app.py

import streamlit as st
import cv2
import numpy as np
import matplotlib.pyplot as plt
import cvlib as cv
from cvlib.object_detection import draw_bbox
from numpy.lib.polynomial import poly
from PIL import Image

# Function to detect circular objects
def detect_circles(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Use HoughCircles to detect circles
    circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp=1.2, minDist=30, param1=50, param2=30, minRadius=10, maxRadius=100)

    # If we found some circles, draw them
    if circles is not None:
        circles = np.uint16(np.around(circles))
        for i in circles[0, :]:
            center = (i[0], i[1])  # center of the circle
            radius = i[2]  # radius of the circle
            # Draw the circle center
            cv2.circle(image, center, 1, (0, 100, 100), 3)
            # Draw the circle outline
            cv2.circle(image, center, radius, (255, 0, 255), 3)

        return image, len(circles[0, :])  # Return image with drawn circles and count
    else:
        return image, 0  # No circles found

# Streamlit UI
st.title('Circular Object Detection')

# Image upload
uploaded_file = st.file_uploader("Choose an image...", type="jpg")

if uploaded_file is not None:
    # Read and process the uploaded image
    image = np.array(Image.open(uploaded_file))
    # Convert RGB to BGR
    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    
    # Detect circles
    result_image, circle_count = detect_circles(image_bgr)
    
    # Convert BGR back to RGB for displaying in Streamlit
    result_image_rgb = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
    
    # Display result
    st.image(result_image_rgb, channels="RGB", caption="Detected Circles")
    st.write(f"Total circular objects detected: {circle_count}")