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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('Copy of age_model_pretrained.h5')
gender_model = load_model('Copy of gender_model_pretrained.h5')
emotion_model = load_model('emotion_model_pretrained.h5')
# Labels on Age, Gender and Emotion to be predicted
age_ranges = ['1-2', '3-9', '10-20', '21-27', '28-45', '46-65', '66-116']
gender_ranges = ['male', 'female']
emotion_ranges= ['positive','negative','neutral']
import streamlit as st
st.write("""
# Customer Age , Gender and Emotion Prediction
"""
)
st.write("This is a simple web app to predict age , gender and emotion of customer.")
file = st.file_uploader("Please upload an image file", type=["jpg", "png","jpeg"])
######
if file is None:
st.text("Please upload an image file")
else:
test_image = Image.open(file)
st.image(test_image, use_column_width=True)
st.write(type(test_image))
test_image = np.asarray(test_image)
gray = cv2.cvtColor(test_image,cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier('Copy of haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
i = 0
for (x,y,w,h) in faces:
i = i+1
cv2.rectangle(test_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(test_image, str(i),(x,y),cv2.FONT_HERSHEY_SIMPLEX,1,col,2)
st.image(cv2.cvtColor(test_image, cv2.COLOR_BGR2RGB))
#st.image(test_image, use_column_width=True)
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