def getSingleFeatureLineFromFile(file, decisions, shot, leave_out_class=None):
"""
This is a less troublesome but slow method to get a featureLine.
"""
beatList, context = getContextAndBeatListFromFile(file)
blockList = coalesceBeats(beatList)
Features.initializeContextVars(context)
lastShotId, context, blockList = applyDecisionsToBeatscript(context, blockList,
decisions)
featureLine = getFeatureLine(context, blockList[len(decisions)], shot, lastShotId,
leave_out_class)
return featureLine
def createFeatureLines(context, beatList, shot, leave_out_class=None):
"""
Returns the list of featureLines converted from the Beats in beatList
"""
featureLines = []
blockList = coalesceBeats(beatList)
Features.initializeContextVars(context)
lastShotId = -1
for block in blockList:
featureLines.append(
getFeatureLine(context, block, shot, lastShotId, leave_out_class))
context["BygoneBlocks"].append(block)
lastShotId = block[-1].shotId
return featureLines
def getFeatureNames(leave_out_class=None):
"""
Returns an array of feature names corresponding to the featureLine.
"""
names = []
featureClassList = Features.getAllFeatureClasses()
context = createContext()
dummy_beat = Beat("0_1\tfull_shot\tfalse\tintroduce\tperson§Nobody", context)
context = Features.createBeatList(context, [dummy_beat])
Features.initializeContextVars(context)
for featureClass in [x for x in featureClassList if x != leave_out_class]:
feature = featureClass(context, [dummy_beat])
names += feature.getNames()
return names
def getSingleFeatureLine(context, blockList, decisions, shot, leave_out_class=None):
"""
Returns a featureLine based on the context and the decisions.
"""
Features.initializeContextVars(context)
lastShotId, context, blockList = applyDecisionsToBeatscript(context, blockList,
decisions)
# This is for preventing the classifier to cheat by using the correct class.
# Activate if you're suspicious.
#for beat in blockList[len(decisions)]:
# beat.shot = 0
featureLine = getFeatureLine(context, blockList[len(decisions)], shot, lastShotId,
leave_out_class)
return featureLine
def main():
beatscriptFile = open(sys.argv[1], "r")
lines = beatscriptFile.readlines()
context = readContext(lines)
beatList = readBeatscript(lines, context)
blockList = coalesceBeats(beatList)
Features.initializeContextVars(context)
dataLines = []
for block in blockList:
dataLines.append(createDataLine(context, block))
context["BygoneBlocks"].append(block)
outputFile = open(sys.argv[2], "w")
for dataLine in dataLines:
outputFile.write(DELIMITER.join([str(x) for x in dataLine]) + "\n")
#outputFile.write(DELIMITER.join(dataLine) + "\n")
outputFile.close()
def onlineFeatureLineCreator(filename, use_classified_shot = False, use_history = True):
# load context and complete beatlist from file
context, beatList = getContextAndBeatListFromFile(filename)
Features.initializeContextVars(context)
blockList = coalesceBeats(beatList)
context["BygoneBlocks"] = []
for block in blockList:
shot_true = block[-1].shot
# get current feature line and true shot class
featureLine = getFeatureLine(context, block, True, -1)
features = np.array(featureLine[:-1], dtype=np.float64)
shot_classified = yield features, shot_true
# update block and lastShotId
if use_classified_shot:
for beat in block:
beat.shot = shot_classified
if use_history:
context["BygoneBlocks"].append(block)
def main():
if len(sys.argv) < 2:
print("Usage: python ClassificationProcess.py <Beatscript-Filename>")
return 1
# Initialization and Training
classifiers, scaler = trainWithAllExamples(True)
cutClassifiers, cutScaler = trainWithAllExamples(False)
try:
beatscript_file = open(sys.argv[1], "r")
except IOError:
print("Error: The Beatscipt could not be opened.")
print("Usage: python ClassificationProcess.py <Beatscript-Filename>")
return 1
lines = beatscript_file.readlines()
context = readContext(lines)#
beatscript = readBeatscript(lines, context)
Features.initializeContextVars(context)
beatList = getBeatsBetweenFrames(beatscript, -1, 0)
current_frame = 0
lastBlock = beatList
context["BygoneBlocks"] = []
sys.stdout.write("Training finished." + "\n")
sys.stdout.flush()
# Get Distribution
dist = classifyForShot(lastBlock, context, classifiers, scaler)
cutBeforeThisClassification = classifyForCut(lastBlock, context, cutClassifiers,
cutScaler)
while True:
choice = raw_input("")
if choice == "e":
printListOfEntities(context)
elif choice == "t": # get the names of the target and the linetarget
determine_targets(context, lastBlock)
elif choice == "p": # print out the classification propabilities
pickle.dump(dist, sys.stdout)
elif choice == "c": # Print out, if we should cut at this point
blockList = []
decisions = []
for block in context["BygoneBlocks"]:
blockList.append(block)
decisions.append(block[0].shot)
blockList.append(lastBlock)
decisions.append(dist.index(max(dist)))
if cutBeforeThisClassification[0] < cutBeforeThisClassification[1]:
sys.stdout.write("yes\n")
else:
sys.stdout.write("no\n")
elif choice == "f": # check framenumber for a new block
new_frame = int(raw_input(""))
beatList = getBeatsBetweenFrames(beatscript, current_frame, new_frame)
current_frame = new_frame
if beatList :
context["BygoneBlocks"].append(lastBlock)
lastBlock = beatList
dist = classifyForShot(lastBlock, context, classifiers, scaler)
cutBeforeThisClassification = classifyForCut(lastBlock, context, cutClassifiers, cutScaler)
sys.stdout.write("yes\n")
else:
sys.stdout.write("no\n")
elif choice == "d": # recieve the decision for the lastBlock
decision = int(raw_input(""))
for beat in lastBlock:
beat.shot = decision
sys.stdout.write("decision recieved\n")
elif choice == "q": # quit...
sys.stdout.write("exiting...\n")
sys.stdout.flush()
break
else:
sys.stdout.write("You didn't enter something useful.\n")
sys.stdout.flush()
