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| from fastai.vision.all import * | |
| import gradio as gr | |
| import cv2 | |
| classifier = cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml') | |
| def label_func(fname): | |
| if int(str(fname)[str(fname).index('_')+1]) == 0: | |
| return "Male" | |
| return "Female" | |
| def get_age(fname): | |
| return int(str(fname).split('/')[1].split('_')[0]) | |
| def detect_face(img): | |
| faces = classifier.detectMultiScale(img) | |
| x, y, w, h = faces[0] | |
| cropped_img = img[y:y+h, x:x+w] | |
| return cropped_img | |
| learn_gender = load_learner('gender.pkl') | |
| learn_age = load_learner('age.pkl') | |
| categories = ('Female', 'Male') | |
| def predict_age(img): | |
| detected_face = detect_face(img) | |
| pred,_,_ = learn_age.predict(detected_face) | |
| return str(pred[0]), detected_face | |
| def classify_image(img): | |
| pred, idx, probs = learn_gender.predict(img) | |
| return dict(zip(categories, map(float, probs))) | |
| def process_image(img): | |
| gender = classify_image(img) | |
| age, face = predict_age(img) | |
| return gender, age, face | |
| image = gr.inputs.Image(shape=(192,192)) | |
| gender_output = gr.outputs.Label() | |
| age_output = gr.outputs.Textbox(label='Predicted Age') | |
| detected_face_output = gr.outputs.Image(type='numpy', label='Detected Face') | |
| examples = ['Male.jpg', 'Female.png'] | |
| iface = gr.Interface(fn=process_image, inputs=image, outputs=[gender_output, age_output, detected_face_output], examples=examples) | |
| iface.launch() |