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module.yml
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76 lines (69 loc) · 2.18 KB
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name: fisheye_line_crossing
parameters:
frame:
width: 1024
height: 1024
output_frame:
codec: ${oc.env:CODEC, 'h264'}
draw_func:
module: samples.fisheye_line_crossing.overlay
class_name: Overlay
kwargs:
config_path: ${oc.env:PROJECT_PATH}/samples/fisheye_line_crossing/polygon_config.yml
batch_size: 1
detected_object_label: person
send_stats: True
pipeline:
elements:
# detector
- element: nvinfer@detector
name: yolov7_obb
model:
remote:
url: s3://savant-data/models/yolov7_obb/yolov7_obb.zip
checksum_url: s3://savant-data/models/yolov7_obb/yolov7_obb.md5
parameters:
endpoint: https://eu-central-1.linodeobjects.com
format: onnx
model_file: yolov7_obb.onnx
batch_size: ${parameters.batch_size}
workspace_size: 6144
input:
shape: [3,640,640]
scale_factor: 0.0039215697906911373
maintain_aspect_ratio: True
output:
num_detected_classes: 1
layer_names: [ 'output' ]
converter:
module: samples.fisheye_line_crossing.converter
class_name: TensorToBBoxConverter
kwargs:
confidence_threshold: 0.5
nms_iou_threshold: 0.5
top_k: 300
objects:
- class_id: 0
label: ${parameters.detected_object_label}
selector:
module: savant.selector.detector
class_name: BBoxSelector
kwargs:
confidence_threshold: 0.5
# pyfunc that applies tracking algorithm
# in this case, using Similari tracking library
- element: pyfunc
module: samples.fisheye_line_crossing.similari_tracker
class_name: Tracker
kwargs:
iou_threshold: 0.2
min_hits: 3
max_age: 15
add_idle: False
- element: pyfunc
module: samples.fisheye_line_crossing.line_crossing
class_name: LineCrossing
kwargs:
config_path: ${oc.env:PROJECT_PATH}/samples/fisheye_line_crossing/polygon_config.yml
stale_track_del_period: 5000
send_stats: ${parameters.send_stats}