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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 = 'model4_0.83/model4_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'] | |
| # face_detector_mtcnn = MTCNN() | |
| classifier = load_model(emotion_model_path) | |
| # def predict_emotion(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) | |
| # frame_clone = 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()] | |
| # # Overlay a box over the detected face | |
| # cv2.putText(frame_clone, label, (fX, fY + 100), | |
| # cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1) | |
| # cv2.rectangle(frame_clone, (fX, fY), (fX + fW, fY + fH), | |
| # (238, 164, 64), 2) | |
| # else: | |
| # label = "Can't find your face" | |
| # return frame_clone | |
| def predict_emotion(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) | |
| for (fX, fY, fW, fH) in 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()] | |
| # Overlay a box over the detected face | |
| cv2.putText(frame, label, (fX, fY + 100), | |
| cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1) | |
| cv2.rectangle(frame, (fX, fY), (fX + fW, fY + fH), | |
| (238, 164, 64), 2) | |
| return frame | |
| demo = gr.Interface( | |
| fn = predict_emotion, | |
| inputs = gr.Image(type="numpy"), | |
| outputs = gr.Image(), | |
| # gr.components.Image(label="Predicted Emotion"), | |
| # gr.components.Label(num_top_classes=2, label="Top 2 Probabilities") | |
| #flagging_options=["blurry", "incorrect", "other"], | |
| examples = [ | |
| os.path.join(os.path.dirname(__file__), "images/chandler.jpeg"), | |
| os.path.join(os.path.dirname(__file__), "images/janice.jpeg"), | |
| os.path.join(os.path.dirname(__file__), "images/joey.jpeg"), | |
| os.path.join(os.path.dirname(__file__), "images/phoebe.jpeg"), | |
| os.path.join(os.path.dirname(__file__), "images/rachel_monica.jpeg"), | |
| os.path.join(os.path.dirname(__file__), "images/ross.jpeg"), | |
| os.path.join(os.path.dirname(__file__), "images/gunther.jpeg") | |
| ], | |
| title = "Whatchu feeling?", | |
| theme = "shivi/calm_seafoam" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |