Python cv2.subtract方法代碼示例

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def _pre_process_input_minimal(self, img, mask, t, darker_fg=True):
        if self._buff_grey is None:
            self._buff_grey = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
            if mask is None:
                mask = np.ones_like(self._buff_grey) * 255

        cv2.cvtColor(img,cv2.COLOR_BGR2GRAY, self._buff_grey)

        cv2.erode(self._buff_grey, self._erode_kern, dst=self._buff_grey)

        if darker_fg:
            cv2.subtract(255, self._buff_grey, self._buff_grey)


        if mask is not None:
            cv2.bitwise_and(self._buff_grey, mask, self._buff_grey)
            return self._buff_grey 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
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 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def maximizeContrast(imgGrayscale):

    height, width = imgGrayscale.shape

    imgTopHat = np.zeros((height, width, 1), np.uint8)
    imgBlackHat = np.zeros((height, width, 1), np.uint8)

    structuringElement = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))

    imgTopHat = cv2.morphologyEx(imgGrayscale, cv2.MORPH_TOPHAT, structuringElement)
    imgBlackHat = cv2.morphologyEx(imgGrayscale, cv2.MORPH_BLACKHAT, structuringElement)

    imgGrayscalePlusTopHat = cv2.add(imgGrayscale, imgTopHat)
    imgGrayscalePlusTopHatMinusBlackHat = cv2.subtract(imgGrayscalePlusTopHat, imgBlackHat)

    return imgGrayscalePlusTopHatMinusBlackHat
# end function 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def imnormalize_(img, mean, std, to_rgb=True):
    """Inplace normalize an image with mean and std.

    Args:
        img (ndarray): Image to be normalized.
        mean (ndarray): The mean to be used for normalize.
        std (ndarray): The std to be used for normalize.
        to_rgb (bool): Whether to convert to rgb.

    Returns:
        ndarray: The normalized image.
    """
    # cv2 inplace normalization does not accept uint8
    assert img.dtype != np.uint8
    mean = np.float64(mean.reshape(1, -1))
    stdinv = 1 / np.float64(std.reshape(1, -1))
    if to_rgb:
        cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)  # inplace
    cv2.subtract(img, mean, img)  # inplace
    cv2.multiply(img, stdinv, img)  # inplace
    return img 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def preprocess(image):
	# load the image
	image = cv2.imread(args["image"])

	#resize image
	image = cv2.resize(image,None,fx=0.7, fy=0.7, interpolation = cv2.INTER_CUBIC)

	#convert to grayscale
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

	#calculate x & y gradient
	gradX = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 1, dy = 0, ksize = -1)
	gradY = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 0, dy = 1, ksize = -1)

	# subtract the y-gradient from the x-gradient
	gradient = cv2.subtract(gradX, gradY)
	gradient = cv2.convertScaleAbs(gradient)

	# blur the image
	blurred = cv2.blur(gradient, (3, 3))

	# threshold the image
	(_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)
	thresh = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	return thresh 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def generateDoGImages(gaussian_images):
    """Generate Difference-of-Gaussians image pyramid
    """
    logger.debug('Generating Difference-of-Gaussian images...')
    dog_images = []

    for gaussian_images_in_octave in gaussian_images:
        dog_images_in_octave = []
        for first_image, second_image in zip(gaussian_images_in_octave, gaussian_images_in_octave[1:]):
            dog_images_in_octave.append(subtract(second_image, first_image))  # ordinary subtraction will not work because the images are unsigned integers
        dog_images.append(dog_images_in_octave)
    return array(dog_images)

###############################
# Scale-space extrema related #
############################### 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
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 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def getAllSquareRecord(all_square_list,types):
    print("將所有的方塊與類型進行比較，轉置成數字矩陣...")
    record = []  # 整個記錄的二維數組
    line = []   # 記錄一行
    for square in all_square_list:   # 把所有的方塊和保存起來的所有類型做對比
        num = 0
        for type in types:    # 所有類型
            res = cv2.subtract(square,type) # 作比較
            if not np.any(res):     # 如果兩個圖片一樣
                line.append(num)    # 將類型的數字記錄進這一行
                break               # 並且跳出循環
            num += 1                # 如果沒有匹配上，則類型數加1

        if len(line) == V_NUM:         # 如果校驗完這一行已經有了11個數據，則另起一行
            record.append(line)
            line = []
    print(record)
    return record

# 自動消除 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def subtract_bgnd(self, image):
        # new method using bitwise not
        def _remove_func(_img, _func, _bg):
            #the reason to use opencv2 instead of numpy is to avoid buffer overflow
            #https://stackoverflow.com/questions/45817037/opencv-image-subtraction-vs-numpy-subtraction/45817868
            new_img = np.zeros_like(_img); #maybe can do this in place
            if image.ndim == 2:
                _func(_img, _bg, new_img)
            else:
                for ii, this_frame in enumerate(_img):
                    _func(this_frame, _bg, new_img[ii])
            return new_img
        
        bg = self.bgnd.astype(np.uint8)
        if self.is_light_background:
            notbg = ~bg
            ss = _remove_func(image, cv2.add, notbg)
        else: # fluorescence
            ss = _remove_func(image, cv2.subtract, bg)
            
        ss = np.clip( ss ,1,255);
        
        return ss 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
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 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def test_write_image_to_disk():
    """Test for write_image_to_disk

    """
    print("testing write_image_to_disk")
    # load the image from disk
    bgr_image = load_image("images/logo.png")
    # write image to disk
    write_image_to_disk("images/temp.png", bgr_image)
    # load the image temp from disk
    temp = load_image("images/temp.png")
    # now we check that the two images are equal
    assert bgr_image.shape == temp.shape
    difference = cv2.subtract(bgr_image, temp)
    b, g, r = cv2.split(difference)
    assert cv2.countNonZero(b) == 0 and cv2.countNonZero(g) == 0 and cv2.countNonZero(r) == 0 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def calculate_diff(self, frame):
        if self.avgframe is not None:
            subframe = cv2.subtract(frame, self.avgframe)
            grayscaled = cv2.cvtColor(subframe, cv2.COLOR_BGR2GRAY)
            retval2,th1 = cv2.threshold(grayscaled,35,255,cv2.THRESH_BINARY)
            self.avgframe = cv2.addWeighted(frame, 0.1, self.avgframe, 0.9, 0.0)
            th1 = th1 / 255
            w, h = th1.shape
            sum = cv2.sumElems(th1)[0]/(w*h)
            return sum
        else:
            self.avgframe = frame
            return 0.0


# Test the code. 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def gradient_and_binary(img_blurred, image_name='1.jpg', save_path='./'):  # 將灰度圖二值化，後麵兩個參數調試用
    """
    求取梯度，二值化
    :param img_blurred: 濾波後的圖片
    :param image_name: 圖片名，測試用
    :param save_path: 保存路徑，測試用
    :return:  二值化後的圖片
    """
    gradX = cv2.Sobel(img_blurred, ddepth=cv2.CV_32F, dx=1, dy=0)
    gradY = cv2.Sobel(img_blurred, ddepth=cv2.CV_32F, dx=0, dy=1)
    img_gradient = cv2.subtract(gradX, gradY)
    img_gradient = cv2.convertScaleAbs(img_gradient)  # sobel算子,計算梯度, 也可以用canny算子替代

    # 這裏改進成自適應閾值,貌似沒用
    img_thresh = cv2.adaptiveThreshold(img_gradient, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 3, -3)
    # cv2.imwrite(os.path.join(save_path, img_name + '_binary.jpg'), img_thresh)  # 二值化 閾值未調整好

    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
    img_closed = cv2.morphologyEx(img_thresh, cv2.MORPH_CLOSE, kernel)
    img_closed = cv2.morphologyEx(img_closed, cv2.MORPH_OPEN, kernel)
    img_closed = cv2.erode(img_closed, None, iterations=9)
    img_closed = cv2.dilate(img_closed, None, iterations=9)  # 腐蝕膨脹
    # 這裏調整了kernel大小(減小),腐蝕膨脹次數後(增大),出錯的概率大幅減小

    return img_closed 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def describe(self, image):
        # convert the image to the HSV color space and initialize
        # the features used to quantify the image
        image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
        features = []

        # grab the dimensions and compute the center of the image
        (h, w) = image.shape[:2]
        (cX, cY) = (int(w * 0.5), int(h * 0.5))

        # divide the image into four rectangles/segments (top-left,
        # top-right, bottom-right, bottom-left)
        segments = [(0, cX, 0, cY), (cX, w, 0, cY),
                    (cX, w, cY, h), (0, cX, cY, h)]

        # construct an elliptical mask representing the center of the
        # image
        (axesX, axesY) = (int(w * 0.75) / 2, int(h * 0.75) / 2)
        ellipMask = np.zeros(image.shape[:2], dtype="uint8")
        cv2.ellipse(ellipMask, (cX, cY), (axesX, axesY), 0, 0, 360, 255, -1)

        # loop over the segments
        for (startX, endX, startY, endY) in segments:
            # construct a mask for each corner of the image, subtracting
            # the elliptical center from it
            cornerMask = np.zeros(image.shape[:2], dtype="uint8")
            cv2.rectangle(cornerMask, (startX, startY), (endX, endY), 255, -1)
            cornerMask = cv2.subtract(cornerMask, ellipMask)

            # extract a color histogram from the image, then update the
            # feature vector
            hist = self.histogram(image, cornerMask)
            features.extend(hist)

        # extract a color histogram from the elliptical region and
        # update the feature vector
        hist = self.histogram(image, ellipMask)
        features.extend(hist)

        # return the feature vector
        return features 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import subtract [as 別名]
def describe(self, image):
		# convert the image to the HSV color space and initialize
		# the features used to quantify the image
		image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
		features = []

		# grab the dimensions and compute the center of the image
		(h, w) = image.shape[:2]
		(cX, cY) = (int(w * 0.5), int(h * 0.5))

		# divide the image into four rectangles/segments (top-left,
		# top-right, bottom-right, bottom-left)
		segments = [(0, cX, 0, cY), (cX, w, 0, cY), (cX, w, cY, h),
			(0, cX, cY, h)]
 
		# construct an elliptical mask representing the center of the
		# image
		(axesX, axesY) = (int(w * 0.75) // 2, int(h * 0.75) // 2)
		ellipMask = np.zeros(image.shape[:2], dtype = "uint8")
		cv2.ellipse(ellipMask, (cX, cY), (axesX, axesY), 0, 0, 360, 255, -1)
 
		# loop over the segments
		for (startX, endX, startY, endY) in segments:
			# construct a mask for each corner of the image, subtracting
			# the elliptical center from it
			cornerMask = np.zeros(image.shape[:2], dtype = "uint8")
			cv2.rectangle(cornerMask, (startX, startY), (endX, endY), 255, -1)
			cornerMask = cv2.subtract(cornerMask, ellipMask)
 
			# extract a color histogram from the image, then update the
			# feature vector
			hist = self.histogram(image, cornerMask)
			features.extend(hist)
 
		# extract a color histogram from the elliptical region and
		# update the feature vector
		hist = self.histogram(image, ellipMask)
		features.extend(hist)
 
		# return the feature vector
		return features