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| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| from keras.preprocessing.image import img_to_array | |
| from keras.models import model_from_json | |
| # Facial expression recognizer initialization | |
| face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') | |
| model = model_from_json(open('facial_expression_model_structure.json', 'r').read()) | |
| model.load_weights('facial_expression_model_weights.h5') | |
| emotions = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral') | |
| def blur_image(img): | |
| blur = cv2.blur(img,(10, 10)) | |
| return blur | |
| def process_image(img): | |
| # Resize the frame | |
| frame = cv2.resize(img, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA) | |
| # Convert to grayscale | |
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| # Run the face detector on the grayscale image | |
| face_rects = face_cascade.detectMultiScale(gray, 1.3, 5) | |
| # Draw a rectangle around the face | |
| for (x,y,w,h) in face_rects: | |
| #cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 3) | |
| cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2) #draw rectangle to main image | |
| detected_face = frame[int(y):int(y+h), int(x):int(x+w)] #crop detected face | |
| detected_face = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY) #transform to gray scale | |
| detected_face = cv2.resize(detected_face, (48, 48)) #resize to 48x48 | |
| img_pixels = img_to_array(detected_face) | |
| img_pixels = np.expand_dims(img_pixels, axis = 0) | |
| img_pixels /= 255 #pixels are in scale of [0, 255]. normalize all pixels in scale of [0, 1] | |
| predictions = model.predict(img_pixels) #store probabilities of 7 expressions | |
| #find max indexed array 0: angry, 1:disgust, 2:fear, 3:happy, 4:sad, 5:surprise, 6:neutral | |
| max_index = np.argmax(predictions[0]) | |
| emotion = emotions[max_index] | |
| #write emotion text above rectangle | |
| cv2.putText(frame, emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2) | |
| return frame | |
| interface = gr.Interface( | |
| fn = process_image, | |
| #inputs='image', | |
| inputs="webcam", | |
| outputs='image', | |
| title='Facial Expression', | |
| description='Facial expression detection test') | |
| interface.launch() |