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Testing at UHS
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@nick-ohi
nick-ohi committed Aug 29, 2016
1 parent 5a41ba6 commit c613158
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Showing 19 changed files with 363 additions and 379 deletions.
Binary file modified computer_vision/data/calibration/calibration_mask.jpg
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12 changes: 6 additions & 6 deletions computer_vision/data/calibration/cameraCalibration.csv
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180 changes: 90 additions & 90 deletions computer_vision/scripts/ClassifierService.py
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Expand Up @@ -123,100 +123,100 @@ def startService(self):
# dictionary to store classifier parameters
classifierDict = {}

# classifier version
classifierVersion = 'CACH_V1'
classifierSampleType = 'CACH'
classifierType = 0

# imgSize
imgSize = 50

# read the mean data
meanData50PathCach = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+"/allData_50_lmdb_"+classifierSampleType+".npy"
meanData50Cach = np.load(meanData50PathCach)
meanData50Cach = np.reshape(meanData50Cach, (1, 3, imgSize, imgSize))

# read the classifier
classifier50PathCach = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+'/best_9epoch_50_'+classifierSampleType+'.npz'

# set dropout parameters for better performance
dropoutParams50Cach = {}
dropoutParams50Cach['conv'] = 0.5
dropoutParams50Cach['fc'] = 0.5

# initialize DeepFishNet50Cach
myClassifier50Cach = DeepFishNet50(loadData = False, imgSize = imgSize, crossvalidid = 0, dropout_params = dropoutParams50Cach, mode='Test', modelToLoad = classifier50PathCach)

# store 50 x 50 classifier details
classifierDict[classifierType] = {}
classifierDict[classifierType]['classifier'] = myClassifier50Cach
classifierDict[classifierType]['mean'] = meanData50Cach
classifierDict[classifierType]['modulePath'] = cvModulePath

# classifier version
classifierVersion = 'HARD_V1'
classifierSampleType = 'HARD'
classifierType = 1

# imgSize
imgSize = 50

# read the mean data
meanData50PathHard = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+"/allData_50_lmdb_"+classifierSampleType+".npy"
meanData50Hard = np.load(meanData50PathHard)
meanData50Hard = np.reshape(meanData50Hard, (1, 3, imgSize, imgSize))

# read the classifier
classifier50PathHard = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+'/best_9epoch_50_'+classifierSampleType+'.npz'

# set dropout parameters for better performance
dropoutParams50Hard = {}
dropoutParams50Hard['conv'] = 0.5
dropoutParams50Hard['fc'] = 0.5

# initialize DeepFishNet50Hard
myClassifier50Hard = DeepFishNet50(loadData = False, imgSize = imgSize, crossvalidid = 0, dropout_params = dropoutParams50Hard, mode='Test', modelToLoad = classifier50PathHard)

# store 50 x 50 classifier details
classifierDict[classifierType] = {}
classifierDict[classifierType]['classifier'] = myClassifier50Hard
classifierDict[classifierType]['mean'] = meanData50Hard
classifierDict[classifierType]['modulePath'] = cvModulePath

# classifier version
classifierVersion = 'ROCK_V1'
classifierSampleType = 'ROCK'
classifierType = 2

# imgSize
imgSize = 50

# read the mean data
meanData50PathRock = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+"/allData_50_lmdb_"+classifierSampleType+".npy"
meanData50Rock = np.load(meanData50PathRock)
meanData50Rock = np.reshape(meanData50Rock, (1, 3, imgSize, imgSize))

# read the classifier
classifier50PathRock = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+'/best_9epoch_50_'+classifierSampleType+'.npz'

# set dropout parameters for better performance
dropoutParams50Rock = {}
dropoutParams50Rock['conv'] = 0.5
dropoutParams50Rock['fc'] = 0.5

# initialize DeepFishNet50Rock
myClassifier50Rock = DeepFishNet50(loadData = False, imgSize = imgSize, crossvalidid = 0, dropout_params = dropoutParams50Rock, mode='Test', modelToLoad = classifier50PathRock)

# store 50 x 15 classifier details
classifierDict[classifierType] = {}
classifierDict[classifierType]['classifier'] = myClassifier50Rock
classifierDict[classifierType]['mean'] = meanData50Rock
classifierDict[classifierType]['modulePath'] = cvModulePath
# # classifier version
# classifierVersion = 'CACH_V1'
# classifierSampleType = 'CACH'
# classifierType = 0

# # imgSize
# imgSize = 50

# # read the mean data
# meanData50PathCach = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+"/allData_50_lmdb_"+classifierSampleType+".npy"
# meanData50Cach = np.load(meanData50PathCach)
# meanData50Cach = np.reshape(meanData50Cach, (1, 3, imgSize, imgSize))

# # read the classifier
# classifier50PathCach = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+'/best_9epoch_50_'+classifierSampleType+'.npz'

# # set dropout parameters for better performance
# dropoutParams50Cach = {}
# dropoutParams50Cach['conv'] = 0.5
# dropoutParams50Cach['fc'] = 0.5

# # initialize DeepFishNet50Cach
# myClassifier50Cach = DeepFishNet50(loadData = False, imgSize = imgSize, crossvalidid = 0, dropout_params = dropoutParams50Cach, mode='Test', modelToLoad = classifier50PathCach)

# # store 50 x 50 classifier details
# classifierDict[classifierType] = {}
# classifierDict[classifierType]['classifier'] = myClassifier50Cach
# classifierDict[classifierType]['mean'] = meanData50Cach
# classifierDict[classifierType]['modulePath'] = cvModulePath

# # classifier version
# classifierVersion = 'HARD_V1'
# classifierSampleType = 'HARD'
# classifierType = 1

# # imgSize
# imgSize = 50

# # read the mean data
# meanData50PathHard = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+"/allData_50_lmdb_"+classifierSampleType+".npy"
# meanData50Hard = np.load(meanData50PathHard)
# meanData50Hard = np.reshape(meanData50Hard, (1, 3, imgSize, imgSize))

# # read the classifier
# classifier50PathHard = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+'/best_9epoch_50_'+classifierSampleType+'.npz'

# # set dropout parameters for better performance
# dropoutParams50Hard = {}
# dropoutParams50Hard['conv'] = 0.5
# dropoutParams50Hard['fc'] = 0.5

# # initialize DeepFishNet50Hard
# myClassifier50Hard = DeepFishNet50(loadData = False, imgSize = imgSize, crossvalidid = 0, dropout_params = dropoutParams50Hard, mode='Test', modelToLoad = classifier50PathHard)

# # store 50 x 50 classifier details
# classifierDict[classifierType] = {}
# classifierDict[classifierType]['classifier'] = myClassifier50Hard
# classifierDict[classifierType]['mean'] = meanData50Hard
# classifierDict[classifierType]['modulePath'] = cvModulePath

# # classifier version
# classifierVersion = 'ROCK_V1'
# classifierSampleType = 'ROCK'
# classifierType = 2

# # imgSize
# imgSize = 50

# # read the mean data
# meanData50PathRock = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+"/allData_50_lmdb_"+classifierSampleType+".npy"
# meanData50Rock = np.load(meanData50PathRock)
# meanData50Rock = np.reshape(meanData50Rock, (1, 3, imgSize, imgSize))

# # read the classifier
# classifier50PathRock = cvModulePath+'/data/classifier/DeepFishNet'+str(imgSize)+'/'+classifierVersion+'/best_9epoch_50_'+classifierSampleType+'.npz'

# # set dropout parameters for better performance
# dropoutParams50Rock = {}
# dropoutParams50Rock['conv'] = 0.5
# dropoutParams50Rock['fc'] = 0.5

# # initialize DeepFishNet50Rock
# myClassifier50Rock = DeepFishNet50(loadData = False, imgSize = imgSize, crossvalidid = 0, dropout_params = dropoutParams50Rock, mode='Test', modelToLoad = classifier50PathRock)

# # store 50 x 15 classifier details
# classifierDict[classifierType] = {}
# classifierDict[classifierType]['classifier'] = myClassifier50Rock
# classifierDict[classifierType]['mean'] = meanData50Rock
# classifierDict[classifierType]['modulePath'] = cvModulePath

# classifier version
classifierVersion = 'ALL_V1'
classifierSampleType = 'ALL'
classifierType = 3
classifierType = 0

# imgSize
imgSize = 50
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