def training(dataDir_Name):
"""Gather the available faces data to return faces, and labels .
Each person's faces folder name should be follow the following convention."""
""" dataDir_Name --->
---> s1
---> s2
and so on...
"""
faces = [] # for storing faces
labels = [] # for storing labels
dirs = os.listdir(dataDir_Name)
for dir in dirs:
if not dir.startswith(
's'
): # check for faces folder name is not matched the convention then skip.
continue
label = int(dir.replace('s', '')) # getting label names
subject_dir = dataDir_Name + '/' + dir # getting subjects directory
subject_images = os.listdir(subject_dir) # getting subjects images
for image in subject_images: # iterating through all the available images.
if image.startswith('.'):
continue
img_path = subject_dir + '/' + image
print(img_path)
person = cv2.imread(img_path)
face, rect = detectFace(person)
if face is not None:
faces.append(face)
labels.append(label)
return faces, labels
def predict(image, face_recognizer):
"""Predicts face on the image and returns the image."""
try:
img = image.copy(
) # getting the copy of image so that original image doesn't get affected.
except AttributeError:
print("Test image is empty.")
exit()
face, rect = detectFace(img)
label, confidence = face_recognizer.predict(face)
labelText = subjects[label]
drawRect(img, rect)
drawText(img, labelText, rect[0], rect[1] - 5)
return img
if(bodyPosition=="front-left-weight"):
jd.moveJoint(jd.RIGHT_FRONT_ARM, arm_zero_pos-50)
jd.moveJoint(jd.LEFT_FRONT_ARM, arm_zero_pos-70)
jd.moveJoint(jd.RIGHT_BACK_ARM, arm_zero_pos)
jd.moveJoint(jd.LEFT_BACK_ARM, arm_zero_pos)
offset_neck = -10
#muove la testa alla ricerca di un volto
for degreeH in [-15, -20, -25, -30, -35]:
for degreeW in [-15, 0, 15, -20, 20]:
#aggiorna la posizione della testa (telecamera)
jd.moveJoint(jd.HEAD, degreeH)
jd.moveJoint(jd.NECK, degreeW)
#cerca una faccia
detected = dt.detectFace(debug=DEBUG)
#se trova la faccia la segue
if detected is not None:
print("found face %s" % (str(detected)))
neckDegree = 0
headDegree = 0
faceRect = detected[-1:]
for _ in range(1000):
faceRect, neckDegree, headDegree = dt.followFace(neckDegree, headDegree)
if(faceRect is None):
print("loss face")
break
if(bodyPosition=="front-left-weight"):
jd.moveJoint(jd.RIGHT_FRONT_ARM, arm_zero_pos-50)
jd.moveJoint(jd.LEFT_FRONT_ARM, arm_zero_pos-70)
jd.moveJoint(jd.RIGHT_BACK_ARM, arm_zero_pos)
jd.moveJoint(jd.LEFT_BACK_ARM, arm_zero_pos)
offset_neck = -10
#muove la testa alla ricerca di un volto
for degreeH in [-15, -20, -25, -30, -35]:
for degreeW in [-15, 0, 15, -20, 20]:
#aggiorna la posizione della testa (telecamera)
jd.moveJoint(jd.HEAD, degreeH)
jd.moveJoint(jd.NECK, degreeW)
#cerca una faccia
detected = dt.detectFace(debug=True)
#se trova la faccia la segue
if detected is not None:
print("found face %s" % (str(detected)))
neckDegree = 0
headDegree = 0
faceRect = detected[-1:]
for _ in range(1000):
faceRect, neckDegree, headDegree = dt.followFace(neckDegree, headDegree)
if(faceRect is None):
print("loss face")
break
# from . import dataCap, detector, train
from detector import detectFace
from dataCap import capture
from train import train
import os
print("Hello User!\nThis is Face Recognition application.")
while True:
print(
"\nWhat do You wanna DO?\n 1. Recognize A Face.\n 2. Upload New Face.\n 3. End Program"
)
c = input(" Choice: ")
if c == "1":
detectFace()
print("Rec Start!")
elif c == "2":
input("Press Enter to Click a Photo (5 photos needed)...")
capture()
a = input("Press Enter to start Training on the new Data...")
train()
elif c == "3":
break
else:
print("Wrong Input")
os.system('cls')
print("The End")