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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| # Set the page config | |
| st.set_page_config(page_title="Emotion Recognition App", layout="centered") | |
| st.title("Emotion Recognition App") | |
| # Upload an image | |
| uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
| # Load OpenCV's face detection model | |
| face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml") | |
| # Load ONNX emotion detection model | |
| emotion_model_path = "emotion_recognition.onnx" # Replace with your model path | |
| emotion_net = cv2.dnn.readNetFromONNX(emotion_model_path) | |
| # Emotion labels (based on model documentation) | |
| emotion_labels = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"] | |
| # Resize image to reduce memory usage | |
| def resize_image(image, max_size=(800, 800)): | |
| """ | |
| Resizes the image to the specified maximum size while maintaining aspect ratio. | |
| """ | |
| image.thumbnail(max_size, Image.Resampling.LANCZOS) | |
| return image | |
| # Process the uploaded image | |
| if uploaded_file is not None: | |
| # Check file size to prevent loading large images | |
| if uploaded_file.size > 10 * 1024 * 1024: # 10 MB limit | |
| st.error("File too large. Please upload an image smaller than 10 MB.") | |
| else: | |
| # Open and resize the image | |
| image = Image.open(uploaded_file) | |
| image = resize_image(image) | |
| # Convert image to numpy array | |
| image_np = np.array(image) | |
| # Convert image to grayscale for face detection | |
| gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) | |
| # Detect faces | |
| faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) | |
| if len(faces) > 0: | |
| for (x, y, w, h) in faces: | |
| # Extract face ROI | |
| face_roi = image_np[y:y+h, x:x+w] | |
| face_blob = cv2.dnn.blobFromImage(face_roi, 1.0, (64, 64), (104, 117, 123), swapRB=True) | |
| # Predict emotion | |
| emotion_net.setInput(face_blob) | |
| predictions = emotion_net.forward() | |
| emotion_idx = np.argmax(predictions) | |
| emotion = emotion_labels[emotion_idx] | |
| # Draw rectangle around the face | |
| cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 2) | |
| # Display emotion | |
| cv2.putText( | |
| image_np, | |
| emotion, | |
| (x, y - 10), | |
| cv2.FONT_HERSHEY_SIMPLEX, | |
| 0.9, | |
| (255, 0, 0), | |
| 2, | |
| ) | |
| # Display the processed image | |
| st.image(image_np, caption="Processed Image", use_column_width=True) | |
| else: | |
| st.warning("No faces detected in the image.") | |