npu.lib.applications package#
Submodules#
npu.lib.applications.videoapps module#
- class npu.lib.applications.videoapps.ColorDetectVideoProcessing(videosource=0)#
Bases:
VideoApplication
Color Detect Video processing
- rtps = {'thresholdValue1l': {'max': 255, 'min': 0, 'name': 'Hue range 0', 'rangehigh': 'thresholdValue1u', 'type': 'hueslider'}, 'thresholdValue2l': {'max': 255, 'min': 0, 'name': 'Hue range 1', 'rangehigh': 'thresholdValue2u', 'type': 'hueslider'}}#
- start()#
Start the video processing
- class npu.lib.applications.videoapps.ColorThresholdVideoProcessing(videosource=0)#
Bases:
VideoApplication
Color Threshold Video processing
- rtps = {'thresholdType': {'name': 'Type', 'options': [('BINARY', [0, 0]), ('BINARY_INV', [1, 0]), ('TRUNC', [2, 0]), ('TOZERO', [3, 0]), ('TOZERO_INV', [4, 0])], 'pair': 'thresholdValue4', 'type': 'dropdownpair'}, 'thresholdValue1': {'max': 255, 'min': 0, 'name': 'Red', 'type': 'slider'}, 'thresholdValue2': {'max': 255, 'min': 0, 'name': 'Green', 'type': 'slider'}, 'thresholdValue3': {'max': 255, 'min': 0, 'name': 'Blue', 'type': 'slider'}}#
- class npu.lib.applications.videoapps.DenoiseDPVideoProcessing(videosource=0)#
Bases:
VideoApplication
Denoising Data Parallel Video processing
- class npu.lib.applications.videoapps.DenoiseTPVideoProcessing(videosource=0)#
Bases:
VideoApplication
Denoising Task Parallel Video processing
- class npu.lib.applications.videoapps.EdgeDetectVideoProcessing(videosource=0)#
Bases:
VideoApplication
Edge Detect Video processing
- rtps = {'alpha': {'max': 16384, 'min': 0, 'name': 'edges', 'type': 'slider'}, 'beta': {'max': 16384, 'min': 0, 'name': 'color overlaid', 'type': 'slider'}, 'thresholdValue': {'max': 255, 'min': 0, 'name': 'threshold', 'type': 'slider'}}#
- start()#
Start the video processing
- class npu.lib.applications.videoapps.ScaledColorThresholdVideoProcessing(videosource=0)#
Bases:
VideoApplication
Color Threshold Video processing
- rtps = {'thresholdType': {'name': 'Type', 'options': [('BINARY', [0, 0]), ('BINARY_INV', [1, 0]), ('TRUNC', [2, 0]), ('TOZERO', [3, 0]), ('TOZERO_INV', [4, 0])], 'pair': 'thresholdValue4', 'type': 'dropdownpair'}, 'thresholdValue1': {'max': 255, 'min': 0, 'name': 'Red', 'type': 'slider'}, 'thresholdValue2': {'max': 255, 'min': 0, 'name': 'Green', 'type': 'slider'}, 'thresholdValue3': {'max': 255, 'min': 0, 'name': 'Blue', 'type': 'slider'}}#
- class npu.lib.applications.videoapps.VideoApplication(filename, videosource=0, pxtype_in=pxtype.RGBA, pxtype_out=pxtype.RGBA)#
Bases:
object
Wrapper class that allows to feed and visualize video stream from the NPU
You must pass an xclbin and optionally the pixel type for the input and output images.
- Parameters:
- Returns:
Object that abstracts away the video handling from a webcam or video file to visualization
- Return type:
- cam_h = None#
- cam_w = None#
- property resolution#
Webcam video feed resolution
- rtps = {}#
- start()#
Start the video processing
- stop()#
Stop the video processing
- property videores#
Video feed resolution to/from the NPU
Module contents#
Applications#
Prebuilt applications can be run as-is with no additional build steps required. The video applications include a variety of computer vision pipelines like color threshold or edge detect.
You can list the available videoapps with
from npu.utils import videoapps videoapps()
Example running edge detect
from npu.lib import EdgeDetectVideoProcessing
app = EdgeDetectVideoProcessing() app.start()