def test_tanprocessing(filename):
from facerec.preprocessing import TanTriggsPreprocessing
path = '/Users/matti/Dropbox/Skjöl/Meistaraverkefni/server/sull'
image = cv2.imread(os.path.join(path, filename))
utils.show_image_and_wait_for_input(image)
# alpha = 0.1, tau = 10.0, gamma = 0.2, sigma0 = 1.0, sigma1 = 2.0):
t = TanTriggsPreprocessing(0.1, 10, 1.0, 3.0)
res = t.extract(image)
utils.show_image_and_wait_for_input(res)
t = TanTriggsPreprocessing()
res = t.extract(image)
utils.show_image_and_wait_for_input(res)
def test_tanprocessing(filename):
from facerec.preprocessing import TanTriggsPreprocessing
path = '/Users/matti/Dropbox/Skjöl/Meistaraverkefni/server/sull'
image = cv2.imread(os.path.join(path, filename))
utils.show_image_and_wait_for_input(image)
# alpha = 0.1, tau = 10.0, gamma = 0.2, sigma0 = 1.0, sigma1 = 2.0):
t = TanTriggsPreprocessing(0.1, 10, 1.0, 3.0)
res = t.extract(image)
utils.show_image_and_wait_for_input(res)
t = TanTriggsPreprocessing()
res = t.extract(image)
utils.show_image_and_wait_for_input(res)
def test_one_method(input_faces, test_faces, feature, classifier, chain=True):
if chain:
feature = ChainOperator(TanTriggsPreprocessing(), feature)
model = PredictableModel(feature, classifier)
id_list, face_list = zip(*input_faces)
start = time.clock()
model.compute(face_list, id_list)
stop = time.clock()
training_time = stop-start
res_list = []
start = time.clock()
for id, image in test_faces:
res = model.predict(image)
res_list.append([id]+res)
stop = time.clock()
predict_time = stop-start
return (training_time, predict_time, res_list)
def __init__(
self,
video_src,
dataset_fn,
face_sz=(130, 130),
cascade_fn="/home/philipp/projects/opencv2/OpenCV-2.3.1/data/haarcascades/haarcascade_frontalface_alt2.xml"
):
self.face_sz = face_sz
self.cam = create_capture(video_src)
ret, self.frame = self.cam.read()
self.detector = CascadedDetector(cascade_fn=cascade_fn,
minNeighbors=5,
scaleFactor=1.1)
# define feature extraction chain & and classifier)
feature = ChainOperator(TanTriggsPreprocessing(), LBP())
classifier = NearestNeighbor(dist_metric=ChiSquareDistance())
# build the predictable model
self.predictor = PredictableModel(feature, classifier)
# read the data & compute the predictor
self.dataSet = DataSet(filename=dataset_fn, sz=self.face_sz)
self.predictor.compute(self.dataSet.data, self.dataSet.labels)
# check_processed_image()
# check_unprocessed_image()
# test_processing()
# test_failed_face()
# test_reading_from_single_folder()
# show_process_steps_of_one_picture('/Users/matti/Dropbox/Skjöl/Meistaraverkefni/sull/3078.jpg')
# test_draw()
# test_tanprocessing('10.png')
# test_face_database()
# test_base64png()
mynd = test_one_face('simi_t1.jpg')
from facerec.preprocessing import TanTriggsPreprocessing
t = TanTriggsPreprocessing(0.1, 10, 1.0, 3.0)
res = t.extract(mynd)
utils.show_image_and_wait_for_input(res)
# test_one_face('simi_t2.jpg')
# test_one_face('simi_t3.jpg')
# test_one_face('simi_t4.jpg')
# test_one_face('gyda_03.jpg')
# test_one_face('3078.jpg')
#test_compare_images(
# '/Users/matti/Documents/forritun/att_faces/arora_01.jpg',
# '/Users/matti/Dropbox/Skjöl/Meistaraverkefni/server/test_faces_to_search_for/arora_01.png'
#)
"""
test_one_face('3.bmp')
# check_processed_image()
# check_unprocessed_image()
# test_processing()
# test_failed_face()
# test_reading_from_single_folder()
# show_process_steps_of_one_picture('/Users/matti/Dropbox/Skjöl/Meistaraverkefni/sull/3078.jpg')
# test_draw()
# test_tanprocessing('10.png')
# test_face_database()
# test_base64png()
mynd = test_one_face('simi_t1.jpg')
from facerec.preprocessing import TanTriggsPreprocessing
t = TanTriggsPreprocessing(0.1, 10, 1.0, 3.0)
res = t.extract(mynd)
utils.show_image_and_wait_for_input(res)
# test_one_face('simi_t2.jpg')
# test_one_face('simi_t3.jpg')
# test_one_face('simi_t4.jpg')
# test_one_face('gyda_03.jpg')
# test_one_face('3078.jpg')
#test_compare_images(
# '/Users/matti/Documents/forritun/att_faces/arora_01.jpg',
# '/Users/matti/Dropbox/Skjöl/Meistaraverkefni/server/test_faces_to_search_for/arora_01.png'
#)
"""
test_one_face('3.bmp')
test_one_face('4.bmp')
print "Appropriation completed in {0:.2f} seconds.\n".format(time.time() - initial_time)
#
# Set up the Haar cascade to detect (not recognize) the faces
#
#
# We're going to use the Fisherfaces face recognition module
#
initial_time = time.time()
print "Initializing Haar cascades for face, eyes, nose and mouth detection: "
#
# This was prior to using the TanTriggsPreprocessing, we can go back
#model = PredictableModel(Fisherfaces(), NearestNeighbor())
feature = ChainOperator(TanTriggsPreprocessing(), Fisherfaces())
classifier = NearestNeighbor()
model = PredictableModel(feature, classifier)
face_cascade = cv2.CascadeClassifier(haarcascade)
eye_cascade = cv2.CascadeClassifier(eyehaarcascade)
nose_cascade = cv2.CascadeClassifier(nosehaarcascade)
mouth_cascade = cv2.CascadeClassifier(mouthhaarcascade)
print "Initialization completed in {0:.2f} seconds.\n".format(time.time() - initial_time)
#
# Main loop
# Press "l" to learn a new image
# Press "r" to reload image database
# Press "v" to toggle voice synthesis
# Press "b" for best guess of image
def __init__(self,
database_folder,
feature_parameter="LPQ",
metric="chi",
k=3):
self.model = None
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger = logging.getLogger("facerec")
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
path = database_folder
start = time.clock()
input_faces = utils.read_images_from_single_folder(path)
stop = time.clock()
print("read {}, images from {} in {} seconds.".format(
len(input_faces), path, stop - start))
feature = None
m = {
"fisher": Fisherfaces,
"fisher80": Fisherfaces,
"pca": PCA,
"pca10": PCA,
"lda": LDA,
"spatial": SpatialHistogram,
"LPQ": SpatialHistogram
}
if feature_parameter in m:
if feature_parameter == 'LPQ':
feature = SpatialHistogram(LPQ())
self.threshold = threshold_function(71.4, 70)
elif feature_parameter == 'fisher80':
feature = Fisherfaces(80)
self.threshold = threshold_function(0.61, 0.5)
elif feature_parameter == 'fisher':
feature = Fisherfaces()
self.threshold = threshold_function(0.61, 0.5)
elif feature_parameter == 'pca80':
feature = PCA(80)
else:
feature = m[feature_parameter]()
metric_param = None
d = {
"euclid": EuclideanDistance,
"cosine": CosineDistance,
"normal": NormalizedCorrelation,
"chi": ChiSquareDistance,
"histo": HistogramIntersection,
"l1b": L1BinRatioDistance,
"chibrd": ChiSquareBRD
}
if metric in d:
metric_param = d[metric]()
else:
metric_param = ChiSquareDistance()
classifier = NearestNeighbor(dist_metric=metric_param, k=k)
feature = ChainOperator(TanTriggsPreprocessing(), feature)
# feature = ChainOperator(TanTriggsPreprocessing(0.1, 10.0, 1.0, 3.0), feature)
self.model = PredictableModel(feature, classifier)
# images in one list, id's on another
id_list, face_list = zip(*input_faces)
print "Train the model"
start = time.clock()
# model.compute(X, y)
self.model.compute(face_list, id_list)
stop = time.clock()
print "Training done in", stop - start, " next...find a face"
# Define a 1-NN classifier with Euclidean Distance:
# classifier = NearestNeighbor(dist_metric=EuclideanDistance(), k=3) # þokkalegt, [1.7472255 , 1.80661233, 1.89985602] bara fremsta rétt
# classifier = NearestNeighbor(dist_metric=CosineDistance(), k=3) # þokkalegt, niðurstöður sem mínus tölur ([-0.72430667, -0.65913855, -0.61865271])
# classifier = NearestNeighbor(dist_metric=NormalizedCorrelation(), k=3) # ágætt 0.28873109, 0.35998333, 0.39835315 (bara fremsta rétt)
classifier = NearestNeighbor(dist_metric=ChiSquareDistance(
), k=3) # gott, 32.49907228, 44.53673458, 45.39480197 bara síðasta rangt
# classifier = NearestNeighbor(dist_metric=HistogramIntersection(), k=3) # sökkar
# classifier = NearestNeighbor(dist_metric=L1BinRatioDistance(), k=3) # nokkuð gott, 36.77156378, 47.84164013, 52.63872497] - síðasta rangt
# classifier = NearestNeighbor(dist_metric=ChiSquareBRD(), k=3) # 36.87781902, 44.06119053, 46.40875114 - síðasta rangt
# Define the model as the combination
# model = PredictableModel(feature=feature, classifier=classifier)
# Compute the model on the given data (in X) and labels (in y):
feature = ChainOperator(TanTriggsPreprocessing(), feature)
# classifier = NearestNeighbor()
model = PredictableModel(feature, classifier)
# images in one list, id's on another
id_list, face_list = zip(*input_faces)
print "Train the model"
start = time.clock()
# model.compute(X, y)
model.compute(face_list, id_list)
stop = time.clock()
print "Training done in", stop - start, " next...find a face"
# test_path = "/Users/matti/Documents/forritun/att_faces/"
test_path = "/Users/matti/Dropbox/Skjöl/Meistaraverkefni/server/test_faces_02"