Jpeg2000 OpenJPEG port
* OpenJPEG based JPEG2000 decoder implementation
Currently, the following input color spaces and depth conversions are
supported:
- 8 bit -> 8 bit
- 16 bit -> 16 bit (IMREAD_UNCHANGED, IMREAD_ANYDEPTH)
- RGB(a) -> BGR
- RGBA -> BGRA (IMREAD_UNCHANGED)
- Y(a) -> Y(a) (IMREAD_ANYCOLOR, IMREAD_GRAY, IMREAD_UNCHANGED))
- YCC -> Y (IMREAD_GRAY)
* Check for OpenJPEG availability
This enables OpenJPEG based JPEG2000 imread support by default, which
can be disabled by -DWITH_OPENJPEG=OFF. In case OpenJPEG is enabled
and found, any checks for Jasper are skipped.
* Implement precision downscaling for precision > 8 without IMREAD_UNCHANGED
With IMREAD_UNCHANGED, values are kept from the input image, without it
components are downscaled to CV_8U range.
* Enable Jpeg2K tests when OpenJPEG is available
* Add support for some more color conversions
Support IMREAD_GRAY when input color space is RGB or unspecified.
Support YUV input color space for BGR output.
* fix: problems with unmanaged memory
* fix: CMake warning - HAVE_OPENJPEG is undefined
Removed trailing whitespaces
* fix: CMake find_package OpenJPEG add minimal version
* Basic JPEG2K encoder
Images with depth CV_8U and CV_16U are supported, with 1 to 4 channels.
* feature: Improved code for OpenJPEG2000 encoder/decoder
- Removed code duplication
- Added error handlers
- Extracted functions
* feature: Update conversion openjpeg array from/to Mat
* feature: Extend ChannelsIterator to fulfill RandomAccessIterator named requirements
- Removed channels split in copyFromMatImpl. With ChannelsIterator no allocations are performed.
- Split whole loop into 2 parts in copyToMat -> where std::copy and std::transforms are called.
* fix: Applied review comments.
- Changed `nullptr` in CV_LOG* functions to `NULL`
- Added `falls through` comment in decoder color space `switch`
- Added warning about unsupported parameters for the encoder
* feature: Added decode from in-memory buffers.
Co-authored-by: Vadim Levin <vadim.levin@xperience.ai>
the float variant was always shadowed by the int version as
Rect2d is implicitly convertible to Rect.
This swaps things which is fine, as the vector of boxes was always
copied and the computation was done in double.
* feature: Add video capture bitrate read-only property for FFMPEG backend
* test: For WIN32 property should be either expected or 0.
Added `IsOneOf` helper function, enabled only for _WIN32.
dnn(darknet-importer): add grouped convolutions, sigmoid, swish, scale_channels
* update darknet importer to support enetb0-yolo
* remove dropout (pr16438) and fix formatting
* add test for scale_channels
* disable batch testing for scale channels
* do not set LayerParams::name
* merge all activations into setActivation
* Add Tengine support .
* Modify printf to CV_LOG_WARNING
* a few minor fixes in the code
* Renew Tengine version
* Add header file for CV_LOG_WARNING
* Add #ifdef HAVE_TENGINE in tengine_graph_convolution.cpp
* remove trailing whitespace
* Remove trailing whitespace
* Modify for compile problem
* Modify some code style error
* remove whitespace
* Move some code style problem
* test
* add ios limit and build problem
* Modified as alalek suggested
* Add cmake 2.8 support
* modify cmake 3.5.1 problem
* test and set BUILD_ANDROID_PROJECTS OFF
* remove some compile error
* remove some extra code in tengine
* close test.
* Test again
* disable android.
* delete ndk version judgement
* Remove setenv() call . and add License information
* Set tengine default OFF. Close test .
Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>
Image sharpness, as well as brightness, are a critical parameter for
accuracte camera calibration. For accessing these parameters for
filtering out problematic calibraiton images, this method calculates
edge profiles by traveling from black to white chessboard cell centers.
Based on this, the number of pixels is calculated required to transit
from black to white. This width of the transition area is a good
indication of how sharp the chessboard is imaged and should be below
~3.0 pixels.
Based on this also motion blur can be detectd by comparing sharpness in
vertical and horizontal direction. All unsharp images should be excluded
from calibration as they will corrupt the calibration result. The same
is true for overexposued images due to a none-linear sensor response.
This can be detected by looking at the average cell brightness of the
detected chessboard.
