Python cv2 模块，DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS 实例源码

def _extract_spots(self) -> None:
        # Dilate and Erode to 'clean' the spot (nb that this harms the number itself, so we only do it to extract spots)
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
        img = cv2.dilate(self._img, kernel, iterations=1)
        img = cv2.erode(img, kernel, iterations=2)
        img = cv2.dilate(img, kernel, iterations=1)

        # Perform a simple blob detect
        params = cv2.SimpleBlobDetector_Params()
        params.filterByArea = True
        params.minArea = 20  # The dot in 20pt font has area of about 30
        params.filterByCircularity = True
        params.minCircularity = 0.7
        params.filterByConvexity = True
        params.minConvexity = 0.8
        params.filterByInertia = True
        params.minInertiaRatio = 0.4
        detector = cv2.SimpleBlobDetector_create(params)
        self.spot_keypoints = detector.detect(img)

        # Log intermediate image
        img_with_keypoints = cv2.drawKeypoints(img, self.spot_keypoints, outImage=np.array([]), color=(0, 0, 255),
                                               flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
        self.intermediate_images.append(NamedImage(img_with_keypoints, 'Spot Detection Image'))

def add_blobs(crop_frame):
    frame=cv2.GaussianBlur(crop_frame, (3, 3), 0)
    # Convert BGR to HSV
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    # define range of green color in HSV
    lower_green = np.array([70,50,50])
    upper_green = np.array([85,255,255])
    # Threshold the HSV image to get only blue colors
    mask = cv2.inRange(hsv, lower_green, upper_green)
    mask = cv2.erode(mask, None, iterations=1)
    mask = cv2.dilate(mask, None, iterations=1)    
    # Bitwise-AND mask and original image
    res = cv2.bitwise_and(frame,frame, mask= mask)
    detector = cv2.SimpleBlobDetector_create(params)
    # Detect blobs.
    reversemask=255-mask
    keypoints = detector.detect(reversemask)
    if keypoints:
        print "found blobs"
        if len(keypoints) > 4:
            keypoints.sort(key=(lambda s: s.size))
            keypoints=keypoints[0:3]
        # 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(frame, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    else:
        print "no blobs"
        im_with_keypoints=crop_frame

    return im_with_keypoints #, max_blob_dist, blob_center, keypoint_in_orders

def execute_BlobDetector(proxy,obj):

    try: img=obj.sourceObject.Proxy.img.copy()
    except: img=cv2.imread(__dir__+'/icons/freek.png')

    im = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    im=255-im
    im2 = img

    params = cv2.SimpleBlobDetector_Params()

    params.filterByArea = True
    params.minArea = obj.Area

    params.filterByConvexity = True
    params.minConvexity = obj.Convexity/200


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

    # Detect blobs.
    keypoints = detector.detect(im)
    # 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
    if not obj.showBlobs:
        im_with_keypoints = cv2.drawKeypoints(im, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
        obj.Proxy.img = im_with_keypoints

        for k in keypoints:
            (x,y)=k.pt
            x=int(round(x))
            y=int(round(y))
#           cv2.circle(im,(x,y),4,0,5)
            cv2.circle(im,(x,y),4,255,5)
            cv2.circle(im,(x,y),4,0,5)
            im[y,x]=255
            im[y,x]=0
        obj.Proxy.img = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)

    else:
        for k in keypoints:
            (x,y)=k.pt
            x=int(round(x))
            y=int(round(y))
            cv2.circle(im2,(x,y),4,(255,0,0),5)
            cv2.circle(im2,(x,y),4,(0,0,0),5)
            im2[y,x]=(255,0,0)
            im2[y,x]=(0,0,0)
        obj.Proxy.img = im2

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 draw_keypoints(self, im, keypoints, filename="keypoints.jpg"):
        self._log("drawing keypoints into '%s'..." % filename)
        rows, cols = im.shape

        def to_cv2_kp(kp):
            # assert kp = [<row>, <col>, <ori>, <octave_ind>, <layer_ind>]
            ratio = get_size_ratio_by_octave(kp[3])
            scale = get_scale_by_ind(kp[3], kp[4])
            return cv2.KeyPoint(kp[1] / ratio, kp[0] / ratio, 10, kp[2] / PI * 180)

        kp_for_draw = list(map(to_cv2_kp, keypoints))
        im_kp = cv2.drawKeypoints(im, kp_for_draw, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
        cv2.imwrite(filename, im_kp)

def cv2_match(im1, im2):
    mysift = SIFT()
    sift = cv2.SIFT()
    bf = cv2.BFMatcher()


    kp1, dp1 = sift.detectAndCompute(im1, None)
    kp2, dp2 = sift.detectAndCompute(im2, None)
    matches_ = bf.knnMatch(dp1, dp2, k=2)

    print(len(matches_))
    good = []
    for m, n in matches_:
        if m.distance < 0.90 * n.distance:
            good.append(m)
    print(len(good))

    pos1 = [(int(kp.pt[1]), int(kp.pt[0])) for kp in kp1]
    pos2 = [(int(kp.pt[1]), int(kp.pt[0])) for kp in kp2]
    matches = [(m.queryIdx, m.trainIdx, 0.15) for m in good]

    cv2.imwrite("cvkp1.jpg", cv2.drawKeypoints(im, kp1, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS))
    cv2.imwrite("cvkp2.jpg", cv2.drawKeypoints(imm, kp2, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS))
    mysift.draw_matches(im, pos1, imm, pos2, matches, 'ckmatch.jpg')

def visualize_keypoints(image, keypoints):
  kp_image = np.array([])
  kp_image = cv2.drawKeypoints(image, keypoints, kp_image, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
  cv2.imshow(PROJ_WIN, kp_image)
  wait()

def draw_image_with_keypoints(img: np.ndarray, keypoints, window_title: str ="Image with keypoints") -> None:
    """An apparently unused method which is actually quite useful when debugging!"""

    # cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
    img_with_keypoints = cv2.drawKeypoints(img, keypoints, outImage=np.array([]), color=(0, 0, 255),
                                           flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    draw_image(img_with_keypoints, window_title)

def sift_thread():
    sift = cv2.xfeatures2d.SIFT_create()
    (kps, descs) = sift.detectAndCompute(gray, None)
    cv2.drawKeypoints(gray, kps, img, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    cv2.imshow('SIFT Algorithm', img)

def surf_thread():
    surf = cv2.xfeatures2d.SURF_create()
    (kps2, descs2) = surf.detectAndCompute(gray, None)
    cv2.drawKeypoints(gray, kps2, img2, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    cv2.imshow('SURF Algorithm', img2)

def fast_thread():
    fast = cv2.FastFeatureDetector_create()
    kps3 = fast.detect(gray, None)
    cv2.drawKeypoints(gray, kps3, img3, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    cv2.imshow('FAST Algorithm', img3)

def orb_thread():
    orb = cv2.ORB_create()
    kps4 = orb.detect(gray, None)
    (kps4, des4) = orb.compute(gray, kps4)
    cv2.drawKeypoints(gray, kps4, img4, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    cv2.imshow('ORB Algorithm', img4)