.DataFrame) -> float:\n",
    "    \"\"\"Evaluate predictions using the metric from the Benetech - Making Graphs Accessible.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ground_truth: pd.DataFrame\n",
    "        Has columns `[data_series, chart_type]` and an index `id`. Values in `data_series` \n",
    "        should be either arrays of floats or arrays of strings.\n",
    "    \n",
    "    predictions: pd.DataFrame\n",
    "    \"\"\"\n",
    "    if not ground_truth.index.equals(predictions.index):\n",
    "        raise ValueError(\"Must have exactly one prediction for each ground-truth instance.\")\n",
    "    if not ground_truth.columns.equals(predictions.columns):\n",
    "        raise ValueError(f\"Predictions must have columns: {ground_truth.columns}.\")\n",
    "    pairs = zip(ground_truth.itertuples(index=False), predictions.itertuples(index=False))\n",
    "    scores = []\n",
    "    for (gt_series, gt_type), (pred_series, pred_type) in pairs:\n",
    "        if gt_type != pred_type:  # Check chart_type condition\n",
    "            print(\"different charts\")\n",
    "            scores.append(0.0)\n",
    "        else:  # Score with RMSE or Levenshtein as appropriate\n",
    "            scores.append(score_series(gt_series, pred_series))\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pth = random.choice(extracted_images)\n",
    "# img = cv2.cvtColor(cv2.imread(pth), cv2.COLOR_BGR2RGB)\n",
    "# results = detect_yolo(pth)\n",
    "# tmppred = [results[:,:4].cpu(),results[:,4].cpu(),results[:,5].cpu()]    \n",
    "# byclass = divide_by_class(*tmppred)\n",
    "# # preds,ctype =get_predictions(byclass,img)\n",
    "# jsonname = os.path.split(pth)[-1].replace(\".jpg\",\"\")\n",
    "# jsonpath = os.path.join(\"../datasets/dataset/train/annotations\",jsonname+\".json\")\n",
    "# annot = json.load(open(jsonpath))\n",
    "# # print(ctype)\n",
    "# # print(len(preds),len(annot[\"data-series\"]))\n",
    "# # print(\"x:\", ([pp[\"x\"] for pp in preds]))\n",
    "# # print(\"y:\", ([pp[\"y\"] for pp in preds]))\n",
    "# # print(\"gt_x:\", [pp[\"x\"] for pp in annot[\"data-series\"] if \"x\" in pp])\n",
    "# # print(\"gt_y:\", [pp[\"y\"] for pp in annot[\"data-series\"] if \"y\" in pp])\n",
    "# # print(len(byclass[0]))\n",
    "# # plot_boxes(pth,[],[],[])\n",
    "# plot_boxes(pth,results[:,:4].cpu().numpy(),results[:,4].cpu().numpy(),results[:,5].cpu())"
   ]
  },
  {
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    }
   ],
   "source": [
    "ST= time.time()\n",
    "predictions = {}\n",
    "groundtruth = {}\n",
    "ec=0\n",
    "for pth in tqdm(random.sample(extracted_images,len(extracted_images))):\n",
    "    img = cv2.cvtColor(cv2.imread(pth), cv2.COLOR_BGR2RGB)\n",
    "    results = detect_yolo(pth)\n",
    "    tmppred = [results[:,:4].cpu(),results[:,5].cpu(),results[:,4].cpu()]    \n",
    "    byclass = divide_by_class(*tmppred)\n",
    "    try:\n",
    "        preds,ctype =get_predictions(byclass,img)\n",
    "    except:\n",
    "        print(\"Error\")\n",
    "        preds =[]\n",
    "        ctype = \"scatter\"\n",
    "        ec+=1\n",
    "    jsonname = os.path.split(pth)[-1].replace(\".jpg\",\"\")\n",
    "    jsonpath = os.path.join(\"../datasets/dataset/train/annotations\",jsonname+\".json\")\n",
    "    annot = json.load(open(jsonpath))\n",
    "    predictions[f\"{jsonname}_x\"] = (\";\".join([str(pp[\"x\"]) for pp in preds]),ctype)\n",
    "    predictions[f\"{jsonname}_y\"] = (\";\".join([str(pp[\"y\"]) for pp in preds]),ctype)\n",
    "    groundtruth[f\"{jsonname}_x\"] = ([pp[\"x\"] for pp in annot[\"data-series\"] if \"x\" in pp],annot[\"chart-type\"])\n",
    "    groundtruth[f\"{jsonname}_y\"] = ([pp[\"y\"] for pp in annot[\"data-series\"] if \"y\" in pp],annot[\"chart-type\"])\n",
    "total = time.time() - ST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "prediction_df = pd.DataFrame.from_dict(predictions, orient='index', columns=['data_series', 'chart_type']).rename_axis('id')\n",
    "groundt_df = pd.DataFrame.from_dict(groundtruth, orient='index', columns=['data_series', 'chart_type']).rename_axis('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (prediction_df[0::2].data_series.apply(lambda x: len(x.split(\";\")))==groundt_df[0::2].data_series.apply(len))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7443946188340808"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mispred=groundt_df[0::2][~(mask&(prediction_df[0::2].chart_type==groundt_df[0::2].chart_type))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# opth = random.choice(mispred.index)\n",
    "# pth = os.path.join(\"../datasets/dataset/keypoint/images/valid/\",opth.split(\"_\")[0]+\".jpg\")\n",
    "# img = cv2.cvtColor(cv2.imread(pth), cv2.COLOR_BGR2RGB)\n",
    "# results = yolo_model.predict(pth, save=False, imgsz=640, conf=0.2,boxes=True)\n",
    "# predictions = [results[0].boxes.xywh.cpu(),results[0].boxes.cls.cpu(),results[0].boxes.conf.cpu()]\n",
    "# byclass = divide_by_class(*predictions)\n",
    "# preds,ctype =get_predictions(byclass,img)\n",
    "# jsonname = os.path.split(pth)[-1].replace(\".jpg\",\"\")\n",
    "# jsonpath = os.path.join(\"../datasets/dataset/train/annotations\",jsonname+\".json\")\n",
    "# annot = json.load(open(jsonpath))\n",
    "# # print(ctype)\n",
    "# print(len(preds),len(annot[\"data-series\"]))\n",
    "# # print(\"x:\", ([pp[\"x\"] for pp in preds]))\n",
    "# # print(\"y:\", ([pp[\"y\"] for pp in preds]))\n",
    "# # print(\"gt_x:\", [pp[\"x\"] for pp in annot[\"data-series\"] if \"x\" in pp])\n",
    "# # print(\"gt_y:\", [pp[\"y\"] for pp in annot[\"data-series\"] if \"y\" in pp])\n",
    "# # print(len(byclass[0]))\n",
    "# plot_boxes(pth,results[0].boxes.xywh.cpu(),results[0].boxes.cls.cpu(),results[0].boxes.conf.cpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "different charts\n",
      "different charts\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "different charts\n",
      "different charts\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "unequal\n",
      "different charts\n",
      "different charts\n",
      "unequal\n",
      "unequal\n"
     ]
    }
   ],
   "source": [
    "scores = benetech_score(groundt_df, prediction_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6166933124997266"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([x for x in scores if np.isfinite(x)])"
   ]
  }
 ],
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