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Commit 0ab70fd8 authored by tihmels's avatar tihmels
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Process Model für Cluster Einsatz vorbereitet

parent c2e98706
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projectmood/process_model.py
View file @ 0ab70fd8
"""
Diese Klasse macht das Training des Models möglich
"""
import argparse
import cv2
import glob
......@@ -9,21 +10,47 @@ import numpy as np
import sys
import logging
from email_service import sendMail
logfile = 'logs/process_model.log'
"""
Erstellt und gibt das Log-File aus
"""
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)-8s %(message)s',
logging.basicConfig(level=logging.NOTSET, format='%(asctime)s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename='logs/process_model.log')
filename=logfile)
"""
Liest Input Parameter
Argument Parser erlaubt Parameter für die Verarbeitung anzugeben.
"""
args = sys.argv
logging.debug('Fisherface training initialized')
file = open("{}.csv".format('_'.join(args[1:]).lower()), "w")
parser = argparse.ArgumentParser(description='Process Model Application')
parser.add_argument('--dataset', action='store', dest='dataset', default='resources/img_data/dataset/', help='path to dataset')
parser.add_argument('-i', action='store', dest='iterations', type=int, default=30, help='declare processing iterations')
parser.add_argument('-e', action='append', dest='emotions', default=['happy', 'neutral', 'surprise'], help='declare emotions that should be processed')
parser.add_argument('-p', action='append', dest='properties', help='pre-processing steps')
parser.add_argument('--test', action='store_true', help='prevent writing new model to file system')
parser.add_argument('--csv', action='store_true', help='activate csv processing')
parser.add_argument('--email', action='store_true', help='activate email notifications')
arguments = parser.parse_args()
logging.debug(arguments)
dataset_path = arguments.dataset
iterations = arguments.iterations
emotions = arguments.emotions
properties = arguments.properties
csv = arguments.csv
email = arguments.email
test = arguments.test
"""
Liest Input Parameter
"""
logging.info('Fisherface training started')
if email:
sendMail('Fisherface training started')
def _get_faces_from_emotion(emotion):
"""
......@@ -31,7 +58,7 @@ def _get_faces_from_emotion(emotion):
:param emotion: Die Emotion
:return: training, prediction
"""
files = glob.glob('img_data/dataset/{}/*'.format(emotion))
files = glob.glob(dataset_path + '{}/*'.format(emotion))
random.shuffle(files)
"""
......@@ -95,27 +122,31 @@ def run_recognizer():
cnt += 1
return ((100 * correct) / (correct + incorrect))
if len(args) > 1:
tags = ', '.join(args[1:])
logging.debug(tags.upper())
"""
Emotions Liste
"""
emotions = ["happy", "neutral", "surprise"]
fishface = cv2.face.FisherFaceRecognizer_create()
metascore = []
for i in range(0, 40):
for i in range(1, iterations+1):
correct = run_recognizer()
file.write("{}\n".format(int(correct)))
logging.debug("{} : {}%".format(i, int(correct)))
logging.info("{} : {}%".format(i, int(correct)))
metascore.append(correct)
file.close()
if i % (int(iterations/4)) == 0 and email:
sendMail(str(i) + ' iterations done', body='up-to-date average: {}%'.format(np.mean(metascore)))
if csv:
file = open("resources/csv/{}.csv".format('_'.join(properties).lower()), "w")
for entry in metascore:
file.write("{}\n".format(int(entry)))
file.close()
logging.info("Fisherface training finished - {}% average\n".format(np.mean(metascore)))
if not test:
fishface.write('img_data/models/detection_model.xml')
logging.debug("{} iterations - {}% average\n".format(len(metascore), np.mean(metascore)))
fishface.write('img_data/models/detection_model.xml')
if email:
sendMail('Fisherface training finished')
projectmood/sorted_set_facedetector.py
View file @ 0ab70fd8
......@@ -10,9 +10,11 @@ import os
from email_service import sendMail
from face_detect import locate_faces
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
logfile = 'logs/sorted_set_facedetector.log'
logging.basicConfig(level=logging.NOTSET, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename='logs/sorted_set_facedetector.log',
filename=logfile,
filemode='w')
"""
Argument Parser erlaubt Parameter für die Verarbeitung anzugeben.
......@@ -23,39 +25,45 @@ parser.add_argument('--source', action='store', dest='img_source', default='reso
help='path to image source')
parser.add_argument('--dataset', action='store', dest='dataset', default='resources/img_data/dataset/', help='path to dataset')
parser.add_argument('--r', action='store', dest='resize', default=150, type=int, help='resize factor')
parser.add_argument('-e', action='append', dest='emotions', default=['happy', 'neutral', 'surprised'], help='declare emotions that should be processed')
parser.add_argument('-e', action='append', dest='emotions', default=['happy', 'neutral', 'surprise'], help='declare emotions that should be processed')
parser.add_argument('-c', action='store', dest='scaleFactor', default=1.1, type=float,
help='scale factor - haar')
parser.add_argument('-n', action='store', dest='minNeighbors', default=6, type=int, help='min neighbors - haar')
parser.add_argument('-s', action='store', dest='minSize', default=40, type=int, help='min size - haar')
parser.add_argument('-x', action='store_true', help='activate email notifications')
parser.add_argument('--email', action='store_true', help='activate email notifications')
arguments = parser.parse_args()
logging.DEBUG(arguments)
logging.debug(arguments)
source_path = arguments.img_source
dataset_path = arguments.dataset
resizeFactor = arguments.resize
emotions = arguments.emotions
scaleFactor = arguments.scaleFactor
minNeighbors = arguments.minNeighbors
minSize = arguments.minSize
email = arguments.email
datasetPath = arguments.dataset
if len(glob.glob(datasetPath + '*')) > 0:
if len(glob.glob(dataset_path + '*')) > 0:
deleteDataset = input(
'Im Dataset befinden sich Dateien. Durch diesen Vorgang werden die existierenden Daten gelöscht. Fortfahren (y/n): ')
if deleteDataset == 'y':
for file in glob.glob(datasetPath + '*'):
for file in glob.glob(dataset_path + '*'):
shutil.rmtree(file)
else:
logging.info('Execution canceled from user')
sys.exit()
totalFiles: int = 0
totalFaces: int = 0
undetected: list = []
img_source = arguments.img_source
def detect_faces(emotion):
"""
Holt alle Dateien zu einer Emotion aus dem sorted_set
"""
files = glob.glob(img_source + '{}/*'.format(emotion))
files = glob.glob(source_path + '{}/*'.format(emotion))
global undetected
global totalFaces
......@@ -68,42 +76,41 @@ def detect_faces(emotion):
frame = cv2.imread(f) # Open image
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Convert image to grayscale
facefeatures = locate_faces(gray, arguments.scaleFactor, arguments.minNeighbors, (arguments.minSize, arguments.minSize))
facefeatures = locate_faces(gray, scaleFactor, minNeighbors, (minSize, minSize))
if facefeatures is '':
logging.info('No face detected ' + f)
undetected.append(f)
if len(undetected) == 150 and arguments.x:
sendMail('Already 150 not detected faces', filepath='logs/sorted_set_facedetector.log')
if len(undetected) % 200 == 0 and email:
sendMail('Already ' + str(len(undetected)) + ' not detected faces', filepath=logfile)
else:
# Cut and save face
for (x, y, w, h) in facefeatures: # get coordinates and size of rectangle containing face
totalFaces += 1
gray = gray[y:y + h, x:x + w] # Cut the frame to size
try:
out = cv2.resize(gray, (arguments.resize, arguments.resize)) # Resize face so all images have same size
success = cv2.imwrite(datasetPath + '{}/{}.jpg'.format(emotion, fileNumber), out) # Write image
out = cv2.resize(gray, (resizeFactor, resizeFactor)) # Resize face so all images have same size
success = cv2.imwrite(dataset_path + '{}/{}.jpg'.format(emotion, fileNumber), out) # Write image
if not success:
logging.error('Problem while writing file occurred...')
if arguments.x:
logging.error('Problem while writing file ' + f + ' occurred...' )
if email:
sendMail('Problem while writing file', body=f + ' to ' + datasetPath + '{}/{}.jpg'.format(emotion, fileNumber))
except:
logging.error('Some error with ' + f)
if arguments.x:
if email:
sendMail('Problem while writing file', body=f)
pass # If error, pass file
totalFiles += 1 # Increment image number
fileNumber += 1
if arguments.x:
if email:
sendMail('Facedetector started notification')
for emotion in arguments.emotions:
for emotion in emotions:
if not os.path.exists(datasetPath + emotion):
os.makedirs(datasetPath + emotion)
if not os.path.exists(dataset_path + emotion):
os.makedirs(dataset_path + emotion)
detect_faces(emotion) # Call functional
......@@ -113,5 +120,5 @@ logging.info('In {} files no face could be detected'.format(totalFiles - totalFa
for f in undetected:
logging.info(f)
if arguments.x:
sendMail('Facedetector finished notification', filepath='logs/sorted_set_facedetector.log')
if email:
sendMail('Facedetector finished notification', filepath=logfile)
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