# BaseClassifier

# Copyright (C) 2024-2026 Collabora Ltd.

#

# This library is free software; you can redistribute it and/or

# modify it under the terms of the GNU Library General Public

# License as published by the Free Software Foundation; either

# version 2 of the License, or (at your option) any later version.

#

# This library is distributed in the hope that it will be useful,

# but WITHOUT ANY WARRANTY; without even the implied warranty of

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU

# Library General Public License for more details.

#

# You should have received a copy of the GNU Library General Public

# License along with this library; if not, write to the

# Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor,

# Boston, MA 02110-1301, USA.

from video_transform import VideoTransform

from backend import analytics

class BaseClassifier(VideoTransform):

"""

GStreamer element for image classification with a machine learning model.

"""

def __init__(self):

super().__init__()

self.logger.info("BaseClassifier initialized.")

def do_forward(self, frame):

"""

Runs classification inference and returns a label with a confidence score.

"""

if self.task_engine:

return self.task_engine.do_forward(frame)

self.logger.error("No model loaded in BaseClassifier.")

return None

def process_frames(self, frames, num_sources, fmt, target):

"""Classify the frame and attach the label as metadata."""

results = self.do_forward(frames)

if not results:

raise RuntimeError("classification returned no results")

self.do_decode(target, results)

def do_decode(self, buf, output):

"""

Decodes classification output and attaches metadata.

"""

if not isinstance(output, dict):

self.logger.error(

f"Unexpected classification output format: {type(output)}"

)

return

label = output.get("label")

score = output.get("score")

if label is None or score is None:

self.logger.warning("Classification result missing label or score.")

return

self.logger.info(f"Classified as {label} with confidence score {score:.2f}")

# Attach classification metadata

meta = analytics.add_relation_meta(buf)

if meta:

analytics.add_object(meta, label, 0, 0, self.width, self.height, score)

self.logger.info(f"Classified as {label} with score {score:.2f}")