Python cv2.pyrMeanShiftFiltering() Examples

The following are 3 code examples of cv2.pyrMeanShiftFiltering(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module cv2 , or try the search function .
Example #1
Source File: ImageMiniLab.py    From ImageMiniLab with GNU General Public License v3.0 6 votes vote down vote up 
def hough_circles(self):
        src = self.cv_read_img(self.src_file)
        if src is None:
            return

        dst = cv.pyrMeanShiftFiltering(src, 10, 100)
        cimage = cv.cvtColor(dst, cv.COLOR_BGR2GRAY)
        circles = cv.HoughCircles(cimage, cv.HOUGH_GRADIENT, 1, 20, param1=50, param2=30, minRadius=0, maxRadius=0)
        circles = np.uint16(np.around(circles))
        for i in circles[0, :]:
            cv.circle(src, (i[0], i[1]), i[2], (0, 0, 255), 2)
            cv.circle(src, (i[0], i[1]), 2, (255, 0, 255), 2)

        self.decode_and_show_dst(src)

    # 轮廓发现
Example #2
Source File: RegionOfInterest.py    From DoNotSnap with GNU General Public License v3.0 5 votes vote down vote up 
def roiMask(image, boundaries):
    scale = max([1.0, np.average(np.array(image.shape)[0:2] / 400.0)])
    shape = (int(round(image.shape[1] / scale)), int(round(image.shape[0] / scale)))

    small_color = cv2.resize(image, shape, interpolation=cv2.INTER_LINEAR)

    # reduce details and remove noise for better edge detection
    small_color = cv2.bilateralFilter(small_color, 8, 64, 64)
    small_color = cv2.pyrMeanShiftFiltering(small_color, 8, 64, maxLevel=1)
    small = cv2.cvtColor(small_color, cv2.COLOR_BGR2HSV)

    hue = small[::, ::, 0]
    intensity = cv2.cvtColor(small_color, cv2.COLOR_BGR2GRAY)

    edges = extractEdges(hue, intensity)
    roi = roiFromEdges(edges)
    weight_map = weightMap(hue, intensity, edges, roi)

    _, final_mask = cv2.threshold(roi, 5, 255, cv2.THRESH_BINARY)
    small = cv2.bitwise_and(small, small, mask=final_mask)

    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (4, 4))

    for (lower, upper) in boundaries:
        lower = np.array([lower, 80, 50], dtype="uint8")
        upper = np.array([upper, 255, 255], dtype="uint8")

        # find the colors within the specified boundaries and apply
        # the mask
        mask = cv2.inRange(small, lower, upper)
        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=3)
        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
        final_mask = cv2.bitwise_and(final_mask, mask)

    # blur the mask for better contour extraction
    final_mask = cv2.GaussianBlur(final_mask, (5, 5), 0)
    return (final_mask, weight_map, scale)
Example #3
Source File: ImageMiniLab.py    From ImageMiniLab with GNU General Public License v3.0 5 votes vote down vote up 
def mean_shift_filter(self):
        src = self.cv_read_img(self.src_file)
        if src is None:
            return

        dst = cv.pyrMeanShiftFiltering(src, 10, 50)  # 均值偏移滤波
        self.decode_and_show_dst(dst)

    # 图像二值化