change links from samples/python2 to samples/python
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Alexander Alekhin
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@ -16,8 +16,7 @@ intensity value of the pixel. But in two-dimensional histograms, you consider tw
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it is used for finding color histograms where two features are Hue & Saturation values of every
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pixel.
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There is a [python sample in the official
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samples](https://github.com/Itseez/opencv/blob/master/samples/python2/color_histogram.py) already
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There is a python sample (samples/python/color_histogram.py) already
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for finding color histograms. We will try to understand how to create such a color histogram, and it
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will be useful in understanding further topics like Histogram Back-Projection.
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@ -106,10 +105,11 @@ You can verify it with any image editing tools like GIMP.
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### Method 3 : OpenCV sample style !!
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There is a [sample code for color-histogram in OpenCV-Python2
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samples](https://github.com/Itseez/opencv/blob/master/samples/python2/color_histogram.py). If you
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run the code, you can see the histogram shows the corresponding color also. Or simply it outputs a
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color coded histogram. Its result is very good (although you need to add extra bunch of lines).
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There is a sample code for color-histogram in OpenCV-Python2 samples
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(samples/python/color_histogram.py).
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If you run the code, you can see the histogram shows the corresponding color also.
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Or simply it outputs a color coded histogram.
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Its result is very good (although you need to add extra bunch of lines).
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In that code, the author created a color map in HSV. Then converted it into BGR. The resulting
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histogram image is multiplied with this color map. He also uses some preprocessing steps to remove
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@ -155,8 +155,8 @@ should be due to the sky)
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Well, here you adjust the values of histograms along with its bin values to look like x,y
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coordinates so that you can draw it using cv2.line() or cv2.polyline() function to generate same
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image as above. This is already available with OpenCV-Python2 official samples. [Check the
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Code](https://github.com/Itseez/opencv/raw/master/samples/python2/hist.py)
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image as above. This is already available with OpenCV-Python2 official samples. Check the
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code at samples/python/hist.py.
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Application of Mask
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-------------------
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@ -13,7 +13,7 @@ OCR of Hand-written Digits
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Our goal is to build an application which can read the handwritten digits. For this we need some
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train_data and test_data. OpenCV comes with an image digits.png (in the folder
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opencv/samples/python2/data/) which has 5000 handwritten digits (500 for each digit). Each digit is
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opencv/samples/data/) which has 5000 handwritten digits (500 for each digit). Each digit is
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a 20x20 image. So our first step is to split this image into 5000 different digits. For each digit,
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we flatten it into a single row with 400 pixels. That is our feature set, ie intensity values of all
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pixels. It is the simplest feature set we can create. We use first 250 samples of each digit as
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@ -81,7 +81,7 @@ Additional Resources
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Exercises
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---------
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-# OpenCV comes with an interactive sample on inpainting, samples/python2/inpaint.py, try it.
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-# OpenCV comes with an interactive sample on inpainting, samples/python/inpaint.py, try it.
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2. A few months ago, I watched a video on [Content-Aware
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Fill](http://www.youtube.com/watch?v=ZtoUiplKa2A), an advanced inpainting technique used in
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Adobe Photoshop. On further search, I was able to find that same technique is already there in
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@ -156,7 +156,7 @@ in image, there is a chance that optical flow finds the next point which may loo
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actually for a robust tracking, corner points should be detected in particular intervals. OpenCV
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samples comes up with such a sample which finds the feature points at every 5 frames. It also run a
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backward-check of the optical flow points got to select only good ones. Check
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samples/python2/lk_track.py).
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samples/python/lk_track.py).
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See the results we got:
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@ -213,7 +213,7 @@ See the result below:
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OpenCV comes with a more advanced sample on dense optical flow, please see
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samples/python2/opt_flow.py.
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samples/python/opt_flow.py.
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Additional Resources
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--------------------
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@ -221,5 +221,5 @@ Additional Resources
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Exercises
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---------
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-# Check the code in samples/python2/lk_track.py. Try to understand the code.
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2. Check the code in samples/python2/opt_flow.py. Try to understand the code.
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-# Check the code in samples/python/lk_track.py. Try to understand the code.
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2. Check the code in samples/python/opt_flow.py. Try to understand the code.
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@ -175,7 +175,7 @@ pattern (every view is described by several 3D-2D point correspondences).
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- A calibration example on stereo matching can be found at
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opencv_source_code/samples/cpp/stereo_match.cpp
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- (Python) A camera calibration sample can be found at
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opencv_source_code/samples/python2/calibrate.py
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opencv_source_code/samples/python/calibrate.py
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@{
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@defgroup calib3d_fisheye Fisheye camera model
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@ -553,7 +553,7 @@ projections, as well as the camera matrix and the distortion coefficients.
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@note
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- An example of how to use solvePnP for planar augmented reality can be found at
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opencv_source_code/samples/python2/plane_ar.py
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opencv_source_code/samples/python/plane_ar.py
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- If you are using Python:
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- Numpy array slices won't work as input because solvePnP requires contiguous
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arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
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@ -1674,7 +1674,7 @@ check, quadratic interpolation and speckle filtering).
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@note
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- (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found
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at opencv_source_code/samples/python2/stereo_match.py
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at opencv_source_code/samples/python/stereo_match.py
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*/
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class CV_EXPORTS_W StereoSGBM : public StereoMatcher
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{
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@ -2035,9 +2035,9 @@ so you need to "flip" the second convolution operand B vertically and horizontal
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- An example using the discrete fourier transform can be found at
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opencv_source_code/samples/cpp/dft.cpp
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- (Python) An example using the dft functionality to perform Wiener deconvolution can be found
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at opencv_source/samples/python2/deconvolution.py
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at opencv_source/samples/python/deconvolution.py
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- (Python) An example rearranging the quadrants of a Fourier image can be found at
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opencv_source/samples/python2/dft.py
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opencv_source/samples/python/dft.py
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@param src input array that could be real or complex.
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@param dst output array whose size and type depends on the flags .
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@param flags transformation flags, representing a combination of the cv::DftFlags
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@ -2848,7 +2848,7 @@ and groups the input samples around the clusters. As an output, \f$\texttt{label
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@note
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- (Python) An example on K-means clustering can be found at
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opencv_source_code/samples/python2/kmeans.py
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opencv_source_code/samples/python/kmeans.py
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@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed.
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Examples of this array can be:
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- Mat points(count, 2, CV_32F);
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@ -73,7 +73,7 @@ namespace cv { namespace cuda {
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- A CUDA example applying the HOG descriptor for people detection can be found at
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opencv_source_code/samples/gpu/hog.cpp
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- (Python) An example applying the HOG descriptor for people detection can be found at
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opencv_source_code/samples/python2/peopledetect.py
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opencv_source_code/samples/python/peopledetect.py
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*/
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class CV_EXPORTS HOG : public Algorithm
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{
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@ -74,7 +74,7 @@ This section describes approaches based on local 2D features and used to categor
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- A complete Bag-Of-Words sample can be found at
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opencv_source_code/samples/cpp/bagofwords_classification.cpp
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- (Python) An example using the features2D framework to perform object categorization can be
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found at opencv_source_code/samples/python2/find_obj.py
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found at opencv_source_code/samples/python/find_obj.py
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@}
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*/
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@ -331,7 +331,7 @@ than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz lap
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than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source
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code which is distributed under GPL.
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- (Python) A complete example showing the use of the %MSER detector can be found at samples/python2/mser.py
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- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py
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*/
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class CV_EXPORTS_W MSER : public Feature2D
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{
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@ -261,7 +261,7 @@ public:
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@note
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- (Python) A face detection example using cascade classifiers can be found at
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opencv_source_code/samples/python2/facedetect.py
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opencv_source_code/samples/python/facedetect.py
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*/
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CV_WRAP void detectMultiScale( InputArray image,
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CV_OUT std::vector<Rect>& objects,
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@ -107,7 +107,7 @@ objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting
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- An example using the inpainting technique can be found at
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opencv_source_code/samples/cpp/inpaint.cpp
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- (Python) An example using the inpainting technique can be found at
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opencv_source_code/samples/python2/inpaint.py
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opencv_source_code/samples/python/inpaint.py
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*/
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CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
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OutputArray dst, double inpaintRadius, int flags );
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@ -74,7 +74,7 @@ See the OpenCV sample camshiftdemo.c that tracks colored objects.
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@note
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- (Python) A sample explaining the camshift tracking algorithm can be found at
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opencv_source_code/samples/python2/camshift.py
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opencv_source_code/samples/python/camshift.py
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*/
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CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_IN_OUT Rect& window,
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TermCriteria criteria );
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@ -166,9 +166,9 @@ The function implements a sparse iterative version of the Lucas-Kanade optical f
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- An example using the Lucas-Kanade optical flow algorithm can be found at
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opencv_source_code/samples/cpp/lkdemo.cpp
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- (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
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opencv_source_code/samples/python2/lk_track.py
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opencv_source_code/samples/python/lk_track.py
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- (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
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opencv_source_code/samples/python2/lk_homography.py
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opencv_source_code/samples/python/lk_homography.py
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*/
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CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg,
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InputArray prevPts, InputOutputArray nextPts,
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@ -213,7 +213,7 @@ The function finds an optical flow for each prev pixel using the @cite Farneback
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- An example using the optical flow algorithm described by Gunnar Farneback can be found at
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opencv_source_code/samples/cpp/fback.cpp
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- (Python) An example using the optical flow algorithm described by Gunnar Farneback can be
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found at opencv_source_code/samples/python2/opt_flow.py
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found at opencv_source_code/samples/python/opt_flow.py
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*/
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CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next, InputOutputArray flow,
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double pyr_scale, int levels, int winsize,
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@ -380,11 +380,11 @@ class can be used: :
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- Another basic video processing sample can be found at
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opencv_source_code/samples/cpp/video_dmtx.cpp
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- (Python) A basic sample on using the VideoCapture interface can be found at
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opencv_source_code/samples/python2/video.py
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opencv_source_code/samples/python/video.py
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- (Python) Another basic video processing sample can be found at
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opencv_source_code/samples/python2/video_dmtx.py
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opencv_source_code/samples/python/video_dmtx.py
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- (Python) A multi threaded video processing sample can be found at
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opencv_source_code/samples/python2/video_threaded.py
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opencv_source_code/samples/python/video_threaded.py
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*/
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class CV_EXPORTS_W VideoCapture
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{
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@ -31,7 +31,7 @@ if(ANDROID AND BUILD_ANDROID_EXAMPLES)
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endif()
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if(INSTALL_PYTHON_EXAMPLES)
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add_subdirectory(python2)
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add_subdirectory(python)
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endif()
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#
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@ -1,6 +1,6 @@
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if(INSTALL_PYTHON_EXAMPLES)
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file(GLOB install_list *.py )
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install(FILES ${install_list}
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DESTINATION ${OPENCV_SAMPLES_SRC_INSTALL_PATH}/python2
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DESTINATION ${OPENCV_SAMPLES_SRC_INSTALL_PATH}/python
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PERMISSIONS OWNER_READ GROUP_READ WORLD_READ COMPONENT samples)
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endif()
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