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peoples.py
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peoples.py
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import cv2
import numpy as np
import os
import time
face_cascade = cv2.CascadeClassifier("MM/haar_facedetect.xml")
names = os.listdir("MM/train/")
recognizer = cv2.face.createLBPHFaceRecognizer()
def face_get(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
face = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5)
if len(face) == 0:
return None, None
(x, y, w, h) = face[0]
return gray[y:y+w, x:x+h], face[0], (x, y, w, h)
def get_user_pics():
cam = cv2.VideoCapture(0)
i = 0
frames = []
while i<20:
retval, frame = cam.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
pic = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5)
for (x, y, w, h) in pic:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("face", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
frames.append(frame)
i += 1
print(i)
if i >= 20:
break
cam.release()
return frames
def add_user_data(name):
frames = get_user_pics()
i = 0
if not os.path.exists("MM/train/"+name):
os.mkdir("MM/train/"+name)
for frame in frames:
img, rect, (x,y,w,h) = face_get(frame)
i+=1
cv2.imwrite("MM/train/"+name+"/"+str(i)+".jpeg", img)
train()
def train():
faces = []
labels = []
i=0
for name in names:
i += 1
print("training from folder"+name)
data = os.listdir("MM/train/"+name+"/")
for imname in data:
img = cv2.imread("MM/train/"+name+"/"+imname, cv2.COLOR_BGR2GRAY)
if not img is None:
labels.append(i)
faces.append(img)
# print(labels[:])
# print(imname)
# recognizer = cv2.face.createLBPHFaceRecognizer()
recognizer.train(faces, np.array(labels))
# recognizer.save("recog.xml")
def recog_camera():
cam = cv2.VideoCapture(0)
time.sleep(1)
while True:
try:
rectval, pict = cam.read()
try:
rectval, pict = cam.read()
face, rect, (x,y,w,h) = face_get(pict)
cv2.rectangle(pict, (x, y), (x + w, y + h), (0, 255, 0), 2)
label, conf = recognizer.predict(face)
cv2.putText(pict, names[label-1]+str(label), (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
except ValueError:
print(ValueError)
cv2.imshow("d", pict)
except ValueError:
print(ValueError)
if cv2.waitKey(1) & 0xFF == ord('q'):
cam.release()
cv2.destroyAllWindows()
break
def train_from_folder(fname, name):
if not os.path.exists("MM/train/"+name):
os.mkdir("MM/train/"+name)
fnames = os.listdir(fname)
i=1
for fn in fnames:
img = cv2.imread(fname+"/"+fn)
frame, rect, (x, y, w, h) = face_get(img)
if not frame is None:
cv2.imwrite("MM/train/"+name+"/"+str(i)+".jpeg", frame)
print(i, end=" ")
i += 1
train()
def recog_file(path):
img = cv2.imread(path)
face, rect, (x,y,w,h) = face_get(img)
recogniser = cv2.face.createLBPHFaceRecognizer()
recogniser.load("recog.xml")
try:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
label, conf = recogniser.predict(face)
cv2.putText(img, names[label - 1]+str(label), (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
except ValueError:
print(ValueError)
cv2.imshow("d", img)
cv2.waitKey(0)
q=2
train()
while q != 0:
print("What would you like to do")
print("1 : for existing user")
print("2 : create new user")
print("3 : let me guess who you are")
print("4 : train from images folder")
print("5 : predict existing image")
q = int(input())
if q == 2:
print("Name:")
name = str(input())
add_user_data(name)
elif q == 3:
recog_camera()
elif q == 4:
print("Folder address:", end=" ")
dest = str(input())
print("Name:")
name = str(input())
train_from_folder(dest, name)
elif q == 5:
print("Image path:", end=" ")
path = input()
recog_file(path)
cv2.destroyAllWindows()