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 + '/*')
