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import gradio as gr |
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from tensorflow.keras.utils import img_to_array |
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import imutils |
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import cv2 |
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from keras.models import load_model |
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import numpy as np |
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detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml' |
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emotion_model_path = 'models/_mini_XCEPTION.102-0.66.hdf5' |
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face_detection = cv2.CascadeClassifier(detection_model_path) |
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emotion_classifier = load_model(emotion_model_path, compile=False) |
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EMOTIONS = ["angry", "disgusted", "scared", "happy", "sad", "surprised", |
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"neutral"] |
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def predict(frame): |
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frame = imutils.resize(frame, width=300) |
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gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) |
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faces = face_detection.detectMultiScale(gray, scaleFactor=1.1, |
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minNeighbors=5, minSize=(30, 30), |
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flags=cv2.CASCADE_SCALE_IMAGE) |
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frameClone = frame.copy() |
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if len(faces) > 0: |
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faces = sorted(faces, reverse=True, |
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key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0] |
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(fX, fY, fW, fH) = faces |
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roi = gray[fY:fY + fH, fX:fX + fW] |
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roi = cv2.resize(roi, (64, 64)) |
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roi = roi.astype("float") / 255.0 |
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roi = img_to_array(roi) |
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roi = np.expand_dims(roi, axis=0) |
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preds = emotion_classifier.predict(roi)[0] |
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label = EMOTIONS[preds.argmax()] |
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else: |
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return frameClone, "Can't find your face" |
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probs = {} |
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cv2.putText(frameClone, label, (fX, fY - 10), |
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cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1) |
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cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH), |
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(238, 164, 64), 2) |
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for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)): |
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probs[emotion] = float(prob) |
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return frameClone, probs |
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inp = gr.inputs.Image(source="webcam", label="Your face") |
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out = [ |
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gr.outputs.Image(label="Predicted Emotion"), |
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gr.outputs.Label(num_top_classes=3, label="Top 3 Probabilities") |
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] |
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title = "Emotion Classification" |
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description = "How well can this model predict your emotions? Take a picture with your webcam, and it will guess if" \ |
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" you are: happy, sad, angry, disgusted, scared, surprised, or neutral." |
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thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-emotion-recognition/master/thumbnail.png" |
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gr.Interface(predict, inp, out, capture_session=True, title=title, thumbnail=thumbnail, |
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description=description).launch(inbrowser=True) |
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