Smile Detection Using Custom LeNet
All about how to detect smile from human face.
• 249 min read
- Importing Necessary Packages.
- Building custom LeNet
- Predicting the Smile, using the trained model and OpenCV Library.
- Summary
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras import backend as K
from tensorflow.keras.models import load_model
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import imutils
import time
import cv2
import os
def lenet(width, height, depth, classes):
#initializing model
model = Sequential()
inputshape = (height, width, depth)
# If we are using "channels first", update the input shape
if K.image_data_format() == "channels_first": #Returns the default image data format convention.
inputshape = (depth, height, width)
# First set of CONV => RELU => POOL layers
model.add(Conv2D(20, (5,5), padding="same", input_shape=inputshape))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
#Second set of CONV => RELU => POOL layers
model.add(Conv2D(50, (5,5), padding="same"))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
# First and only set of FC => RELU layers
model.add(Flatten())
model.add(Dense(500))
model.add(Activation("relu"))
# Softmax Classifier
model.add(Dense(classes))
model.add(Activation("softmax"))
#return the constructed network architechture
return model
Load the SMILES dataset
- You can get dataset from here LINK
- In this preprocessing step, we will
- convert image into GRAY scale
- resize image to (28, 28)
- convert image into Numpy array
data = []
labels = []
for imagePath in sorted(list(paths.list_images("./dataset/SMILEs"))):
# Load the image,pre-process it, and store it in the data list.
image = cv2.imread(imagePath)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = imutils.resize(image, width =28)
image = img_to_array(image)
data.append(image)
# extract the class label from the image path and update the labels list
label = imagePath.split(os.path.sep)[-3]
label = "smiling" if label == "positives" else "not_smiling"
labels.append(label)
Our data and labels are constructed, we can scale the raw pixel intensities to the range [0, 1] and then apply one-hot encoding to the labels.
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
# Convert the labels from integers to vectors
le = LabelEncoder().fit(labels)
labels = to_categorical(le.transform(labels), 2)
We know our dataset is imbalanced so we have to fix it otherwise our trained model will be biased toward dataset that is large in number. So we fix it by computing the class weights.
# initialize a dictionary to store the class weights
classTotals = labels.sum(axis=0)
classWeight = dict()
# Loop over all classes and calculate the class weight
for i in range(0, len(classTotals)):
classWeight[i] = classTotals.max() / classTotals[i]
d:\AnacondaInstallation\envs\smile\lib\site-packages\ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in float_scalars
Splitting the data into training and testing sets using 80% of the data for training and the remaining 20 % for testing
X_train, X_test, y_train, y_test = train_test_split(data, labels,
test_size = 0.20, stratify=labels, random_state=42)
Now training LeNet.
model = lenet(width=28, height=28, depth=1, classes=2)
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
INFO Compiling our Model...
H = model.fit(X_train, y_train, validation_data=(X_test, y_test),
class_weight=classWeight, batch_size=64, epochs=30,verbose=1)
INFO Training our model..
Epoch 1/30 165/165 [==============================] - 1s 6ms/step - loss: 0.2219 - accuracy: 0.9397 - val_loss: 0.2706 - val_accuracy: 0.9009 Epoch 2/30 165/165 [==============================] - 1s 5ms/step - loss: 0.2038 - accuracy: 0.9424 - val_loss: 0.2665 - val_accuracy: 0.9066 Epoch 3/30 165/165 [==============================] - 1s 5ms/step - loss: 0.1893 - accuracy: 0.9487 - val_loss: 0.2791 - val_accuracy: 0.8987 Epoch 4/30 165/165 [==============================] - 1s 5ms/step - loss: 0.1771 - accuracy: 0.9509 - val_loss: 0.2639 - val_accuracy: 0.9040 Epoch 5/30 165/165 [==============================] - 1s 5ms/step - loss: 0.1680 - accuracy: 0.9533 - val_loss: 0.2766 - val_accuracy: 0.9009 Epoch 6/30 165/165 [==============================] - 1s 5ms/step - loss: 0.1517 - accuracy: 0.9587 - val_loss: 0.2948 - val_accuracy: 0.9002 Epoch 7/30 165/165 [==============================] - 1s 5ms/step - loss: 0.1398 - accuracy: 0.9618 - val_loss: 0.2778 - val_accuracy: 0.9142 Epoch 8/30 165/165 [==============================] - 1s 5ms/step - loss: 0.1233 - accuracy: 0.9682 - val_loss: 0.3248 - val_accuracy: 0.8998 Epoch 9/30 165/165 [==============================] - 1s 5ms/step - loss: 0.1141 - accuracy: 0.9691 - val_loss: 0.3057 - val_accuracy: 0.9078 Epoch 10/30 165/165 [==============================] - 1s 5ms/step - loss: 0.1021 - accuracy: 0.9733 - val_loss: 0.3409 - val_accuracy: 0.9013 Epoch 11/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0985 - accuracy: 0.9732 - val_loss: 0.3394 - val_accuracy: 0.9104 Epoch 12/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0759 - accuracy: 0.9806 - val_loss: 0.3554 - val_accuracy: 0.9047 Epoch 13/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0805 - accuracy: 0.9798 - val_loss: 0.3826 - val_accuracy: 0.8937 Epoch 14/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0703 - accuracy: 0.9817 - val_loss: 0.3746 - val_accuracy: 0.9032 Epoch 15/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0500 - accuracy: 0.9880 - val_loss: 0.3860 - val_accuracy: 0.9123 Epoch 16/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0502 - accuracy: 0.9880 - val_loss: 0.3924 - val_accuracy: 0.8979 Epoch 17/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0614 - accuracy: 0.9847 - val_loss: 0.4086 - val_accuracy: 0.8877 Epoch 18/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0444 - accuracy: 0.9884 - val_loss: 0.3861 - val_accuracy: 0.9112 Epoch 19/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0427 - accuracy: 0.9891 - val_loss: 0.4135 - val_accuracy: 0.9116 Epoch 20/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0298 - accuracy: 0.9933 - val_loss: 0.4430 - val_accuracy: 0.9104 Epoch 21/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0259 - accuracy: 0.9940 - val_loss: 0.4711 - val_accuracy: 0.9078 Epoch 22/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0228 - accuracy: 0.9947 - val_loss: 0.4577 - val_accuracy: 0.9089 Epoch 23/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0267 - accuracy: 0.9934 - val_loss: 0.4535 - val_accuracy: 0.9044 Epoch 24/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0483 - accuracy: 0.9876 - val_loss: 0.4633 - val_accuracy: 0.9089 Epoch 25/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0182 - accuracy: 0.9958 - val_loss: 0.5287 - val_accuracy: 0.9066 Epoch 26/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0403 - accuracy: 0.9896 - val_loss: 0.4862 - val_accuracy: 0.9055 Epoch 27/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0292 - accuracy: 0.9931 - val_loss: 0.5270 - val_accuracy: 0.9104 Epoch 28/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0155 - accuracy: 0.9971 - val_loss: 0.5431 - val_accuracy: 0.9006 Epoch 29/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0102 - accuracy: 0.9987 - val_loss: 0.5666 - val_accuracy: 0.9066 Epoch 30/30 165/165 [==============================] - 1s 5ms/step - loss: 0.0089 - accuracy: 0.9987 - val_loss: 0.5738 - val_accuracy: 0.9120
Printing the performance of our model.
- It seems our model is abit overfitted, but for this project its ok.
predictions = model.predict(X_test, batch_size = 64)
print(classification_report(y_test.argmax(axis=1),
predictions.argmax(axis=1), target_names=le.classes_))
INFO Evaluating our model..
precision recall f1-score support
not_smiling 0.93 0.95 0.94 1897
smiling 0.86 0.82 0.84 738
accuracy 0.91 2635
macro avg 0.89 0.88 0.89 2635
weighted avg 0.91 0.91 0.91 2635
Load the face detector cascade and smile detector trained model
- You can get haarcascade file from here LINK
detector = cv2.CascadeClassifier("./haarcascade_frontalface_default.xml")
model = load_model("./trained_model/dip.h5")
vs = cv2.VideoCapture(0)
time.sleep(2.0)
# keep looping
while True:
# grab the current frame
_, frame = vs.read()
# resize the frame, convert it to grayscale, and then clone the
# original frame so we can draw on it later in the program
frame = imutils.resize(frame, width=300)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frameClone = frame.copy()
# detect faces in the input frame, then clone the frame so that
# we can draw on it
rects = detector.detectMultiScale(gray, scaleFactor=1.1,
minNeighbors=5, minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
# loop over the face bounding boxes
for (fX, fY, fW, fH) in rects:
# extract the ROI of the face from the grayscale image,
# resize it to a fixed 28x28 pixels, and then prepare the
# ROI for classification via the CNN
roi = gray[fY:fY + fH, fX:fX + fW]
roi = cv2.resize(roi, (28, 28))
roi = roi.astype("float") / 255.0
roi = img_to_array(roi)
roi = np.expand_dims(roi, axis=0)
# determine the probabilities of both "smiling" and "not
# smiling", then set the label accordingly
(notSmiling, smiling) = model.predict(roi)[0]
label = "Smiling" if smiling > notSmiling else "Not Smiling"
# display the label and bounding box rectangle on the output
# frame
cv2.putText(frameClone, label, (fX, fY - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (255,255,0), 2)
cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH),
(0,128,128), 2)
# show our detected faces along with smiling/not smiling labels
# cv2.imshow("Face", frameClone)
# show the output frame
cv2.imshow("frameClone", frameClone)
# if the 'q' key is pressed, stop the loop
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
#
# if the `q` key was pressed, break from the loop
# cleanup the camera and close any open windows
vs.release()
cv2.destroyAllWindows()
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