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import cv2
import numpy as np
import pandas as pd
import streamlit as st
def calculate_parameters(annotations, scale_factor):
"""Calculates parameters for each segmented object, including Feret diameter."""
df = pd.DataFrame(columns=['Object', 'Area', 'Perimeter', 'Roundness',
'Aspect Ratio (Elongation)', 'Longest Feret Diameter'])
if len(annotations) > 0:
for i, mask in enumerate(annotations):
binary_mask = mask.cpu().numpy().astype(np.uint8)
area_pixel = np.sum(binary_mask)
area_micron = area_pixel * (scale_factor ** 2)
# Find contours with all points (no approximation)
contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if contours: # Check if any contours were found
perimeter_pixel = cv2.arcLength(contours[0], True)
perimeter_micron = perimeter_pixel * scale_factor
# Fit ellipse for roundness and aspect ratio (check for sufficient points)
if len(contours[0]) >= 5:
ellipse = cv2.fitEllipse(contours[0])
major_axis, minor_axis = ellipse[1]
else:
major_axis = minor_axis = 0
major_axis_micron = major_axis * scale_factor
minor_axis_micron = minor_axis * scale_factor
roundness = (4 * np.pi * area_micron) / (perimeter_micron ** 2)
aspect_ratio = major_axis_micron / minor_axis_micron if minor_axis_micron != 0 else "Undefined"
# Calculate Feret diameter and Elongation
hull = cv2.convexHull(contours[0])
distances = np.linalg.norm(hull - hull[:, 0, :], axis=2)
max_feret_diameter_micron = np.max(distances) * scale_factor
new_row = pd.DataFrame({
'Object': [f"Object {i}"],
'Area': [area_micron],
'Perimeter': [perimeter_micron],
'Roundness': [roundness],
'Aspect Ratio (Elongation)': [aspect_ratio],
'Longest Feret Diameter': [max_feret_diameter_micron],
})
df = pd.concat([df, new_row], ignore_index=True)
# Eliminate artifacts with undefined parameters
df = df[(df['Longest Feret Diameter'] != 0) & (df['Roundness'] >= 0) & (df['Roundness'] <= 1)]
return df |