Python+OpenCV：模板匹配(Template Matching)

我会带着你远行 2022-12-21 14:55 91阅读 0赞

## Python+OpenCV：模板匹配(Template Matching) ##

### Template Matching with Single Objects ###

####################################################################################################
    # 图像模板匹配(Template Matching)
    def lmc_cv_image_template_matching():
        """
            函数功能: 图像模板匹配(Template Matching)。
        """
    
        # 读取图像
        image1 = lmc_cv.imread('D:/99-Research/Python/Image/messi.jpg', flags=lmc_cv.IMREAD_GRAYSCALE)
        image2 = image1.copy()
        template = lmc_cv.imread('D:/99-Research/Python/Image/messi_template.jpg', flags=lmc_cv.IMREAD_GRAYSCALE)
        w, h = template.shape[::-1]
    
        # 图像模板匹配(Template Matching)
        # All the 6 methods for comparison in a list
        methods = ['lmc_cv.TM_CCOEFF', 'lmc_cv.TM_CCOEFF_NORMED', 'lmc_cv.TM_CCORR', 'lmc_cv.TM_CCORR_NORMED',
                   'lmc_cv.TM_SQDIFF', 'lmc_cv.TM_SQDIFF_NORMED']
        titles = ['TM_SQDIFF', 'TM_SQDIFF_NORMED', 'TM_CCORR', 'TM_CCORR_NORMED', 'TM_CCOEFF', 'TM_CCOEFF_NORMED']
        for i in methods:
            image = image2.copy()
            method = eval(i)
            # Apply template Matching: Compares a template against overlapped image regions.
            result_image = lmc_cv.matchTemplate(image, template, method)
            min_val, max_val, min_loc, max_loc = lmc_cv.minMaxLoc(result_image)
            # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
            if method in [lmc_cv.TM_SQDIFF, lmc_cv.TM_SQDIFF_NORMED]:
                top_left = min_loc
            else:
                top_left = max_loc
            bottom_right = (top_left[0] + w, top_left[1] + h)
            # 绘制矩形
            lmc_cv.rectangle(image, top_left, bottom_right, 255, 2)
            # 显示图像
            pyplot.figure(titles[method])
            pyplot.subplot(1, 2, 1)
            pyplot.imshow(result_image, cmap='gray')
            pyplot.title('Matching Result')
            pyplot.xticks([])
            pyplot.yticks([])
            pyplot.subplot(1, 2, 2)
            pyplot.imshow(image, cmap='gray')
            pyplot.title('Detected Point')
            pyplot.xticks([])
            pyplot.yticks([])
            # pyplot.suptitle(titles[method])
            pyplot.show()
    
        # 根据用户输入保存图像
        if ord("q") == (lmc_cv.waitKey(0) & 0xFF):
            # 销毁窗口
            pyplot.close('all')
        return

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![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xpdWJpbmc4NjA5_size_16_color_FFFFFF_t_70 1][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xpdWJpbmc4NjA5_size_16_color_FFFFFF_t_70 2][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xpdWJpbmc4NjA5_size_16_color_FFFFFF_t_70 3][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xpdWJpbmc4NjA5_size_16_color_FFFFFF_t_70 4][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xpdWJpbmc4NjA5_size_16_color_FFFFFF_t_70 4][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xpdWJpbmc4NjA5_size_16_color_FFFFFF_t_70 5][]

### Template Matching with Multiple Objects ###

####################################################################################################
    # 多目标模板匹配(Template Matching Multiple Objects)
    def lmc_cv_image_template_matching_multiple_objects():
        """
            函数功能: 多目标模板匹配(Template Matching Multiple Objects)。
        """
    
        # 读取图像
        image = lmc_cv.imread('D:/99-Research/Python/Image/mary.jpg', flags=lmc_cv.IMREAD_UNCHANGED)
        image_rgb = lmc_cv.cvtColor(image, lmc_cv.COLOR_BGR2RGB)
        image_gray = lmc_cv.cvtColor(image_rgb, lmc_cv.COLOR_RGB2GRAY)
        template = lmc_cv.imread('D:/99-Research/Python/Image/mary_template.jpg', flags=lmc_cv.IMREAD_UNCHANGED)
        template_rgb = lmc_cv.cvtColor(template, lmc_cv.COLOR_BGR2RGB)
        template_gray = lmc_cv.cvtColor(template_rgb, lmc_cv.COLOR_RGB2GRAY)
        w, h = template_gray.shape[::-1]
        pyplot.figure('Image Display')
        pyplot.subplot(1, 3, 1)
        pyplot.imshow(image_rgb, cmap='gray')
        pyplot.title('Original Image')
        pyplot.xticks([])
        pyplot.yticks([])
        pyplot.subplot(1, 3, 2)
        pyplot.imshow(template_rgb, cmap='gray')
        pyplot.title('Template Image')
        pyplot.xticks([])
        pyplot.yticks([])
    
        # 多目标模板匹配(Template Matching Multiple Objects)
        result_image = lmc_cv.matchTemplate(image_gray, template_gray, lmc_cv.TM_CCOEFF_NORMED)
        threshold = 0.8
        loc = np.where(result_image >= threshold)
        for pt in zip(*loc[::-1]):
            lmc_cv.rectangle(image_rgb, pt, (pt[0] + w, pt[1] + h), (255, 0, 0), 2)
    
        pyplot.subplot(1, 3, 3)
        pyplot.imshow(image_rgb, cmap='gray')
        pyplot.title('Result Image')
        pyplot.xticks([])
        pyplot.yticks([])
        pyplot.show()
    
        # 根据用户输入保存图像
        if ord("q") == (lmc_cv.waitKey(0) & 0xFF):
            # 销毁窗口
            pyplot.close('all')
        return

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xpdWJpbmc4NjA5_size_16_color_FFFFFF_t_70 6][]

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