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import streamlit as st
from imageai.Detection import ObjectDetection
import os
import time
from PIL import Image
def main():
st.title("Object Detection App")
st.write("Upload an image and get object detections!")
# File uploader
uploaded_file = st.file_uploader("Choose an image...", type=['jpg', 'jpeg', 'png'])
if uploaded_file is not None:
# Display the uploaded image
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
# Save the uploaded file temporarily
with open("temp_image.jpg", "wb") as f:
f.write(uploaded_file.getbuffer())
# Button to start detection
if st.button('Start Detection'):
# Spinner while model loads
with st.spinner('Loading model and performing detection...'):
try:
execution_path = os.getcwd()
detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()
detector.setModelPath(os.path.join(execution_path, "retinanet_resnet50_fpn_coco-eeacb38b.pth"))
detector.loadModel()
# Perform detection
detections = detector.detectObjectsFromImage(
input_image="temp_image.jpg",
output_image_path="output_image.jpg",
minimum_percentage_probability=10
)
# Display results
st.success('Detection Complete!')
# Display detected image
detected_image = Image.open("output_image.jpg")
st.image(detected_image, caption='Detected Objects', use_column_width=True)
# Display detections with probabilities
st.write("### Detected Objects:")
for obj in detections:
st.write(f"- {obj['name']}: {obj['percentage_probability']:.2f}%")
except Exception as e:
st.error(f"An error occurred: {str(e)}")
# Clean up temporary files
if os.path.exists("temp_image.jpg"):
os.remove("temp_image.jpg")
if os.path.exists("output_image.jpg"):
os.remove("output_image.jpg")
if __name__ == "__main__":
main() |