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import streamlit as st |
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from ultralyticsplus import YOLO, render_result |
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import PIL.Image as Image |
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import numpy as np |
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import requests |
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from io import BytesIO |
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st.title("Blood Cell Detection with YOLOv8") |
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model = YOLO('keremberke/yolov8m-blood-cell-detection') |
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model.overrides['conf'] = 0.25 |
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model.overrides['iou'] = 0.45 |
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model.overrides['agnostic_nms'] = False |
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model.overrides['max_det'] = 1000 |
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uploaded_file = st.file_uploader("Upload an image for detection", type=["jpg", "png"]) |
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if uploaded_file: |
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image = Image.open(uploaded_file) |
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results = model.predict(np.array(image)) |
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st.image(image, caption="Uploaded Image", use_column_width=True) |
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rendered_image = render_result(model=model, image=image, result=results[0]) |
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st.image(rendered_image, caption="Detection Results", use_column_width=True) |
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st.write("Detection Results:") |
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for box in results[0].boxes: |
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st.write(f"Bounding box: {box.xyxy}") |
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st.write(f"Confidence: {box.conf}") |
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st.write(f"Class: {box.cls}") |
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else: |
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st.write("Upload an image to start detection") |
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