pcb defect detetcion application

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / data / lib_coco / PythonAPI / pycocoEvalDemo.ipynb

diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocoEvalDemo.ipynb b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocoEvalDemo.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pycocotools.coco import COCO\n",
+    "from pycocotools.cocoeval import COCOeval\n",
+    "import numpy as np\n",
+    "import skimage.io as io\n",
+    "import pylab\n",
+    "pylab.rcParams['figure.figsize'] = (10.0, 8.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running demo for *bbox* results.\n"
+     ]
+    }
+   ],
+   "source": [
+    "annType = ['segm','bbox','keypoints']\n",
+    "annType = annType[1]      #specify type here\n",
+    "prefix = 'person_keypoints' if annType=='keypoints' else 'instances'\n",
+    "print 'Running demo for *%s* results.'%(annType)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading annotations into memory...\n",
+      "Done (t=8.01s)\n",
+      "creating index...\n",
+      "index created!\n"
+     ]
+    }
+   ],
+   "source": [
+    "#initialize COCO ground truth api\n",
+    "dataDir='../'\n",
+    "dataType='val2014'\n",
+    "annFile = '%s/annotations/%s_%s.json'%(dataDir,prefix,dataType)\n",
+    "cocoGt=COCO(annFile)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading and preparing results...     \n",
+      "DONE (t=0.05s)\n",
+      "creating index...\n",
+      "index created!\n"
+     ]
+    }
+   ],
+   "source": [
+    "#initialize COCO detections api\n",
+    "resFile='%s/results/%s_%s_fake%s100_results.json'\n",
+    "resFile = resFile%(dataDir, prefix, dataType, annType)\n",
+    "cocoDt=cocoGt.loadRes(resFile)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "imgIds=sorted(cocoGt.getImgIds())\n",
+    "imgIds=imgIds[0:100]\n",
+    "imgId = imgIds[np.random.randint(100)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running per image evaluation...      \n",
+      "DONE (t=0.46s).\n",
+      "Accumulating evaluation results...   \n",
+      "DONE (t=0.38s).\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505\n",
+      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.697\n",
+      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.573\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.586\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.519\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.501\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.387\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.594\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.595\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.640\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.566\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.564\n"
+     ]
+    }
+   ],
+   "source": [
+    "# running evaluation\n",
+    "cocoEval = COCOeval(cocoGt,cocoDt,annType)\n",
+    "cocoEval.params.imgIds  = imgIds\n",
+    "cocoEval.evaluate()\n",
+    "cocoEval.accumulate()\n",
+    "cocoEval.summarize()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}