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#!/usr/bin/env python3
"""
File: count_by_class.py
Location: 6_Data_metrics/count_by_class.py
Description:
Streamlit application to count and report metrics per sample for specified classes.
For each sample, outputs:
- Sample name
- Sample URL (including labelset)
- Number of frames (ignoring a 31st frame if present)
- Total number of annotations
- Number of annotations matching any of the user-specified classes
- Labeled by (from label data)
- Reviewed by (from label data)
Usage:
streamlit run 6_Data_metrics/count_by_class.py
"""
import streamlit as st
import io
import csv
from segments import SegmentsClient
from datetime import datetime
import sys
import os
from get_labels_from_samples import get_samples as get_samples_objects
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 _count_from_frames(frames, target_set, class_ids):
"""Helper to count frames, totals, and per-class counts directly."""
if not frames:
return 0, 0, 0, {cid: 0 for cid in class_ids}
num_frames = len(frames)
total_annotations = 0
matching_annotations = 0
class_counts = {cid: 0 for cid in class_ids}
for f in frames:
anns = getattr(f, 'annotations', [])
total_annotations += len(anns)
if target_set:
for ann in anns:
cid = getattr(ann, 'category_id', None)
if cid in target_set:
matching_annotations += 1
if cid in class_counts:
class_counts[cid] += 1
return num_frames, total_annotations, matching_annotations, class_counts
def compute_metrics_for_sample(sample, api_key, target_set, class_ids, is_multisensor, sensor_select):
"""
Fetch label for a single sample and compute metrics.
Returns a list of metric dicts (one per sensor if 'All sensors', otherwise one).
"""
try:
client = init_client(api_key)
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 ''
metrics_rows = []
if is_multisensor:
sensors = getattr(getattr(label, 'attributes', None), 'sensors', None) or []
if sensor_select and sensor_select != 'All sensors':
# single sensor
for sensor in sensors:
if getattr(sensor, 'name', None) == sensor_select:
frames = getattr(getattr(sensor, 'attributes', None), 'frames', [])
num_frames, total_annotations, matching_annotations, class_counts = _count_from_frames(frames, target_set, class_ids)
metrics_rows.append({
'name': getattr(sample, 'name', sample.uuid),
'uuid': sample.uuid,
'labelset': labelset,
'sensor': sensor_select,
'num_frames': num_frames,
'total_annotations': total_annotations,
'matching_annotations': matching_annotations,
'class_counts': class_counts,
'labeled_by': labeled_by,
'reviewed_by': reviewed_by
})
break
else:
# all sensors
for sensor in sensors:
sensor_name = getattr(sensor, 'name', 'Unknown')
frames = getattr(getattr(sensor, 'attributes', None), 'frames', [])
num_frames, total_annotations, matching_annotations, class_counts = _count_from_frames(frames, target_set, class_ids)
metrics_rows.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,
'class_counts': class_counts,
'labeled_by': labeled_by,
'reviewed_by': reviewed_by
})
else:
# single-sensor dataset
frames = getattr(getattr(label, 'attributes', None), 'frames', [])
num_frames, total_annotations, matching_annotations, class_counts = _count_from_frames(frames, target_set, class_ids)
metrics_rows.append({
'name': getattr(sample, 'name', sample.uuid),
'uuid': sample.uuid,
'labelset': labelset,
'sensor': '',
'num_frames': num_frames,
'total_annotations': total_annotations,
'matching_annotations': matching_annotations,
'class_counts': class_counts,
'labeled_by': labeled_by,
'reviewed_by': reviewed_by
})
return metrics_rows
except Exception:
return []
def generate_csv(metrics: list, dataset_identifier: str, target_classes: list[int]) -> 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)
header = [
'name', 'sample_url', 'sensor', 'num_frames',
'total_annotations', 'matching_annotations'
]
# dynamic per-class columns
header.extend([f'class_{cid}' for cid in target_classes])
header.extend(['labeled_by', 'reviewed_by'])
writer.writerow(header)
for m in metrics:
url = f"https://app.segments.ai/{dataset_identifier}/samples/{m['uuid']}/{m['labelset']}"
row = [
m['name'], url, m['sensor'],
m['num_frames'], m['total_annotations'],
m['matching_annotations']
]
# add per-class counts in the same order as header
class_counts = m.get('class_counts', {})
row.extend([class_counts.get(cid, 0) for cid in target_classes])
row.extend([m['labeled_by'], m['reviewed_by']])
writer.writerow(row)
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:
# Show loader/status message while checking dataset type and generating CSV
status_ctx = None
try:
status_ctx = st.status("Checking dataset type...", expanded=True)
except AttributeError:
st.info("Checking dataset type...")
try:
target_classes = parse_classes(classes_input)
target_set = set(target_classes)
metrics = []
# Update loader after dataset type check
total = len(samples_objects)
if status_ctx is not None:
status_ctx.update(label=f"Dataset type checked. Processing {total} samples...", state="running")
progress = st.progress(0)
done = 0
failed_samples = []
# Process samples sequentially instead of in parallel
for i, sample in enumerate(samples_objects):
try:
rows = compute_metrics_for_sample(
sample,
api_key,
target_set,
target_classes,
is_multisensor,
sensor_select,
)
if rows:
metrics.extend(rows)
else:
failed_samples.append(f"Sample {sample.uuid}: No metrics generated")
except Exception as e:
failed_samples.append(f"Sample {sample.uuid}: {str(e)}")
done += 1
if total:
progress.progress(min(done / total, 1.0))
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, target_classes)
success_msg = f"CSV generated! Processed {len(metrics)} samples"
if failed_samples:
success_msg += f" ({len(failed_samples)} samples failed)"
if status_ctx is not None:
status_ctx.update(label=success_msg, state="complete")
if failed_samples:
st.warning(f"{len(failed_samples)} samples failed processing. First few errors:")
for failure in failed_samples[:5]: # Show first 5 failures
st.text(failure)
except Exception as e:
st.session_state.error = f"An error occurred: {e}"
if status_ctx is not None:
status_ctx.update(label="Error occurred.", state="error")
if st.session_state.error:
st.error(st.session_state.error)
if st.session_state.csv_content:
today_str = datetime.now().strftime("%Y%m%d")
filename = f"{today_str}_{dataset_identifier}_count-by-class.csv"
st.download_button(
"Download CSV",
data=st.session_state.csv_content,
file_name=filename,
mime="text/csv"
)
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