Python cv2.moments方法代碼示例

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def get_contour_centers(contours: np.ndarray) -> np.ndarray:
    """
    Calculate the centers of the contours
    :param contours: Contours detected with find_contours
    :return: object centers as numpy array
    """

    if len(contours) == 0:
        return np.array([])

    # ((x, y), radius) = cv2.minEnclosingCircle(c)
    centers = np.zeros((len(contours), 2), dtype=np.int16)
    for i, c in enumerate(contours):
        M = cv2.moments(c)
        center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
        centers[i] = center
    return centers 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def blob_mean_and_tangent(contour):

    moments = cv2.moments(contour)

    area = moments['m00']

    mean_x = moments['m10'] / area
    mean_y = moments['m01'] / area

    moments_matrix = np.array([
        [moments['mu20'], moments['mu11']],
        [moments['mu11'], moments['mu02']]
    ]) / area

    _, svd_u, _ = cv2.SVDecomp(moments_matrix)

    center = np.array([mean_x, mean_y])
    tangent = svd_u[:, 0].flatten().copy()

    return center, tangent 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def find_red(img):
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv,(130,130,180),(255,255,255))
    mask = cv2.erode(mask, np.ones((2,1)) , iterations=1)
    mask = cv2.dilate(mask, None, iterations=3)
    cnts = cv2.findContours(mask, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2]
    frame=img.copy()    
    ###based on example from  http://www.pyimagesearch.com/2015/09/14/ball-tracking-with-opencv
    if len(cnts) > 0:
        c = max(cnts, key=cv2.contourArea)
        ((x, y), radius) = cv2.minEnclosingCircle(c)
        M = cv2.moments(c)
        center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
        if radius > 3:
            cv2.circle(frame, (int(x), int(y)), 12,(0, 255, 255), 2)
    return frame 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def deskew(image, image_shape, negated=False):
    """
    This method deskwes an image using moments
    :param image: a numpy nd array input image
    :param image_shape: a tuple denoting the image`s shape
    :param negated: a boolean flag telling  whether the input image is a negated one

    :returns: a numpy nd array deskewd image
    """
    
    # negate the image
    if not negated:
        image = 255-image

    # calculate the moments of the image
    m = cv2.moments(image)
    if abs(m['mu02']) < 1e-2:
        return image.copy()

    # caclulating the skew
    skew = m['mu11']/m['mu02']
    M = numpy.float32([[1, skew, -0.5*image_shape[0]*skew], [0,1,0]])
    img = cv2.warpAffine(image, M, image_shape, flags=cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR)
    
    return img 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def findLargestSign(image, contours, threshold, distance_theshold):
    max_distance = 0
    coordinate = None
    sign = None
    for c in contours:
        M = cv2.moments(c)
        if M["m00"] == 0:
            continue
        cX = int(M["m10"] / M["m00"])
        cY = int(M["m01"] / M["m00"])
        is_sign, distance = contourIsSign(c, [cX, cY], 1-threshold)
        if is_sign and distance > max_distance and distance > distance_theshold:
            max_distance = distance
            coordinate = np.reshape(c, [-1,2])
            left, top = np.amin(coordinate, axis=0)
            right, bottom = np.amax(coordinate, axis = 0)
            coordinate = [(left-2,top-2),(right+3,bottom+1)]
            sign = cropSign(image,coordinate)
    return sign, coordinate 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def findSigns(image, contours, threshold, distance_theshold):
    signs = []
    coordinates = []
    for c in contours:
        # compute the center of the contour
        M = cv2.moments(c)
        if M["m00"] == 0:
            continue
        cX = int(M["m10"] / M["m00"])
        cY = int(M["m01"] / M["m00"])
        is_sign, max_distance = contourIsSign(c, [cX, cY], 1-threshold)
        if is_sign and max_distance > distance_theshold:
            sign = cropContour(image, [cX, cY], max_distance)
            signs.append(sign)
            coordinate = np.reshape(c, [-1,2])
            top, left = np.amin(coordinate, axis=0)
            right, bottom = np.amax(coordinate, axis = 0)
            coordinates.append([(top-2,left-2),(right+1,bottom+1)])
    return signs, coordinates 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def calculate_contour_features(contour):
    """Calculates interesting properties (features) of a contour.

    We use these features to match shapes (contours). In this script,
    we are interested in finding shapes in our input image that look like
    a corner. We do that by calculating the features for many contours
    in the input image and comparing these to the features of the corner
    contour. By design, we know exactly what the features of the real corner
    contour look like - check out the calculate_corner_features function.

    It is crucial for these features to be invariant both to scale and rotation.
    In other words, we know that a corner is a corner regardless of its size
    or rotation. In the past, this script implemented its own features, but
    OpenCV offers much more robust scale and rotational invariant features
    out of the box - the Hu moments.
    """
    moments = cv2.moments(contour)
    return cv2.HuMoments(moments) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def __init__(self, rubiks_parent, index, contour, heirarchy, debug):
        self.rubiks_parent = rubiks_parent
        self.index = index
        self.contour = contour
        self.heirarchy = heirarchy
        peri = cv2.arcLength(contour, True)
        self.approx = cv2.approxPolyDP(contour, 0.1 * peri, True)
        self.area = cv2.contourArea(contour)
        self.corners = len(self.approx)
        self.width = None
        self.debug = debug

        # compute the center of the contour
        M = cv2.moments(contour)

        if M["m00"]:
            self.cX = int(M["m10"] / M["m00"])
            self.cY = int(M["m01"] / M["m00"])

            # if self.cX == 188 and self.cY == 93:
            #    log.warning("CustomContour M %s" % pformat(M))
        else:
            self.cX = None
            self.cY = None 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def image_callback(self, msg):
    image = self.bridge.imgmsg_to_cv2(msg,desired_encoding='bgr8')
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    lower_yellow = numpy.array([ 10,  10,  10])
    upper_yellow = numpy.array([255, 255, 250])
    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
    
    h, w, d = image.shape
    search_top = 3*h/4
    search_bot = 3*h/4 + 20
    mask[0:search_top, 0:w] = 0
    mask[search_bot:h, 0:w] = 0
    M = cv2.moments(mask)
    if M['m00'] > 0:
      cx = int(M['m10']/M['m00'])
      cy = int(M['m01']/M['m00'])
      cv2.circle(image, (cx, cy), 20, (0,0,255), -1)
      # BEGIN CONTROL
      err = cx - w/2
      self.twist.linear.x = 0.2
      self.twist.angular.z = -float(err) / 100
      self.cmd_vel_pub.publish(self.twist)
      # END CONTROL
    cv2.imshow("window", image)
    cv2.waitKey(3) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def deskew(img):
	m = cv2.moments(img)
	if abs(m['mu02']) < 1e-2:
		return img.copy()
	skew = m['mu11']/m['mu02']
	M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
	img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
	return img
#來自opencv的sample，用於svm訓練 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def de_skew(image, width):
	# Grab the width and height of the image and compute moments for the image
	(h, w) = image.shape[:2]
	moments = cv2.moments(image)
	
	# De-skew the image by applying an affine transformation
	skew = moments["mu11"] / moments["mu02"]
	matrix = np.float32([[1, skew, -0.5 * w * skew], [0, 1, 0]])
	image = cv2.warpAffine(image, matrix, (w, h), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)

	# Resize the image to have a constant width
	image = imutils.resize(image, width=width)
	
	# Return the de-skewed image
	return image 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def detect_iris(frame, marks, side='left'):
    """
    return:
       x: the x coordinate of the iris.
       y: the y coordinate of the iris.
       x_rate: how much the iris is toward the left. 0 means totally left and 1 is totally right.
       y_rate: how much the iris is toward the top. 0 means totally top and 1 is totally bottom.
    """
    mask = np.full(frame.shape[:2], 255, np.uint8)
    if side == 'left':
        region = marks[36:42].astype(np.int32)
    elif side == 'right':
        region = marks[42:48].astype(np.int32)
    try:
        cv2.fillPoly(mask, [region], (0, 0, 0))
        eye = cv2.bitwise_not(frame, frame.copy(), mask=mask)
        # Cropping on the eye
        margin = 4
        min_x = np.min(region[:, 0]) - margin
        max_x = np.max(region[:, 0]) + margin
        min_y = np.min(region[:, 1]) - margin
        max_y = np.max(region[:, 1]) + margin

        eye = eye[min_y:max_y, min_x:max_x]
        eye = cv2.cvtColor(eye, cv2.COLOR_RGB2GRAY)

        eye_binarized = cv2.threshold(eye, np.quantile(eye, 0.2), 255, cv2.THRESH_BINARY)[1]
        contours, _ = cv2.findContours(eye_binarized, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
        contours = sorted(contours, key=cv2.contourArea)
        moments = cv2.moments(contours[-2])
        x = int(moments['m10'] / moments['m00']) + min_x
        y = int(moments['m01'] / moments['m00']) + min_y
        return x, y, (x-min_x-margin)/(max_x-min_x-2*margin), (y-min_y-margin)/(max_y-min_y-2*margin)
    except:
        return 0, 0, 0.5, 0.5 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def deskew(img):
    m = cv2.moments(img)
    if abs(m['mu02']) < 1e-2:
        return img.copy()
    skew = m['mu11']/m['mu02']
    M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
    img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
    return img 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def deskew(img):
    m = cv2.moments(img)
    if abs(m['mu02']) < 1e-2:
        return img.copy()
    skew = m['mu11']/m['mu02']
    M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
    img = cv2.warpAffine(img,M,(SZ, SZ),flags=affine_flags)
    return img
## [deskew]

## [hog] 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import moments [as 別名]
def deskew(img):
    m = cv2.moments(img)
    if abs(m['mu02']) < 1e-2:
        return img.copy()
    skew = m['mu11'] / m['mu02']
    M = np.float32([[1, skew, -0.5 * SZ * skew], [0, 1, 0]])
    img = cv2.warpAffine(img, M, (SZ, SZ), flags=affine_flags)
    return img


# 計算圖像 X 方向和 Y 方向的 Sobel 導數