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use object-anchored snapshot crops for classification wizard examples (#22985)

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Josh Hawkins 2026-04-23 08:53:48 -05:00 committed by GitHub
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1 changed files with 96 additions and 52 deletions
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frigate/util/classification.py
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@ -24,8 +24,12 @@ from frigate.log import redirect_output_to_logger, suppress_stderr_during
from frigate.models import Event, Recordings, ReviewSegment
from frigate.types import ModelStatusTypesEnum
from frigate.util.downloader import ModelDownloader
from frigate.util.file import get_event_thumbnail_bytes
from frigate.util.image import get_image_from_recording
from frigate.util.file import get_event_thumbnail_bytes, load_event_snapshot_image
from frigate.util.image import (
calculate_region,
get_image_from_recording,
relative_box_to_absolute,
)
from frigate.util.process import FrigateProcess
BATCH_SIZE = 16
@ -713,7 +717,7 @@ def collect_object_classification_examples(
This function:
1. Queries events for the specified label
2. Selects 100 balanced events across different cameras and times
3. Retrieves thumbnails for selected events (with 33% center crop applied)
3. Crops each event's clean snapshot around the object bounding box
4. Selects 24 most visually distinct thumbnails
5. Saves to dataset directory
@ -832,66 +836,106 @@ def _select_balanced_events(
def _extract_event_thumbnails(events: list[Event], output_dir: str) -> list[str]:
"""
Extract thumbnails from events and save to disk.
Extract a training image for each event.
Preferred path: load the full-frame clean snapshot and crop around the
stored bounding box with the same calculate_region(..., max(w, h), 1.0)
call the live ObjectClassificationProcessor uses, so wizard examples
are framed like inference-time inputs.
Fallback: if no clean snapshot exists (snapshots disabled, or only a
legacy annotated JPG is on disk), center-crop the stored thumbnail
using a step ladder sized from the box/region area ratio.
Args:
events: List of Event objects
output_dir: Directory to save thumbnails
output_dir: Directory to save crops
Returns:
List of paths to successfully extracted thumbnail images
List of paths to successfully extracted images
"""
thumbnail_paths = []
image_paths = []
for idx, event in enumerate(events):
try:
thumbnail_bytes = get_event_thumbnail_bytes(event)
img = _load_event_classification_crop(event)
if img is None:
continue
if thumbnail_bytes:
nparr = np.frombuffer(thumbnail_bytes, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
if img is not None:
height, width = img.shape[:2]
crop_size = 1.0
if event.data and "box" in event.data and "region" in event.data:
box = event.data["box"]
region = event.data["region"]
if len(box) == 4 and len(region) == 4:
box_w, box_h = box[2], box[3]
region_w, region_h = region[2], region[3]
box_area = (box_w * box_h) / (region_w * region_h)
if box_area < 0.05:
crop_size = 0.4
elif box_area < 0.10:
crop_size = 0.5
elif box_area < 0.20:
crop_size = 0.65
elif box_area < 0.35:
crop_size = 0.80
else:
crop_size = 0.95
crop_width = int(width * crop_size)
crop_height = int(height * crop_size)
x1 = (width - crop_width) // 2
y1 = (height - crop_height) // 2
x2 = x1 + crop_width
y2 = y1 + crop_height
cropped = img[y1:y2, x1:x2]
resized = cv2.resize(cropped, (224, 224))
output_path = os.path.join(output_dir, f"thumbnail_{idx:04d}.jpg")
cv2.imwrite(output_path, resized)
thumbnail_paths.append(output_path)
resized = cv2.resize(img, (224, 224))
output_path = os.path.join(output_dir, f"thumbnail_{idx:04d}.jpg")
cv2.imwrite(output_path, resized)
image_paths.append(output_path)
except Exception as e:
logger.debug(f"Failed to extract thumbnail for event {event.id}: {e}")
logger.debug(f"Failed to extract image for event {event.id}: {e}")
continue
return thumbnail_paths
return image_paths
def _load_event_classification_crop(event: Event) -> np.ndarray | None:
"""Prefer a snapshot-based object crop; fall back to a center-cropped thumbnail."""
if event.data and "box" in event.data:
snapshot, _ = load_event_snapshot_image(event, clean_only=True)
if snapshot is not None:
abs_box = relative_box_to_absolute(snapshot.shape, event.data["box"])
if abs_box is not None:
xmin, ymin, xmax, ymax = abs_box
box_w = xmax - xmin
box_h = ymax - ymin
if box_w > 0 and box_h > 0:
x1, y1, x2, y2 = calculate_region(
snapshot.shape,
xmin,
ymin,
xmax,
ymax,
max(box_w, box_h),
1.0,
)
cropped = snapshot[y1:y2, x1:x2]
if cropped.size > 0:
return cropped
thumbnail_bytes = get_event_thumbnail_bytes(event)
if not thumbnail_bytes:
return None
nparr = np.frombuffer(thumbnail_bytes, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
if img is None or img.size == 0:
return None
height, width = img.shape[:2]
crop_size = 1.0
if event.data and "box" in event.data and "region" in event.data:
box = event.data["box"]
region = event.data["region"]
if len(box) == 4 and len(region) == 4:
box_w, box_h = box[2], box[3]
region_w, region_h = region[2], region[3]
box_area = (box_w * box_h) / (region_w * region_h)
if box_area < 0.05:
crop_size = 0.4
elif box_area < 0.10:
crop_size = 0.5
elif box_area < 0.20:
crop_size = 0.65
elif box_area < 0.35:
crop_size = 0.80
else:
crop_size = 0.95
crop_width = int(width * crop_size)
crop_height = int(height * crop_size)
x1 = (width - crop_width) // 2
y1 = (height - crop_height) // 2
cropped = img[y1 : y1 + crop_height, x1 : x1 + crop_width]
if cropped.size == 0:
return None
return cropped