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import streamlit as st
import numpy as np
import cv2
from ultralytics import YOLO
import supervision as sv
import time
model = YOLO("yolov8x.pt")
def callback(x: np.ndarray) -> sv.Detections:
result = model(x, verbose=False, conf=0.25)[0]
return sv.Detections.from_ultralytics(result)
def main():
st.title("Small Object Detection with SAHI")
st.write("Slicing Aided Hyper Inference (SAHI) implementaion with Supervsion for small object detection")
example_image_loaded = st.checkbox("Load example image")
uploaded_image = None
if example_image_loaded:
image = cv2.imread("example-image.jpg")
else:
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
if uploaded_image is not None:
image = cv2.imdecode(np.fromstring(uploaded_image.read(), np.uint8), 1)
if uploaded_image is not None or example_image_loaded:
with st.spinner("Loading..."):
start_time_sahi = time.time()
slicer = sv.InferenceSlicer(callback=callback)
sliced_detections = slicer(image=image)
end_time_sahi = time.time()
start_time_yolo = time.time()
yolo_results = model(image, verbose=False, conf=0.25)
end_time_yolo = time.time()
st.header("Original Image")
st.image(image, channels="BGR")
st.header("SAHI-Processed Image")
sliced_image = sv.BoxAnnotator().annotate(image.copy(), detections=sliced_detections)
st.image(sliced_image, channels="BGR")
st.header("YOLO-Detected Image (Without SAHI)")
yolo_image = sv.BoxAnnotator().annotate(image.copy(), detections=sv.Detections.from_ultralytics(yolo_results[0]))
st.image(yolo_image, channels="BGR")
st.subheader("Method Comparison")
st.write("SAHI Inference Time:", round(end_time_sahi - start_time_sahi, 2), "seconds")
st.write("YOLOv8 Inference Time:", round(end_time_yolo - start_time_yolo, 2), "seconds")
st.write("SAHI Detection Count:", len(sliced_detections))
st.write("YOLOv8 Detection Count:", len(yolo_results[0]))
if __name__ == "__main__":
main() |