Detect

class lib.infer.detect.Detect(plugin: str, rotation: str | None = None, min_size: int = 0, max_size: int = 0, compile_model: bool = False, config_file: str | None = None)

Bases: ExtractHandler

Responsible for handling Detection plugins within the extract pipeline

Parameters:

plugin (str) – The plugin that this runner is to use
None (rotation |) – The rotation arguments. Either a list of angles between 0 and 360 to rotate at or a single step size. Default: None, no rotations
min_size (int) – Minimum percentage of the frame’s shortest edge to accept as a successful detection along the detection’s longest edge Default: 0 (accept all detections)
max_size (int) – Maximum percentage of the frame’s shortest edge to accept as a successful detection along the detection’s longest edge Default: 0 (accept all detections)
compile_model (bool) – True to compile any PyTorch models
config_file (str | None) – Full path to a custom config file to load. None for default config
rotation (str | None)

Attributes Summary

`batch_size`	The batch size of the plugin
`processors`	The processors which should have thread's launched for this handler
`runner`	The runner that runs this handler

Methods Summary

`__call__`([input_plugin, profile])	Build and start the plugin handler's runner
`init_model`()	Load the model, compile it, if requested, and send a warmup batch through.
`output_info`()	Output the counts of filtered items
`post_process`(batch)	Perform detection post processing.
`pre_process`(batch)	Perform pre-processing for detection plugins.
`process`(batch)	Obtain the output from the plugin's model.

Attributes Documentation

batch_size: The batch size of the plugin

processors: tuple[Literal['pre_process', 'process', 'post_process'], ...] = ('pre_process', 'process', 'post_process'): The processors which should have thread’s launched for this handler

runner: The runner that runs this handler

Methods Documentation

__call__(input_plugin: ExtractHandler | ExtractRunner | None = None, profile: bool = False) → ExtractRunner

Build and start the plugin handler’s runner

Parameters:

input_plugin (ExtractHandler | ExtractRunner | None) – The input plugin handler or it’s runner that feeds this handler. None if data is to be fed through the handler runner’s put method (ie, the first handler in an extraction chain). Default: None
profile (bool) – True if the runner is to be profiled, indicating that threads will not be started. Default: False

Return type:

The extract plugin handler’s runner for this handler

init_model() → None

Load the model, compile it, if requested, and send a warmup batch through. Called either from the main thread, if compiling, or from the inference thread if not.

Return type:: None

output_info() → None

Output the counts of filtered items

Return type:: None

post_process(batch: ExtractBatch) → None

Perform detection post processing.

If no rotations were requested, any plugin post-processing will be done here.

Detection boxes are:

stacked into a single array
scaled back to frame dimensions,
filtered for faces which fall outside min/max thresholds
Added to the batch object along with frame to face mapping information.

Parameters:: batch (ExtractBatch) – The incoming ExtractBatch to use for post-processing
Return type:: None

pre_process(batch: ExtractBatch) → None

Perform pre-processing for detection plugins.

Gets the scale and padding to take the batch of images to model input size
Formats the image to the correct color order, dtype and scale for the plugin
Performs any plugin specific pre-processing

Parameters:: batch (ExtractBatch) – The incoming ExtractBatch to use for pre-processing
Return type:: None

process(batch: ExtractBatch) → None

Obtain the output from the plugin’s model.

Executes the plugin’s predict function and stores the output prior to post-processing.

If rotations have been selected, plugin post-processing is done as part of this process as the computed bounding boxes are required for re-feeding the model future rotations

Parameters:: batch (ExtractBatch) – The incoming ExtractBatch to use for processing
Return type:: None