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Camera_Thread_class.py
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import time
import tensorflow as tf
import numpy as np
from PyQt5 import QtCore, QtGui
from PyQt5.QtCore import QThread
import cv2
from tensorflow.python.keras import Sequential
from tensorflow.python.keras.layers import Dense
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
class Camera_Thread_class(QThread):
def __init__(self, ui):
super().__init__()
self.running_flag = True
self.ui = ui
# 初始化keras模型
self.model = self.get_student_model()
self.types = ['Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']
self.initEmojis()
self.initBar()
def get_student_model(self):
stu_base = tf.keras.applications.MobileNetV2(
input_shape=None,
alpha=1.0,
include_top=True,
# weights='imagenet',
input_tensor=None,
pooling=None,
classes=1000,
classifier_activation='softmax'
)
# 生成一个model
student_model = Sequential(stu_base)
student_model.add(Dense(5, activation="softmax"))
student_model.load_weights("weight.h5")
return student_model
def initBar(self):
self.F = MyFigure(width=3, height=2, dpi=100)
self.ui.gridLayout.addWidget(self.F)
def initCamera_timer(self):
self.timer_camera = QtCore.QTimer()
self.timer_camera.start(10)
self.timer_camera.timeout.connect(self.showCamera)
def showCamera(self):
flag, self.image = self.cap.read()
show = cv2.resize(self.image, (160, 150))
show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)
showImage = QtGui.QImage(show.data, show.shape[1], show.shape[0], QtGui.QImage.Format_RGB888)
self.ui.Cameralabel.setPixmap(QtGui.QPixmap.fromImage(showImage))
self.getDetectCamera(self.image)
# showCamera的附属函数
def getDetectCamera(self, pics):
frame = pics
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame_raw = pics.copy()
# OpenCV人脸识别分类器
classifier = cv2.CascadeClassifier(
"haarcascade_frontalface_default.xml"
)
color = (255, 0, 0)
# 调用识别人脸
faceRects = classifier.detectMultiScale(
gray, scaleFactor=1.2, minNeighbors=3, minSize=(150, 150))
x, y, w, h = (0, 0, 0, 0)
if len(faceRects): # 大于0则检测到人脸
for faceRect in faceRects: # 单独框出每一张人脸
x, y, w, h = faceRect
cv2.rectangle(frame, (x, y), (x + h, y + w), color, 5)
cv2.circle(frame, (x + w // 4, y + h // 4 + 30), min(w // 8, h // 8),
color, 5)
cv2.circle(frame, (x + 3 * w // 4, y + h // 4 + 30), min(w // 8, h // 8),
color, 5)
cv2.rectangle(frame, (x + 3 * w // 8, y + 3 * h // 4),
(x + 5 * w // 8, y + 7 * h // 8), color, 5)
show = cv2.resize(frame, (160, 150))
show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)
showImage = QtGui.QImage(show.data, show.shape[1], show.shape[0], QtGui.QImage.Format_RGB888)
self.ui.Detectlabel.setPixmap(QtGui.QPixmap.fromImage(showImage))
# 把送入的图片送入深度学习模型中推理
if len(faceRects) > 0:
self.pridectEmotion(frame_raw[y:y + h, x:x + w])
# self.pridectEmotion(frame_raw[y-40:y+h+40,x-40:x+w+40])
face_area = frame_raw[y:y + h, x:x + w]
else:
self.ui.emtiontextlabel.setText("未检测到人脸！")
def initEmojis(self):
shapee = (160, 150)
self.angry_emoji = cv2.imread("./emoji_pics/angry.png")
self.angry_emoji = cv2.cvtColor(self.angry_emoji, cv2.COLOR_BGR2RGB)
self.angry_emoji = cv2.resize(self.angry_emoji, shapee)
self.happy_emoji = cv2.imread("./emoji_pics/happy.png")
self.happy_emoji = cv2.cvtColor(self.happy_emoji, cv2.COLOR_BGR2RGB)
self.happy_emoji = cv2.resize(self.happy_emoji, shapee)
self.neutral_emoji = cv2.imread("./emoji_pics/neutral.png")
self.neutral_emoji = cv2.cvtColor(self.neutral_emoji, cv2.COLOR_BGR2RGB)
self.neutral_emoji = cv2.resize(self.neutral_emoji, shapee)
self.sad_emoji = cv2.imread("./emoji_pics/sad.png")
self.sad_emoji = cv2.cvtColor(self.sad_emoji, cv2.COLOR_BGR2RGB)
self.sad_emoji = cv2.resize(self.sad_emoji, shapee)
self.surprise_emoji = cv2.imread("./emoji_pics/surprise.png")
self.surprise_emoji = cv2.cvtColor(self.surprise_emoji, cv2.COLOR_BGR2RGB)
self.surprise_emoji = cv2.resize(self.surprise_emoji, shapee)
def pridectEmotion(self, frame):
img = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_LINEAR)
img_show = cv2.resize(img, (160, 150))
showImage = QtGui.QImage(img_show.data, img_show.shape[1], img_show.shape[0], QtGui.QImage.Format_RGB888)
self.ui.Face_Label.setPixmap(QtGui.QPixmap.fromImage(showImage))
img = (img[..., ::-1].astype(np.float32)) / 255.0
img = img.reshape((1, 224, 224, 3))
pred = self.model.predict(img)
emotion_possible = pred * 100
type_index = np.argmax(pred)
emotion_label = self.types[type_index]
self.ui.emtiontextlabel.setText(emotion_label)
if (type_index == 0):
img_showw = self.angry_emoji
showImage = QtGui.QImage(img_showw.data, img_showw.shape[1], img_showw.shape[0],
QtGui.QImage.Format_RGB888)
self.ui.EmojiLabel.setPixmap(QtGui.QPixmap.fromImage(showImage))
if (type_index == 1):
img_showw = self.happy_emoji
showImage = QtGui.QImage(img_showw.data, img_showw.shape[1], img_showw.shape[0],
QtGui.QImage.Format_RGB888)
self.ui.EmojiLabel.setPixmap(QtGui.QPixmap.fromImage(showImage))
if (type_index == 2):
img_showw = self.neutral_emoji
showImage = QtGui.QImage(img_showw.data, img_showw.shape[1], img_showw.shape[0],
QtGui.QImage.Format_RGB888)
self.ui.EmojiLabel.setPixmap(QtGui.QPixmap.fromImage(showImage))
if (type_index == 3):
img_showw = self.sad_emoji
showImage = QtGui.QImage(img_showw.data, img_showw.shape[1], img_showw.shape[0],
QtGui.QImage.Format_RGB888)
self.ui.EmojiLabel.setPixmap(QtGui.QPixmap.fromImage(showImage))
if (type_index == 4):
img_showw = self.surprise_emoji
showImage = QtGui.QImage(img_showw.data, img_showw.shape[1], img_showw.shape[0],
QtGui.QImage.Format_RGB888)
self.ui.EmojiLabel.setPixmap(QtGui.QPixmap.fromImage(showImage))
possible = list(emotion_possible[0])
self.F.axes.cla()
self.F.axes.set_ylim([0, 100])
self.F.axes.bar(["angry", "happy", "neutral", "sad", "surprise"], possible,
color=["r", "gold", "deepskyblue", "slategray", "g"])
self.F.axes.set_title("Prediction Probability Distribution")
self.F.draw()
def startRunning(self):
if (self.running_flag):
return 1
self.running_flag = True
self.start()
def stopRunning(self):
self.running_flag = False
time.sleep(1)
self.ui.emtiontextlabel.setText("检测暂停！")
def run(self):
fps = 30 #这个是帧数参数，大家可以根据需要自行设定！
self.cap = cv2.VideoCapture(0)
while (self.running_flag):
self.showCamera()
time.sleep(1 / fps)
class MyFigure(FigureCanvas):
def __init__(self, width, height, dpi):
self.fig = Figure(figsize=(width, height), dpi=dpi)
super(MyFigure, self).__init__(self.fig)
self.axes = self.fig.add_subplot(111)
def refresh(self):
super(MyFigure, self).__init__(self.fig)