Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| import cv2 | |
| from PIL import Image as PilImage | |
| from PIL import ImageDraw | |
| import numpy as np | |
| import io | |
| import base64 | |
| # Load the face detection classifier | |
| face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') | |
| def detect_and_write_on_faces(image, texts_to_write): | |
| # Convert the uploaded image to grayscale for face detection | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| # Detect faces in the image | |
| faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) | |
| # Convert the OpenCV image to a Pillow image | |
| pil_image = PilImage.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) | |
| draw = ImageDraw.Draw(pil_image) | |
| for i, (x, y, w, h) in enumerate(faces): | |
| # Write text on the detected face | |
| if i < len(texts_to_write): | |
| text_to_write = texts_to_write[i] | |
| draw.text((x, y - 10), text_to_write, fill=(255, 0, 0, 0)) | |
| # Convert the Pillow image back to OpenCV format | |
| image_with_text = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR) | |
| return image_with_text | |
| st.title("Face Detection and Text Writing") | |
| uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"]) | |
| if uploaded_image is not None: | |
| if st.button("Process Image"): | |
| input_image = cv2.imdecode(np.frombuffer(uploaded_image.read(), np.uint8), -1) | |
| # Define different texts for each face | |
| texts_to_write = [ | |
| "Happy face, this person has a retention rate of 69.", | |
| "Smiling face, spreading positivity!", | |
| "Serious face, focused and determined.", | |
| "Surprised face, something caught their attention!", | |
| "Confused face, deep in thought.", | |
| "Excited face, full of energy!", | |
| "Calm face, a picture of tranquility." | |
| ] | |
| result_image = detect_and_write_on_faces(input_image, texts_to_write) | |
| st.image(result_image, caption="Processed Image", use_column_width=True) | |
| # Allow the user to download the processed image as a JPEG file | |
| output_buffer = io.BytesIO() | |
| PilImage.fromarray(result_image).save(output_buffer, format="JPEG") | |
| st.markdown("### Download Processed Image") | |
| st.markdown( | |
| f"Download your processed image [here](data:file/jpeg;base64,{base64.b64encode(output_buffer.getvalue()).decode()})" | |
| ) | |