print "Test Error mini:", testErrorMini
'''
# Run the same caffe test set using python
DEBUG = False # Set this to true if you wish to plot the images
if 'testError' in STEPS:
with open('testSet.pickle','r') as f:
dataRowsTrainValid = load(f)
testError=ErrorAcum()
predictor = Predictor(protoTXTPath=PATH_TO_DEPLOY_TXT, weightsPath=PATH_TO_WEIGHTS)
for i, dataRow in enumerate(dataRowsTrainValid):
dataRow40 = dataRow.copyCroppedByBBox(dataRow.fbbox)
image, lm40 = predictor.preprocess(dataRow40.image, dataRow40.landmarks())
prediction = predictor.predict(image)
testError.add(lm40, prediction)
dataRow40.prediction = (prediction+0.5)*40.
if DEBUG and i%40 ==0:
dataRow.prediction = dataRow40.inverseScaleAndOffset(dataRow40.prediction)
dataRow.show()
break
print "Test Error:", testError
# AFW test - Make the pickle data set
if 'createAFW_TestSet' in STEPS:
if 'testSetHD5' in STEPS:
writeHD5(dataRowsTestValid, ROOT+'/caffeData/hd5/test.hd5', ROOT+'/caffeData/test.txt', MEAN_TRAIN_SET, STD_TRAIN_SET)
print "Finished writing test to caffeData/test.txt"
DEBUG = True
if 'testErrorMini' in STEPS:
with open('testSetMini.pickle','r') as f:
dataRowsTrainValid = load(f)
testErrorMini=ErrorAcum()
predictor = Predictor(protoTXTPath=PATH_TO_DEPLOY_TXT, weightsPath=PATH_TO_WEIGHTS)
for i, dataRow in enumerate(dataRowsTrainValid):
dataRow40 = dataRow.copyCroppedByBBox(dataRow.fbbox).copyMirrored()
image, lm_0_5 = predictor.preprocess(dataRow40.image, dataRow40.landmarks())
prediction = predictor.predict(image)
testErrorMini.add(lm_0_5, prediction)
dataRow40.prediction = (prediction+0.5)*40. # Scale -0.5..+0.5 to 0..40
if DEBUG:
dataRow.prediction = dataRow40.inverseScaleAndOffset(dataRow40.prediction) # Scale up to the original image scale
dataRow.show()
if i>40:
print "Debug breaked after %d rows:" % i
print "Test Error mini:", testErrorMini
if 'trainSetHD5' in STEPS:
PATH_TO_DEPLOY_TXT_DM = os.path.join('/data/b0216.yu/LD_Train/Parts/DownMouth/', 'parts_40x40.prototxt')
predictorDM = Predictor(protoTXTPath=PATH_TO_DEPLOY_TXT_DM, weightsPath=PATH_TO_WEIGHTS_DM, meanPath=MEAN_TRAIN_DM, stdPath=STD_TRAIN_DM)
'''
testError=ErrorAcum()
ttime = 0.0
for i, dataRow in enumerate(dataRowsTestValid):
start_time = time.time()
fbbox = BBox(dataRow.fbbox.left,dataRow.fbbox.top,dataRow.fbbox.right,dataRow.fbbox.bottom)
dataRow40 = dataRow.copyCroppedByBBox_68(fbbox)
image, lm40 = root_predictor.preprocess(dataRow40.image, dataRow40.landmarks_68())
prediction = root_predictor.predict(image)
prediction = dataRow40.inverseScaleAndOffset((prediction + 0.5) * GLOBAL_IMAGE_SIZE)
# Rotate & refine - yubing 20160727
dataRowRot, rotangleT = dataRow.copyCropped_68_rot(prediction,fbbox)
imageRot, lmRot = root_predictor.preprocess(dataRowRot.image, dataRow40.landmarks_68())
predictionRot = root_predictor.predict(imageRot)
predictionRot = dataRowRot.inverseScaleAndOffset((predictionRot + 0.5) * GLOBAL_IMAGE_SIZE)
prediction = dataRow.reverseRotate(predictionRot, -rotangleT)
# Local part refine - yubing 20160701
# Left Eye
dataRowLE, rotangle = dataRow.copyCropped_patch(prediction,'LE',rotangleT)
imageLE, lm40 = predictorLE.preprocess(dataRowLE.image, dataRow40.landmarks_68())
predictionLE = predictorLE.predict(imageLE)
predictionLE = dataRowLE.inverseScaleAndOffset((predictionLE + 0.5) * 40)