diff --git a/doc/js_tutorials/js_core/js_basic_ops/js_basic_ops.markdown b/doc/js_tutorials/js_core/js_basic_ops/js_basic_ops.markdown index ee110c37cb..19b7cc6157 100644 --- a/doc/js_tutorials/js_core/js_basic_ops/js_basic_ops.markdown +++ b/doc/js_tutorials/js_core/js_basic_ops/js_basic_ops.markdown @@ -105,7 +105,7 @@ let mat = new cv.Mat(); let matVec = new cv.MatVector(); // Push a Mat back into MatVector matVec.push_back(mat); -// Get a Mat fom MatVector +// Get a Mat from MatVector let cnt = matVec.get(0); mat.delete(); matVec.delete(); cnt.delete(); @endcode diff --git a/doc/js_tutorials/js_imgproc/js_morphological_ops/js_morphological_ops.markdown b/doc/js_tutorials/js_imgproc/js_morphological_ops/js_morphological_ops.markdown index 09cad1003c..5564c8a6e9 100644 --- a/doc/js_tutorials/js_imgproc/js_morphological_ops/js_morphological_ops.markdown +++ b/doc/js_tutorials/js_imgproc/js_morphological_ops/js_morphological_ops.markdown @@ -27,7 +27,7 @@ foreground object (Always try to keep foreground in white). So what it does? The through the image (as in 2D convolution). A pixel in the original image (either 1 or 0) will be considered 1 only if all the pixels under the kernel is 1, otherwise it is eroded (made to zero). -So what happends is that, all the pixels near boundary will be discarded depending upon the size of +So what happens is that, all the pixels near boundary will be discarded depending upon the size of kernel. So the thickness or size of the foreground object decreases or simply white region decreases in the image. It is useful for removing small white noises (as we have seen in colorspace chapter), detach two connected objects etc. @@ -174,4 +174,4 @@ Try it -\endhtmlonly \ No newline at end of file +\endhtmlonly diff --git a/doc/tutorials/app/highgui_wayland_ubuntu.markdown b/doc/tutorials/app/highgui_wayland_ubuntu.markdown index 2b8020ad19..c1aa7d19c5 100644 --- a/doc/tutorials/app/highgui_wayland_ubuntu.markdown +++ b/doc/tutorials/app/highgui_wayland_ubuntu.markdown @@ -103,4 +103,4 @@ int main(void) Limitation/Known problem ------------------------ -- cv::moveWindow() is not implementated. ( See. https://github.com/opencv/opencv/issues/25478 ) +- cv::moveWindow() is not implemented. ( See. https://github.com/opencv/opencv/issues/25478 ) diff --git a/doc/tutorials/app/orbbec_astra_openni.markdown b/doc/tutorials/app/orbbec_astra_openni.markdown index ed828956e1..57d2326b98 100644 --- a/doc/tutorials/app/orbbec_astra_openni.markdown +++ b/doc/tutorials/app/orbbec_astra_openni.markdown @@ -72,8 +72,8 @@ In order to use the Astra camera's depth sensor with OpenCV you should do the fo @note The last tried version `2.3.0.86_202210111154_4c8f5aa4_beta6` does not work correctly with modern Linux, even after libusb rebuild as recommended by the instruction. The last know good - configuration is version 2.3.0.63 (tested with Ubuntu 18.04 amd64). It's not provided officialy - with the downloading page, but published by Orbbec technical suport on Orbbec community forum + configuration is version 2.3.0.63 (tested with Ubuntu 18.04 amd64). It's not provided officially + with the downloading page, but published by Orbbec technical support on Orbbec community forum [here](https://3dclub.orbbec3d.com/t/universal-download-thread-for-astra-series-cameras/622). -# Now you can configure OpenCV with OpenNI support enabled by setting the `WITH_OPENNI2` flag in CMake. diff --git a/doc/tutorials/calib3d/camera_calibration_pattern/camera_calibration_pattern.markdown b/doc/tutorials/calib3d/camera_calibration_pattern/camera_calibration_pattern.markdown index 5e8063c361..b5729fc2c2 100644 --- a/doc/tutorials/calib3d/camera_calibration_pattern/camera_calibration_pattern.markdown +++ b/doc/tutorials/calib3d/camera_calibration_pattern/camera_calibration_pattern.markdown @@ -63,7 +63,7 @@ Example code to generate features coordinates for calibration with symmetric gri } } ``` -Example code to generate features corrdinates for calibration with asymmetic grid (object points): +Example code to generate features coordinates for calibration with asymmetric grid (object points): ``` std::vector objectPoints; for (int i = 0; i < boardSize.height; i++) { @@ -84,7 +84,7 @@ about ArUco pairs. In opposite to the previous pattern partially occluded board corners are labeled. The board is rotation invariant, but set of ArUco markers and their order should be known to detector apriori. It cannot detect ChAruco board with predefined size and random set of markers. -Example code to generate features corrdinates for calibration (object points) for board size in units: +Example code to generate features coordinates for calibration (object points) for board size in units: ``` std::vector objectPoints; for (int i = 0; i < boardSize.height-1; ++i) { diff --git a/doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown b/doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown index b675c1fd29..852d7c4181 100644 --- a/doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown +++ b/doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown @@ -64,25 +64,25 @@ We encourage you to add new algorithms to these APIs. ``` crnn.onnx: -url: https://drive.google.com/uc?export=dowload&id=1ooaLR-rkTl8jdpGy1DoQs0-X0lQsB6Fj +url: https://drive.google.com/uc?export=download&id=1ooaLR-rkTl8jdpGy1DoQs0-X0lQsB6Fj sha: 270d92c9ccb670ada2459a25977e8deeaf8380d3, -alphabet_36.txt: https://drive.google.com/uc?export=dowload&id=1oPOYx5rQRp8L6XQciUwmwhMCfX0KyO4b +alphabet_36.txt: https://drive.google.com/uc?export=download&id=1oPOYx5rQRp8L6XQciUwmwhMCfX0KyO4b parameter setting: -rgb=0; description: The classification number of this model is 36 (0~9 + a~z). The training dataset is MJSynth. crnn_cs.onnx: -url: https://drive.google.com/uc?export=dowload&id=12diBsVJrS9ZEl6BNUiRp9s0xPALBS7kt +url: https://drive.google.com/uc?export=download&id=12diBsVJrS9ZEl6BNUiRp9s0xPALBS7kt sha: a641e9c57a5147546f7a2dbea4fd322b47197cd5 -alphabet_94.txt: https://drive.google.com/uc?export=dowload&id=1oKXxXKusquimp7XY1mFvj9nwLzldVgBR +alphabet_94.txt: https://drive.google.com/uc?export=download&id=1oKXxXKusquimp7XY1mFvj9nwLzldVgBR parameter setting: -rgb=1; description: The classification number of this model is 94 (0~9 + a~z + A~Z + punctuations). The training datasets are MJsynth and SynthText. crnn_cs_CN.onnx: -url: https://drive.google.com/uc?export=dowload&id=1is4eYEUKH7HR7Gl37Sw4WPXx6Ir8oQEG +url: https://drive.google.com/uc?export=download&id=1is4eYEUKH7HR7Gl37Sw4WPXx6Ir8oQEG sha: 3940942b85761c7f240494cf662dcbf05dc00d14 -alphabet_3944.txt: https://drive.google.com/uc?export=dowload&id=18IZUUdNzJ44heWTndDO6NNfIpJMmN-ul +alphabet_3944.txt: https://drive.google.com/uc?export=download&id=18IZUUdNzJ44heWTndDO6NNfIpJMmN-ul parameter setting: -rgb=1; description: The classification number of this model is 3944 (0~9 + a~z + A~Z + Chinese characters + special characters). The training dataset is ReCTS (https://rrc.cvc.uab.es/?ch=12). @@ -96,25 +96,25 @@ You can train more models by [CRNN](https://github.com/meijieru/crnn.pytorch), a ``` - DB_IC15_resnet50.onnx: -url: https://drive.google.com/uc?export=dowload&id=17_ABp79PlFt9yPCxSaarVc_DKTmrSGGf +url: https://drive.google.com/uc?export=download&id=17_ABp79PlFt9yPCxSaarVc_DKTmrSGGf sha: bef233c28947ef6ec8c663d20a2b326302421fa3 recommended parameter setting: -inputHeight=736, -inputWidth=1280; description: This model is trained on ICDAR2015, so it can only detect English text instances. - DB_IC15_resnet18.onnx: -url: https://drive.google.com/uc?export=dowload&id=1vY_KsDZZZb_svd5RT6pjyI8BS1nPbBSX +url: https://drive.google.com/uc?export=download&id=1vY_KsDZZZb_svd5RT6pjyI8BS1nPbBSX sha: 19543ce09b2efd35f49705c235cc46d0e22df30b recommended parameter setting: -inputHeight=736, -inputWidth=1280; description: This model is trained on ICDAR2015, so it can only detect English text instances. - DB_TD500_resnet50.onnx: -url: https://drive.google.com/uc?export=dowload&id=19YWhArrNccaoSza0CfkXlA8im4-lAGsR +url: https://drive.google.com/uc?export=download&id=19YWhArrNccaoSza0CfkXlA8im4-lAGsR sha: 1b4dd21a6baa5e3523156776970895bd3db6960a recommended parameter setting: -inputHeight=736, -inputWidth=736; description: This model is trained on MSRA-TD500, so it can detect both English and Chinese text instances. - DB_TD500_resnet18.onnx: -url: https://drive.google.com/uc?export=dowload&id=1sZszH3pEt8hliyBlTmB-iulxHP1dCQWV +url: https://drive.google.com/uc?export=download&id=1sZszH3pEt8hliyBlTmB-iulxHP1dCQWV sha: 8a3700bdc13e00336a815fc7afff5dcc1ce08546 recommended parameter setting: -inputHeight=736, -inputWidth=736; description: This model is trained on MSRA-TD500, so it can detect both English and Chinese text instances. @@ -133,11 +133,11 @@ This model is based on https://github.com/argman/EAST ``` Text Recognition: -url: https://drive.google.com/uc?export=dowload&id=1nMcEy68zDNpIlqAn6xCk_kYcUTIeSOtN +url: https://drive.google.com/uc?export=download&id=1nMcEy68zDNpIlqAn6xCk_kYcUTIeSOtN sha: 89205612ce8dd2251effa16609342b69bff67ca3 Text Detection: -url: https://drive.google.com/uc?export=dowload&id=149tAhIcvfCYeyufRoZ9tmc2mZDKE_XrF +url: https://drive.google.com/uc?export=download&id=149tAhIcvfCYeyufRoZ9tmc2mZDKE_XrF sha: ced3c03fb7f8d9608169a913acf7e7b93e07109b ``` diff --git a/doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown b/doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown index 9df97d7370..3d7df26988 100644 --- a/doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown +++ b/doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown @@ -129,7 +129,7 @@ than YOLOX) in case it is needed. However, usually each YOLO repository has pred #### Exporting YOLOv10 model -In oder to run YOLOv10 one needs to cut off postporcessing with dynamic shapes from torch and then convert it to ONNX. If someone is looking for on how to cut off the postprocessing, there is this [forked branch](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. The forked branch cuts of the postprocessing by [returning output](https://github.com/Abdurrahheem/yolov10/blob/4fdaafd912c8891642bfbe85751ea66ec20f05ad/ultralytics/nn/modules/head.py#L522) of the model before postprocessing procedure itself. To convert torch model to ONNX follow this proceduce. +In order to run YOLOv10 one needs to cut off postprocessing with dynamic shapes from torch and then convert it to ONNX. If someone is looking for on how to cut off the postprocessing, there is this [forked branch](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. The forked branch cuts off the postprocessing by [returning output](https://github.com/Abdurrahheem/yolov10/blob/4fdaafd912c8891642bfbe85751ea66ec20f05ad/ultralytics/nn/modules/head.py#L522) of the model before postprocessing procedure itself. To convert torch model to ONNX follow this procedure. @code{.bash} git clone git@github.com:Abdurrahheem/yolov10.git diff --git a/doc/tutorials/objdetect/aruco_faq/aruco_faq.markdown b/doc/tutorials/objdetect/aruco_faq/aruco_faq.markdown index 11338369c8..886929e966 100644 --- a/doc/tutorials/objdetect/aruco_faq/aruco_faq.markdown +++ b/doc/tutorials/objdetect/aruco_faq/aruco_faq.markdown @@ -167,7 +167,7 @@ for `cv::aruco::Dictionary`. The data member of board classes are public and can To do so, you will need to use an external rendering engine library, such as OpenGL. The aruco module only provides the functionality to obtain the camera pose, i.e. the rotation and translation vectors, which is necessary to create the augmented reality effect. However, you will need to adapt the rotation -and traslation vectors from the OpenCV format to the format accepted by your 3d rendering library. +and translation vectors from the OpenCV format to the format accepted by your 3d rendering library. The original ArUco library contains examples of how to do it for OpenGL and Ogre3D. diff --git a/doc/tutorials/objdetect/charuco_diamond_detection/charuco_diamond_detection.markdown b/doc/tutorials/objdetect/charuco_diamond_detection/charuco_diamond_detection.markdown index 04ae79ded0..a962861d80 100644 --- a/doc/tutorials/objdetect/charuco_diamond_detection/charuco_diamond_detection.markdown +++ b/doc/tutorials/objdetect/charuco_diamond_detection/charuco_diamond_detection.markdown @@ -12,7 +12,7 @@ It is similar to a ChArUco board in appearance, however they are conceptually di In both, ChArUco board and Diamond markers, their detection is based on the previous detected ArUco markers. In the ChArUco case, the used markers are selected by directly looking their identifiers. This means that if a marker (included in the board) is found on a image, it will be automatically assumed to belong to the board. Furthermore, -if a marker board is found more than once in the image, it will produce an ambiguity since the system wont +if a marker board is found more than once in the image, it will produce an ambiguity since the system won't be able to know which one should be used for the Board. On the other hand, the detection of Diamond marker is not based on the identifiers. Instead, their detection