Spaces:
Sleeping
Sleeping
File size: 2,008 Bytes
1f8d1c4 2c552be 1f8d1c4 df27f53 1f8d1c4 9794e2a 1f8d1c4 9794e2a 1f8d1c4 9794e2a 1f8d1c4 9794e2a 1f8d1c4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import streamlit as st
import cv2
import numpy as np
from PIL import Image
import tensorflow as tf
face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# Load the Keras model
model = tf.keras.models.load_model("./affectnet_CNN_VGG_FIVEEMO_FINE_FINAL.h5")
# Mapping of emotion labels to their indices
emotion_label_dict = {
0: 'neutral',
1: 'happiness',
2: 'sadness',
3: 'surprise',
4: 'fear',
}
# Function to detect faces in an image
def detect_face(image):
img =image
face = face_detector.detectMultiScale(img, 1.1, 5, minSize=(40, 40))
if len(face) > 0:
x, y, w, h = face[0]
crop_img = img[y:y+h, x:x+w]
cropped = cv2.resize(crop_img, (224, 224))
img_rgb = cv2.cvtColor(cropped, cv2.COLOR_BGR2RGB)
return img_rgb
else:
print("No face detected.")
return None
# Function to classify emotion using the loaded model
def classify_emotion(image):
# Preprocess the image
image = detect_face(image)
image = np.array(image)
image = np.expand_dims(image, axis=0)
# image = image / 255.0
# Make prediction using the model
predictions = model.predict(image)
predictions = tf.nn.softmax(predictions)
print(predictions)
emotion_index = np.argmax(predictions)
emotion_name = emotion_label_dict[emotion_index]
return emotion_name
# Streamlit app
def main():
st.title("Emotion Prediction App")
uploaded_file = st.file_uploader("Upload Image", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
image_array = np.array(image)
detected_face = detect_face(image_array)
if detected_face is not None:
predicted_emotion = classify_emotion(detected_face)
st.write('Predicted Emotion:', predicted_emotion)
if __name__ == '__main__':
main()
|