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| 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_frontalface_default.xml' | |
| emotion_model_path = '_mini_XCEPTION.102-0.66.hdf5' | |
| # 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 = ["angry", "disgusted", "scared", "happy", "sad", "surprised", | |
| "neutral"] | |
| 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, (64, 64)) | |
| 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=3, label="Top 3 Probabilities") | |
| ] | |
| title = "Emotion Classification" | |
| 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) |