ImpulseRunner

Parameters

• model_path: str

Subclasses

• edge_impulse_linux.audio.AudioImpulseRunner

• edge_impulse_linux.image.ImageImpulseRunner

Methods

classify

Parameters

• self

• data

hello

Parameters

• self

init

Parameters

• self

• debug=False

send_msg

Parameters

• self

• msg

stop

Parameters

• self

now

Submodules

• edge_impulse_linux.audio

• edge_impulse_linux.image

• edge_impulse_linux.runner

Package Info

• Name: edge_impulse_linux

• Summary: Python runner for real-time ML classification

• Version: 1.0.10

• Requires: numpy, psutil

• License: None

AudioImpulseRunner

class edge_impulse_linux.audio.AudioImpulseRunner(
		model_path: str
)

Parameters

• model_path: str

Bases

• edge_impulse_linux.runner.ImpulseRunner

Methods

classifier

edge_impulse_linux.audio.classifier(
		self,
		device_id=None
)

Parameters

• self

• device_id=None

classify

edge_impulse_linux.audio.classify(
		self,
		data
)

Parameters

• self

• data

init

edge_impulse_linux.audio.init(
		self,
		debug=False
)

Parameters

• self

• debug=False

Microphone

class edge_impulse_linux.audio.Microphone(
		rate,
		chunk_size,
		device_id=None,
		channels=1
)

Parameters

• rate

• chunk_size

• device_id=None

• channels=1

Methods

checkDeviceModelCompatibility

edge_impulse_linux.audio.checkDeviceModelCompatibility(
		self,
		device_id
)

Parameters

• self

• device_id

fill_buffer

edge_impulse_linux.audio.fill_buffer(
		self,
		in_data,
		frame_count,
		time_info,
		status_flags
)

Parameters

• self

• in_data

• frame_count

• time_info

• status_flags

generator

edge_impulse_linux.audio.generator(
		self
)

Parameters

• self

listAvailableDevices

edge_impulse_linux.audio.listAvailableDevices(
		self
)

Parameters

• self

now

edge_impulse_linux.audio.now(
		
)

ImageImpulseRunner

class edge_impulse_linux.image.ImageImpulseRunner(
		model_path: str
)

Parameters

• model_path: str

Bases

• edge_impulse_linux.runner.ImpulseRunner

Methods

classifier

edge_impulse_linux.image.classifier(
		self,
		videoDeviceId=0
)

Parameters

• self

• videoDeviceId=0

classify

edge_impulse_linux.image.classify(
		self,
		data
)

Parameters

• self

• data

get_features_from_image

edge_impulse_linux.image.get_features_from_image(
		self,
		img,
		crop_direction_x='center',
		crop_direction_y='center'
)

Parameters

• self

• img

• crop_direction_x='center'

• crop_direction_y='center'

get_features_from_image_auto_studio_setings

edge_impulse_linux.image.get_features_from_image_auto_studio_setings(
		self,
		img
)

Parameters

• self

• img

get_frames

edge_impulse_linux.image.get_frames(
		self,
		videoDeviceId=0
)

Parameters

• self

• videoDeviceId=0

init

edge_impulse_linux.image.init(
		self,
		debug=False
)

Parameters

• self

• debug=False

get_features_from_image_with_studio_mode

edge_impulse_linux.image.get_features_from_image_with_studio_mode(
		img,
		mode,
		output_width,
		output_height,
		is_grayscale
)

Extract features from an image using different resizing modes suitable for Edge Impulse Studio.

Parameters

• img

• mode

• output_width

• output_height

• is_grayscale

resize_with_letterbox

edge_impulse_linux.image.resize_with_letterbox(
		image,
		target_width,
		target_height
)

Resize an image while maintaining aspect ratio using letterboxing.

Parameters

• image

• target_width

• target_height