realtime_hog.py

import cv2
import numpy as np
import os
import _pickle as cPickle
from skimage.feature import hog
import dlib

'''
Showing gradients from hog

Joshua Kranabetter and Taif Anjum
2022
'''


def fetch_landmarks(image, rects, predictor):
    if len(rects) > 1:
        raise BaseException('too many faces')
    if len(rects) == 0:
        raise BaseException('no faces')
    return np.matrix([[p.x, p.y] for p in predictor(image, rects[0]).parts()])


# (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral)\


emotions = ['Anger', 'disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
font = cv2.FONT_HERSHEY_DUPLEX

# load svm model
if os.path.isfile('svm_model.bin'):
    with open('svm_model.bin', 'rb') as f:
        model = cPickle.load(f)

# load Dlib predictor for face landmarks
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')

webcam = cv2.VideoCapture(0)
#webcam.open(0, cv2.CAP_DSHOW)

last_emotion = ''

while (webcam.isOpened()):
    ret, frame = webcam.read()

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in faces:
        image = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)

        face_crop = gray[y:y+h, x:x+w]
        face_crop = cv2.resize(face_crop, (48, 48),
                               interpolation=cv2.INTER_AREA)

        hog_features, hog_image = hog(face_crop, orientations=8, pixels_per_cell=(16, 16),
                                      cells_per_block=(1, 1), visualize=True)

        hog_features1, hog_image1 = hog(gray, orientations=8, pixels_per_cell=(16, 16),
                                        cells_per_block=(1, 1), visualize=True)

        image_int8 = np.uint8(face_crop)
        face_rectangles = [dlib.rectangle(left=1, top=1, right=47, bottom=47)]
        face_landmarks = fetch_landmarks(
            image_int8, face_rectangles, predictor)
        face_landmarks = face_landmarks.getA1()

        datas = np.concatenate((face_landmarks, hog_features), axis=None)

        results = model.predict([datas])
        probabilities = model.predict_proba([datas])
        probabilities = probabilities[0]  # unwrap

        max_value = max(probabilities)

        if max_value > 0.5:
            emotion = emotions[results[0]]
            last_emotion = emotion
            print(emotion + " confidence level of " + str(max_value*100) + "%")
            cv2.putText(image, emotion, (x + 6, y - 6),
                        font, 1.0, (255, 255, 255), 1)
            cv2.imshow('FER', hog_image1)
            cv2.waitKey(25)
        else:
            cv2.putText(image, last_emotion, (x + 6, y - 6),
                        font, 1.0, (255, 255, 255), 1)
            cv2.imshow('FER', hog_image1)
            cv2.waitKey(25)


# After the loop release the cap object
webcam.release()
# Destroy all the windows
cv2.destroyAllWindows()