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| from keras.models import load_model | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| #the following are to do with this interactive notebook code | |
| from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks | |
| import pylab # this allows you to control figure size | |
| pylab.rcParams['figure.figsize'] = (10.0, 8.0) # this controls figure size in the notebook | |
| ###loading model### | |
| age_model = load_model('age_model_pretrained.h5') | |
| gender_model = load_model('gender_model_pretrained.h5') | |
| emotion_model = load_model('emotion_model_pretrained.h5') | |
| # Labels on Age, Gender and Emotion to be predicted | |
| age_ranges = ['1-5', '6-10', '11-15', '16-20', '21-25', '26-30', '31-35','36-40','41-50','51-60','61-70','71-80','81-90'] | |
| gender_ranges = ['male', 'female'] | |
| emotion_ranges= ['positive','negative','neutral'] | |
| ############################ | |
| import io | |
| import streamlit as st | |
| bytes_data=None | |
| img_file_buffer=st.camera_input("Take a picture") | |
| if img_file_buffer is not None: | |
| test_image = Image.open(img_file_buffer) | |
| st.image(test_image, use_column_width=True) | |
| st.write(type(test_image)) | |
| image = np.asarray(test_image) | |
| gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) | |
| face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') | |
| faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5) | |
| i = 0 | |
| for (x,y,w,h) in faces: | |
| i = i+1 | |
| cv2.rectangle(image,(x,y),(x+w,y+h),(203,12,255),2) | |
| img_gray=gray[y:y+h,x:x+w] | |
| emotion_img = cv2.resize(img_gray, (48, 48), interpolation = cv2.INTER_AREA) | |
| emotion_image_array = np.array(emotion_img) | |
| emotion_input = np.expand_dims(emotion_image_array, axis=0) | |
| output_emotion= emotion_ranges[np.argmax(emotion_model.predict(emotion_input))] | |
| gender_img = cv2.resize(img_gray, (100, 100), interpolation = cv2.INTER_AREA) | |
| gender_image_array = np.array(gender_img) | |
| gender_input = np.expand_dims(gender_image_array, axis=0) | |
| output_gender=gender_ranges[np.argmax(gender_model.predict(gender_input))] | |
| age_image=cv2.resize(img_gray, (200, 200), interpolation = cv2.INTER_AREA) | |
| age_input = age_image.reshape(-1, 200, 200, 1) | |
| output_age = age_ranges[np.argmax(age_model.predict(age_input))] | |
| output_str = str(i) + ": "+ output_gender + ', '+ output_age + ', '+ output_emotion | |
| st.write(output_str) | |
| col = (0,255,0) | |
| cv2.putText(image, str(i),(x,y),cv2.FONT_HERSHEY_SIMPLEX,1,col,2) | |
| st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) | |
| if bytes_data is None: | |
| st.stop() | |