reduce_lr = ReduceLROnPlateau('val_loss',
factor=0.1,
patience=int(patience / 4),
verbose=1)
trained_models_path = base_path + '_mini_XCEPTION'
model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(model_names,
'val_loss',
verbose=1,
save_best_only=True)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]
# loading dataset
faces, emotions = load_fer2013()
faces = preprocess_input(faces)
num_samples, num_classes = emotions.shape
xtrain, xtest, ytrain, ytest = train_test_split(faces,
emotions,
test_size=0.2,
shuffle=True)
model.fit_generator(data_generator.flow(xtrain, ytrain, batch_size),
steps_per_epoch=len(xtrain) / batch_size,
epochs=num_epochs,
verbose=1,
callbacks=callbacks,
validation_data=(xtest, ytest))
model.save("hdf.h5")
def run(self, frame_in, canvas, label_face, label_result):
# frame_in 摄像画面或图像
# canvas 用于显示的背景图
# label_face 用于人脸显示画面的label对象
# label_result 用于显示结果的label对象
# 调节画面大小
frame = imutils.resize(frame_in, width=300) # 缩放画面
# frame = cv2.resize(frame, (300,300)) # 缩放画面
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 转为灰度图
# 检测人脸
faces = self.face_detection.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
preds = [] # 预测的结果
label = None # 预测的标签
(fX, fY, fW, fH) = None, None, None, None # 人脸位置
frameClone = frame.copy() # 复制画面
if len(faces) > 0:
faces = sorted(faces,
reverse=False,
key=lambda x: (x[2] - x[0]) *
(x[3] - x[1])) # 按面积从小到大排序
for i in range(len(faces)):
# if i == 0:
# i = -1
# else:
# break
(fX, fY, fW, fH) = faces[i]
roi = gray[fY:fY + fH, fX:fX + fW]
roi = cv2.resize(roi, self.emotion_classifier.input_shape[1:3])
roi = preprocess_input(roi)
roi = img_to_array(roi)
roi = np.expand_dims(roi, axis=0)
preds = self.emotion_classifier.predict(roi)[0]
# emotion_probability = np.max(preds) #
label = self.EMOTIONS[preds.argmax()] #
cv2.putText(frameClone, label, (fX, fY - 10),
cv2.FONT_HERSHEY_TRIPLEX, 0.4, (0, 255, 0), 1)
cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH),
(255, 255, 0), 1)
# canvas = 255* np.ones((250, 300, 3), dtype="uint8")
# canvas = cv2.imread('slice.png', flags=cv2.IMREAD_UNCHANGED)
for (i, (emotion, prob)) in enumerate(zip(self.EMOTIONS, preds)):
# 用于显示各类别概率
text = "{}: {:.2f}%".format(emotion, prob * 100)
w = int(prob * 300) + 7
cv2.rectangle(canvas, (7, (i * 35) + 5), (w, (i * 35) + 35),
(224, 200, 130), -1)
cv2.putText(canvas, text, (10, (i * 35) + 23),
cv2.FONT_HERSHEY_DUPLEX, 0.6, (0, 0, 0), 1)
frameClone = cv2.resize(frameClone, (420, 280))
show = cv2.cvtColor(frameClone, cv2.COLOR_BGR2RGB)
showImage = QtGui.QImage(show.data, show.shape[1], show.shape[0],
QtGui.QImage.Format_RGB888)
label_face.setPixmap(QtGui.QPixmap.fromImage(showImage))
QtWidgets.QApplication.processEvents()
show = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB)
showImage = QtGui.QImage(show.data, show.shape[1], show.shape[0],
QtGui.QImage.Format_RGB888)
label_result.setPixmap(QtGui.QPixmap.fromImage(showImage))
return (label)
