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# import libraries
import streamlit as st
from streamlit_image_select import image_select
import tensorflow as tf
import numpy as np
import cv2
from PIL import Image
# Project title
st.title('Emotion Detector App')
# display banner image
st.image("banner_emotions.jpg")
img = image_select(
label = "Select an image to run model",
images = ["test_images/angry1.jpg",
"test_images/angry2.jpg",
"test_images/happy1.jpg",
"test_images/happy2.jpg",
"test_images/sad1.jpg",
"test_images/sad2.jpg"]
)
uploaded_img = st.file_uploader("Upload an image file",
type = ["png", "jpg", "jpeg"])
# load model
@st.cache_resource
def cache_model(model_add):
model = tf.keras.models.load_model(model_add)
return model
model = cache_model("emotion_detector")
# creating predict button
predict = st.button("Predict")
# defining harcascade classifier and class_names
face_detector=cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
class_names = ["Angry", "Happy", "Sad"]
def model_pred(model, image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
results = face_detector.detectMultiScale(gray,scaleFactor=1.05,
minNeighbors=10,
minSize=(100, 100))
if len(results) != 0:
for x,y,w,h in results:
img_crp = image[y:y+h, x:x+w]
img_crp = cv2.resize(img_crp,(350,350))
y_pred_prob = model.predict(tf.expand_dims(img_crp,
axis = 0))
y_pred = np.argmax(y_pred_prob, axis = -1)
# print(y_pred_prob)
label = class_names[int(y_pred)]
cv2.rectangle(image, (x,y), (x+w, y+h),
color=(0, 255, 0),
thickness = 10)
cv2.putText(image,f"{label},{np.round(np.max(y_pred_prob),2)}%",
(x,y-20),cv2.FONT_HERSHEY_COMPLEX,2,
(0,255,255),2)
return image
if predict:
if uploaded_img:
# img_array = np.array(uploaded_img)
img_array = np.array(Image.open(uploaded_img))
result_img = model_pred(model,img_array)
st.image(result_img)
else:
st.write("Please upload a valid image")
else:
image_array = np.array(Image.open(img))
result_img = model_pred(model, image_array)
st.image(result_img)
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