def _test_mAP_pipeline(): inDir = '/mnt/Ext/data/' db.connect() #convert to frames and store in a temp directory imProd = imc.File2Im() bgr = imc.RGB2BGR() #run rcnn on these frames prms = cfg.set_rcnn_prms(trainDataSet='coco', netName='vgg16-coco-rcnn') print cfg.get_rcnn_prms(**prms) rcnn = cc.Im2RCNNDet(prms) # glue to Jpkg jlbl = RCNN2Labels() fmt = MetricList2FormatMetricList() scoredImDir = [] sessionQuery={'required_status': 'MANUAL_STEP_1|CHECKED'} for session in NSVideoSession.fetch_many(query=sessionQuery, count=12): seshCheck = [session] # inside mutable object because no nonlocal keyword in python2 while not seshCheck[0].annotations: seshCheck[0] = NSVideoSession.fetch_one(query=sessionQuery) session = seshCheck[0] vidName = osp.join(inDir, session.storage_path) #tmp directory to store the frames tmpDir = '/tmp/NSBackEnd/alg_output' hashStr = hashlib.sha256(vidName).hexdigest() hashDir = osp.join(tmpDir, hashStr) vid2im = imc.Video2ImNames({'op_dir': hashDir, 'vidName': vidName}) # chain 1: vid --> rcnn detections chain1 = ch.Chainer([vid2im, imProd, bgr, rcnn]) chain1Out = chain1.produce(vidName) # seperate chains to obtain aggregated output of chain1 # chain2: detections --> mAP format pred = Labels2MetricList() chain2 = ch.Chainer([jlbl, pred]) allDet = [] while chain1Out: allDet.append(chain1Out) chain1Out = chain1.produce(vidName) chain2Out = chain2.produce(allDet) # chain3: annotations to mAP format act = Labels2MetricList() chain3 = ch.Chainer([act]) chain3Out = chain3.produce(session.annotations) # chain4: mAP calc mapget = mc.Labels2mAP({'object': 'Person', 'occlusion': None}) chain4 = ch.Chainer([fmt, mapget]) chain4Out = chain4.produce([chain3Out, chain2Out]) print 'mAP at {0}:\n{1}'.format(vidName, chain4Out) scoredImDir.append((hashDir, chain2Out, chain4Out)) viser = vc.VismAP({'outDir': 'successes_failures', 'n': 3}) viser.produce(scoredImDir) os.system('rm -r ' + tmpDir + '/*')