def testfromcamera(chkpoint):
camera = cv2.VideoCapture(0)
haar = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
pathlabelpair, indextoname = getfileandlabel('./image/trainfaces')
output = myconv.cnnLayer(len(pathlabelpair))
#predict = tf.equal(tf.argmax(output, 1), tf.argmax(y_data, 1))
predict = output
saver = tf.train.Saver()
with tf.Session() as sess:
#sess.run(tf.global_variables_initializer())
saver.restore(sess, chkpoint)
n = 1
while 1:
if (n <= 20000):
print('It`s processing %s image.' % n)
# 读帧
success, img = camera.read()
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = haar.detectMultiScale(gray_img, 1.3, 5)
for f_x, f_y, f_w, f_h in faces:
face = img[f_y:f_y + f_h, f_x:f_x + f_w]
face = cv2.resize(face, (IMGSIZE, IMGSIZE))
#could deal with face to train
test_x = np.array([face])
test_x = test_x.astype(np.float32) / 255.0
res = sess.run([predict, tf.argmax(output, 1)],\
feed_dict={myconv.x_data: test_x,\
myconv.keep_prob_5:1.0, myconv.keep_prob_75: 1.0})
print(res)
cv2.putText(img, indextoname[res[1][0]], (f_x, f_y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) #显示名字
img = cv2.rectangle(img, (f_x, f_y),
(f_x + f_w, f_y + f_h), (255, 0, 0), 2)
n += 1
cv2.imshow('img', img)
key = cv2.waitKey(30) & 0xff
if key == 27:
break
else:
break
camera.release()
cv2.destroyAllWindows()
def testfromcamera(chkpoint):
#camera = cv2.VideoCapture(0)
haar = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
#generateface([['./image/testimages', './image/testfaces']])
pathlabelpair, indextoname = getfileandlabel('./image/trainfaces')
print(pathlabelpair)
print(indextoname)
output = myconv.cnnLayer(len(pathlabelpair))
#output = myconv.cnnLayer(4)
#predict = tf.equal(tf.argmax(output, 1), tf.argmax(y_data, 1))
predict = output
saver = tf.train.Saver()
with tf.Session() as sess:
#sess.run(tf.global_variables_initializer())
saver.restore(sess, chkpoint)
n = 1
while (n <= 4):
if (n <= 4):
print('It`s processing %s image.' % n)
# 读帧
#success, img = camera.read()
#img = cv2.imread(os.path.join('./image/testimages/david/', 'me_' + str(n+200) + '.jpg'))
#img = cv2.imread(os.path.join('./image/testimages/songpeng/', 'songpeng' + str(n+12) + '.jpg'))
img = cv2.imread(
os.path.join('./image/testimages/Ariel_Sharon/',
'Ariel_Sharon_00' + str(n + 73) + '.jpg'))
#img = cv2.imread(os.path.join('./image/testimages/Fan_Bingbing/', 'fbb' + str(n+21) + '.jpg'))
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#gray_img=cv2.equalizeHist(gray_img)
faces = haar.detectMultiScale(gray_img, 1.3, 5)
print(faces)
for f_x, f_y, f_w, f_h in faces:
face = img[f_y:f_y + f_h, f_x:f_x + f_w]
print('inx output0')
face = cv2.resize(face, (IMGSIZE, IMGSIZE))
#could deal with face to train
test_x = np.array([face])
test_x = test_x.astype(np.float32) / 255.0
res = sess.run([predict, tf.argmax(output, 1)],\
feed_dict={myconv.x_data: test_x,\
myconv.keep_prob_5:1.0, myconv.keep_prob_75: 1.0})
print('inx output1')
print(res)
cv2.putText(img, indextoname[res[1][0]], (f_x, f_y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) #显示名字
img = cv2.rectangle(img, (f_x, f_y),
(f_x + f_w, f_y + f_h), (255, 0, 0), 2)
n += 1
cv2.imshow('img', img)
time.sleep(5)
key = cv2.waitKey(30) & 0xff
if key == 27:
break
else:
break
#camera.release()
cv2.destroyAllWindows()
def testfromcamera(train_x, train_y, chkpoint):
# 打开默认摄像头
camera = cv2.VideoCapture(0)
#haar = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
pathlabelpair, indextoname = getfileandlabel(rootpath +
'\\image\\trainfaces')
# 得到预测值
output = myconv.cnnLayer(len(pathlabelpair), False)
predict = output
# 得到dlib的人脸检测器
detector = dlib.get_frontal_face_detector()
# 加载模型
saver = tf.train.Saver()
with tf.Session() as sess:
#sess.run(tf.global_variables_initializer())
saver.restore(sess, chkpoint)
n = 1
while 1:
if (n <= 20000):
print('It`s processing %s image.' % n)
sys.stdout.flush()
# 间隔0.2s
time.sleep(0.2)
# 读帧
success, img = camera.read()
# 得到灰度图
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 使用detector进行人脸检测
faces = detector(gray_img, 1)
#faces = haar.detectMultiScale(gray_img, 1.3, 5)
for i, d in enumerate(faces):
x1 = d.top() if d.top() > 0 else 0
y1 = d.bottom() if d.bottom() > 0 else 0
x2 = d.left() if d.left() > 0 else 0
y2 = d.right() if d.right() > 0 else 0
face = img[x1:y1, x2:y2]
face = cv2.resize(face, (IMGSIZE, IMGSIZE))
#could deal with face to train
test_x = np.array([face])
test_x = test_x.astype(np.float32) / 255.0
res = sess.run([predict, tf.argmax(output, 1)],\
feed_dict={myconv.x: test_x,\
myconv.keep_prob_5:1.0, myconv.keep_prob_75: 1.0})
print(res, indextoname[res[1][0]], res[0][0][res[1][0]])
sys.stdout.flush()
# 得到一组随机的颜色值
r = random.randint(0, 255)
g = random.randint(0, 255)
b = random.randint(0, 255)
# 绘制检测到的人脸的方框
cv2.rectangle(img, (x2, x1), (y2, y1), (r, g, b), 3)
# if res[0][0][res[1][0]] >= 500:
# cv2.putText(img, 'others', (x1, y1 + 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (col, col, col), 2) #显示名字
# else:
# cv2.putText(img, indextoname[res[1][0]], (x1, y1 - 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (col, col, col), 2) #显示名字
cv2.putText(img, indextoname[res[1][0]],
(x2 + 25, y1 + 40), cv2.FONT_HERSHEY_SIMPLEX,
1, (r, g, b), 2) #显示名字
n += 1
cv2.imshow('img', img)
key = cv2.waitKey(30) & 0xff
if key == 27:
break
else:
break
camera.release()
cv2.destroyAllWindows()