File size: 8,143 Bytes
69591a9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import streamlit as st
st.set_page_config(
page_title="DN-AI",
page_icon="🔬",
layout="wide",
)
def build_multichannel_loader():
if (
st.session_state.get("files_uploaded", None) is None
or len(st.session_state.files_uploaded) == 0
):
st.session_state["files_uploaded"] = st.file_uploader(
label="Upload files",
accept_multiple_files=True,
type=["czi", "jpeg", "jpg", "png", "tiff", "tif"],
)
else:
st.session_state["files_uploaded"] += st.file_uploader(
label="Upload files",
accept_multiple_files=True,
type=["czi", "jpeg", "jpg", "png", "tiff", "tif"],
)
st.write("### Channel interpretation")
st.markdown("The goal is to obtain an RGB image in the order of <span style='color: red;'>First analog</span>, <span style='color: green;'>Second analog</span>, <span style='color: blue;'>Empty</span>.", unsafe_allow_html=True)
st.markdown("By default, we assume that the first channel in CZI/TIFF file is <span style='color: green;'>the second analog</span>, (which happens to be the case in Zeiss microscope) " \
"which means that we swap the order of the two channels for processing.", unsafe_allow_html=True)
st.write("If this not the intented behavior, please tick the box below:")
st.session_state["reverse_channels"] = st.checkbox(
"Reverse the channels interpretation",
value=False,
)
st.warning("Please note that we only swap the channels for raw (CZI, TIFF) files. JPEG and PNG files "\
"are assumed to be already in the correct order (First analog in red and second analog in green). " \
)
st.info("" \
"The channels order in CZI files does not necessarily match the order in which they are displayed in ImageJ or equivalent. " \
"Indeed, such viewers will usually look at the metadata of the file to determine the order of the channels, which we don't. " \
"In doubt, we recommend visualizing the image in ImageJ and compare with our viewer. If the channels appear reversed, tick the option above.")
def build_individual_loader():
cols = st.columns(2)
with cols[1]:
st.markdown(f"<h3 style='color: {st.session_state['color2']};'>Second analog</h3>", unsafe_allow_html=True)
if (
st.session_state.get("analog_2_files", None) is None
or len(st.session_state.analog_2_files) == 0
):
st.session_state["analog_2_files"] = st.file_uploader(
label="Upload second analog file(s)",
accept_multiple_files=True,
type=["czi", "jpeg", "jpg", "png", "tiff", "tif"],
)
else:
st.session_state["analog_2_files"] += st.file_uploader(
label="Upload second analog file(s)",
accept_multiple_files=True,
type=["czi", "jpeg", "jpg", "png", "tiff", "tif"],
)
with cols[0]:
st.markdown(f"<h3 style='color: {st.session_state['color1']};'>First analog</h3>", unsafe_allow_html=True)
if (
st.session_state.get("analog_1_files", None) is None
or len(st.session_state.analog_1_files) == 0
):
st.session_state["analog_1_files"] = st.file_uploader(
label="Upload first analog file(s)",
accept_multiple_files=True,
type=["czi", "jpeg", "jpg", "png", "tiff", "tif"],
)
else:
st.session_state["analog_1_files"] += st.file_uploader(
label="Upload first analog file(s)",
accept_multiple_files=True,
type=["czi", "jpeg", "jpg", "png", "tiff", "tif"],)
analog_1_files=st.session_state.get("analog_1_files", None)
analog_2_files=st.session_state.get("analog_2_files", None)
# Remove duplicates from the list of files. We loop through the files and keep only the first occurrence of each file_id.
def remove_duplicates(files):
seen_ids = set()
unique_files = []
for file in files:
if file and file.name not in seen_ids:
unique_files.append(file)
seen_ids.add(file.name)
return unique_files
analog_1_files = remove_duplicates(analog_1_files or [])
analog_2_files = remove_duplicates(analog_2_files or [])
if analog_1_files is None and analog_2_files is None:
return
else:
if len(analog_1_files)>0 and len(analog_2_files)>0 and len(analog_1_files) != len(analog_2_files):
st.error("Please upload the same number of analogs files.")
return
# Always make sure we don't have duplicates in the list of files
analog_1_files = sorted(analog_1_files, key=lambda x: x.name)
analog_2_files = sorted(analog_2_files, key=lambda x: x.name)
max_size = max(len(analog_1_files), len(analog_2_files))
# Pad the shorter list with None
if len(analog_1_files) < max_size:
analog_1_files += [None] * (max_size - len(analog_1_files))
if len(analog_2_files) < max_size:
analog_2_files += [None] * (max_size - len(analog_2_files))
combined_files = list(zip(analog_1_files, analog_2_files))
if (
st.session_state.get("files_uploaded", None) is None
or len(st.session_state.files_uploaded) == 0
):
st.session_state["files_uploaded"] = combined_files
else:
st.session_state["files_uploaded"] += combined_files
# If any of the files (analog_1_files or analog_2_files) was included previously in the files_uploaded,
# We remove the previous occurence from the files_uploaded list.
current_ids = set()
for f in analog_1_files + analog_2_files:
if f:
current_ids.add(f.name)
# Safely filter the list to exclude any files with matching file_ids
def is_not_duplicate(file):
if isinstance(file, tuple):
f1, f2 = file
if f1 and f2:
return True
return (f1 is None or f1.name not in current_ids) and (f2 is None or f2.name not in current_ids)
else:
return True
st.session_state.files_uploaded = [f for f in st.session_state.files_uploaded if is_not_duplicate(f)]
cols = st.columns(2)
with cols[1]:
st.write("### Pixel size")
st.session_state["pixel_size"] = st.number_input(
"Please indicate the pixel size of the image in µm (default: 0.13 µm).",
value=st.session_state.get("pixel_size", 0.13),
)
# In small, lets precise the tehnical details
st.write(
"The pixel size is used to convert the pixel coordinates to µm. " \
"The model is trained on images with a pixel size of 0.26 µm, and the application automatically " \
"resizes the images to match this pixel size using your provided choice."
)
st.write("### Labels color")
color_choices = st.columns(2)
with color_choices[0]:
st.session_state["color1"] = st.color_picker(
"Select the color for first analog",
value=st.session_state.get("color1", "#FF0000"),
help="This color will be used to display the first analog segments.")
with color_choices[1]:
st.session_state["color2"] = st.color_picker(
"Select the color for second analog",
value=st.session_state.get("color2", "#00FF00"),
help="This color will be used to display the second analog segments.")
with cols[0]:
choice = st.segmented_control(
"Please select the type of images you want to upload:",
options=["Multichannel", "Individual channel"],
default="Multichannel",
)
if choice == "Individual channel":
build_individual_loader()
else:
build_multichannel_loader()
|