import gradio as gr from keras.preprocessing.image import img_to_array import imutils import cv2 from keras.models import load_model import numpy as np # parameters for loading data and images detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml' emotion_model_path = 'models/model2_entire_model.h5' # hyper-parameters for bounding boxes shape # loading models face_detection = cv2.CascadeClassifier(detection_model_path) emotion_classifier = load_model(emotion_model_path, compile=False) EMOTIONS = ['neutral','happiness','surprise','sadness','anger','disgust','fear','contempt','unknown'] def predict(frame): frame = imutils.resize(frame, width=300) gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) faces = face_detection.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE) frameClone = frame.copy() if len(faces) > 0: faces = sorted(faces, reverse=True, key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0] (fX, fY, fW, fH) = faces # 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, (48, 48)) roi = roi.astype("float") / 255.0 roi = img_to_array(roi) roi = np.expand_dims(roi, axis=0) preds = emotion_classifier.predict(roi)[0] label = EMOTIONS[preds.argmax()] else: return frameClone, "Can't find your face" probs = {} cv2.putText(frameClone, label, (fX, fY - 10), cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1) cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH), (238, 164, 64), 2) for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)): probs[emotion] = float(prob) return frameClone, probs inp = gr.inputs.Image(source="webcam", label="Your face") out = [ gr.outputs.Image(label="Predicted Emotion"), gr.outputs.Label(num_top_classes=2, label="Top 2 Probabilities") ] title = "Facial Emotion Recognition" description = "How well can this model predict your emotions? Take a picture with your webcam, and it will guess if" \ " you are: happy, sad, angry, disgusted, scared, surprised, or neutral." # thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-emotion-recognition/master/thumbnail.png" gr.Interface(predict, inp, out, capture_session=True, title=title, thumbnail=thumbnail, description=description).launch(inbrowser=True)