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Cleanup

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This commit is contained in:
Nicolas Mowen 2025-09-25 07:19:32 -06:00
parent a93c1e5653
commit 2a40e3d9ab
3 changed files with 89 additions and 91 deletions
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3
frigate/track/norfair_tracker.py
View File

@ -5,7 +5,6 @@ from typing import Any, Sequence, cast
import cv2 import cv2
import numpy as np import numpy as np
from frigate.track.stationary_classifier import StationaryMotionClassifier
from norfair.drawing.draw_boxes import draw_boxes from norfair.drawing.draw_boxes import draw_boxes
from norfair.drawing.drawer import Drawable, Drawer from norfair.drawing.drawer import Drawable, Drawer
from norfair.filter import OptimizedKalmanFilterFactory from norfair.filter import OptimizedKalmanFilterFactory
@ -13,12 +12,12 @@ from norfair.tracker import Detection, TrackedObject, Tracker
from rich import print from rich import print
from rich.console import Console from rich.console import Console
from rich.table import Table from rich.table import Table
from scipy.ndimage import gaussian_filter
from frigate.camera import PTZMetrics from frigate.camera import PTZMetrics
from frigate.config import CameraConfig from frigate.config import CameraConfig
from frigate.ptz.autotrack import PtzMotionEstimator from frigate.ptz.autotrack import PtzMotionEstimator
from frigate.track import ObjectTracker from frigate.track import ObjectTracker
from frigate.track.stationary_classifier import StationaryMotionClassifier
from frigate.util.image import ( from frigate.util.image import (
SharedMemoryFrameManager, SharedMemoryFrameManager,
get_histogram, get_histogram,

6
frigate/track/stationary_classifier.py
View File

@ -80,7 +80,7 @@ class StationaryMotionClassifier:
y1, y1,
x2, x2,
y2, y2,
crop.shape if 'crop' in locals() else None, crop.shape if "crop" in locals() else None,
) )
return result return result
@ -115,9 +115,7 @@ class StationaryMotionClassifier:
""" """
if id not in self.anchor_crops or id not in self.anchor_boxes: if id not in self.anchor_crops or id not in self.anchor_boxes:
logger.debug( logger.debug("evaluate: id=%s has no anchor; default keep stationary", id)
"evaluate: id=%s has no anchor; default keep stationary", id
)
return True return True
# Compare same spatial region across frames # Compare same spatial region across frames

171
notebooks/YOLO_NAS_Pretrained_Export.ipynb
View File

@ -1,88 +1,89 @@
{ {
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"id": "rmuF9iKWTbdk" "id": "rmuF9iKWTbdk"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"! pip install -q git+https://github.com/Deci-AI/super-gradients.git" "! pip install -q git+https://github.com/Deci-AI/super-gradients.git"
] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NiRCt917KKcL"
},
"outputs": [],
"source": [
"! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.12/dist-packages/super_gradients/training/pretrained_models.py\n",
"! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.12/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dTB0jy_NNSFz"
},
"outputs": [],
"source": [
"from super_gradients.common.object_names import Models\n",
"from super_gradients.conversion import DetectionOutputFormatMode\n",
"from super_gradients.training import models\n",
"\n",
"model = models.get(Models.YOLO_NAS_S, pretrained_weights=\"coco\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GymUghyCNXem"
},
"outputs": [],
"source": [
"# export the model for compatibility with Frigate\n",
"\n",
"model.export(\"yolo_nas_s.onnx\",\n",
" output_predictions_format=DetectionOutputFormatMode.FLAT_FORMAT,\n",
" max_predictions_per_image=20,\n",
" num_pre_nms_predictions=300,\n",
" confidence_threshold=0.4,\n",
" input_image_shape=(320,320),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uBhXV5g4Nh42"
},
"outputs": [],
"source": [
"from google.colab import files\n",
"\n",
"files.download('yolo_nas_s.onnx')"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
}, },
"nbformat": 4, {
"nbformat_minor": 0 "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NiRCt917KKcL"
},
"outputs": [],
"source": [
"! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.12/dist-packages/super_gradients/training/pretrained_models.py\n",
"! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.12/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dTB0jy_NNSFz"
},
"outputs": [],
"source": [
"from super_gradients.common.object_names import Models\n",
"from super_gradients.conversion import DetectionOutputFormatMode\n",
"from super_gradients.training import models\n",
"\n",
"model = models.get(Models.YOLO_NAS_S, pretrained_weights=\"coco\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GymUghyCNXem"
},
"outputs": [],
"source": [
"# export the model for compatibility with Frigate\n",
"\n",
"model.export(\n",
" \"yolo_nas_s.onnx\",\n",
" output_predictions_format=DetectionOutputFormatMode.FLAT_FORMAT,\n",
" max_predictions_per_image=20,\n",
" num_pre_nms_predictions=300,\n",
" confidence_threshold=0.4,\n",
" input_image_shape=(320, 320),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uBhXV5g4Nh42"
},
"outputs": [],
"source": [
"from google.colab import files\n",
"\n",
"files.download(\"yolo_nas_s.onnx\")"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} }