Improved classification sample #25519
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sync 5.x
gursimarsingh
added
category: samples
cleanup
vpisarev
reviewed
May 1, 2024
vpisarev
reviewed
May 1, 2024
6 tasks
vpisarev
reviewed
May 15, 2024
WanliZhong
reviewed
May 15, 2024
asmorkalov
reviewed
Jun 4, 2024
samples/dnn/classification.cpp
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| "{ backend | default | Choose one of computation backends: " | ||
| "default: automatically (by default), " | ||
| "inference_engine: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), " | ||
| "openvino: OpenCV implementation, " |
| if (parser.has("input")) { | ||
| string input = parser.get<String>("input"); | ||
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|
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| if (input.find('.')==string::npos) { |
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It may be network stream. I propose always use VideoCapture. It supports sequence of images too.
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@asmorkalov
VideoCapture does support sequences of images, but it expects them to be in a numerically ordered format (e.g., image1.jpg, image2.jpg). It cannot handle images with unordered prefixes (e.g., ab.jpg, bc.jpg, xyz.jpg). For cases where images are not named in a numerical sequence, this was implemented to read and process these images from a directory.
samples/dnn/common.hpp
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| {"default", 0}, | ||
| {"inference_engine", 1}, | ||
| {"openvino", 2}, | ||
| {"vkcom", 3}, | ||
| {"cuda", 4}, | ||
| {"webnn", 5} |
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Why not OpenCV constants?
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#25006 #25314
This pull requests replaces the caffe model for classification with onnx versions. It also adds resnet in model.yml.
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Patch to opencv_extra has the same branch name.