 | 1 | initial version |
In the dnn openpose.cpp using the network definition from CMU at prototxt link the input layer shape is given as [1,3,368,368]. Does it mean that the network is expecting a square image as an input? What happens if an image is given in its original aspect ratio? The current implementation of the openpose library defines the input layer shape at runtime: prototxt link, is it possible to do something similar with opencv?
In the dnn openpose.cpp using the network definition from CMU at prototxt link the input layer shape is given as [1,3,368,368].
Does it mean that the network is expecting a square image as an input? What happens if an image is given in its original aspect ratio?
The current implementation of the openpose library defines the input layer shape at runtime: prototxt link, is it possible to do something similar with opencv?
| | 3 | retagged |
In the dnn openpose.cpp using the network definition from CMU at prototxt link the input layer shape is given as [1,3,368,368].
Does it mean that the network is expecting a square image as an input? What happens if an image is given in its original aspect ratio?
The current implementation of the openpose library defines the input layer shape at runtime: prototxt link, is it possible to do something similar with opencv?
In the dnn openpose.cpp dnn/openpose.cpp using the network definition from CMU at prototxt link the input layer shape is given as [1,3,368,368].
Does it mean that the network is expecting a square image as an input? What happens if an image is given in its original aspect ratio?
The current implementation of the openpose library defines the input layer shape at runtime: prototxt link, is it possible to do something similar with opencv?
