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import streamlit as st
import io
import csv
from segments import SegmentsClient
from get_labels_from_samples import (
get_samples as get_samples_objects,
export_frames_and_annotations,
export_sensor_frames_and_annotations,
export_all_sensor_frames_and_annotations
)
def init_session_state():
if 'csv_content' not in st.session_state:
st.session_state.csv_content = None
if 'error' not in st.session_state:
st.session_state.error = None
def init_client(api_key: str) -> SegmentsClient:
"""Initialize the Segments.ai API client using the provided API key."""
return SegmentsClient(api_key)
def parse_classes(input_str: str) -> list:
"""
Parse user input for classes (ranges and comma-separated lists). Returns unique sorted list of ints.
"""
classes = []
tokens = input_str.split(',')
for token in tokens:
token = token.strip()
if '-' in token:
try:
start, end = map(int, token.split('-'))
classes.extend(range(start, end + 1))
except ValueError:
continue
else:
try:
classes.append(int(token))
except ValueError:
continue
return sorted(set(classes))
def generate_csv(metrics: list, dataset_identifier: str) -> str:
"""
Generate CSV content from list of per-sample metrics.
Columns: name, sample_url, sensor, num_frames, total_annotations,
matching_annotations, labeled_by, reviewed_by
"""
output = io.StringIO()
writer = csv.writer(output)
writer.writerow([
'name', 'sample_url', 'sensor', 'num_frames',
'total_annotations', 'matching_annotations',
'labeled_by', 'reviewed_by'
])
for m in metrics:
url = f"https://app.segments.ai/{dataset_identifier}/samples/{m['uuid']}/{m['labelset']}"
writer.writerow([
m['name'], url, m['sensor'],
m['num_frames'], m['total_annotations'],
m['matching_annotations'], m['labeled_by'],
m['reviewed_by']
])
content = output.getvalue()
output.close()
return content
# ----------------------
# Streamlit UI
# ----------------------
init_session_state()
st.title("Per-Sample Annotation Counts by Class")
api_key = st.text_input("API Key", type="password", key="api_key_input")
dataset_identifier = st.text_input("Dataset Identifier (e.g., username/dataset)", key="dataset_identifier_input")
classes_input = st.text_input("Classes (e.g., 1,2,5 or 1-3)", key="classes_input")
run_button = st.button("Generate CSV", key="run_button")
sensor_names = []
is_multisensor = False
sensor_select = None
samples_objects = []
if api_key and dataset_identifier:
try:
client = init_client(api_key)
samples_objects = get_samples_objects(client, dataset_identifier)
if samples_objects:
label = client.get_label(samples_objects[0].uuid)
sensors = getattr(getattr(label, 'attributes', None), 'sensors', None)
if sensors is not None:
is_multisensor = True
sensor_names = [getattr(sensor, 'name', 'Unknown') for sensor in sensors]
except Exception as e:
st.warning(f"Could not inspect dataset sensors: {e}")
if is_multisensor:
sensor_select = st.selectbox("Choose sensor (optional)", options=['All sensors'] + sensor_names)
if run_button:
st.session_state.csv_content = None
st.session_state.error = None
if not api_key:
st.session_state.error = "API Key is required."
elif not dataset_identifier:
st.session_state.error = "Dataset identifier is required."
elif not classes_input:
st.session_state.error = "Please specify at least one class."
elif is_multisensor and not sensor_select:
st.session_state.error = "Please select a sensor or 'All sensors' before generating CSV."
else:
with st.spinner("Processing samples..."):
try:
target_classes = parse_classes(classes_input)
client = init_client(api_key)
metrics = []
for sample in samples_objects:
try:
label = client.get_label(sample.uuid)
labelset = getattr(label, 'labelset', '') or ''
labeled_by = getattr(label, 'created_by', '') or ''
reviewed_by = getattr(label, 'reviewed_by', '') or ''
if is_multisensor and sensor_select and sensor_select != 'All sensors':
frames_list = export_sensor_frames_and_annotations(label, sensor_select)
sensor_val = sensor_select
num_frames = len(frames_list)
total_annotations = sum(len(f['annotations']) for f in frames_list)
matching_annotations = sum(
1
for f in frames_list
for ann in f['annotations']
if getattr(ann, 'category_id', None) in target_classes
)
elif is_multisensor and (not sensor_select or sensor_select == 'All sensors'):
all_sensor_frames = export_all_sensor_frames_and_annotations(label)
for sensor_name, frames_list in all_sensor_frames.items():
num_frames = len(frames_list)
total_annotations = sum(len(f['annotations']) for f in frames_list)
matching_annotations = sum(
1
for f in frames_list
for ann in f['annotations']
if getattr(ann, 'category_id', None) in target_classes
)
metrics.append({
'name': getattr(sample, 'name', sample.uuid),
'uuid': sample.uuid,
'labelset': labelset,
'sensor': sensor_name,
'num_frames': num_frames,
'total_annotations': total_annotations,
'matching_annotations': matching_annotations,
'labeled_by': labeled_by,
'reviewed_by': reviewed_by
})
continue
else:
frames_list = export_frames_and_annotations(label)
sensor_val = ''
num_frames = len(frames_list)
total_annotations = sum(len(f['annotations']) for f in frames_list)
matching_annotations = sum(
1
for f in frames_list
for ann in f['annotations']
if getattr(ann, 'category_id', None) in target_classes
)
metrics.append({
'name': getattr(sample, 'name', sample.uuid),
'uuid': sample.uuid,
'labelset': labelset,
'sensor': sensor_val if is_multisensor else '',
'num_frames': num_frames,
'total_annotations': total_annotations,
'matching_annotations': matching_annotations,
'labeled_by': labeled_by,
'reviewed_by': reviewed_by
})
except Exception as e:
continue
if not metrics:
st.session_state.error = "No metrics could be generated for the dataset."
else:
st.session_state.csv_content = generate_csv(metrics, dataset_identifier)
except Exception as e:
st.session_state.error = f"An error occurred: {e}"
if st.session_state.error:
st.error(st.session_state.error)
if st.session_state.csv_content:
st.download_button(
label="Download Metrics CSV",
data=st.session_state.csv_content,
file_name="sample_metrics.csv",
mime="text/csv"
) |