|
from keras.models import load_model |
|
from PIL import Image |
|
import numpy as np |
|
import cv2 |
|
|
|
|
|
|
|
|
|
from matplotlib import pyplot as plt |
|
import pylab |
|
pylab.rcParams['figure.figsize'] = (10.0, 8.0) |
|
|
|
|
|
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') |
|
|
|
|
|
age_ranges = ['1-5', '6-10', '11-15', '16-20', '21-25', '26-30', '31-35','36-40','41-45','46-50','51-60','61-70','71-80','81-90','91-100'] |
|
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)) |
|
|
|
|
|
|
