[GSoC] High Level API and Samples for Scene Text Detection and Recognition
* APIs and samples for scene text detection and recognition
* update APIs and tutorial for Text Detection and Recognition
* API updates:
(1) put decodeType into struct Voc
(2) optimize the post-processing of DB
* sample update:
(1) add transformation into scene_text_spotting.cpp
(2) modify text_detection.cpp with API update
* update tutorial
* simplify text recognition API
update tutorial
* update impl usage in recognize() and detect()
* dnn: refactoring public API of TextRecognitionModel/TextDetectionModel
* update provided models
update opencv.bib
* dnn: adjust text rectangle angle
* remove points ordering operation in model.cpp
* update gts of DB test in test_model.cpp
* dnn: ensure to keep text rectangle angle
- avoid 90/180 degree turns
* dnn(text): use quadrangle result in TextDetectionModel API
* dnn: update Text Detection API
(1) keep points' order consistent with (bl, tl, tr, br) in unclip
(2) update contourScore with boundingRect
Add option for NMS for boxes with different labels
* DetectionModel impl
* Add option for NMS for boxes with different labels
In the detect function in modules/dnn/include/opencv2/dnn/dnn.hpp, whose implementation can be found at modules/dnn/src/model.cpp, the Non Max Suppression (NMS) is applied only for objects of the same label. Thus, a flag
was added with the purpose to allow developers to choose if they want to keep the default implementation or wether they would like NMS to be applied to all the boxes, regardless of label.
The flag is called nmsDifferentLabels, and is given a default value of false, which applies the current default implementation, thus allowing existing projects to update opencv without disruption
Solves issue opencv#18832
* Change return type of set & Add default constr
* Add assertions due to default constructor
Support for Pool1d layer for OpenCV and OpenCL targets
* Initial version of Pool1d support
* Fix variable naming
* Fix 1d pooling for OpenCL
* Change support logic, remove unnecessary variable, split the tests
* Remove other depricated variables
* Fix warning. Check tests
* Change support check logic
* Change support check logic, 2
Fixing dnn Resize layer for variable input size
* Fix onnx loading of resize/upsample layers for different opset
* group all DynamicResize tests
* cleaned up scales checks
* Simplify branching
Fix loading issue for Faster RCNN model from #16783
* Add a reproducer with multi-output Gather
* Fix an issue with ONNX graph simplifier
* fix build
* Move checks to correct class
* Minor changes for better code appearence
Add support for Conv1D on OpenCV backend
* Add support for Conv1D on OpenCV backend
* disable tests on other targets/backends
* Fix formatting
* Restore comment
* Remove unnecessary flag and fix test logic
* Fix perf test
* fix braces
* Fix indentation, assert check and remove unnecessary condition
* Remove unnecessary changes
* Add test cases for variable weights and bias
* dnn(conv): fallback on OpenCV+CPU instead of failures
* coding style
Fix loading of ONNX models with Resize operation with Opset 11 for newer versions of Pytorch
* Add reproducer for Resize operation from newer versions of Pytorch
* Fix loading of scales parameter for Resize layer
* Change check type for better diagnostic messages
* Fix ONNX loading in issues opencv#17516, opencv#17531
* Add tests for Linear and Matmul layers
* Disable tests for IE versions lower than 20.4
* Skip unstable tests with OpenCL FP16 on Intel GPU
* Add correct test filtering for OpenCL FP16 tests
revise default proto to match the filename in documentations
fix a bug
beautify python codes
fix bug
beautify codes
add test samples with larger/smaller size
remove unless code
using bytearray without creating tmp file
remove useless codes
add relu option
add relu as activation option in darknet
simplify the setParams if-else ladder
add relu as activation option in darknet
correct activation_param type
format
format
add relu as activation option in darknet
spacing
spacing
add relu as activation option in darknet