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Extending template_matching tutorial with Java (#8043)
* Extending template_matching tutorial with Java * adding mask to java version of the tutorial * adding the python toggle and code * updating table of content * adding py and java to table of content * adding mask to python * going back to markdown with duplicated text * non duplicated text
This commit is contained in:
Cartucho
Maksim Shabunin
parent
3b669149d2
commit
2055bcc807
@ -67,7 +67,7 @@ $("h2").each(function() {
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$smallerHeadings = $(this).nextUntil("h2").filter("h3").add($(this).nextUntil("h2").find("h3"));
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if ($smallerHeadings.length) {
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$smallerHeadings.each(function() {
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var $elements = $(this).nextUntil("h3").filter("div.newInnerHTML");
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var $elements = $(this).nextUntil("h2,h3").filter("div.newInnerHTML");
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buttonsToAdd($elements, $(this), "h3");
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});
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} else {
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@ -1,14 +1,17 @@
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Template Matching {#tutorial_template_matching}
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=================
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@prev_tutorial{tutorial_back_projection}
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@next_tutorial{tutorial_find_contours}
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Goal
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----
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In this tutorial you will learn how to:
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- Use the OpenCV function @ref cv::matchTemplate to search for matches between an image patch and
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- Use the OpenCV function **matchTemplate()** to search for matches between an image patch and
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an input image
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- Use the OpenCV function @ref cv::minMaxLoc to find the maximum and minimum values (as well as
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- Use the OpenCV function **minMaxLoc()** to find the maximum and minimum values (as well as
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their positions) in a given array.
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Theory
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@ -42,7 +45,7 @@ that should be used to find the match.
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- By **sliding**, we mean moving the patch one pixel at a time (left to right, up to down). At
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each location, a metric is calculated so it represents how "good" or "bad" the match at that
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location is (or how similar the patch is to that particular area of the source image).
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- For each location of **T** over **I**, you *store* the metric in the *result matrix* **(R)**.
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- For each location of **T** over **I**, you *store* the metric in the *result matrix* **R**.
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Each location \f$(x,y)\f$ in **R** contains the match metric:
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@ -51,9 +54,8 @@ that should be used to find the match.
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The brightest locations indicate the highest matches. As you can see, the location marked by the
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red circle is probably the one with the highest value, so that location (the rectangle formed by
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that point as a corner and width and height equal to the patch image) is considered the match.
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- In practice, we use the function @ref cv::minMaxLoc to locate the highest value (or lower,
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depending of the type of matching method) in the *R* matrix.
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- In practice, we locate the highest value (or lower, depending of the type of matching method) in
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the *R* matrix, using the function **minMaxLoc()**
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### How does the mask work?
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- If masking is needed for the match, three components are required:
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@ -81,7 +83,7 @@ that should be used to find the match.
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### Which are the matching methods available in OpenCV?
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Good question. OpenCV implements Template matching in the function @ref cv::matchTemplate . The
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Good question. OpenCV implements Template matching in the function **matchTemplate()**. The
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available methods are 6:
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-# **method=CV_TM_SQDIFF**
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@ -117,119 +119,176 @@ Code
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- **What does this program do?**
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- Loads an input image, an image patch (*template*), and optionally a mask
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- Perform a template matching procedure by using the OpenCV function @ref cv::matchTemplate
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- Perform a template matching procedure by using the OpenCV function **matchTemplate()**
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with any of the 6 matching methods described before. The user can choose the method by
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entering its selection in the Trackbar. If a mask is supplied, it will only be used for
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the methods that support masking
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- Normalize the output of the matching procedure
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- Localize the location with higher matching probability
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- Draw a rectangle around the area corresponding to the highest match
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@add_toggle_cpp
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp)
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- **Code at glance:**
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@include samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp
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@end_toggle
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@add_toggle_java
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/ImgProc/tutorial_template_matching/MatchTemplateDemo.java)
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- **Code at glance:**
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@include samples/java/tutorial_code/ImgProc/tutorial_template_matching/MatchTemplateDemo.java
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@end_toggle
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@add_toggle_python
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/imgProc/match_template/match_template.py)
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- **Code at glance:**
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@include samples/python/tutorial_code/imgProc/match_template/match_template.py
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@end_toggle
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Explanation
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-----------
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-# Declare some global variables, such as the image, template and result matrices, as well as the
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- Declare some global variables, such as the image, template and result matrices, as well as the
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match method and the window names:
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@code{.cpp}
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Mat img; Mat templ; Mat result;
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char* image_window = "Source Image";
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char* result_window = "Result window";
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int match_method;
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int max_Trackbar = 5;
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@endcode
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-# Load the source image, template, and optionally, if supported for the matching method, a mask:
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@code{.cpp}
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bool method_accepts_mask = (CV_TM_SQDIFF == match_method || match_method == CV_TM_CCORR_NORMED);
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if (use_mask && method_accepts_mask)
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{ matchTemplate( img, templ, result, match_method, mask); }
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else
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{ matchTemplate( img, templ, result, match_method); }
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp declare
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@end_toggle
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@endcode
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-# Create the windows to show the results:
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@code{.cpp}
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namedWindow( image_window, WINDOW_AUTOSIZE );
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namedWindow( result_window, WINDOW_AUTOSIZE );
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@endcode
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-# Create the Trackbar to enter the kind of matching method to be used. When a change is detected
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the callback function **MatchingMethod** is called.
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@code{.cpp}
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char* trackbar_label = "Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED";
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createTrackbar( trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod );
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@endcode
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-# Wait until user exits the program.
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@code{.cpp}
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waitKey(0);
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return 0;
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@endcode
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-# Let's check out the callback function. First, it makes a copy of the source image:
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@code{.cpp}
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Mat img_display;
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img.copyTo( img_display );
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@endcode
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-# Next, it creates the result matrix that will store the matching results for each template
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location. Observe in detail the size of the result matrix (which matches all possible locations
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for it)
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@code{.cpp}
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int result_cols = img.cols - templ.cols + 1;
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int result_rows = img.rows - templ.rows + 1;
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/tutorial_template_matching/MatchTemplateDemo.java declare
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@end_toggle
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result.create( result_rows, result_cols, CV_32FC1 );
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@endcode
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-# Perform the template matching operation:
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@code{.cpp}
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bool method_accepts_mask = (CV_TM_SQDIFF == match_method || match_method == CV_TM_CCORR_NORMED);
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if (use_mask && method_accepts_mask)
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{ matchTemplate( img, templ, result, match_method, mask); }
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else
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{ matchTemplate( img, templ, result, match_method); }
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@endcode
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the arguments are naturally the input image **I**, the template **T**, the result **R**, the
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match_method (given by the Trackbar), and optionally the mask image **M**
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/match_template/match_template.py global_variables
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@end_toggle
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-# We normalize the results:
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@code{.cpp}
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normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );
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@endcode
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-# We localize the minimum and maximum values in the result matrix **R** by using @ref
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cv::minMaxLoc .
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@code{.cpp}
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double minVal; double maxVal; Point minLoc; Point maxLoc;
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Point matchLoc;
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- Load the source image, template, and optionally, if supported for the matching method, a mask:
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minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
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@endcode
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the function calls as arguments:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp load_image
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@end_toggle
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- **result:** The source array
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- **&minVal** and **&maxVal:** Variables to save the minimum and maximum values in **result**
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- **&minLoc** and **&maxLoc:** The Point locations of the minimum and maximum values in the
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array.
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- **Mat():** Optional mask
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/tutorial_template_matching/MatchTemplateDemo.java load_image
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@end_toggle
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-# For the first two methods ( TM_SQDIFF and MT_SQDIFF_NORMED ) the best match are the lowest
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/match_template/match_template.py load_image
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@end_toggle
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- Create the Trackbar to enter the kind of matching method to be used. When a change is detected
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the callback function is called.
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp create_trackbar
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/tutorial_template_matching/MatchTemplateDemo.java create_trackbar
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/match_template/match_template.py create_trackbar
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@end_toggle
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- Let's check out the callback function. First, it makes a copy of the source image:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp copy_source
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/tutorial_template_matching/MatchTemplateDemo.java copy_source
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/match_template/match_template.py copy_source
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@end_toggle
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- Perform the template matching operation. The arguments are naturally the input image **I**,
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the template **T**, the result **R** and the match_method (given by the Trackbar),
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and optionally the mask image **M**.
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp match_template
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/tutorial_template_matching/MatchTemplateDemo.java match_template
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/match_template/match_template.py match_template
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@end_toggle
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- We normalize the results:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp normalize
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/tutorial_template_matching/MatchTemplateDemo.java normalize
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/match_template/match_template.py normalize
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@end_toggle
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- We localize the minimum and maximum values in the result matrix **R** by using **minMaxLoc()**.
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp best_match
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/tutorial_template_matching/MatchTemplateDemo.java best_match
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/match_template/match_template.py best_match
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@end_toggle
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- For the first two methods ( TM_SQDIFF and MT_SQDIFF_NORMED ) the best match are the lowest
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values. For all the others, higher values represent better matches. So, we save the
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corresponding value in the **matchLoc** variable:
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@code{.cpp}
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if( match_method == TM_SQDIFF || match_method == TM_SQDIFF_NORMED )
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{ matchLoc = minLoc; }
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else
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{ matchLoc = maxLoc; }
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@endcode
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-# Display the source image and the result matrix. Draw a rectangle around the highest possible
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matching area:
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@code{.cpp}
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rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
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rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
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imshow( image_window, img_display );
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imshow( result_window, result );
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@endcode
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp match_loc
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/tutorial_template_matching/MatchTemplateDemo.java match_loc
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/match_template/match_template.py match_loc
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@end_toggle
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- Display the source image and the result matrix. Draw a rectangle around the highest possible
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matching area:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp imshow
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/tutorial_template_matching/MatchTemplateDemo.java imshow
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/match_template/match_template.py imshow
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@end_toggle
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Results
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-------
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@ -173,6 +173,8 @@ In this section you will learn about the image processing (manipulation) functio
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- @subpage tutorial_template_matching
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*Languages:* C++, Java, Python
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*Compatibility:* \> OpenCV 2.0
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*Author:* Ana Huamán
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@ -12,6 +12,7 @@
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using namespace std;
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using namespace cv;
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//! [declare]
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/// Global Variables
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bool use_mask;
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Mat img; Mat templ; Mat mask; Mat result;
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@ -20,6 +21,7 @@ const char* result_window = "Result window";
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int match_method;
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int max_Trackbar = 5;
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//! [declare]
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/// Function Headers
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void MatchingMethod( int, void* );
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@ -36,6 +38,7 @@ int main( int argc, char** argv )
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return -1;
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}
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//! [load_image]
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/// Load image and template
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img = imread( argv[1], IMREAD_COLOR );
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templ = imread( argv[2], IMREAD_COLOR );
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@ -50,19 +53,26 @@ int main( int argc, char** argv )
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cout << "Can't read one of the images" << endl;
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return -1;
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}
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//! [load_image]
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//! [create_windows]
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/// Create windows
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namedWindow( image_window, WINDOW_AUTOSIZE );
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namedWindow( result_window, WINDOW_AUTOSIZE );
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//! [create_windows]
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//! [create_trackbar]
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/// Create Trackbar
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const char* trackbar_label = "Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED";
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createTrackbar( trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod );
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//! [create_trackbar]
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MatchingMethod( 0, 0 );
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//! [wait_key]
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waitKey(0);
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return 0;
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//! [wait_key]
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}
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/**
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@ -71,44 +81,57 @@ int main( int argc, char** argv )
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*/
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void MatchingMethod( int, void* )
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{
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//! [copy_source]
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/// Source image to display
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Mat img_display;
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img.copyTo( img_display );
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//! [copy_source]
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//! [create_result_matrix]
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/// Create the result matrix
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int result_cols = img.cols - templ.cols + 1;
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int result_rows = img.rows - templ.rows + 1;
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result.create( result_rows, result_cols, CV_32FC1 );
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//! [create_result_matrix]
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//! [match_template]
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/// Do the Matching and Normalize
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bool method_accepts_mask = (CV_TM_SQDIFF == match_method || match_method == CV_TM_CCORR_NORMED);
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if (use_mask && method_accepts_mask)
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{ matchTemplate( img, templ, result, match_method, mask); }
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else
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{ matchTemplate( img, templ, result, match_method); }
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//! [match_template]
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//! [normalize]
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normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );
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//! [normalize]
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//! [best_match]
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/// Localizing the best match with minMaxLoc
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double minVal; double maxVal; Point minLoc; Point maxLoc;
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Point matchLoc;
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minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
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//! [best_match]
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//! [match_loc]
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/// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
|
||||
if( match_method == TM_SQDIFF || match_method == TM_SQDIFF_NORMED )
|
||||
{ matchLoc = minLoc; }
|
||||
else
|
||||
{ matchLoc = maxLoc; }
|
||||
//! [match_loc]
|
||||
|
||||
//! [imshow]
|
||||
/// Show me what you got
|
||||
rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
|
||||
rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
|
||||
|
||||
imshow( image_window, img_display );
|
||||
imshow( result_window, result );
|
||||
//! [imshow]
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
@ -0,0 +1,196 @@
|
||||
import org.opencv.core.*;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.imgcodecs.Imgcodecs;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
|
||||
import javax.swing.*;
|
||||
import javax.swing.event.ChangeEvent;
|
||||
import javax.swing.event.ChangeListener;
|
||||
import java.awt.*;
|
||||
import java.awt.image.BufferedImage;
|
||||
import java.awt.image.DataBufferByte;
|
||||
|
||||
import java.util.*;
|
||||
|
||||
class MatchTemplateDemoRun implements ChangeListener{
|
||||
|
||||
//! [declare]
|
||||
/// Global Variables
|
||||
Boolean use_mask = false;
|
||||
Mat img = new Mat(), templ = new Mat();
|
||||
Mat mask = new Mat();
|
||||
|
||||
int match_method;
|
||||
|
||||
JLabel imgDisplay = new JLabel(), resultDisplay = new JLabel();
|
||||
//! [declare]
|
||||
|
||||
public void run(String[] args) {
|
||||
|
||||
if (args.length < 2)
|
||||
{
|
||||
System.out.println("Not enough parameters");
|
||||
System.out.println("Program arguments:\n<image_name> <template_name> [<mask_name>]");
|
||||
System.exit(-1);
|
||||
}
|
||||
|
||||
//! [load_image]
|
||||
/// Load image and template
|
||||
img = Imgcodecs.imread( args[0], Imgcodecs.IMREAD_COLOR );
|
||||
templ = Imgcodecs.imread( args[1], Imgcodecs.IMREAD_COLOR );
|
||||
//! [load_image]
|
||||
|
||||
if(args.length > 2) {
|
||||
use_mask = true;
|
||||
mask = Imgcodecs.imread( args[2], Imgcodecs.IMREAD_COLOR );
|
||||
}
|
||||
|
||||
if(img.empty() || templ.empty() || (use_mask && mask.empty()))
|
||||
{
|
||||
System.out.println("Can't read one of the images");
|
||||
System.exit(-1);
|
||||
}
|
||||
|
||||
matchingMethod();
|
||||
createJFrame();
|
||||
|
||||
}
|
||||
|
||||
private void matchingMethod() {
|
||||
|
||||
Mat result = new Mat();
|
||||
|
||||
//! [copy_source]
|
||||
/// Source image to display
|
||||
Mat img_display = new Mat();
|
||||
img.copyTo( img_display );
|
||||
//! [copy_source]
|
||||
|
||||
//! [create_result_matrix]
|
||||
/// Create the result matrix
|
||||
int result_cols = img.cols() - templ.cols() + 1;
|
||||
int result_rows = img.rows() - templ.rows() + 1;
|
||||
|
||||
result.create( result_rows, result_cols, CvType.CV_32FC1 );
|
||||
//! [create_result_matrix]
|
||||
|
||||
//! [match_template]
|
||||
/// Do the Matching and Normalize
|
||||
Boolean method_accepts_mask = (Imgproc.TM_SQDIFF == match_method ||
|
||||
match_method == Imgproc.TM_CCORR_NORMED);
|
||||
if (use_mask && method_accepts_mask)
|
||||
{ Imgproc.matchTemplate( img, templ, result, match_method, mask); }
|
||||
else
|
||||
{ Imgproc.matchTemplate( img, templ, result, match_method); }
|
||||
//! [match_template]
|
||||
|
||||
//! [normalize]
|
||||
Core.normalize( result, result, 0, 1, Core.NORM_MINMAX, -1, new Mat() );
|
||||
//! [normalize]
|
||||
|
||||
//! [best_match]
|
||||
/// Localizing the best match with minMaxLoc
|
||||
double minVal; double maxVal;
|
||||
Point matchLoc;
|
||||
|
||||
Core.MinMaxLocResult mmr = Core.minMaxLoc( result );
|
||||
//! [best_match]
|
||||
|
||||
//! [match_loc]
|
||||
/// For SQDIFF and SQDIFF_NORMED, the best matches are lower values.
|
||||
// For all the other methods, the higher the better
|
||||
if( match_method == Imgproc.TM_SQDIFF || match_method == Imgproc.TM_SQDIFF_NORMED )
|
||||
{ matchLoc = mmr.minLoc; }
|
||||
else
|
||||
{ matchLoc = mmr.maxLoc; }
|
||||
//! [match_loc]
|
||||
|
||||
//! [imshow]
|
||||
/// Show me what you got
|
||||
Imgproc.rectangle(img_display, matchLoc, new Point(matchLoc.x + templ.cols(),
|
||||
matchLoc.y + templ.rows()), new Scalar(0, 0, 0), 2, 8, 0);
|
||||
Imgproc.rectangle(result, matchLoc, new Point(matchLoc.x + templ.cols(),
|
||||
matchLoc.y + templ.rows()), new Scalar(0, 0, 0), 2, 8, 0);
|
||||
|
||||
Image tmpImg = toBufferedImage(img_display);
|
||||
ImageIcon icon = new ImageIcon(tmpImg);
|
||||
imgDisplay.setIcon(icon);
|
||||
|
||||
result.convertTo(result, CvType.CV_8UC1, 255.0);
|
||||
tmpImg = toBufferedImage(result);
|
||||
icon = new ImageIcon(tmpImg);
|
||||
resultDisplay.setIcon(icon);
|
||||
//! [imshow]
|
||||
}
|
||||
|
||||
public void stateChanged(ChangeEvent e) {
|
||||
JSlider source = (JSlider) e.getSource();
|
||||
if (!source.getValueIsAdjusting()) {
|
||||
match_method = (int)source.getValue();
|
||||
matchingMethod();
|
||||
}
|
||||
}
|
||||
|
||||
public Image toBufferedImage(Mat m) {
|
||||
int type = BufferedImage.TYPE_BYTE_GRAY;
|
||||
if ( m.channels() > 1 ) {
|
||||
type = BufferedImage.TYPE_3BYTE_BGR;
|
||||
}
|
||||
int bufferSize = m.channels()*m.cols()*m.rows();
|
||||
byte [] b = new byte[bufferSize];
|
||||
m.get(0,0,b); // get all the pixels
|
||||
BufferedImage image = new BufferedImage(m.cols(),m.rows(), type);
|
||||
final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
|
||||
System.arraycopy(b, 0, targetPixels, 0, b.length);
|
||||
return image;
|
||||
}
|
||||
|
||||
private void createJFrame() {
|
||||
|
||||
String title = "Source image; Control; Result image";
|
||||
JFrame frame = new JFrame(title);
|
||||
frame.setLayout(new GridLayout(2, 2));
|
||||
frame.add(imgDisplay);
|
||||
|
||||
//! [create_trackbar]
|
||||
int min = 0, max = 5;
|
||||
JSlider slider = new JSlider(JSlider.VERTICAL, min, max, match_method);
|
||||
//! [create_trackbar]
|
||||
|
||||
slider.setPaintTicks(true);
|
||||
slider.setPaintLabels(true);
|
||||
|
||||
// Set the spacing for the minor tick mark
|
||||
slider.setMinorTickSpacing(1);
|
||||
|
||||
// Customizing the labels
|
||||
Hashtable labelTable = new Hashtable();
|
||||
labelTable.put( new Integer( 0 ), new JLabel("0 - SQDIFF") );
|
||||
labelTable.put( new Integer( 1 ), new JLabel("1 - SQDIFF NORMED") );
|
||||
labelTable.put( new Integer( 2 ), new JLabel("2 - TM CCORR") );
|
||||
labelTable.put( new Integer( 3 ), new JLabel("3 - TM CCORR NORMED") );
|
||||
labelTable.put( new Integer( 4 ), new JLabel("4 - TM COEFF") );
|
||||
labelTable.put( new Integer( 5 ), new JLabel("5 - TM COEFF NORMED : (Method)") );
|
||||
slider.setLabelTable( labelTable );
|
||||
|
||||
slider.addChangeListener(this);
|
||||
|
||||
frame.add(slider);
|
||||
|
||||
frame.add(resultDisplay);
|
||||
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
|
||||
frame.pack();
|
||||
frame.setVisible(true);
|
||||
}
|
||||
}
|
||||
|
||||
public class MatchTemplateDemo
|
||||
{
|
||||
public static void main(String[] args) {
|
||||
// load the native OpenCV library
|
||||
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
|
||||
|
||||
// run code
|
||||
new MatchTemplateDemoRun().run(args);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,96 @@
|
||||
import sys
|
||||
import cv2
|
||||
|
||||
## [global_variables]
|
||||
use_mask = False
|
||||
img = None
|
||||
templ = None
|
||||
mask = None
|
||||
image_window = "Source Image"
|
||||
result_window = "Result window"
|
||||
|
||||
match_method = 0
|
||||
max_Trackbar = 5
|
||||
## [global_variables]
|
||||
|
||||
def main(argv):
|
||||
|
||||
if (len(sys.argv) < 3):
|
||||
print 'Not enough parameters'
|
||||
print 'Usage:\nmatch_template_demo.py <image_name> <template_name> [<mask_name>]'
|
||||
return -1
|
||||
|
||||
## [load_image]
|
||||
global img
|
||||
global templ
|
||||
img = cv2.imread(sys.argv[1], cv2.IMREAD_COLOR)
|
||||
templ = cv2.imread(sys.argv[2], cv2.IMREAD_COLOR)
|
||||
|
||||
if (len(sys.argv) > 3):
|
||||
global use_mask
|
||||
use_mask = True
|
||||
global mask
|
||||
mask = cv2.imread( sys.argv[3], cv2.IMREAD_COLOR )
|
||||
|
||||
if ((img is None) or (templ is None) or (use_mask and (mask is None))):
|
||||
print 'Can\'t read one of the images'
|
||||
return -1
|
||||
## [load_image]
|
||||
|
||||
## [create_windows]
|
||||
cv2.namedWindow( image_window, cv2.WINDOW_AUTOSIZE )
|
||||
cv2.namedWindow( result_window, cv2.WINDOW_AUTOSIZE )
|
||||
## [create_windows]
|
||||
|
||||
## [create_trackbar]
|
||||
trackbar_label = 'Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED'
|
||||
cv2.createTrackbar( trackbar_label, image_window, match_method, max_Trackbar, MatchingMethod )
|
||||
## [create_trackbar]
|
||||
|
||||
MatchingMethod(match_method)
|
||||
|
||||
## [wait_key]
|
||||
cv2.waitKey(0)
|
||||
return 0
|
||||
## [wait_key]
|
||||
|
||||
def MatchingMethod(param):
|
||||
|
||||
global match_method
|
||||
match_method = param
|
||||
|
||||
## [copy_source]
|
||||
img_display = img.copy()
|
||||
## [copy_source]
|
||||
## [match_template]
|
||||
method_accepts_mask = (cv2.TM_SQDIFF == match_method or match_method == cv2.TM_CCORR_NORMED)
|
||||
if (use_mask and method_accepts_mask):
|
||||
result = cv2.matchTemplate(img, templ, match_method, None, mask)
|
||||
else:
|
||||
result = cv2.matchTemplate(img, templ, match_method)
|
||||
## [match_template]
|
||||
|
||||
## [normalize]
|
||||
cv2.normalize( result, result, 0, 1, cv2.NORM_MINMAX, -1 )
|
||||
## [normalize]
|
||||
## [best_match]
|
||||
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(result, None)
|
||||
## [best_match]
|
||||
|
||||
## [match_loc]
|
||||
if (match_method == cv2.TM_SQDIFF or match_method == cv2.TM_SQDIFF_NORMED):
|
||||
matchLoc = minLoc
|
||||
else:
|
||||
matchLoc = maxLoc
|
||||
## [match_loc]
|
||||
|
||||
## [imshow]
|
||||
cv2.rectangle(img_display, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 )
|
||||
cv2.rectangle(result, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 )
|
||||
cv2.imshow(image_window, img_display)
|
||||
cv2.imshow(result_window, result)
|
||||
## [imshow]
|
||||
pass
|
||||
|
||||
if __name__ == "__main__":
|
||||
main(sys.argv[1:])
|
||||
Loading…
Reference in New Issue
Block a user