Face Expression Recognition Blog

dir-tree.md

•
├── test
│   ├── modi-obama-1.jpg
│   ├── modi-obama-2.jpg
│   └── face-demographics-walking.mp4
├── models
│   ├── facial_expression_model_weights.h5
│   └── facial_expression_model_structure.json
├── facial-expression-recognition-from-image.py
└── realtime-facial-expression-recognition.py

face-expression-recognition-image.py

# importing required libraries
import cv2
import numpy as np
from keras.preprocessing import image
from keras.models import model_from_json
import  face_recognition

# only for google colab
# from google.colab.patches import cv2_imshow

# face expression model initialization
face_exp_model = model_from_json(open("models/facial_expression_model_structure.json","r").read())

# load weights into model
face_exp_model.load_weights("models/facial_expression_model_weights.h5")

# list of emotions labels
emotions_label = ('Angry','Digust','Fear','Happy','Sad','Surprise','Neutral')

# load the image to be detected
img = cv2.imread('test/modi-obama-1.jpg');

# find all face locations using face_locations() function
# model can be "cnn" or "hog"
# number_of_times_to_upsample = 1 higher and detect more faces

all_face_locations = face_recognition.face_locations(img, model="hog")

# loop over each face detected in the image
for index, face_location in enumerate(all_face_locations):
  
  top_pos, right_pos, bottom_pos, left_pos = face_location
  
  #-----------------------------------------------------------------------------
  # Face Expression Detection
  #-----------------------------------------------------------------------------

  # Slice frame image array by positions face_locations
  face_image = img[top_pos:bottom_pos,left_pos:right_pos]
  
  # preprocessing input, convert it to an image like as the data in dataset
  # convert face image to grayscale
  face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)

  # resize to 48X48 px size
  face_image = cv2.resize(face_image, (48, 48))

  # convert the PIL image into a 3D numpy array
  img_pixels = image.img_to_array(face_image)

  # expand the shape of an array into single row multiple columns
  img_pixels = np.expand_dims(img_pixels, axis = 0)

  # pixels are in range of [0, 255]. Normalize all pixels in scale of [0, 1]
  img_pixels /= 255

  # do prediction using model, get the prediction values for all 7 expressions
  exp_predictions = face_exp_model.predict(img_pixels)

  # find max indexed prediction value (0 till 7)
  max_index = np.argmax(exp_predictions[0])

  # get the corresponding label from emotions_label list
  emotion_label = emotions_label[max_index]
    
  # draw rectangle around each face
  cv2.rectangle(img, (left_pos,top_pos), (right_pos,bottom_pos), (0,0,255), 2)

  # disply the name of emotion as text along the face
  font = cv2.FONT_HERSHEY_SIMPLEX
  cv2.rectangle(img, (left_pos-1, bottom_pos + 18), (right_pos+1, bottom_pos), (0, 0, 255), -1)
  cv2.putText(img, emotion_label, (left_pos+2,bottom_pos+11), font, 0.5, (255,255,255), 1)

  #-----------------------------------------------------------------------------

# show image
cv2.imshow("Face Expression Recognition",image)

# only for google colab 
# cv2_imshow(img)

import-libraries.py

# importing required libraries
import cv2
import numpy as np
from keras.preprocessing import image
from keras.models import model_from_json
import  face_recognition

# only for google colab
# from google.colab.patches import cv2_imshow

initialize-model-and-weights.py

# face expression model initialization
face_exp_model = model_from_json(open("models/facial_expression_model_structure.json","r").read())

# load weights into model
face_exp_model.load_weights("models/facial_expression_model_weights.h5")

# list of emotions labels
emotions_label = ('Angry','Digust','Fear','Happy','Sad','Surprise','Neutral')

recognize-expression.py

# load the image to be detected
img = cv2.imread('test/modi-obama-1.jpg');

# find all face locations using face_locations() function
# model can be "cnn" or "hog"
# number_of_times_to_upsample = 1 higher and detect more faces

all_face_locations = face_recognition.face_locations(img, model="hog")

# loop over each face detected in the image
for index, face_location in enumerate(all_face_locations):
  
  top_pos, right_pos, bottom_pos, left_pos = face_location
  
  #-----------------------------------------------------------------------------
  # Face Expression Detection
  #-----------------------------------------------------------------------------

  # Slice frame image array by positions face_locations
  face_image = img[top_pos:bottom_pos,left_pos:right_pos]
  
  # preprocessing input, convert it to an image like as the data in dataset
  # convert face image to grayscale
  face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)

  # resize to 48X48 px size
  face_image = cv2.resize(face_image, (48, 48))

  # convert the PIL image into a 3D numpy array
  img_pixels = image.img_to_array(face_image)

  # expand the shape of an array into single row multiple columns
  img_pixels = np.expand_dims(img_pixels, axis = 0)

  # pixels are in range of [0, 255]. Normalize all pixels in scale of [0, 1]
  img_pixels /= 255

  # do prediction using model, get the prediction values for all 7 expressions
  exp_predictions = face_exp_model.predict(img_pixels)

  # find max indexed prediction value (0 till 7)
  max_index = np.argmax(exp_predictions[0])

  # get the corresponding label from emotions_label list
  emotion_label = emotions_label[max_index]
    
  # draw rectangle around each face
  cv2.rectangle(img, (left_pos,top_pos), (right_pos,bottom_pos), (0,0,255), 2)

  # disply the name of emotion as text along the face
  font = cv2.FONT_HERSHEY_SIMPLEX
  cv2.rectangle(img, (left_pos-1, bottom_pos + 18), (right_pos+1, bottom_pos), (0, 0, 255), -1)
  cv2.putText(img, emotion_label, (left_pos+2,bottom_pos+11), font, 0.5, (255,255,255), 1)

  #-----------------------------------------------------------------------------

# show image
cv2.imshow("Face Expression Recognition",image)

# only for google colab 
# cv2_imshow(img)