[MAJOR UPDATE] cocoeval.py: adding python detection evaluation code

PythonAPI/pycocoEvalDemo.py

@@ -0,0 +1,39 @@
+__author__ = 'tsungyi'
+
+import os
+import sys
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+import numpy as np
+import skimage.io as io
+import matplotlib.pyplot as plt
+import json
+import mask
+
+##Demo demonstrating the algorithm result formats for COCO
+##select results type for demo (either bbox or segm)
+annType = ['segm','bbox']
+annType = annType[0]      #specify type here
+print 'Running demo for *%s* results.\n\n'%annType
+
+##initialize COCO ground truth api
+dataDir='../'
+dataType='val2014'
+annFile = '%s/annotations/instances_%s.json'%(dataDir,dataType)
+cocoGt=COCO(annFile)
+
+##initialize COCO detections api
+resFile='%s/results/instances_%s_fake%s100_results.json'
+resFile = resFile%(dataDir, dataType, annType)
+cocoDt=cocoGt.loadRes(resFile)
+
+##run Coco evaluation code (see CocoEval.m)
+cocoEval = COCOeval(cocoGt,cocoDt)
+imgIds = sorted(cocoGt.getImgIds())
+imgIds = imgIds[0:100]
+cocoEval.params.imgIds  = imgIds
+cocoEval.params.useSegm = (annType == 'segm')
+cocoEval.params.useCats = True
+cocoEval.evaluate()
+cocoEval.accumulate()
+print cocoEval