Python cv2.bitwise_or方法代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def contour_filter(self, frame):
        _, contours, _ = cv2.findContours(frame,
            cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        new_frame = np.zeros(frame.shape, np.uint8)
        for i, contour in enumerate(contours):
            c_area = cv2.contourArea(contour)
            if self.contour_min_area <= c_area <= self.contour_max_area:
                mask = np.zeros(frame.shape, np.uint8)
                cv2.drawContours(mask, contours, i, 255, cv2.FILLED)
                mask = cv2.bitwise_and(frame, mask)
                new_frame = cv2.bitwise_or(new_frame, mask)
        frame = new_frame

        if self.contour_disp_flag:
            frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
            cv2.drawContours(frame, contours, -1, (255, 0, 0), 1)

        return frame


    # A number of methods corresponding to the various trackbars available. 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def remove_other_color(img):
    frame = cv2.GaussianBlur(img, (3,3), 0) 
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    # define range of blue color in HSV
    lower_blue = np.array([100,128,0])
    upper_blue = np.array([215,255,255])
    # Threshold the HSV image to get only blue colors
    mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)

    lower_white = np.array([0,0,128], dtype=np.uint8)
    upper_white = np.array([255,255,255], dtype=np.uint8)
    # Threshold the HSV image to get only blue colors
    mask_white = cv2.inRange(hsv, lower_white, upper_white)

    lower_black = np.array([0,0,0], dtype=np.uint8)
    upper_black = np.array([170,150,50], dtype=np.uint8)

    mask_black = cv2.inRange(hsv, lower_black, upper_black)

    mask_1 = cv2.bitwise_or(mask_blue, mask_white)
    mask = cv2.bitwise_or(mask_1, mask_black)
    # Bitwise-AND mask and original image
    #res = cv2.bitwise_and(frame,frame, mask= mask)
    return mask 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def skeletonize(image_in):
    '''Inputs and grayscale image and outputs a binary skeleton image'''
    size = np.size(image_in)
    skel = np.zeros(image_in.shape, np.uint8)

    ret, image_edit = cv2.threshold(image_in, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))
    done = False

    while not done:
        eroded = cv2.erode(image_edit, element)
        temp = cv2.dilate(eroded, element)
        temp = cv2.subtract(image_edit, temp)
        skel = cv2.bitwise_or(skel, temp)
        image_edit = eroded.copy()

        zeros = size - cv2.countNonZero(image_edit)
        if zeros == size:
            done = True

    return skel 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def binarize(self, src):
        """Image binarization.

        Args:
            src (int): Input image BGR.
                       numpy.ndarray, (256, 512, 3), 0~255

        Returns:
            dst (int): Output image.
                       numpy.ndarray, (256, 512), 0~1

        """
        # resize
        if src.shape[0] != self.__height or src.shape[1] != self.__width:
            src = cv2.resize(src, (self.__width, self.__height), interpolation=cv2.INTER_LINEAR)

        # image thresholding
        image_binary_canny = self.__apply_canny(src)
        image_binary_mthreshold = self.__apply_multi_threshold(src)
        image_binary_all = cv2.bitwise_or(image_binary_canny, image_binary_mthreshold)

        # mask top and bottom
        dst = self.__mask_image(image_binary_all)

        return dst 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def paste_patch(self,pos,w,paste_img):
        pos_x = int(pos.x()/self.scale)
        pos_y = int(pos.y()/self.scale)
        w = int(w)

        paste_img = cv2.resize(paste_img,(self.img_size,self.img_size))

        mask = np.full((self.img_size, self.img_size), 0 ,dtype = np.uint8)
        mask = cv2.rectangle(mask,(pos_x,pos_y),( pos_x + w , pos_y + w  ),(255,255,255),thickness=-1)

        img = self.img
        foreground_img = cv2.bitwise_or(paste_img,paste_img,mask=mask)
        back_mask = cv2.bitwise_not(mask)
        background_img =  cv2.bitwise_or(img, img, mask = back_mask)

        self.img = cv2.bitwise_or(foreground_img,background_img) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def add_background(img, img_box, img_background=None):
    if img_background is None: 
        # create random image   
        img_background = draw_random_img(img.shape)
    else:
        # check if we have to resize img_background
        if img_background.shape != img.shape: 
            #print('resizing img background')
            (h, w) = img.shape[:2]
            img_background = cv2.resize(img_background,(w,h))
            # check if we have to convert to gray image            
            if img.ndim == 2:   
                img_background = cv2.cvtColor(img_background,cv2.COLOR_RGB2GRAY) 
        #print('img.shape:',img.shape,', img_background.shape:',img_background.shape)            
    mask = mask_from_polygon(img.shape,img_box) 
    inverse_mask = cv2.bitwise_not(mask)
    img_background = cv2.bitwise_or(img_background, img_background, mask=inverse_mask)
    # combine foreground+background
    final = cv2.bitwise_or(img, img_background)
    return final 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def skeletize(img):
    size = np.size(img)
    skel = np.zeros(img.shape, np.uint8)
    element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
    done = False

    while not done:
        eroded = cv2.erode(img, element)
        temp = cv2.dilate(eroded, element)
        temp = cv2.subtract(img, temp)
        skel = cv2.bitwise_or(skel, temp)
        img = eroded.copy()

        zeroes = size - cv2.countNonZero(img)
        if zeroes == size:
            done = True

    return skel 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def extract_color( src, h_th_low, h_th_up, s_th, v_th ):
    hsv = cv2.cvtColor(src, cv2.COLOR_BGR2HSV)
    h, s, v = cv2.split(hsv)
    if h_th_low > h_th_up:
        ret, h_dst_1 = cv2.threshold(h, h_th_low, 255, cv2.THRESH_BINARY) 
        ret, h_dst_2 = cv2.threshold(h, h_th_up,  255, cv2.THRESH_BINARY_INV)

        dst = cv2.bitwise_or(h_dst_1, h_dst_2)
    else:
        ret, dst = cv2.threshold(h,   h_th_low, 255, cv2.THRESH_TOZERO) 
        ret, dst = cv2.threshold(dst, h_th_up,  255, cv2.THRESH_TOZERO_INV)
        ret, dst = cv2.threshold(dst, 0, 255, cv2.THRESH_BINARY)

    ret, s_dst = cv2.threshold(s, s_th, 255, cv2.THRESH_BINARY)
    ret, v_dst = cv2.threshold(v, v_th, 255, cv2.THRESH_BINARY)
    dst = cv2.bitwise_and(dst, s_dst)
    dst = cv2.bitwise_and(dst, v_dst)
    return dst 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def skeletonize(img):
    """ OpenCV function to return a skeletonized version of img, a Mat object"""

    #  hat tip to http://felix.abecassis.me/2011/09/opencv-morphological-skeleton/

    img = img.copy() # don't clobber original
    skel = img.copy()

    skel[:,:] = 0
    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))

    while True:
        eroded = cv2.morphologyEx(img, cv2.MORPH_ERODE, kernel)
        temp = cv2.morphologyEx(eroded, cv2.MORPH_DILATE, kernel)
        temp  = cv2.subtract(img, temp)
        skel = cv2.bitwise_or(skel, temp)
        img[:,:] = eroded[:,:]
        if cv2.countNonZero(img) == 0:
            break

    return skel 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def _maskBackground(self, img, mask_corners = True):
        h,w,c = np.shape(img)

        blur_img=cv2.blur(img, (5,5))
        hsv = cv2.cvtColor(blur_img, cv2.COLOR_BGR2HSV)

        lower_color = np.array([22,28,26])
        upper_color = np.array([103,255,255])

        # create binary mask by finding background color range
        mask = cv2.inRange(hsv, self.lower_mask_color, self.upper_mask_color)
        # remove the corners from mask since they are prone to illumination problems
        if(mask_corners):
            circle_mask = np.zeros((h, w), np.uint8)
            circle_mask[:, :] = 255
            cv2.circle(circle_mask,(w/2, h/2), min(w/2, h/2), 0, -1)
            mask = cv2.bitwise_or(mask,circle_mask)
        # invert mask to get white objects on black background
        #inverse_mask = 255 - mask

        if self._interactive: cv2.imshow("binary mask", mask)
        if self._interactive: cv2.waitKey(0)

        return mask 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def logical_or(bin_img1, bin_img2):
    """Join two images using the bitwise OR operator.

    Inputs:
    bin_img1   = Binary image data to be compared to bin_img2
    bin_img2   = Binary image data to be compared to bin_img1

    Returns:
    merged     = joined binary image

    :param bin_img1: numpy.ndarray
    :param bin_img2: numpy.ndarray
    :return merged: numpy.ndarray
    """

    params.device += 1
    merged = cv2.bitwise_or(bin_img1, bin_img2)
    if params.debug == 'print':
        print_image(merged, os.path.join(params.debug_outdir, str(params.device) + '_or_joined.png'))
    elif params.debug == 'plot':
        plot_image(merged, cmap='gray')
    return merged 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def overlay_masks(self, image, masks, classes, ids=None):
        colors = self.compute_colors_for_labels(classes).tolist()

        segments_image = image.copy()
        aggregated_mask = np.zeros(image.shape[:2], dtype=np.uint8)
        aggregated_colored_mask = np.zeros(image.shape, dtype=np.uint8)
        black = np.zeros(3, dtype=np.uint8)

        for i, (mask, color) in enumerate(zip(masks, colors)):
            color_idx = i if ids is None else ids[i]
            mask_color = self.instance_color_palette[color_idx % len(self.instance_color_palette)].tolist()
            cv2.bitwise_or(aggregated_mask, mask, dst=aggregated_mask)
            cv2.bitwise_or(aggregated_colored_mask, np.asarray(mask_color, dtype=np.uint8),
                           dst=aggregated_colored_mask, mask=mask)

        # Fill the area occupied by all instances with a colored instances mask image.
        cv2.bitwise_and(segments_image, black, dst=segments_image, mask=aggregated_mask)
        cv2.bitwise_or(segments_image, aggregated_colored_mask, dst=segments_image, mask=aggregated_mask)
        # Blend original image with the one, where instances are colored.
        # As a result instances masks become transparent.
        cv2.addWeighted(image, 0.5, segments_image, 0.5, 0, dst=image)

        return image 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def handsegment(frame):
    lower, upper = boundaries[0]
    lower = np.array(lower, dtype="uint8")
    upper = np.array(upper, dtype="uint8")
    mask1 = cv2.inRange(frame, lower, upper)

    lower, upper = boundaries[1]
    lower = np.array(lower, dtype="uint8")
    upper = np.array(upper, dtype="uint8")
    mask2 = cv2.inRange(frame, lower, upper)

    # for i,(lower, upper) in enumerate(boundaries):
    # 	# create NumPy arrays from the boundaries
    # 	lower = np.array(lower, dtype = "uint8")
    # 	upper = np.array(upper, dtype = "uint8")

    # 	# find the colors within the specified boundaries and apply
    # 	# the mask
    # 	if(i==0):
    # 		print "Harish"
    # 		mask1 = cv2.inRange(frame, lower, upper)
    # 	else:
    # 		print "Aadi"
    # 		mask2 = cv2.inRange(frame, lower, upper)
    mask = cv2.bitwise_or(mask1, mask2)
    output = cv2.bitwise_and(frame, frame, mask=mask)
    # show the images
    # cv2.imshow("images", mask)
    # cv2.imshow("images", output)
    return output 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def combine_images():
    im1 = cv2.imread('/Users/fogleman/Workspace/maptiles/zoom18t.png')
    im2 = cv2.imread('/Users/fogleman/Workspace/maptiles/zoom19t.png')
    im3 = cv2.imread('/Users/fogleman/Workspace/maptiles/zoom20s.png')
    im1 = isolate_buildings(im1)
    im2 = isolate_buildings(im2)
    im3 = isolate_buildings(im3)
    im = cv2.bitwise_or(cv2.bitwise_or(im1, im2), im3)
    return im 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_or [as 别名]
def mask_overlay(src, mask):
    """Image overlaying.

    Args:
        src (int): Input image BGR.
            numpy.ndarray, (720, 1280, 3), 0~255
        mask (int): Input image BGR.
            numpy.ndarray, (720, 1280, 3), 0~255

    Returns:
        dst (int): Output image BGR.
                   numpy.ndarray, (720, 1280, 3), 0~255

    """
    # binarize
    gray = cv2.cvtColor(mask, cv2.COLOR_RGB2GRAY)
    ret, mask_bynary = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
    mask_bynary = mask_bynary // 255

    # resize
    if src.shape[0] != mask.shape[0] or src.shape[1] != mask.shape[1]:
        mask = cv2.resize(mask, (src.shape[1], src.shape[0]), interpolation=cv2.INTER_LINEAR)
        mask_bynary = cv2.resize(mask_bynary, (src.shape[1], src.shape[0]), interpolation=cv2.INTER_LINEAR)

    # prepare white image
    mask_white = np.dstack((mask_bynary*255, mask_bynary*255, mask_bynary*255))

    # combine src and thresholded image
    not_mask = cv2.bitwise_not(mask_white)
    src_masked = cv2.bitwise_and(src, not_mask)
    dst = cv2.bitwise_or(src_masked, mask)

    return dst