Python cv2.MORPH_ELLIPSE Examples

def _morphological_process(image, kernel_size=5):
        """

        :param image:
        :param kernel_size:
        :return:
        """
        if image.dtype is not np.uint8:
            image = np.array(image, np.uint8)
        if len(image.shape) == 3:
            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

        kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size))

        # close operation fille hole
        closing = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1)

        return closing 

def _morphological_process(image, kernel_size=5):
    """
    morphological process to fill the hole in the binary segmentation result
    :param image:
    :param kernel_size:
    :return:
    """
    if len(image.shape) == 3:
        raise ValueError('Binary segmentation result image should be a single channel image')

    if image.dtype is not np.uint8:
        image = np.array(image, np.uint8)

    kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size))

    # close operation fille hole
    closing = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1)

    return closing 

def border(self, alpha, size, kernel_type = 'RECT'):
        
        kdict = {'RECT':cv2.MORPH_RECT, 'ELLIPSE':cv2.MORPH_ELLIPSE,
                 'CROSS':cv2.MORPH_CROSS}
        kernel = cv2.getStructuringElement(kdict[kernel_type], (size, size))
        border = cv2.dilate(alpha, kernel, iterations = 1) # - alpha
        return border 

def border(self, alpha, size, kernel_type='RECT'):
        """
        alpha : alpha layer of the text
        size  : size of the kernel
        kernel_type : one of [rect,ellipse,cross]

        @return : alpha layer of the border (color to be added externally).
        """
        kdict = {'RECT':cv.MORPH_RECT, 'ELLIPSE':cv.MORPH_ELLIPSE,
                 'CROSS':cv.MORPH_CROSS}
        kernel = cv.getStructuringElement(kdict[kernel_type],(size,size))
        border = cv.dilate(alpha,kernel,iterations=1) # - alpha
        return border 

def build_kernel(kernel_type, kernel_size):
    """Creates the specific kernel: MORPH_ELLIPSE, MORPH_CROSS or MORPH_RECT"""

    if kernel_type == cv2.MORPH_ELLIPSE:
        # We build a elliptical kernel
        return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, kernel_size)
    elif kernel_type == cv2.MORPH_CROSS:
        # We build a cross-shape kernel
        return cv2.getStructuringElement(cv2.MORPH_CROSS, kernel_size)
    else:  # cv2.MORPH_RECT
        # We build a rectangular kernel:
        return cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size)


# This function erodes the image 

def filter_lane_points(self,
                           img,
                           filter_type='bilateral',
                           ksize_r=25,
                           C_r=8,
                           ksize_b=35,
                           C_b=5,
                           mask_noise=False,
                           ksize_noise=65,
                           C_noise=10,
                           noise_thresh=135):
        '''
        Filter an image to isolate lane lines and return a binary version.

        All image color space conversion, thresholding, filtering and morphing
        happens inside this method. It takes an RGB color image as input and
        returns a binary filtered version.
        '''

        # Define structuring elements for cv2 functions
        strel_lab_b = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(55,55))
        strel_rgb_r = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(29,29))
        strel_open = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(5,5))
        # Extract RGB R-channel and LAB B-channel
        rgb_r_channel = img[:,:,0]
        lab_b_channel = (cv2.cvtColor(img, cv2.COLOR_RGB2LAB))[:,:,2]
        # Apply tophat morphology
        rgb_r_tophat = cv2.morphologyEx(rgb_r_channel, cv2.MORPH_TOPHAT, strel_rgb_r, iterations=1)
        lab_b_tophat = cv2.morphologyEx(lab_b_channel, cv2.MORPH_TOPHAT, strel_lab_b, iterations=1)
        if filter_type == 'bilateral':
            # Apply bilateral adaptive color thresholding
            rgb_r_thresh = bilateral_adaptive_threshold(rgb_r_tophat, ksize=ksize_r, C=C_r)
            lab_b_thresh = bilateral_adaptive_threshold(lab_b_tophat, ksize=ksize_b, C=C_b)
        elif filter_type == 'neighborhood':
            rgb_r_thresh = cv2.adaptiveThreshold(rgb_r_channel, 255, adaptiveMethod=cv2.ADAPTIVE_THRESH_MEAN_C, thresholdType=cv2.THRESH_BINARY, blockSize=ksize_r, C=-C_r)
            lab_b_thresh = cv2.adaptiveThreshold(lab_b_channel, 255, adaptiveMethod=cv2.ADAPTIVE_THRESH_MEAN_C, thresholdType=cv2.THRESH_BINARY, blockSize=ksize_b, C=-C_b)
        else:
            raise ValueError("Unexpected filter mode. Expected modes are 'bilateral' or 'neighborhood'.")
        if mask_noise: # Merge both color channels and the noise mask
            # Create a mask to filter out noise such as trees and other greenery based on the LAB B-channel
            noise_mask_part1 = cv2.inRange(lab_b_channel, noise_thresh, 255) # This catches the noise, but unfortunately also the yellow line, therefore...
            noise_mask_part2 = bilateral_adaptive_threshold(lab_b_channel, ksize=ksize_noise, C=C_noise) # ...this brings the yellow line back...
            noise_bool = np.logical_or(np.logical_not(noise_mask_part1), noise_mask_part2) # ...once we combine the two.
            noise_mask = np.zeros_like(rgb_r_channel, dtype=np.uint8)
            noise_mask[noise_bool] = 255

            merged_bool = np.logical_and(np.logical_or(rgb_r_thresh, lab_b_thresh), noise_mask)
            merged = np.zeros_like(rgb_r_channel, dtype=np.uint8)
            merged[merged_bool] = 255
        else: # Only merge the two color channels
            merged_bool = np.logical_or(rgb_r_thresh, lab_b_thresh)
            merged = np.zeros_like(rgb_r_channel, dtype=np.uint8)
            merged[merged_bool] = 255

        # Apply open morphology
        opened = cv2.morphologyEx(merged, cv2.MORPH_OPEN, strel_open, iterations=1)

        return opened