基于深度学习的人脸情感识别代码

""" Reference: https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_gui/py_video_display/py_video_display.html In this python file we will be be using our pretrained emotions and gestures models and we will be finally running them and executing """ from tensorflow.keras.models import load_model from tensorflow.keras.preprocessing import image from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.preprocessing.image import img_to_array import numpy as np import cv2 from playsound import playsound import time # Load the respective emotions and gesture Models emotion_model = load_model("emotions.h5") gesture_model = load_model("gesture.h5") # Load the Haar Cascade classifier for face detection face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') # Sort the emotions and gesture labels emotion_label = ['Angry', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise'] gesture_label = ['loser', 'punch', 'super', 'victory'] # Analyze users choice # 1 for emotions detector # 2 for gestures detector print(""" Enter Your Choice: 1. Emotions 2. Gestures """) choice = int(input()) if choice == 1: """ In this choice we will be running the emotions model. While webcam is detected we will read the frames and then we will draw a rectangle box when the haar cascade classifier detects a face. We will convert the facial image into a grayscale of dimensions 48, 48 similar to the trained images for better predictions. The Prediction is only made when the np.sum detects at least one face. The keras commands img_to_array converts the image to array dimensions and in case more images are detected we expand the dimensions. The Predictions are made according to the labels and the recordings will be played accordingly. """ cap = cv2.VideoCapture(0) while True: # Grab a single frame of video ret, frame = cap.read() labels = [] gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) faces = face_classifier.detectMultiScale(gray,1.3,5) for (x,y,w,h) in faces: cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2) roi_gray = gray[y:y+h,x:x+w] roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA) # rect,face,image = face_detector(frame) if np.sum([roi_gray])!=0: roi = roi_gray.astype('float')/255.0 roi = img_to_array(roi) roi = np.expand_dims(roi,axis=0) # make a prediction on the ROI, then lookup the class preds = emotion_model.predict(roi)[0] label = emotion_label[preds.argmax()] # We are starting the clock here and after every 10 seconds # we will give a voice prediction. start = time.clock() print(start) if int(start) % 10 == 0: if label == "Angry": playsound("reactions/anger.mp3") elif label == "Fear": playsound("reactions/fear.mp3") elif label == "Happy": playsound("reactions/happy.mp3") if label == "Neutral": playsound("reactions/neutral.mp3") elif label == "Sad": playsound("reactions/sad.mp3") elif label == "Surprise": playsound("reactions/surprise.mp3") label_position = (x,y) cv2.putText(frame, label, label_position, cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3) else: cv2.putText(frame, 'No Face Found', (20,60), cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3) cv2.imshow('Emotion Detector',frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() else: """ In this choice we will be running the gestures model. While webcam is detected we will read the frames and then we will draw a rectangle box in the middle of the screen unlike the emotions model. The User will have to place the fingers in the required box to make the following work. The Prediction is only made when the np.sum detects at least one finger model. The keras commands img_to_array converts the image to array dimensions and in case more images are detected we expand the dimensions. The Predictions are made according to the labels and the recordings will be played accordingly. """ cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() cv2.rectangle(frame, (100, 100), (500, 500), (255, 255, 255), 2) roi = frame[100:500, 100:500] img = cv2.resize(roi, (200, 200)) img = image.img_to_array(img) img = np.expand_dims(img, axis=0) img = img.astype('float32')/255 pred = np.argmax(gesture_model.predict(img)) color = (0,0,255) start = time.clock() print(start) if int(start) % 10 == 0: if pred == 0: playsound("reactions/loser.mp3") elif pred == 1: playsound("reactions/punch.mp3") elif pred == 2: playsound("reactions/super.mp3") if pred == 3: playsound("reactions/victory.mp3") cv2.putText(frame, gesture_label[pred], (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, color, 2) cv2.imshow('Gesture Detector', frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows()