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| import gradio as gr | |
| import os | |
| import cv2 | |
| import numpy as np | |
| import imutils | |
| from keras.preprocessing.image import img_to_array | |
| from keras.models import load_model | |
| # Load the pre-trained models and define parameters | |
| detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml' | |
| emotion_model_path = 'model_2_aug_nocall_BEST/model_2_aug_nocall_entire_model.h5' | |
| 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'] | |
| # Function to predict emotions from a frame | |
| def predict(frame_or_path): | |
| if isinstance(frame_or_path, np.ndarray): # If input is a webcam frame | |
| frame = imutils.resize(frame_or_path, width=300) | |
| else: # If input is a file path | |
| frame = cv2.imread(frame_or_path) | |
| if frame is None: | |
| return None, "Error: Unable to read image or video." | |
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| faces = face_detection.detectMultiScale(gray, scaleFactor=1.1, | |
| minNeighbors=5, minSize=(30, 30), | |
| flags=cv2.CASCADE_SCALE_IMAGE) | |
| if len(faces) == 0: | |
| return frame, "No face detected." | |
| (fX, fY, fW, fH) = faces[0] | |
| 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()] | |
| cv2.putText(frame, label, (fX, fY - 10), | |
| cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1) | |
| cv2.rectangle(frame, (fX, fY), (fX + fW, fY + fH), | |
| (238, 164, 64), 2) | |
| return frame, {emotion: float(prob) for emotion, prob in zip(EMOTIONS, preds)} | |
| # Define input and output components for Gradio | |
| image_input = gr.components.Image(sources=["webcam", "upload"], label="Your face") | |
| image_output = gr.components.Image(label="Predicted Emotion") | |
| label_output = gr.components.Label(num_top_classes=2, label="Top 2 Probabilities") | |
| # Launch the Gradio interface | |
| title = "Facial Emotion Recognition" | |
| description = "How well can this model predict your emotions? Take a picture with your webcam, or upload an image, 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(fn=predict, inputs=image_input, outputs=[image_output, label_output], | |
| title=title, description=description, thumbnail=thumbnail).launch() | |