I have this simple Python code that makes predictions on the emotions of the face (took it from here in case you need to run it), and shows it on the rectangle around the face on the camera. But the problem is it has many noises. For instance, Fearful -- Sad -- -- Sad and such. I want to smooth out the predictions and filter out singled out predictions. How can I make a change that if n number of predictions in a row said Sad, then display it as Sad?
You'll only need to change the last few lines as the initial parts are all for predictions.
import numpy as np
import argparse
import matplotlib.pyplot as plt
import cv2
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# command line argument
ap = argparse.ArgumentParser()
ap.add_argument("--mode",help="train/display")
mode = ap.parse_args().mode
# plots accuracy and loss curves
def plot_model_history(model_history):
"""
Plot Accuracy and Loss curves given the model_history
"""
fig, axs = plt.subplots(1,2,figsize=(15,5))
# summarize history for accuracy
axs[0].plot(range(1,len(model_history.history['accuracy']) 1),model_history.history['accuracy'])
axs[0].plot(range(1,len(model_history.history['val_accuracy']) 1),model_history.history['val_accuracy'])
axs[0].set_title('Model Accuracy')
axs[0].set_ylabel('Accuracy')
axs[0].set_xlabel('Epoch')
axs[0].set_xticks(np.arange(1,len(model_history.history['accuracy']) 1),len(model_history.history['accuracy'])/10)
axs[0].legend(['train', 'val'], loc='best')
# summarize history for loss
axs[1].plot(range(1,len(model_history.history['loss']) 1),model_history.history['loss'])
axs[1].plot(range(1,len(model_history.history['val_loss']) 1),model_history.history['val_loss'])
axs[1].set_title('Model Loss')
axs[1].set_ylabel('Loss')
axs[1].set_xlabel('Epoch')
axs[1].set_xticks(np.arange(1,len(model_history.history['loss']) 1),len(model_history.history['loss'])/10)
axs[1].legend(['train', 'val'], loc='best')
fig.savefig('plot.png')
plt.show()
# Define data generators
train_dir = 'data/train'
val_dir = 'data/test'
num_train = 28709
num_val = 7178
batch_size = 64
num_epoch = 50
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(48,48),
batch_size=batch_size,
color_mode="grayscale",
class_mode='categorical')
validation_generator = val_datagen.flow_from_directory(
val_dir,
target_size=(48,48),
batch_size=batch_size,
color_mode="grayscale",
class_mode='categorical')
# Create the model
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))
# If you want to train the same model or try other models, go for this
if mode == "train":
model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.0001, decay=1e-6),metrics=['accuracy'])
model_info = model.fit_generator(
train_generator,
steps_per_epoch=num_train // batch_size,
epochs=num_epoch,
validation_data=validation_generator,
validation_steps=num_val // batch_size)
plot_model_history(model_info)
model.save_weights('model.h5')
# emotions will be displayed on your face from the webcam feed
elif mode == "display":
model.load_weights('model.h5')
# prevents openCL usage and unnecessary logging messages
cv2.ocl.setUseOpenCL(False)
# dictionary which assigns each label an emotion (alphabetical order)
emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
# start the webcam feed
cap = cv2.VideoCapture(1)
while True:
# Find haar cascade to draw bounding box around face
ret, frame = cap.read()
if not ret:
break
facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = facecasc.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y-50), (x w, y h 10), (255, 0, 0), 2)
roi_gray = gray[y:y h, x:x w]
cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0)
prediction = model.predict(cropped_img)
maxindex = int(np.argmax(prediction))
text = emotion_dict[maxindex]
if ("Sad" in text) or ("Angry" in text) or ("Disgusted" in text):
text = "Sad"
if ("Happy" in text) or ("Sad" in text):
cv2.putText(frame, text, (x 20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
cv2.imshow('Video', cv2.resize(frame,(1600,960),interpolation = cv2.INTER_CUBIC))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
CodePudding user response：
I'd make a list of predictions and take the mode, something like this:
prediction_history = []
LOOKBACK = 5 # how far you want to look back
# in loop:
prediction_history.append(maxindex)
most_common_index = max(set(prediction_history[-LOOKBACK:][::-1]), key = prediction_history.count)
text = emotion_dict[most_common_index]
specifically in your code:
import os
...
prediction_history = []
LOOKBACK = 5 # how far you want to look back
...
cap = cv2.VideoCapture(1)
while True:
# Find haar cascade to draw bounding box around face
ret, frame = cap.read()
if not ret:
break
facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = facecasc.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y-50), (x w, y h 10), (255, 0, 0), 2)
roi_gray = gray[y:y h, x:x w]
cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0)
prediction = model.predict(cropped_img)
# updates
prediction_history.append(int(np.argmax(prediction)))
most_common_index = max(set(prediction_history[-LOOKBACK:][::-1]), key = prediction_history.count)
text = emotion_dict[most_common_index]
if ("Sad" in text) or ("Angry" in text) or ("Disgusted" in text):
text = "Sad"
if ("Happy" in text) or ("Sad" in text):
cv2.putText(frame, text, (x 20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
cv2.imshow('Video', cv2.resize(frame,(1600,960),interpolation = cv2.INTER_CUBIC))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()