add exception for empty situation

result_processing/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

result_processing/.ipynb_checkpoints/box_plot-checkpoint.ipynb

@@ -0,0 +1,272 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "from glob import glob\n",
+    "from os.path import join as pjoin\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def resize_label(bboxes, d_height, gt_height, bias=0):\n",
+    "    bboxes_new = []\n",
+    "    scale = gt_height/d_height\n",
+    "    for bbox in bboxes:\n",
+    "        bbox = [int(b * scale + bias) for b in bbox]\n",
+    "        bboxes_new.append(bbox)\n",
+    "    return bboxes_new\n",
+    "\n",
+    "\n",
+    "def draw_bounding_box(org, corners, color=(0, 255, 0), line=2, show=False):\n",
+    "    board = org.copy()\n",
+    "    for i in range(len(corners)):\n",
+    "        board = cv2.rectangle(board, (corners[i][0], corners[i][1]), (corners[i][2], corners[i][3]), color, line)\n",
+    "    if show:\n",
+    "        cv2.imshow('a', cv2.resize(board, (300, 600)))\n",
+    "        cv2.waitKey(0)\n",
+    "    return board\n",
+    "\n",
+    "\n",
+    "def load_detect_result_json(reslut_file_root):\n",
+    "    result_files = glob(pjoin(reslut_file_root, '*.json'))\n",
+    "    compos_reform = {}\n",
+    "    print('Loading %d detection results' % len(result_files))\n",
+    "    for reslut_file in tqdm(result_files):\n",
+    "        img_name = reslut_file.split('\\\\')[-1].split('_')[0]\n",
+    "        compos = json.load(open(reslut_file, 'r'))['compos']\n",
+    "        for compo in compos:\n",
+    "            if img_name not in compos_reform:\n",
+    "                compos_reform[img_name] = {'bboxes': [[compo['column_min'], compo['row_min'], compo['column_max'], compo['row_max']]],\n",
+    "                                           'categories': [compo['category']]}\n",
+    "            else:\n",
+    "                compos_reform[img_name]['bboxes'].append([compo['column_min'], compo['row_min'], compo['column_max'], compo['row_max']])\n",
+    "                compos_reform[img_name]['categories'].append(compo['category'])\n",
+    "    return compos_reform\n",
+    "\n",
+    "\n",
+    "def load_ground_truth_json(gt_file):\n",
+    "    def get_img_by_id(img_id):\n",
+    "        for image in images:\n",
+    "            if image['id'] == img_id:\n",
+    "                return image['file_name'].split('/')[-1][:-4], (image['height'], image['width'])\n",
+    "\n",
+    "    def cvt_bbox(bbox):\n",
+    "        '''\n",
+    "        :param bbox: [x,y,width,height]\n",
+    "        :return: [col_min, row_min, col_max, row_max]\n",
+    "        '''\n",
+    "        bbox = [int(b) for b in bbox]\n",
+    "        return [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]\n",
+    "\n",
+    "    data = json.load(open(gt_file, 'r'))\n",
+    "    images = data['images']\n",
+    "    annots = data['annotations']\n",
+    "    compos = {}\n",
+    "    print('Loading %d ground truth' % len(annots))\n",
+    "    for annot in tqdm(annots):\n",
+    "        img_name, size = get_img_by_id(annot['image_id'])\n",
+    "        if img_name not in compos:\n",
+    "            compos[img_name] = {'bboxes': [cvt_bbox(annot['bbox'])], 'categories': [annot['category_id']], 'size':size}\n",
+    "        else:\n",
+    "            compos[img_name]['bboxes'].append(cvt_bbox(annot['bbox']))\n",
+    "            compos[img_name]['categories'].append(annot['category_id'])\n",
+    "    return compos\n",
+    "\n",
+    "\n",
+    "def eval(detection, ground_truth, img_root, show=True):\n",
+    "    def match(org, d_bbox, gt_bboxes, matched):\n",
+    "        '''\n",
+    "        :param matched: mark if the ground truth component is matched\n",
+    "        :param d_bbox: [col_min, row_min, col_max, row_max]\n",
+    "        :param gt_bboxes: list of ground truth [[col_min, row_min, col_max, row_max]]\n",
+    "        :return: Boolean: if IOU large enough or detected box is contained by ground truth\n",
+    "        '''\n",
+    "        area_d = (d_bbox[2] - d_bbox[0]) * (d_bbox[3] - d_bbox[1])\n",
+    "        for i, gt_bbox in enumerate(gt_bboxes):\n",
+    "            if matched[i] == 0:\n",
+    "                continue\n",
+    "            area_gt = (gt_bbox[2] - gt_bbox[0]) * (gt_bbox[3] - gt_bbox[1])\n",
+    "            col_min = max(d_bbox[0], gt_bbox[0])\n",
+    "            row_min = max(d_bbox[1], gt_bbox[1])\n",
+    "            col_max = min(d_bbox[2], gt_bbox[2])\n",
+    "            row_max = min(d_bbox[3], gt_bbox[3])\n",
+    "            # if not intersected, area intersection should be 0\n",
+    "            w = max(0, col_max - col_min)\n",
+    "            h = max(0, row_max - row_min)\n",
+    "            area_inter = w * h\n",
+    "            if area_inter == 0:\n",
+    "                continue\n",
+    "            iod = area_inter / area_d\n",
+    "            iou = area_inter / (area_d + area_gt - area_inter)\n",
+    "\n",
+    "            # if show:\n",
+    "            #     print(\"IoDetection: %.3f, IoU: %.3f\" % (iod, iou))\n",
+    "            #     broad = draw_bounding_box(org, [d_bbox], color=(0, 0, 255))\n",
+    "            #     draw_bounding_box(broad, [gt_bbox], color=(0, 255, 0), show=True)\n",
+    "\n",
+    "            if iou >= 0.5 or iod == 1:\n",
+    "                matched[i] = 0\n",
+    "                return True\n",
+    "        return False\n",
+    "\n",
+    "    pres = []\n",
+    "    recalls = []\n",
+    "    amount = len(detection)\n",
+    "    for i, image_id in enumerate(detection):\n",
+    "        TP, FP, FN = 0, 0, 0\n",
+    "        img = cv2.imread(pjoin(img_root, image_id + '.jpg'))\n",
+    "        d_compos = detection[image_id]\n",
+    "        gt_compos = ground_truth[image_id]\n",
+    "        d_compos['bboxes'] = resize_label(d_compos['bboxes'], 800, gt_compos['size'][0])\n",
+    "        matched = np.ones(len(gt_compos['bboxes']), dtype=int)\n",
+    "        for d_bbox in d_compos['bboxes']:\n",
+    "            if match(img, d_bbox, gt_compos['bboxes'], matched):\n",
+    "                TP += 1\n",
+    "            else:\n",
+    "                FP += 1\n",
+    "        FN += sum(matched)\n",
+    "\n",
+    "        precesion = TP / (TP+FP)\n",
+    "        recall = TP / (TP+FN)\n",
+    "\n",
+    "        pres.append(precesion)\n",
+    "        recalls.append(recall)\n",
+    "        if show:\n",
+    "            print(\"Number of gt boxes: %d, Number of detected boxes: %d\" % (\n",
+    "            len(gt_compos['bboxes']), len(d_compos['bboxes'])))\n",
+    "            print('[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f' % (i, amount, TP, FP, FN, precesion, recall))\n",
+    "\n",
+    "            broad = draw_bounding_box(img,  d_compos['bboxes'], color=(0, 0, 255), line=3)\n",
+    "            draw_bounding_box(broad, gt_compos['bboxes'], color=(0, 255, 0), show=True, line=2)\n",
+    "\n",
+    "        if i % 200 == 0:\n",
+    "            print('[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f' % (i, amount, TP, FP, FN, precesion, recall))\n",
+    "\n",
+    "    return pres, recalls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "  0%|                                                                                         | 0/9419 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading 9419 detection results\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 18%|██████████████▏                                                              | 1742/9419 [00:06<00:26, 288.34it/s]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-46d485acea1a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdetect\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_detect_result_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'E:\\\\Mulong\\\\Result\\\\rico\\\\rico_uied\\\\rico_new_uied_cls\\\\ip'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mgt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_ground_truth_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'E:/Mulong/Datasets/rico/test.json'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mpres\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrecalls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdetect\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'E:\\\\Mulong\\\\Datasets\\\\rico\\\\combined'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-4-89050ccdec40>\u001b[0m in \u001b[0;36mload_detect_result_json\u001b[1;34m(reslut_file_root)\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mreslut_file\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult_files\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[0mimg_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreslut_file\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\\\'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'_'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0mcompos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreslut_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'compos'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mcompo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcompos\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mimg_name\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcompos_reform\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "detect = load_detect_result_json('E:\\\\Mulong\\\\Result\\\\rico\\\\rico_uied\\\\rico_new_uied_cls\\\\ip')\n",
+    "gt = load_ground_truth_json('E:/Mulong/Datasets/rico/test.json')\n",
+    "pres, recalls = eval(detect, gt, 'E:\\\\Mulong\\\\Datasets\\\\rico\\\\combined', show=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\figure.py:448: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "  % get_backend())\n"
+     ]
+    },
+    {
+     "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",
+    "\n",
+    "fig = plt.figure()  # 创建画布\n",
+    "ax = plt.subplot()  # 创建作图区域\n",
+    "ax.boxplot([pres, recalls])\n",
+    "ax.set_xticklabels(['precisions', 'recalls'])\n",
+    "plt.grid(axis='y')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "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": "3.5.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}