PyElispotAnalysis_StreamlitApp.py

#!/usr/bin/env python3
# encoding: utf-8
#
# Copyright (C) 2022 Max Planck Institute for Multidisclplinary Sciences
# Copyright (C) 2022 University Medical Center Goettingen
# Copyright (C) 2022 Ajinkya Kulkarni <ajinkya.kulkarni@mpinat.mpg.de>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.

##########################################################################

import streamlit as st
from io import BytesIO

from cv2 import MORPH_OPEN, morphologyEx, resize
import numpy as np
from skimage.measure import label, regionprops

import sys
# Don't generate the __pycache__ folder locally
sys.dont_write_bytecode = True 
# Print exception without the buit-in python warning
sys.tracebacklimit = 0 

##########################################################################

from modules import *

allowed_image_size = 1000 # Only images with sizes less than 1000x1000 allowed

##########################################################################

# Open the logo file in binary mode and read its contents into memory
with open("logo.jpg", "rb") as f:
	image_data = f.read()

# Create a BytesIO object from the image data
image_bytes = BytesIO(image_data)

# Configure the page settings using the "set_page_config" method of Streamlit
st.set_page_config(
	page_title='PyElispotAnalysis',
	page_icon=image_bytes,  # Use the logo image as the page icon
	layout="centered",
	initial_sidebar_state="expanded",
	menu_items={
		'Get help': 'mailto:ajinkya.kulkarni@mpinat.mpg.de',
		'Report a bug': 'mailto:ajinkya.kulkarni@mpinat.mpg.de',
		'About': 'This is an application for demonstrating the PyElispotAnalysis package. Developed, tested, and maintained by Ajinkya Kulkarni: https://github.com/ajinkya-kulkarni at the MPI-NAT, Goettingen.'
	}
)

##########################################################################

# Set the title of the web app
st.title(':blue[Spot detection for Elispot assay images]')

st.caption('Application screenshots and source code available [here](https://github.com/ajinkya-kulkarni/PyElispotAnalysis). Sample image to test this application is available [here](https://github.com/ajinkya-kulkarni/PyElispotAnalysis/blob/main/image.tif).', unsafe_allow_html = False)

# Add some vertical space between the title and the next section
st.markdown("")

##########################################################################

# # Create a form using the "form" method of Streamlit
# with st.form(key = 'form1', clear_on_submit = False):

# Add some text explaining what the user should do next
st.markdown(':blue[Upload a single Elispot assay image/slide to be analyzed.]')

# Add a file uploader to allow the user to upload an image file
uploaded_file = st.file_uploader("Upload a file", type = ["tif", "tiff", "png", "jpg", "jpeg"], accept_multiple_files = False, label_visibility = 'collapsed')

######################################################################

st.markdown("")

left_column, right_column = st.columns(2)

with left_column:

	st.slider('Local window size to identify dark spots [pixels]. Larger values cover broader regions.', min_value = 5, max_value = 101, value = 31, step = 2, format = '%d', label_visibility = "visible", key = '-BlockSizeKey-')

	BlockSize = int(st.session_state['-BlockSizeKey-'])

with right_column:

	st.slider('Determines sensitivity for detecting dark spots. Lower values detect more spots.', min_value = 2, max_value = 20, value = 8, step = 2, format = '%d', label_visibility = "visible", key = '-ConstantKey-')

	Constant = int(st.session_state['-ConstantKey-'])

######################################################################

st.markdown("")

######################################################################

left_column, right_column = st.columns(2)

with left_column:

	st.slider('Minimum area of spots in the image [pixels^2]. Should be the average area of the small spots.', min_value = 2, max_value = 50, value = 10, step = 2, format = '%d', label_visibility = "visible", key = '-MinimumAreaKey-')

	MinimumAreaKey = int(st.session_state['-MinimumAreaKey-'])

with right_column:

	st.slider('Maximum area of spots in the image [pixels^2]. Should be the average area of the large spots.', min_value = 500, max_value = 2000, value = 1000, step = 100, format = '%d', label_visibility = "visible", key = '-MaximumAreaKey-')

	MaximumAreaKey = int(st.session_state['-MaximumAreaKey-'])

######################################################################

st.markdown("""---""")

######################################################################

# # Add a submit button to the form
# submitted = st.form_submit_button('Analyze')

######################################################################

# If no file was uploaded, stop processing and exit early
if uploaded_file is None:
	st.stop()

######################################################################

# if submitted:

# Read and process the image: convert it to grayscale and scale it
img_scaled = read_image(uploaded_file)

if img_scaled.shape[0] > allowed_image_size or img_scaled.shape[1] > allowed_image_size:
	st.error('Uploaded image exceeds the allowed image size. Please reduce the image size to 1000x1000.')
	st.stop()

# Resize image to suit the UI for image comparision
img_scaled = resize_image(img_scaled)

# Segment the processed image to highlight regions of interest
mask_image = make_segmented_image(img_scaled, BlockSize, Constant)

# Define a kernel for morphological operations
# Erosion helps in detaching closely packed regions
kernel_size = (5, 5)
kernel = np.ones(kernel_size, np.uint8)

# Perform morphological opening on the segmented image
# Opening is an operation that consists of erosion followed by dilation
# It helps to remove noise and to separate regions that are close to each other
opened = morphologyEx(mask_image, MORPH_OPEN, kernel)

# Invert the opened image
# This is done so the regions of interest are now considered as foreground (labelled as 1)
inverted_opened = 1 - opened

# Label the regions in the inverted image
# Each connected component/region gets a unique label
labeled_image = label(inverted_opened, connectivity=2)

# Use the counts_spots function to draw circles around detected spots
# Spots are determined based on the area and eccentricity criteria defined in the function
circled_image, counter, filtered_labelled_image = counts_spots(labeled_image, img_scaled, MinimumAreaKey, MaximumAreaKey)

# # Generate the results figure
# result_figure = make_figure(img_scaled, circled_image, counter)

##############################################################

st.markdown('Results')

image_comparison(img1=img_scaled, img2=circled_image, label1="", label2="")

st.markdown(f'{counter} spots detected.')

##############################################################

img_pil = Image.fromarray(circled_image)

buf = BytesIO()
img_pil.save(buf, format="TIFF")
byte_im = buf.getvalue()

btn = st.download_button(
      label="Download Segmented Image",
      data=byte_im,
      file_name="Result.tif")

##############################################################

st.markdown("""---""")

st.markdown("Detailed Report")

# Collect areas of all regions
areas = [region.area for region in regionprops(filtered_labelled_image)]

# Plot histogram
fig = plt.figure(figsize=(7, 3), dpi = 200)
plt.hist(areas, bins='auto', color='tab:blue', alpha=0.8)
plt.title('Histogram of detected spots', fontsize=14, pad = 10)
plt.xlim(0, )
plt.ylim(0, )
plt.xlabel('Size of spots (Area)', fontsize=10)
plt.ylabel('Number of spots', fontsize=10)
plt.xticks(fontsize=8)
plt.yticks(fontsize=8)
plt.grid(True, which='both', linestyle='--', linewidth=0.5)

st.pyplot(fig)

##############################################################

st.markdown("")

dataframe = create_dataframe(filtered_labelled_image)

st.dataframe(dataframe.style.format("{:.2f}"), use_container_width = True)

##############################################################

@st.cache_data
def convert_df(df):
    # IMPORTANT: Cache the conversion to prevent computation on every rerun
    return df.to_csv().encode('utf-8')

csv = convert_df(dataframe)

##############################################################

st.download_button(
    label="Download data as CSV",
    data=csv,
    file_name='dataframe.csv',
    mime='text/csv',
)

##############################################################

st.stop()

##########################################################################