def __init__(self,filename,color,cascade,READ_FILE):
labeled=zip(filename,color)
label=[]
feature_train=[]
for (fd,c) in labeled:
print fd
if READ_FILE is True:
img=cv2.imread(fd,0)
else:
img = filename
if img is None:
print "image can't be loaded in face recog"
sys.exit(0)
face_cascade=cv2.CascadeClassifier(cascade)
features=Feature_extractor(img)
face=face_cascade.detectMultiScale(
img,
scaleFactor=1.1,
minNeighbors=2,
minSize=(10,10)
)
window=face[0]
(x,y,w,h)=window
#print "selectkp"
#print window
kp=features.keypoints()
selectKp=features.select_kp(window)
#print selectKp
for (X,Y) in selectKp:
plt.scatter(X,Y,color=c)
label.append(c)
feature_train.append([X,Y])
#cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),1)
#cv2.imshow("image",img)
self.label=label
self.feature_train=feature_train
face=face_cascade.detectMultiScale(
image,
scaleFactor=1.1,
minNeighbors=2,
minSize=(400,400)
)
window=face[0]
print face
(x,y,w,h)=window
kp=features.keypoints()
selectKp=features.select_kp(window)
if len(selectKp)==0 :
print "NO KEYPOINTS IN WINDOW"
sys.exit(0)
clf=Training(filenames,color,cascade,READ_FILE=True)
clf.train()
points= clf.predict(selectKp)
print "counting frequency"
