python类SimpleBlobDetector()的实例源码

def create_blob_detector(roi_size=(128, 128), blob_min_area=3, 
                         blob_min_int=.5, blob_max_int=.95, blob_th_step=10):
    params = cv2.SimpleBlobDetector_Params()
    params.filterByArea = True
    params.minArea = blob_min_area
    params.maxArea = roi_size[0]*roi_size[1]
    params.filterByCircularity = False
    params.filterByColor = False
    params.filterByConvexity = False
    params.filterByInertia = False
    # blob detection only works with "uint8" images.
    params.minThreshold = int(blob_min_int*255)
    params.maxThreshold = int(blob_max_int*255)
    params.thresholdStep = blob_th_step
    ver = (cv2.__version__).split('.')
    if int(ver[0]) < 3:
        return cv2.SimpleBlobDetector(params)
    else:
        return cv2.SimpleBlobDetector_create(params)

def mask_to_objects(mask, threshold):
    """
    applies a blob detection algorithm to the image
    Args:
        mask: image mask scaled between 0 and 255 
        threshold: min pixel intensity of interest
    Returns:
        list of objects [(x,y)]
    """

    params = cv2.SimpleBlobDetector_Params()
    params.minThreshold = threshold
    params.maxThreshold = 255

    params.filterByArea = True
    params.minArea = 150
    params.maxArea = 10000

    params.filterByCircularity = False
    params.filterByInertia = False
    params.filterByConvexity = False
    params.filterByColor = False
    params.blobColor = 255

    # Create a detector with the parameters
    ver = (cv2.__version__).split('.')
    if int(ver[0]) < 3:
        detector = cv2.SimpleBlobDetector(params)
    else: 
        detector = cv2.SimpleBlobDetector_create(params)

    keypoints = detector.detect(mask)

    objects = []
    for k in keypoints:
        objects.append(Rect(int(k.pt[0] - k.size), int(k.pt[1] - k.size), int(k.size * 2), int(k.size * 2)))

    return objects
# ============================================================= #

def find_blobs(img):
    # Setup SimpleBlobDetector parameters.
    params = cv2.SimpleBlobDetector_Params()

    # Change thresholds
    params.minThreshold = 100;
    params.maxThreshold = 5000;

    # Filter by Area.
    params.filterByArea = True
    params.minArea = 200

    # Filter by Circularity
    params.filterByCircularity = False
    params.minCircularity = 0.785

    # Filter by Convexity
    params.filterByConvexity = False
    params.minConvexity = 0.87

    # Filter by Inertia
    #params.filterByInertia = True
    #params.minInertiaRatio = 0.01

    # Set up the detector with default parameters.
    detector = cv2.SimpleBlobDetector(params)

    # Detect blobs.
    keypoints = detector.detect(img)
    print keypoints

    # Draw detected blobs as red circles.
    # cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
    im_with_keypoints = cv2.drawKeypoints(img, keypoints, np.array([]),
            (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    cv2.imwrite("blobs.jpg", im_with_keypoints);

def blobdetect(img):
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    yellowMin = (20,100,100)
    yellowMax = (30, 255, 255)

    blueMin=(50,100,100)
    blueMax=(100,255,255)

    brownMin=(0,100,0)
    brownMax=(20,255,255)
    yellow=cv2.inRange(hsv,yellowMin, yellowMax)
    blue=cv2.inRange(hsv,blueMin,blueMax)
    brown=cv2.inRange(hsv,brownMin,brownMax)

    params = cv2.SimpleBlobDetector_Params()
    params.filterByArea = True
    params.minArea=150
    detector=cv2.SimpleBlobDetector(params)
    keypoints=detector.detect(255-yellow)
    food=[]
    for i in keypoints:
        x=i.pt[0]; y=i.pt[1]
        food.append([x,y])

    params.maxArea=250
    detector=cv2.SimpleBlobDetector(params)
    keypoints=detector.detect(255-yellow)

    wood=[]
    for i in keypoints:
        x=i.pt[0]; y=i.pt[1]
        wood.append([x,y])

    params=cv2.SimpleBlobDetector()
    keypoints=params.detect(255-blue)
    rivers=[]
    for i in keypoints:
        x=i.pt[0]; y=i.pt[1]
        rivers.append([x,y])

    keypoints=params.detect(255-brown)
    centre=[]
    for i in keypoints:
        x=i.pt[0]; y=i.pt[1]
        centre.append([x,y])

    return [food, wood, rivers, centre]