moodmeter/parse_output.py

import os
from PIL import Image, ImageDraw
import argparse
import json
import time
import glob
import numpy as np


parser = argparse.ArgumentParser(description='Parses opencv-webcam-demo json output files and collects statistics')
parser.add_argument('--frameOutput', '-o', required=True, help='directory to look for frames & json')
parser.add_argument('--status', '-s', action='store_true', help='Keep status of last frame')
parser.add_argument('--cutAllFaces', action='store_true', help='Cut out all faces from all frames')
parser.add_argument('--sum', action='store_true', help='Get total scores over all time')
parser.add_argument('--disonant', action='store_true', help='Get most disonant faces over time')

args = parser.parse_args()

faces = []
class Face:
	def __init__(self, frame, data):
		self.id = data['id']
		self.frame = frame # Frame class
		self.data = data # json data
		self.disonanceScore = None # a first attempt, can be deprecated?
		self.anomalyScore = None
	
	def getFaceImg(self):
		r = self.data['rect']
		return self.frame.getImg().crop((int(r['x']), int(r['y']), int(r['x']+r['w']), int(r['y']+r['h'])))
	
	def getCharacteristicVector(self):
		self.vector = np.array([
			self.data["smile"],
			self.data["innerBrowRaise"],
			self.data["browRaise"],
			self.data["browFurrow"],
			self.data["noseWrinkle"],
			self.data["upperLipRaise"],
			self.data["lipCornerDepressor"],
			self.data["chinRaise"],
			self.data["lipPucker"],
			self.data["lipPress"],
			self.data["lipSuck"],
			self.data["mouthOpen"],
			self.data["smirk"],
			self.data["eyeClosure"],
			# self.data["attention"],
			self.data["eyeWiden"],
			self.data["cheekRaise"],
			self.data["lidTighten"],
			self.data["dimpler"],
			self.data["lipStretch"],
			self.data["jawDrop"],
		])
		return self.vector
	
	def setAnomalyScore(self, score):
		self.anomalyScore = score

class Frame:
	"""
	Everything for an analysed frame
	"""
	def __init__(self, outputPath, nr):
		self.outputPath = outputPath
		self.nr = nr
		self.name = "frame%06d" % nr
		self.jsonPath = os.path.join(outputPath, ("frame%06d" % (nr)) + ".json")
		self.imgPath = os.path.join(outputPath, self.name + ".jpg")
		self.faces = None # init with getFaces
	
	def getTime(self):
		return os.path.getmtime(self.imgPath)
		
	def getJson(self):
		#~ try:
			with open(self.jsonPath) as fp:
				return json.load(fp)
		#~ except Exception as e:
			#~ # no json file yet?
			#~ return None
			
	def getImg(self):
		return Image.open(self.imgPath)
		
	def getFaces(self):
		if self.faces is None:
			j = self.getJson()
			
			self.faces = [Face(self, f) for f in j['faces']]
			faces.extend(self.faces)
		
		return self.faces
	
	def updateDisonanceScores(self):
		totalValence = 0.0
		totalFaces = 0
		for face in self.getFaces():
			totalValence += face.data['valence']
			totalFaces += 1

		if totalFaces == 0:
			return
		
		avgValence = totalValence / totalFaces
		
		for face in self.getFaces():
			face.disonanceScore = abs(face.data['valence'] - avgValence)
	
	def getAverageV(self):
		vectors = [face.getCharacteristicVector() for face in self.getFaces()]
		vAvg = np.mean(vectors, axis=0)
		return vAvg
	
	def updateAnomalyScores(self):
		vAvg = self.getAverageV()
		for face in self.getFaces():
			face.setAnomalyScore(np.linalg.norm(face.getCharacteristicVector() - vAvg))
		
	
	def exists(self):
		return os.path.exists(self.jsonPath) and os.path.exists(self.imgPath)

frames = {}

def loadFrames(frameDir):
	global frames
	nr = 2
	nextFrame = Frame(frameDir, nr)
	# TODO; make threaded and infinite loop that updates global frames
	while nextFrame.exists():
		frames[nr] = nextFrame
		nr+=1
		nextFrame = Frame(frameDir, nr)
	return frames

def getLastFrame(frameDir):
	jsons = sorted(glob.glob(os.path.join(frameDir, "*.json")))
	if len(jsons):
		lastJson = jsons[-1]
		lastNr = int(lastJson[-11:-5])
		frame = Frame(frameDir, lastNr)
		return frame
	return None

def cutOutFaces(frame, targetDir):
	for faceNr, face in enumerate(frame.getFaces()):
		print(faceNr, face)
		img = face.getFaceImg()
		faceImgPath = os.path.join(targetDir, frame.name + "-%s.jpg" % face.id)
		print(faceImgPath)
		img.save(faceImgPath)
	pass

def validateJsonTimes():
	lastTime = None
	for frameNr, frame in loadFrames(args.frameOutput).items():
		thisTime = frame.getJson()['t']
		#print(frameNr, thisTime)
		if not (lastTime is None) and lastTime > thisTime:
			print "ERRROR!! Time error at %s. Restarted scanner there?" % frameNr
		lastTime = thisTime

def sumEmotions():
	total = 0.
	summed = 0.
	items = 0
	for frameNr, frame in loadFrames(args.frameOutput).items():
		for face in frame.getFaces():
			total += abs(face.data['valence'])
			summed += face.data['valence']
			items += 1
	
	average = summed / items
	print ("Total emotion %d, positivity score %d (average: %s)" % (total, summed, average))
	
def getMostDisonant(nr = 5):
	for frameNr, frame in loadFrames(args.frameOutput).items():
		frame.updateDisonanceScores()
	faces.sort(key=lambda x: x.disonanceScore, reverse=True)
	
	mostDisonantFaces = faces[:nr]
	for face in mostDisonantFaces:
		print("Frame %d, face %d, score %d, valence %d" % (face.frame.nr, face.id, face.disonanceScore, face.data['valence']))
		face.getFaceImg().show()

def getAnomalies(nr = 5):
	for frameNr, frame in loadFrames(args.frameOutput).items():
		frame.updateAnomalyScores()
	faces.sort(key=lambda x: x.anomalyScore, reverse=True)
	
	anomalies = faces[:nr]
	for face in anomalies:
		print("Frame %d, face %d, score %d" % (face.frame.nr, face.id, face.anomalyScore))
		#~ getCharacteristicVector
		face.getFaceImg().show()

def printFrameStats(frame):
	os.system('clear')
	print(time.time())
	print( ("Nr: %d" % frame.nr).ljust(40) + ("t: %f" % frame.getJson()['t']) )
	#~ print
	faces = frame.getFaces()
	print("Faces: %d" % len(faces))
	
	if len(faces) < 1:
		return
	
	params = ['smile', 'browFurrow']
	
	q0s = [np.percentile(np.array([f.data[param] for f in faces]),0) for param in params]
	q1s = [np.percentile(np.array([f.data[param] for f in faces]),25) for param in params]
	q2s = [np.percentile(np.array([f.data[param] for f in faces]),50) for param in params]
	q3s = [np.percentile(np.array([f.data[param] for f in faces]),75) for param in params]
	q4s = [np.percentile(np.array([f.data[param] for f in faces]),100) for param in params]
	
	print " ".ljust(8),
	for p in params:
		print p.center(20),
	print ""
	
	print("     0% " + "".join([("%f%%" % q).rjust(20) for q in q0s]))
	print("     q1 " + "".join([("%f%%" % q).rjust(20) for q in q1s]))
	print(" median " + "".join([("%f%%" % q).rjust(20) for q in q2s]))
	print("     q3 " + "".join([("%f%%" % q).rjust(20) for q in q3s]))
	print("   100% " + "".join([("%f%%" % q).rjust(20) for q in q4s]))
	
	#~ TODO: speaker stats
	
	frame.updateDisonanceScores()
	dissonantFace = max(faces,key=lambda f: f.disonanceScore)
	#~ dissonantFace.getFaceImg()
	

def monitorStatus(frameDir):
	while True:
		frame = getLastFrame(frameDir)
		if not frame is None:
			printFrameStats(frame)
			
		# don't check too often	
		time.sleep(.5)
			

validateJsonTimes()
	
if args.sum:
	sumEmotions()

if args.disonant:
	getMostDisonant()
	
if args.cutAllFaces:
	faceDir = os.path.join(args.frameOutput, 'faces')

	if not os.path.exists(faceDir):
		os.mkdir(faceDir)
	for frameNr, frame in loadFrames(args.frameOutput).items():
		cutOutFaces(faceDir)


if args.status:
	monitorStatus(args.frameOutput)