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| import numpy as np | |
| import pandas as pd | |
| from deepface import DeepFace | |
| import streamlit as st | |
| import cv2 | |
| import base64 | |
| import time | |
| st.set_page_config(layout="wide") | |
| cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') | |
| def upload(): | |
| image=None | |
| initial_image = st.camera_input('Take a picture') | |
| original_image = initial_image | |
| temp_path = None | |
| if initial_image is not None: | |
| bytes_data = initial_image.getvalue() | |
| image = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR) | |
| return image, original_image | |
| def main(options): | |
| if st.checkbox('Take a picture for prediction'): | |
| image, original_image= upload() | |
| if original_image is not None and original_image is not None and st.button('Prediction'): # Check if original_image is not None | |
| st.warning('Wait for few seconds!!') | |
| progress_bar = st.progress(0.0) | |
| status_text = st.empty() | |
| result = DeepFace.analyze(image,detector_backend=options,actions=['age','gender','emotion','race']) | |
| for i in range(100): | |
| progress_bar.progress((i + 1) / 100) | |
| status_text.text(f"Processing {i+1}%") | |
| time.sleep(0.01) | |
| progress_bar.empty() | |
| gray_frame = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| faces = cascade.detectMultiScale(gray_frame, 1.1, 3) | |
| faces = sorted(faces, key=lambda f: -f[2] * f[3]) | |
| if len(faces) > 0: | |
| x,y,w,h=faces[0] | |
| cv2.rectangle(image, (x, y), (x+w, y+h), (4, 29, 255), 2, cv2.LINE_4) | |
| user_selected_items = list(result[0].keys()) | |
| if 'age' in user_selected_items: | |
| age_label='Age: '+str(result[0]['age']) | |
| cv2.putText(image, age_label, (x ,y+h+30), cv2.FONT_ITALIC,1 ,(255,255,0), 2) | |
| if 'dominant_gender' in user_selected_items: | |
| gender_label='Gender: '+str(result[0]['dominant_gender']) | |
| cv2.putText(image, gender_label, (x, y+h+70), cv2.FONT_ITALIC,1, (0,255,255), 2) | |
| if 'dominant_emotion' in user_selected_items: | |
| emotion_label='Emotion: '+str(result[0]['dominant_emotion']).title() | |
| cv2.putText(image, emotion_label, (x, y+h+110), cv2.FONT_ITALIC,1 ,(255,0,255), 2) | |
| if 'dominant_race' in user_selected_items: | |
| emotion_label='Race: '+str(result[0]['dominant_race']).title() | |
| cv2.putText(image, emotion_label, (x, y+h+150), cv2.FONT_ITALIC,1 ,(255,255,255), 2) | |
| st.image(image, channels='BGR') | |
| st.balloons() | |
| if __name__ == '__main__': | |
| def get_options(): | |
| actions = ['opencv','mtcnn','retinaface'] | |
| option2 = st.selectbox('Choose the following backend:', actions) | |
| return option2 | |
| main(get_options()) | |