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import streamlit as st
import cv2
import supervision as sv
from ultralytics import YOLO
import numpy as np
from PIL import Image
import io
import torch
# Set page config
st.set_page_config(page_title="Building Detection App", page_icon="🏢", layout="wide")
# Custom CSS with theme compatibility
st.markdown("""
<style>
.reportview-container {
background: var(--background-color);
}
.main {
background-color: var(--background-color);
padding: 2rem;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.stButton>button {
background-color: #4CAF50;
color: white;
font-weight: bold;
border: none;
border-radius: 5px;
padding: 0.5rem 1rem;
transition: all 0.3s;
}
.stButton>button:hover {
background-color: #45a049;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.2);
}
.upload-box {
border: 2px dashed #4CAF50;
border-radius: 10px;
padding: 2rem;
text-align: center;
}
.theme-text {
color: var(--text-color);
}
</style>
""", unsafe_allow_html=True)
# Load the YOLO model
@st.cache_resource
def load_model():
model = YOLO("mosaic_medium_100_tiny_object.pt") # Update this to the filename of your model
model.to('cpu') # Ensure the model is on CPU
return model
model = load_model()
def process_image(image):
# Convert PIL Image to numpy array
image_np = np.array(image)
# Convert RGB to BGR (OpenCV uses BGR)
image_cv2 = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
def callback(image_slice: np.ndarray) -> sv.Detections:
result = model(image_slice)[0]
return sv.Detections.from_ultralytics(result)
slicer = sv.InferenceSlicer(callback=callback, slice_wh=(256, 256), iou_threshold=0.8)
detections = slicer(image_cv2)
# Filter detections for building class (assuming class_id 2 is for buildings)
building_detections = detections[detections.class_id == 2]
label_annotator = sv.LabelAnnotator()
box_annotator = sv.BoxAnnotator()
annotated_image = box_annotator.annotate(scene=image_cv2.copy(), detections=building_detections)
annotated_image = label_annotator.annotate(scene=annotated_image, detections=building_detections)
# Convert BGR back to RGB for displaying in Streamlit
return cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
def main():
st.title("Building Detection App")
st.markdown('<p class="theme-text">Upload an image to detect buildings using our advanced AI model.</p>', unsafe_allow_html=True)
col1, col2 = st.columns(2)
with col1:
st.markdown('<h3 class="theme-text">Upload Image</h3>', unsafe_allow_html=True)
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
if st.button("Detect Buildings"):
with st.spinner("Processing..."):
result_image = process_image(image)
with col2:
st.markdown('<h3 class="theme-text">Detection Results</h3>', unsafe_allow_html=True)
st.image(result_image, caption="Processed Image", use_column_width=True)
st.success("Detection completed successfully!")
else:
st.markdown(
"""
<div class="upload-box theme-text">
<h3>📁 Upload an image to get started</h3>
<p>Supported formats: JPG, JPEG, PNG</p>
</div>
""",
unsafe_allow_html=True
)
with col2:
if uploaded_file is None:
st.markdown('<h3 class="theme-text">How it works</h3>', unsafe_allow_html=True)
st.markdown(
"""
<p class="theme-text">
1. <strong>Upload</strong> an image using the file uploader on the left.<br>
2. Click the <strong>Detect Buildings</strong> button to process the image.<br>
3. View the results with bounding boxes around detected buildings.<br><br>
Our AI model is trained to identify various types of buildings in different environments.
</p>
""",
unsafe_allow_html=True
)
st.markdown("---")
#st.markdown('<p class="theme-text"></p>', unsafe_allow_html=True)
if __name__ == "__main__":
main() |