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import streamlit as st
from ultralyticsplus import YOLO, render_result
import PIL.Image as Image
import numpy as np
import pandas as pd
import requests
from io import BytesIO
# Initialize Streamlit app
st.title("Blood Cell Detection with YOLOv8")
# Load YOLO model
model = YOLO('keremberke/yolov8m-blood-cell-detection')
# Set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # Maximum number of detections per image
# File uploader for image input
uploaded_file = st.file_uploader("Upload an image for detection", type=["jpg", "png"])
if uploaded_file:
# Open the uploaded image
image = Image.open(uploaded_file)
# Perform inference
results = model.predict(np.array(image))
# Display results
st.image(image, caption="Uploaded Image", use_column_width=True)
# Render detection results
rendered_image = render_result(model=model, image=image, result=results[0])
# Show the rendered result
st.image(rendered_image, caption="Detection Results", use_column_width=True)
# Count the number of each cell type
cell_counts = {"RBC": 0, "WBC": 0, "Platelets": 0}
# Display details of detected boxes
st.write("Detection Results:")
for box in results[0].boxes:
class_index = int(box.cls) # Get the class index
if class_index == 1: # RBC
cell_counts["RBC"] += 1
elif class_index == 2: # WBC
cell_counts["WBC"] += 1
elif class_index == 0: # Platelets
cell_counts["Platelets"] += 1
# Display bounding box information
#st.write(f"Bounding box: {box.xyxy}")
#st.write(f"Confidence: {box.conf}")
#st.write(f"Class: {box.cls}")
# Display the counts of each cell type
st.write("Cell Type Counts:")
st.write(pd.DataFrame.from_dict(cell_counts, orient='index', columns=['Count']))
else:
st.write("Upload an image to start detection.")
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