diff --git a/doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/py_2d_histogram.markdown b/doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/py_2d_histogram.markdown index d7c0d4649c..d0f650e958 100644 --- a/doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/py_2d_histogram.markdown +++ b/doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/py_2d_histogram.markdown @@ -16,8 +16,7 @@ intensity value of the pixel. But in two-dimensional histograms, you consider tw it is used for finding color histograms where two features are Hue & Saturation values of every pixel. -There is a [python sample in the official -samples](https://github.com/Itseez/opencv/blob/master/samples/python2/color_histogram.py) already +There is a python sample (samples/python/color_histogram.py) already for finding color histograms. We will try to understand how to create such a color histogram, and it will be useful in understanding further topics like Histogram Back-Projection. @@ -106,10 +105,11 @@ You can verify it with any image editing tools like GIMP. ### Method 3 : OpenCV sample style !! -There is a [sample code for color-histogram in OpenCV-Python2 -samples](https://github.com/Itseez/opencv/blob/master/samples/python2/color_histogram.py). If you -run the code, you can see the histogram shows the corresponding color also. Or simply it outputs a -color coded histogram. Its result is very good (although you need to add extra bunch of lines). +There is a sample code for color-histogram in OpenCV-Python2 samples +(samples/python/color_histogram.py). +If you run the code, you can see the histogram shows the corresponding color also. +Or simply it outputs a color coded histogram. +Its result is very good (although you need to add extra bunch of lines). In that code, the author created a color map in HSV. Then converted it into BGR. The resulting histogram image is multiplied with this color map. He also uses some preprocessing steps to remove diff --git a/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown b/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown index 59c8d69586..540b0906ec 100644 --- a/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown +++ b/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown @@ -155,8 +155,8 @@ should be due to the sky) Well, here you adjust the values of histograms along with its bin values to look like x,y coordinates so that you can draw it using cv2.line() or cv2.polyline() function to generate same -image as above. This is already available with OpenCV-Python2 official samples. [Check the -Code](https://github.com/Itseez/opencv/raw/master/samples/python2/hist.py) +image as above. This is already available with OpenCV-Python2 official samples. Check the +code at samples/python/hist.py. Application of Mask ------------------- diff --git a/doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown b/doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown index ced85a7123..195afdf5a1 100644 --- a/doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown +++ b/doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown @@ -13,7 +13,7 @@ OCR of Hand-written Digits Our goal is to build an application which can read the handwritten digits. For this we need some train_data and test_data. OpenCV comes with an image digits.png (in the folder -opencv/samples/python2/data/) which has 5000 handwritten digits (500 for each digit). Each digit is +opencv/samples/data/) which has 5000 handwritten digits (500 for each digit). Each digit is a 20x20 image. So our first step is to split this image into 5000 different digits. For each digit, we flatten it into a single row with 400 pixels. That is our feature set, ie intensity values of all pixels. It is the simplest feature set we can create. We use first 250 samples of each digit as diff --git a/doc/py_tutorials/py_photo/py_inpainting/py_inpainting.markdown b/doc/py_tutorials/py_photo/py_inpainting/py_inpainting.markdown index 8dbfee0213..2612e031e5 100644 --- a/doc/py_tutorials/py_photo/py_inpainting/py_inpainting.markdown +++ b/doc/py_tutorials/py_photo/py_inpainting/py_inpainting.markdown @@ -81,7 +81,7 @@ Additional Resources Exercises --------- --# OpenCV comes with an interactive sample on inpainting, samples/python2/inpaint.py, try it. +-# OpenCV comes with an interactive sample on inpainting, samples/python/inpaint.py, try it. 2. A few months ago, I watched a video on [Content-Aware Fill](http://www.youtube.com/watch?v=ZtoUiplKa2A), an advanced inpainting technique used in Adobe Photoshop. On further search, I was able to find that same technique is already there in diff --git a/doc/py_tutorials/py_video/py_lucas_kanade/py_lucas_kanade.markdown b/doc/py_tutorials/py_video/py_lucas_kanade/py_lucas_kanade.markdown index 2c21394069..2c8861710e 100644 --- a/doc/py_tutorials/py_video/py_lucas_kanade/py_lucas_kanade.markdown +++ b/doc/py_tutorials/py_video/py_lucas_kanade/py_lucas_kanade.markdown @@ -156,7 +156,7 @@ in image, there is a chance that optical flow finds the next point which may loo actually for a robust tracking, corner points should be detected in particular intervals. OpenCV samples comes up with such a sample which finds the feature points at every 5 frames. It also run a backward-check of the optical flow points got to select only good ones. Check -samples/python2/lk_track.py). +samples/python/lk_track.py). See the results we got: @@ -213,7 +213,7 @@ See the result below: ![image](images/opticalfb.jpg) OpenCV comes with a more advanced sample on dense optical flow, please see -samples/python2/opt_flow.py. +samples/python/opt_flow.py. Additional Resources -------------------- @@ -221,5 +221,5 @@ Additional Resources Exercises --------- --# Check the code in samples/python2/lk_track.py. Try to understand the code. -2. Check the code in samples/python2/opt_flow.py. Try to understand the code. +-# Check the code in samples/python/lk_track.py. Try to understand the code. +2. Check the code in samples/python/opt_flow.py. Try to understand the code. diff --git a/modules/calib3d/include/opencv2/calib3d.hpp b/modules/calib3d/include/opencv2/calib3d.hpp index 3f5d02050b..ddffffecd0 100644 --- a/modules/calib3d/include/opencv2/calib3d.hpp +++ b/modules/calib3d/include/opencv2/calib3d.hpp @@ -175,7 +175,7 @@ pattern (every view is described by several 3D-2D point correspondences). - A calibration example on stereo matching can be found at opencv_source_code/samples/cpp/stereo_match.cpp - (Python) A camera calibration sample can be found at - opencv_source_code/samples/python2/calibrate.py + opencv_source_code/samples/python/calibrate.py @{ @defgroup calib3d_fisheye Fisheye camera model @@ -553,7 +553,7 @@ projections, as well as the camera matrix and the distortion coefficients. @note - An example of how to use solvePnP for planar augmented reality can be found at - opencv_source_code/samples/python2/plane_ar.py + opencv_source_code/samples/python/plane_ar.py - If you are using Python: - Numpy array slices won't work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of @@ -1674,7 +1674,7 @@ check, quadratic interpolation and speckle filtering). @note - (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found - at opencv_source_code/samples/python2/stereo_match.py + at opencv_source_code/samples/python/stereo_match.py */ class CV_EXPORTS_W StereoSGBM : public StereoMatcher { diff --git a/modules/core/include/opencv2/core.hpp b/modules/core/include/opencv2/core.hpp index c41868b3bd..559226079b 100644 --- a/modules/core/include/opencv2/core.hpp +++ b/modules/core/include/opencv2/core.hpp @@ -2035,9 +2035,9 @@ so you need to "flip" the second convolution operand B vertically and horizontal - An example using the discrete fourier transform can be found at opencv_source_code/samples/cpp/dft.cpp - (Python) An example using the dft functionality to perform Wiener deconvolution can be found - at opencv_source/samples/python2/deconvolution.py + at opencv_source/samples/python/deconvolution.py - (Python) An example rearranging the quadrants of a Fourier image can be found at - opencv_source/samples/python2/dft.py + opencv_source/samples/python/dft.py @param src input array that could be real or complex. @param dst output array whose size and type depends on the flags . @param flags transformation flags, representing a combination of the cv::DftFlags @@ -2848,7 +2848,7 @@ and groups the input samples around the clusters. As an output, \f$\texttt{label @note - (Python) An example on K-means clustering can be found at - opencv_source_code/samples/python2/kmeans.py + opencv_source_code/samples/python/kmeans.py @param data Data for clustering. An array of N-Dimensional points with float coordinates is needed. Examples of this array can be: - Mat points(count, 2, CV_32F); diff --git a/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp b/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp index 8f3745cfc2..f29ed53677 100644 --- a/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp +++ b/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp @@ -73,7 +73,7 @@ namespace cv { namespace cuda { - A CUDA example applying the HOG descriptor for people detection can be found at opencv_source_code/samples/gpu/hog.cpp - (Python) An example applying the HOG descriptor for people detection can be found at - opencv_source_code/samples/python2/peopledetect.py + opencv_source_code/samples/python/peopledetect.py */ class CV_EXPORTS HOG : public Algorithm { diff --git a/modules/features2d/include/opencv2/features2d.hpp b/modules/features2d/include/opencv2/features2d.hpp index d1e05b4fd2..692d3d9fd9 100644 --- a/modules/features2d/include/opencv2/features2d.hpp +++ b/modules/features2d/include/opencv2/features2d.hpp @@ -74,7 +74,7 @@ This section describes approaches based on local 2D features and used to categor - A complete Bag-Of-Words sample can be found at opencv_source_code/samples/cpp/bagofwords_classification.cpp - (Python) An example using the features2D framework to perform object categorization can be - found at opencv_source_code/samples/python2/find_obj.py + found at opencv_source_code/samples/python/find_obj.py @} */ @@ -331,7 +331,7 @@ than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz lap than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source code which is distributed under GPL. -- (Python) A complete example showing the use of the %MSER detector can be found at samples/python2/mser.py +- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py */ class CV_EXPORTS_W MSER : public Feature2D { diff --git a/modules/objdetect/include/opencv2/objdetect.hpp b/modules/objdetect/include/opencv2/objdetect.hpp index bd932e6e6f..6587b3d77a 100644 --- a/modules/objdetect/include/opencv2/objdetect.hpp +++ b/modules/objdetect/include/opencv2/objdetect.hpp @@ -261,7 +261,7 @@ public: @note - (Python) A face detection example using cascade classifiers can be found at - opencv_source_code/samples/python2/facedetect.py + opencv_source_code/samples/python/facedetect.py */ CV_WRAP void detectMultiScale( InputArray image, CV_OUT std::vector& objects, diff --git a/modules/photo/include/opencv2/photo.hpp b/modules/photo/include/opencv2/photo.hpp index 3d969124e7..c093f65521 100644 --- a/modules/photo/include/opencv2/photo.hpp +++ b/modules/photo/include/opencv2/photo.hpp @@ -107,7 +107,7 @@ objects from still images or video. See