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"""
Merge new predictions into existing CVAT XML, preserving frame 0 annotations
"""
import xml.etree.ElementTree as ET
from typing import Dict, List
from pathlib import Path
import cv2
from cvat_xml_generator import create_cvat_xml
from src.types import TrackedObject, Event
def parse_existing_xml(xml_path: str) -> Dict:
"""
Parse existing CVAT XML to extract frame 0 annotations and metadata
Args:
xml_path: Path to existing CVAT XML file
Returns:
Dictionary with:
- frame_0_tracks: Dict mapping track_id -> track element
- video_metadata: Dict with width, height, fps, frame_count
- events: List of event elements
"""
tree = ET.parse(xml_path)
root = tree.getroot()
# Extract video metadata
meta = root.find('.//meta/task')
if meta is not None:
size_elem = meta.find('size')
frame_count = int(size_elem.text) if size_elem is not None else 0
else:
frame_count = 0
# Get video path from XML if available
video_path = None
source_elem = root.find('.//source')
if source_elem is not None:
video_path = source_elem.text
# Extract video metadata from video file if available
video_metadata = {"width": 1920, "height": 1080, "fps": 30.0, "frame_count": frame_count}
if video_path and Path(video_path).exists():
cap = cv2.VideoCapture(video_path)
if cap.isOpened():
video_metadata = {
"width": int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
"height": int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
"fps": cap.get(cv2.CAP_PROP_FPS),
"frame_count": int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
}
cap.release()
# Extract frame 0 annotations (preserve these)
frame_0_tracks = {}
all_tracks = root.findall('.//track')
for track in all_tracks:
track_id = track.get('id')
label = track.get('label', 'player')
source = track.get('source', 'manual')
# Find boxes in frame 0
frame_0_boxes = track.findall('.//box[@frame="0"]')
if frame_0_boxes:
# Create a copy of the track with ALL boxes (not just frame 0)
# This preserves existing annotations beyond frame 0
frame_0_track = ET.Element('track', {
'id': track_id,
'label': label,
'source': source # Preserve source attribute
})
# Add ALL boxes from this track (preserve existing annotations)
for box in track.findall('.//box'):
frame_0_track.append(box)
frame_0_tracks[track_id] = frame_0_track
# Extract events
events = root.findall('.//tag')
return {
'frame_0_tracks': frame_0_tracks,
'video_metadata': video_metadata,
'events': events
}
def convert_tracked_objects_to_dict(
tracked_objects_by_frame: Dict[int, List[TrackedObject]]
) -> Dict[int, List[Dict]]:
"""
Convert TrackedObject list to dictionary format expected by create_cvat_xml
Args:
tracked_objects_by_frame: Dict mapping frame_id -> List[TrackedObject]
Returns:
Dict mapping frame_id -> List of box dicts
"""
result = {}
for frame_id, tracked_objects in tracked_objects_by_frame.items():
frame_boxes = []
for tracked_obj in tracked_objects:
det = tracked_obj.detection
x, y, w, h = det.bbox
frame_boxes.append({
"frame": frame_id,
"xtl": x,
"ytl": y,
"xbr": x + w,
"ybr": y + h,
"outside": 0,
"occluded": 0,
"keyframe": 1,
"confidence": det.confidence,
"track_id": tracked_obj.object_id,
"label": det.class_name
})
result[frame_id] = frame_boxes
return result
def merge_annotations(
original_xml_path: str,
video_path: str,
new_tracked_objects_by_frame: Dict[int, List[TrackedObject]],
output_xml_path: str
):
"""
Merge new predictions into existing XML, preserving frame 0
Args:
original_xml_path: Path to original CVAT XML with frame 0 annotations
video_path: Path to video file
new_tracked_objects_by_frame: New predictions for frames 1+
output_xml_path: Path to save merged XML
"""
# Parse existing XML
print(f"Parsing existing XML: {original_xml_path}")
existing_data = parse_existing_xml(original_xml_path)
frame_0_tracks = existing_data['frame_0_tracks']
video_metadata = existing_data['video_metadata']
# Convert new tracked objects to format for XML generation
print(f"Converting {len(new_tracked_objects_by_frame)} frames of new predictions...")
new_boxes_by_frame = convert_tracked_objects_to_dict(new_tracked_objects_by_frame)
# Merge frame 0 boxes with new boxes
# Group boxes by track_id across all frames
all_tracks_dict = {}
# Add frame 0 tracks (preserve original)
for track_id, track_elem in frame_0_tracks.items():
label = track_elem.get('label', 'player')
source = track_elem.get('source', 'manual')
boxes = []
for box_elem in track_elem.findall('.//box'):
frame = int(box_elem.get('frame'))
boxes.append({
"frame": frame,
"xtl": float(box_elem.get('xtl')),
"ytl": float(box_elem.get('ytl')),
"xbr": float(box_elem.get('xbr')),
"ybr": float(box_elem.get('ybr')),
"outside": int(box_elem.get('outside', 0)),
"occluded": int(box_elem.get('occluded', 0)),
"keyframe": int(box_elem.get('keyframe', 1)),
"confidence": 1.0, # Manual annotations have full confidence
"track_id": track_id,
"label": label
})
all_tracks_dict[track_id] = {
'label': label,
'source': source,
'boxes': boxes
}
# Add new predictions (frames 1+)
for frame_id, frame_boxes in new_boxes_by_frame.items():
for box in frame_boxes:
track_id = box['track_id']
label = box.get('label', 'player')
if track_id not in all_tracks_dict:
all_tracks_dict[track_id] = {
'label': label,
'source': 'auto',
'boxes': []
}
all_tracks_dict[track_id]['boxes'].append(box)
# Instead of using create_cvat_xml (which reassigns track IDs),
# we'll directly modify the original XML to preserve track IDs
print(f"Preserving original XML structure and track IDs...")
# Load original XML tree
tree = ET.parse(original_xml_path)
root = tree.getroot()
# Remove all existing tracks and tags (we'll rebuild tracks preserving IDs)
# But keep meta, version, and other structure
for track in root.findall('.//track'):
root.remove(track)
for tag in root.findall('.//tag'):
root.remove(tag)
# Rebuild tracks preserving original track IDs
for track_id, track_data in all_tracks_dict.items():
# Create track element with original ID
track_elem = ET.Element('track', {
'id': str(track_id),
'label': track_data['label'],
'source': track_data.get('source', 'manual')
})
# Sort boxes by frame
sorted_boxes = sorted(track_data['boxes'], key=lambda b: b['frame'])
# Add boxes to track
for box in sorted_boxes:
box_elem = ET.SubElement(track_elem, 'box', {
'frame': str(box['frame']),
'xtl': f"{box['xtl']:.2f}",
'ytl': f"{box['ytl']:.2f}",
'xbr': f"{box['xbr']:.2f}",
'ybr': f"{box['ybr']:.2f}",
'outside': str(box.get('outside', 0)),
'occluded': str(box.get('occluded', 0)),
'keyframe': str(box.get('keyframe', 1))
})
# Add confidence attribute if present
if 'confidence' in box:
conf_attr = ET.SubElement(box_elem, 'attribute', {'name': 'confidence'})
conf_attr.text = f"{box['confidence']:.3f}"
# Append track to root
root.append(track_elem)
# Preserve events from original XML
# (events are already in the tree, we just need to make sure they're not removed)
# Generate pretty-printed XML
from cvat_xml_generator import prettify_xml
xml_content = prettify_xml(root)
# Save merged XML
output_path = Path(output_xml_path)
output_path.parent.mkdir(parents=True, exist_ok=True)
with open(output_xml_path, 'w', encoding='utf-8') as f:
f.write(xml_content)
print(f"✅ Merged XML saved to: {output_xml_path}")
print(f" - Preserved {len(frame_0_tracks)} tracks from frame 0")
print(f" - Added {len(new_tracked_objects_by_frame)} frames of new predictions")
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