Python feature.match_template方法代碼示例

# 需要導入模塊: from skimage import feature [as 別名]
# 或者: from skimage.feature import match_template [as 別名]
def match_template1(template, img, plot=False, method=cv2.TM_SQDIFF_NORMED):
    img = cv2.imread(img, 0).copy()
    template = cv2.imread(template, 0)
    w, h = template.shape[::-1]
    if lib == OPENCV:
        res = cv2.matchTemplate(img, template, method)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
            top_left = min_loc
        else:
            top_left = max_loc
    else:
        result = match_template(img, template)
        ij = np.unravel_index(np.argmax(result), result.shape)
        top_left = ij[::-1]

    bottom_right = (top_left[0] + w, top_left[1] + h)

    if plot:
        cv2.rectangle(img, top_left, bottom_right, 255, 5)
        plt.subplot(121)
        plt.imshow(img)
        plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
        plt.subplot(122)
        plt.imshow(template)

        plt.show()

    return top_left, bottom_right 

# 需要導入模塊: from skimage import feature [as 別名]
# 或者: from skimage.feature import match_template [as 別名]
def find_peaks_xc(z, disc_image, min_distance=5, peak_threshold=0.2):
    """
    Find peaks using the the correlation between the image and a reference peaks

    Parameters
    ----------

    z: numpy.ndarray
        Array of image intensities.
    disc_image: numpy.ndarray (square)
        Array containing a single bright disc, similar to those to detect.
    min_distance: int
        The minimum expected distance between peaks (in pixels)
    peak_threshold: float between 0 and 1
        Larger values will lead to fewer peaks in the output.

    Returns
    -------
    peaks : numpy.ndarray
        Array of peak pixel coordinates with shape (n_peaks, 2).

    """
    response_image = match_template(z, disc_image, pad_input=True)
    peaks = corner_peaks(
        response_image, min_distance=min_distance, threshold_rel=peak_threshold
    )
    # make return format the same as the other peak finders
    peaks -= 1

    return clean_peaks(peaks) 

# 需要導入模塊: from skimage import feature [as 別名]
# 或者: from skimage.feature import match_template [as 別名]
def _template_match_binary_image_single_frame(frame, binary_image):
    """Template match a binary image (template) with a single image.

    Parameters
    ----------
    frame : NumPy 2D array
    binary_image : NumPy 2D array
        Must be smaller than frame

    Returns
    -------
    template_match : NumPy 2D array
        Same size as frame

    Examples
    --------
    >>> frame = np.random.randint(1000, size=(256, 256))
    >>> from skimage import morphology
    >>> binary_image = morphology.disk(4, np.uint16)
    >>> import pyxem.utils.dask_tools as dt
    >>> template_match = dt._template_match_binary_image_single_frame(
    ...     frame, binary_image)

    """
    template_match = match_template(frame, binary_image, pad_input=True)
    template_match = template_match - np.min(template_match)
    return template_match 

# 需要導入模塊: from skimage import feature [as 別名]
# 或者: from skimage.feature import match_template [as 別名]
def template_match(needle, haystack):
    result = feature.match_template(haystack, needle)
    ij = np.unravel_index(np.argmax(result), result.shape)
    x, y = ij[1], ij[0]
    score = result[y, x]
    return x, y, score 

# 需要導入模塊: from skimage import feature [as 別名]
# 或者: from skimage.feature import match_template [as 別名]
def match_templates_1(search_image, template_image, n=0):
    '''
    Calculates the n closest matches of some template image in another image and
    displays a figure illustrating the results.

    Args:
        search_image: image within which to match template.
        template_image: image to be matched.
        n: number of matches to be found. ie. closest n matches.
    '''
    Point = namedtuple('Point', ['x', 'y'])

    # Calculate template matches
    match_result = match_template(search_image, template_image);

    # Get closest n matches
    print(match_result.shape)
    if(n == 0):
        n = int(match_result.shape[1]);
    matched_point_list = []
    max_indices = get_n_max_indices(match_result, n)
    for index in max_indices:
        ij = np.unravel_index(int(index), match_result.shape)
        x, y = ij[::-1]
        point = Point(x,y)
        #print(point)
        matched_point_list.append(point)

    # Display
    fig = plt.figure(figsize=(8, 3))
    plt.gray()
    ax1 = plt.subplot(1, 3, 1)
    ax2 = plt.subplot(1, 3, 2, adjustable='box-forced')
    ax3 = plt.subplot(1, 3, 3, sharex=ax2, sharey=ax2, adjustable='box-forced')

    ax1.imshow(template_image)
    ax1.set_axis_off()
    ax1.set_title('grain template')

    # highlight matched regions
    ax2.imshow(search_image)
    ax2.set_axis_off()
    ax2.set_title('image')
    himage, wimage = template_image.shape
    for point in matched_point_list:
        rect = plt.Rectangle((point.x, point.y), wimage, himage, edgecolor='r', facecolor='none')
        ax2.add_patch(rect)

    # highlight matched regions
    ax3.imshow(match_result)
    ax3.set_axis_off()
    ax3.set_title('`match_template`\nresult')
    ax3.autoscale(False)
    for point in matched_point_list:
        ax3.plot(point.x, point.y, 'o', markeredgecolor='r', markerfacecolor='none', markersize=10)

    plt.show()