データサイエンスに向いたPython用可視化ツールstreamlit　その２：画像を読み込んで判定するスクリプト

実際のところ

import streamlit as st
from skimage import io, color, feature, draw
from skimage.transform import hough_circle, hough_circle_peaks
import numpy as np

def find_circles(image, min_radius, max_radius):
    gray_image = color.rgb2gray(image)

    edges = feature.canny(gray_image)
    hough_radii = np.arange(min_radius, max_radius,2)
    hough_res = hough_circle(edges, hough_radii)

    for _, x, y, radius in zip(*hough_circle_peaks(hough_res, hough_radii,total_num_peaks=3)):
        rr, cc = draw.circle_perimeter(int(y), int(x), int(radius), shape=image.shape)
        image[rr, cc] = (255, 0, 0)
    return image

st.title("Circle Detection using Streamlit and scikit-image")
uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])

if uploaded_file is not None:
    image = io.imread(uploaded_file)
    st.image(image, caption="Uploaded Image", use_column_width=True)

    st.sidebar.header("Hough Radii Parameters")  # Added a header in the sidebar
    min_radius = st.sidebar.slider("Min Radius", 1, 100, 40)
    max_radius = st.sidebar.slider("Max Radius", 1, 1000, 90)

    result = find_circles(image, min_radius, max_radius)
    st.image(result, caption="Result with Circles Detected", use_column_width=True)