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app.py

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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 and YOLOv8")
+
+    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()

requirements.txt

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+ultralytics
+supervision
+numpy