EdgeDetector
Edge detector uses Sobel filter to create an image that emphasises edges.How to place it
Python
C++
Python
1pipeline = dai.Pipeline()
2edgeDetector = pipeline.create(dai.node.EdgeDetector)Inputs and Outputs
Command Line
1/
2 ┌───────────────────┐
3 inputImage │ │
4 ──────────────►│ │
5 │ │ outputImage
6 │ EdgeDetector ├───────────►
7 inputConfig │ │
8 ──────────────►│ │
9 │ │
10 └───────────────────┘inputImage- ImgFrameinputConfig- EdgeDetectorConfigoutputImage- ImgFrame
Usage
Python
C++
Python
1pipeline = dai.Pipeline()
2edgeDetector = pipeline.create(dai.node.EdgeDetector)
3
4sobelHorizontalKernel = [[1, 0, -1], [2, 0, -2], [1, 0, -1]]
5sobelVerticalKernel = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]
6edgeDetector.initialConfig.setSobelFilterKernels(sobelHorizontalKernel, sobelVerticalKernel)Examples of functionality
Reference
class
depthai.node.EdgeDetector(depthai.Node)
method
getWaitForConfigInput(self) -> bool: boolSee also:
setWaitForConfigInput
Returns:
True if wait for inputConfig message, false otherwisemethod
setMaxOutputFrameSize(self, arg0: int)Specify maximum size of output image.
Parameter ``maxFrameSize``:
Maximum frame size in bytesmethod
setNumFramesPool(self, arg0: int)Specify number of frames in pool.
Parameter ``numFramesPool``:
How many frames should the pool havemethod
setWaitForConfigInput(self, wait: bool)Specify whether or not wait until configuration message arrives to inputConfig
Input.
Parameter ``wait``:
True to wait for configuration message, false otherwise.property
initialConfig
Initial config to use for edge detection.
property
inputConfig
Input EdgeDetectorConfig message with ability to modify parameters in runtime. Default queue is non-blocking with size 4.
property
inputImage
Input image on which edge detection is performed. Default queue is non-blocking with size 4.
property
outputImage
Outputs image frame with detected edges
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