def predict_emotion(self, detected_face):
'''
预测表情，返回一个形状为(7, )的向量
'''
tensor = image_to_tensor(detected_face)
result = sess.run(probs, feed_dict={face_x: tensor})
return result
def facial_detact(modelPath, showBox=True):
face_x = tf.placeholder(tf.float32, [None, 2304])
y_conv = deepnn(face_x)
probs = tf.nn.softmax(y_conv)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(modelPath)
sess = tf.Session()
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print(
'Restore model sucsses!!\n\nNOTE: Press SPACE on keyboard to capture face.'
)
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(
cv2.imread('./data/emojis/' + emotion + '.png', -1))
video_captor = cv2.VideoCapture(0)
emoji_face = []
result = None
while True:
ret, frame = video_captor.read()
detected_face, face_coor = format_image(frame)
if showBox:
if face_coor is not None:
[x, y, w, h] = face_coor
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
if cv2.waitKey(1) & 0xFF == ord(' '):
if detected_face is not None:
cv2.imwrite('a.jpg', detected_face)
tensor = image_to_tensor(detected_face)
result = sess.run(probs, feed_dict={face_x: tensor})
print(EMOTIONS[np.argmax(result[0])])
if result is not None:
for index, emotion in enumerate(EMOTIONS):
cv2.putText(frame, emotion, (10, index * 20 + 20),
cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1)
cv2.rectangle(frame, (130, index * 20 + 10),
(130 + int(result[0][index] * 100),
(index + 1) * 20 + 4), (255, 0, 0), -1)
emoji_face = feelings_faces[np.argmax(result[0])]
for c in range(0, 3):
frame[200:320, 10:130, c] = emoji_face[:, :, c] * (
emoji_face[:, :, 3] /
255.0) + frame[200:320, 10:130,
c] * (1.0 - emoji_face[:, :, 3] / 255.0)
cv2.imshow('face', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_captor.release()
cv2.destroyAllWindows()
def demo(modelPath, showBox=True):
face_x = tf.placeholder(tf.float32, [None, 2304])
y_conv = deepnn(face_x)
probs = tf.nn.softmax(y_conv)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(modelPath)
sess = tf.Session()
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(
cv2.imread('./data/emojis/' + emotion + '.png', -1))
emoji_face = []
img = input("Enter the image file name: ")
while True:
frame = cv2.imread(img)
detected_face, face_coor = format_image(frame)
if face_coor is not None:
[x, y, w, h] = face_coor
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
result = None
if detected_face is not None:
cv2.imwrite('face.jpg', detected_face)
tensor = image_to_tensor(detected_face)
result = sess.run(probs, feed_dict={face_x: tensor})
if result is not None:
for index, emotion in enumerate(EMOTIONS):
cv2.putText(frame, emotion, (10, index * 20 + 20),
cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1)
cv2.rectangle(frame, (130, index * 20 + 10),
(130 + int(result[0][index] * 100),
(index + 1) * 20 + 4), (255, 0, 0), -1)
emoji_face = feelings_faces[np.argmax(result[0])]
for c in range(0, 3):
frame[200:320, 10:130, c] = emoji_face[:, :, c] * (
emoji_face[:, :, 3] /
255.0) + frame[200:320, 10:130,
c] * (1.0 - emoji_face[:, :, 3] / 255.0)
else:
comment = 'We cannot detect a face....Too bad!'
cv2.putText(frame, comment, (115, 250), cv2.FONT_HERSHEY_PLAIN,
1.5, (0, 0, 0), 1)
cv2.imwrite('result1.jpg', frame)
cv2.namedWindow('Guess how I feel', cv2.WINDOW_NORMAL)
cv2.resizeWindow('Guess how I feel', 450, 600)
cv2.imshow('Guess how I feel', frame)
k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video
if k == 27:
break
def demo(modelPath, showBox=False,SAMPLE_IMAGE_PATH=SAMPLE_IMAGE):
face_x = tf.placeholder(tf.float32, [None, 2304])
y_conv = deepnn(face_x)
probs = tf.nn.softmax(y_conv)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(modelPath)
sess = tf.Session()
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
result = None
frame = SAMPLE_IMAGE_PATH
detected_face, face_coor = format_image(frame) #face Coordinates
if showBox:
if face_coor is not None:
[x,y,w,h] = face_coor
cv2.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 2)
if detected_face is not None:
cv2.imwrite('temp.jpg', detected_face)
tensor = image_to_tensor(detected_face)
result = sess.run(probs, feed_dict={face_x: tensor})
print(result)
jsonString='{'
maximum=0.00
FINAL_EMOTION=''
if result is not None:
for index, emotion in enumerate(EMOTIONS):
if(index!=0):
jsonString+=','
percentage= float("{0:.2f}".format(result[0][index]*100))
if(percentage==0.0):
percentage=0
jsonString+='"'+str(emotion)+'":'+str(percentage)
if(maximum<percentage):
maximum=copy.copy(percentage)
FINAL_EMOTION=emotion
print('--------------------------------')
print(' ')
print(str(emotion)+" : "+str(percentage)+"%")
print(' ')
print('=========================================================')
print('The Result for Input Image is : '+str(FINAL_EMOTION)+" ( "+str(maximum)+"% )")
print('=========================================================')
jsonString+='}'
return jsonString
def stdface_expression_recognition(stdface):
"""
直接识别stdface中人物的表情
:param stdface: 传入的stdface
:return: 返回值1为表情id(int) 返回值2位表情指数数组(ndarray)
"""
# 先对stdface进行format操作 再将其拉直变为一维张量
tensor = image_to_tensor(format_image(stdface))
# 将图片转为的一维张量输入到神经网络中得到输出
result = sess.run(probs, feed_dict={face_x: tensor})
# 表情识别结果为指数最大的那个
expression_id = np.argmax(result[0])
return expression_id, result[0]
def demo(modelPath, showBox=False):
face_x = tf.placeholder(tf.float32, [None, 2304])
y_conv = deepnn(face_x)
probs = tf.nn.softmax(y_conv)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(modelPath)
sess = tf.Session()
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Restore model sucsses!!\nNOTE: Press SPACE on keyboard to capture face.')
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./data/emojis/' + emotion + '.png', -1))
video_captor = cv2.VideoCapture(0)
emoji_face = []
result = None
while True:
ret, frame = video_captor.read()
detected_face, face_coor = format_image(frame)
if showBox:
if face_coor is not None:
[x,y,w,h] = face_coor
cv2.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 2)
if cv2.waitKey(1) & 0xFF == ord(' '):
if detected_face is not None:
cv2.imwrite('a.jpg', detected_face)
tensor = image_to_tensor(detected_face)
result = sess.run(probs, feed_dict={face_x: tensor})
# print(result)
if result is not None:
for index, emotion in enumerate(EMOTIONS):
cv2.putText(frame, emotion, (10, index * 20 + 20), cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1)
cv2.rectangle(frame, (130, index * 20 + 10), (130 + int(result[0][index] * 100), (index + 1) * 20 + 4),
(255, 0, 0), -1)
emoji_face = feelings_faces[np.argmax(result[0])]
for c in range(0, 3):
frame[200:320, 10:130, c] = emoji_face[:, :, c] * (emoji_face[:, :, 3] / 255.0) + frame[200:320, 10:130, c] * (1.0 - emoji_face[:, :, 3] / 255.0)
cv2.imshow('face', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def main():
#create RKNN object
rknn = RKNN(verbose=True)
#Direct Load RKNN Model
rknn.load_rknn('./emotion.rknn') #만들어진 rknn을 로드
print('--> load success') #성공 메세지 출력
result = None
#이미지 읽기
input_image = cv2.imread('./data/image/happy.jpg', cv2.IMREAD_COLOR)
#esize한 이미지, 가장 큰 얼굴 object
detected_face, face_coor = format_image(input_image)
#탐지된 이미지가 있다면,
if detected_face is not None:
#image를 tenxor로 변환 & float32로 변환 (rknn이 float64는 지원하지 않음)
"tensor 사이즈는 (1,2304), detected_face는 48X48"
tensor = image_to_tensor(detected_face).astype(np.float32)
#init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime()
#오류 메세지 출력
if ret != 0:
print('Init runtime environment failed')
#rknn 모델 실행
result = rknn.inference(inputs=[tensor])
print('run success')
#list를 array로 변환
#result는 감정 예측 배열
result = np.array(result)
#result가 존재하면
if result is not None:
#감정 배열이 7개의 값을 가지므로 range(7)의 범위를 가짐
for i in range(7):
#감정 배열 중 1인 값이 있다면,
if result[0][0][i] == 1:
#감정 예측 메세지 출력
print('당신의 감정은 ' + EMOTIONS[i] + '입니다.')
def expression_recognition(image):
"""
识别图像中人物的表情
:param image: 传入的图像
:return: 返回值1为表情id(int) 返回值2位表情指数数组(ndarray)
"""
stdface = Faces.get_stdface(image)
if stdface is None:
return None, None
# 先对stdface进行format操作 再将其拉直变为一维张量
tensor = image_to_tensor(format_image(stdface))
# 将图片转为的一维张量输入到神经网络中得到输出
result = sess.run(probs, feed_dict={face_x: tensor})
# 表情识别结果为指数最大的那个
expression_id = np.argmax(result[0])
return expression_id, result[0]
def demo(modelPath, showBox=False):
face_x = tf.compat.v1.placeholder(tf.float32, [None, 2304])
y_conv = deepnn(face_x)
probs = tf.nn.softmax(y_conv)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(modelPath)
sess = tf.Session()
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print(
'Restore model sucsses!!\nNOTE: Press SPACE on keyboard to capture face.'
)
video_path = "test.mp4"
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS) # 获取视频的帧率
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) # 获取视频的大小
fourcc = cv2.VideoWriter_fourcc(*'mpeg') # 要保存的视频格式
# 把处理过的视频保存下来
output_viedo = cv2.VideoWriter()
# 保存的视频地址
video_save_path = 'trans.mp4'
output_viedo.open(video_save_path, fourcc, fps, size, True)
result = None
while True:
ret, frame = cap.read()
detected_face, face_coor = format_image(frame)
if showBox:
if face_coor is not None:
specify(face_coor, frame)
if detected_face is not None:
tensor = image_to_tensor(detected_face)
result = sess.run(probs, feed_dict={face_x: tensor})
print(result)
output_viedo.write(frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def demo(modelPath, showBox=True):
face_x = tf.placeholder(tf.float32, [None, 2304])
y_conv = deepnn(face_x)
probs = tf.nn.softmax(y_conv)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(modelPath)
sess = tf.Session()
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print(
'Restore model sucsses!!\nNOTE: Press SPACE on keyboard to capture face.'
)
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(
cv2.imread('./data/emojis/' + emotion + '.png', -1))
# video_captor = cv2.VideoCapture(0)
demo_image = cv2.imread('./demo_image.jpg')
detected_face, face_coor = format_image(demo_image)
if showBox:
if face_coor is not None:
[x, y, w, h] = face_coor
cv2.rectangle(demo_image, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.imshow('demo_image', demo_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
if detected_face is not None:
cv2.imwrite('a.jpg', detected_face)
tensor = image_to_tensor(detected_face)
result = sess.run(y_conv, feed_dict={face_x: tensor})
print('result.shape: ', result.shape)
print('result: ', result)
most_likely_index = np.argmax(result)
# print(most_likely_index)
print(EMOTIONS[most_likely_index])
sess.close()
def process_image(ip_img):
img=ip_img
img_bkup = img
alignedFaces = []
img_matlab = np.copy(img)
tmp = img_matlab[:,:,2].copy()
img_matlab[:,:,2] = img_matlab[:,:,0]
img_matlab[:,:,0] = tmp
# Getting the bounding box and the landmarks and drawing the bounding box around the image
boundingboxes, points = detect_face(img_matlab, minsize, PNet, RNet, ONet, threshold, False, factor)
x1 = boundingboxes[:,0]
#print(x1)
# Code for aligning the detected faces
imgSize = (112, 96)
# This part is needed by the DeepFace code
x_ = [30.2946, 65.5318, 48.0252, 33.5493, 62.7299]
y_ = [51.6963, 51.5014, 71.7366, 92.3655, 92.2041]
src = np.array( zip(x_, y_) ).astype(np.float32).reshape(1,5,2)
out = None
emotion = ""
features = []
name_dict_arr = []
for i in range(0, len(x1)):
# Drawing the landmarks
x = points[i][0:5]
y = points[i][5:10]
# Code for alginign the faces
dst = np.array( zip(x, y) ).astype(np.float32).reshape(1,5,2)
transmat = cv2.estimateRigidTransform( dst, src, False )
if(transmat is not None):
sys.stdout.flush()
print "Person found"
out = cv2.warpAffine(img_bkup, transmat, (imgSize[1], imgSize[0]))
# DEBUG cv2.imwrite("new"+str(count_num)+".jpg", out)
alignedFaces.append(out)
if INFERENCE:
npstore = out
npstore = npstore.astype(float)
npstore = (npstore - 127.5)/128
npstore = np.expand_dims(npstore, axis=0)
npstore = np.swapaxes(npstore,1,3)
npstore = np.swapaxes(npstore,2,3)
center_facenet.blobs['data'].data[...] = npstore
center_facenet.forward()
features.append(copy.deepcopy(center_facenet.blobs["fc5"].data[0]))
# Face expression part
if EXPRESSION_DETECTION_ENABLED:
face_coor = np.ceil(boundingboxes[i]).astype(int)
image = img[face_coor[1]:face_coor[3], face_coor[0]:face_coor[2]]
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#print("coord size", len(face_coor))
try:
image = cv2.resize(image, (48, 48), interpolation=cv2.INTER_CUBIC)
import scipy.misc
scipy.misc.imsave('outfile.jpg', image)
except Exception:
continue
tensor = image_to_tensor(image)
result = sess.run(probs, feed_dict={face_x: tensor})
print("debug", result, np.argmax(result[0]))
emotion = EMOTIONS[np.argmax(result[0])]
else:
print "none detected"
return alignedFaces, boundingboxes, features, emotion
# Check if confidence is less them 100 ==> "0" is perfect match
if (confidence < 100):
id = names[id]
confidence = " {0}%".format(round(100 - confidence))
else:
id = "unknown"
confidence = " {0}%".format(round(100 - confidence))
cv2.putText(frame, str(id), (x + 5, y - 5), cv2.FONT_HERSHEY_TRIPLEX,
1, (0, 0, 0), 2)
cv2.putText(frame, str(confidence), (x + 5, y + h - 5),
cv2.FONT_HERSHEY_TRIPLEX, 1, (255, 255, 255), 1)
if cv2.waitKey(1) & 0xFF == ord(' '):
result = None
if detected_face is not None:
tensor = image_to_tensor(detected_face)
result = sess.run(probs, feed_dict={face_x: tensor})
if result is not None:
for index, emotion in enumerate(EMOTIONS):
cv2.putText(frame, emotion, (10, index * 20 + 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 1)
cv2.rectangle(frame, (130, index * 20 + 10),
(130 + int(result[0][index] * 100),
(index + 1) * 20 + 4), (255, 0, 0), -1)
emoji_face = feelings_faces[np.argmax(result[0])]
for c in range(0, 3):
frame[200:320, 10:130, c] = emoji_face[:, :, c] * (
emoji_face[:, :, 3] / 255.0) + frame[200:320, 10:130, c] * (
1.0 - emoji_face[:, :, 3] / 255.0)
else:
comment = 'We cannot detect your emotion!'
def demo(modelPath, showBox=False):
face_x = tf.placeholder(dtype=tf.float32, name='inputs', shape=[None, 2304])
#parser = argparse.ArgumentParser()
#parser.add_argument("--fz_model_fn",default = "./emotion_model_frozen.pb",type=str,help="Frozen model file to import")
#args = parser.parse_args()
#graph = load_graph(args.fz_model_fn)
#for op in graph.get_operations():
#print(op.name,op.values())
#x = graph.get_tensor_by_name('prefix/inputs:0')
#probs = graph.get_tensor_by_name('prefix/output_node:0')
y_conv = deepnn(face_x)
probs = tf.nn.softmax(y_conv, name='output_node')
print("probs",probs)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(modelPath)
sess = tf.Session()
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Restore model sucsses!!\nNOTE: Press SPACE on keyboard to capture face.')
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./data/emojis/' + emotion + '.png', -1))
video_captor = cv2.VideoCapture(0)
emoji_face = []
result = None
while True:
ret, frame = video_captor.read()
detected_face, face_coor = format_image(frame)
if showBox:
if face_coor is not None:
[x,y,w,h] = face_coor
cv2.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 2)
if cv2.waitKey(1):
if detected_face is not None:
cv2.imwrite('a.jpg', detected_face)
tensor = image_to_tensor(detected_face)
result = sess.run(probs, feed_dict={face_x: tensor})
print(result)
print("probs",probs)
tf.train.write_graph(sess.graph_def, './', "nn_model.pbtxt", as_text=True)
for op in tf.get_default_graph().get_operations():
print(op.name)
# print(result)
if result is not None:
for index, emotion in enumerate(EMOTIONS):
cv2.putText(frame, emotion, (10, index * 20 + 20), cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1)
cv2.rectangle(frame, (130, index * 20 + 10), (130 + int(result[0][index] * 100), (index + 1) * 20 + 4),
(255, 0, 0), -1)
emoji_face = feelings_faces[np.argmax(result[0])]
for c in range(0, 3):
frame[200:320, 10:130, c] = emoji_face[:, :, c] * (emoji_face[:, :, 3] / 255.0) + frame[200:320, 10:130, c] * (1.0 - emoji_face[:, :, 3] / 255.0)
cv2.imshow('face', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def demo(modelPath, showBox=False):
face_x = tf.placeholder(tf.float32, [None, 2304])
y_conv = deepnn(face_x)
probs = tf.nn.softmax(y_conv)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(modelPath)
sess = tf.Session()
# Get the model
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Restore model sucsses!!')
# print('NOTE: Press SPACE on keyboard to capture face.')
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(
cv2.imread('./data/emojis/' + emotion + '.png', -1))
video_captor = cv2.VideoCapture(0)
emoji_face = []
result = None
while True:
ret, frame = video_captor.read()
# time.sleep(0.1)
detected_face, face_coor = format_image(frame)
if showBox:
if face_coor is not None:
[x, y, w, h] = face_coor
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
# if cv2.waitKey(1) & 0xFF == ord(' '):
# Not using because it calls the model again and again
if detected_face is not None:
# cv2.imwrite('a.jpg', detected_face)
tensor = image_to_tensor(detected_face)
result = sess.run(probs, feed_dict={face_x: tensor})
# print(result)
if result is not None:
for index, emotion in enumerate(EMOTIONS):
# Label(emotion) display
cv2.putText(frame, emotion, (10, index * 20 + 20),
cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1)
# Histogram display
cv2.rectangle(frame, (130, index * 20 + 10),
(130 + int(result[0][index] * 100),
(index + 1) * 20 + 4), (255, 0, 0), -1)
# emoji<-emotion of the highest score
emoji_face = feelings_faces[np.argmax(result[0])]
# Emoji(120*120) display
for c in range(0, 3):
frame[200:320, 10:130, c] = emoji_face[:, :, c] * (
emoji_face[:, :, 3] /
255.0) + frame[200:320, 10:130,
c] * (1.0 - emoji_face[:, :, 3] / 255.0)
# Real-time capture
cv2.imshow('face', frame)
if cv2.waitKey(1) & 0xFF == ord(' '):
cv2.imwrite('fer_result/' + str(uuid.uuid4().hex) + '.png', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def demo(modelPath, showBox=True):
face_x = tf.placeholder(tf.float32, [None, 2304])
y_conv = deepnn(face_x)
probs = tf.nn.softmax(y_conv)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(modelPath)
sess = tf.Session()
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print(
'Restore model sucsses!!\nNOTE: Press SPACE on keyboard to capture face.'
)
# feelings_faces = []
# for index, emotion in enumerate(EMOTIONS):
# num = random.randint(1,3)
# feelings_faces.append(cv2.imread('./data/emojis/' + emotion + str(num) + '.jpg', -1))
# emoji_face = []
result = None
video_captor = cv2.VideoCapture(0)
while True:
ret, frame = video_captor.read()
detected_face, face_coor = format_image(frame)
window_name = 'Face Expression Recognition'
cv2.namedWindow(window_name, cv2.WND_PROP_FULLSCREEN)
cv2.moveWindow(window_name, 0, 0)
if showBox:
if face_coor is not None:
[x, y, w, h] = face_coor
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
ratio = 0.8
frame = cv2.resize(frame,
None,
fx=ratio,
fy=ratio,
interpolation=cv2.INTER_CUBIC)
move_dx = (1 - ratio) * 1280
move_dy = (1 - ratio) * 360
M = np.float32([[1, 0, move_dx], [0, 1, move_dy]])
new_height, new_width, _ = frame.shape
frame = cv2.warpAffine(frame, M, (1280, 720))
cv2.rectangle(frame, (0, 0), (int(move_dx), 720), (255, 255, 255), -1)
if cv2.waitKey(10) & 0xFF == ord(' '):
if detected_face is not None:
cv2.imwrite('a.jpg', detected_face)
tensor = image_to_tensor(detected_face)
result = sess.run(probs, feed_dict={face_x: tensor})
# print(result)
if result is not None:
for index, emotion in enumerate(EMOTIONS):
cv2.putText(frame, emotion, (10, index * 20 + 100),
cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), 1)
cv2.rectangle(frame, (130, index * 20 + 90),
(130 + int(result[0][index] * 100),
(index + 1) * 20 + 84), (255, 0, 0), -1)
emotion_type = EMOTIONS[np.argmax(result[0])]
prob = float(np.max(result[0]) * 100)
cv2.putText(frame, emotion_type, (20, 490),
cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 0), 1)
cv2.putText(frame, str('%.2f' % prob + "%"), (150, 490),
cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 0), 1)
# emoji_face = feelings_faces[np.argmax(result[0])]
# print("1")
# for c in range(0, 3):
# frame[200:320, 10:130, c] = emoji_face[:, :, c] * (emoji_face[:, :, 3] / 255.0) + frame[200:320, 10:130, c] * (1.0 - emoji_face[:, :, 3] / 255.0)
detected_img = cv2.imread('a.jpg', 0)
detected_img = cv2.resize(detected_img,
None,
fx=2,
fy=2,
interpolation=cv2.INTER_CUBIC)
for c in range(0, 3):
frame[340:436,
int(move_dx / 2) - 48:int(move_dx / 2) + 48,
c] = detected_img
cv2.imshow(window_name, frame)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
def demo(modelPath, showBox=False):
face_x = tf.placeholder(tf.float32, [None, 2304]) # 定义一个TensorFlow的占位符
y_conv = deepnn(face_x) # 输出一个维度为（1*7）的结果
probs = tf.nn.softmax(y_conv) # 使用softmax激活函数将结果计算为哪种表情的概率
saver = tf.train.Saver() # 定义一个Saver对象
ckpt = tf.train.get_checkpoint_state(
modelPath) # 得到check_point路径，生成一个check_point对象
sess = tf.Session() # 定义一个TensorFlow的对话，在对话中去执行代码和训练模型
if ckpt and ckpt.model_checkpoint_path:
# 如果check_point对象存在，并且check_point的路径存在
saver.restore(
sess, ckpt.model_checkpoint_path) # 保存Session对话 这一行代码可能实在训练完之后才会执行
print(
'Restore model sucsses!!\nNOTE: Press SPACE on keyboard to capture face.'
)
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
# 将EMOTION 转换为枚举类型后提取 索引和值
feelings_faces.append(
cv2.imread('./data/emojis/' + emotion + '.png', -1))
# cv2.imread（）读取emojis文件夹下的表情图像转化为二进制数据存储到felling_facs中
video_captor = cv2.VideoCapture(0) # CV2.VideoCapture（）打开摄像头获取照片 按空格拍照
emoji_face = []
result = None
while True:
ret, frame = video_captor.read() # 读取拍到的照片转化为二进制数据 存储到frame中
detected_face, face_coor = format_image(
frame) # format_image（）函数定义在 14 行，得到脸部图像和坐标
if showBox:
if face_coor is not None:
[x, y, w, h] = face_coor
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
if cv2.waitKey(1) & 0xFF == ord(' '): # 不知道干嘛用的，以后再说
if detected_face is not None:
cv2.imwrite('a.jpg', detected_face) # 保存脸的图像
tensor = image_to_tensor(detected_face)
# 将图像转化为一个48*48的0到255灰度图像并转化为np.array数据
result = sess.run(probs, feed_dict={
face_x: tensor
}) # 运行TensorFlow模型，计算表情的概率，返回模型训练结果
# print(result)
if result is not None:
for index, emotion in enumerate(EMOTIONS):
cv2.putText(frame, emotion, (10, index * 20 + 20),
cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1)
# 将人脸的表情的文字添加到人脸图片上， 参数是什么以后站再说
cv2.rectangle(frame, (130, index * 20 + 10),
(130 + int(result[0][index] * 100),
(index + 1) * 20 + 4), (255, 0, 0), -1)
emoji_face = feelings_faces[np.argmax(result[0])]
for c in range(0, 3):
frame[200:320, 10:130, c] = emoji_face[:, :, c] * (
emoji_face[:, :, 3] /
255.0) + frame[200:320, 10:130,
c] * (1.0 - emoji_face[:, :, 3] / 255.0)
cv2.imshow('face', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
import model
# import graph
if __name__ == '__main__':
if len(sys.argv) >= 2:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
encoder = model.encoder_load("models/encoder.pth")
encoder.eval()
# graph.show(encoder)
encoder.to(device)
decoder = model.decoder_load("models/decoder.pth")
# graph.show(decoder)
decoder.eval()
decoder.to(device)
img = Image.open(sys.argv[1])
# x -> z -> y
x = model.image_to_tensor(img).to(device)
z = encoder(x)
y = decoder(z)
y = torch.Tensor(y.cpu())
y.clamp_(0, 1)
img = model.image_from_tensor(y)
img.show()
