Python cv2.CC_STAT_LEFT Examples

The following are 2 code examples of cv2.CC_STAT_LEFT(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module cv2 , or try the search function .
Example #1
Source File: detect_by_simple_dense_optical_flow.py    From open_model_zoo with Apache License 2.0 5 votes vote down vote up 
def _convert_connection_components(retval, labels, stats, centroids, original_mask):
    assert np.amax(labels) == retval - 1
    connected_components = [None] * retval
    for i in range(retval):
        mask = np.array(labels == i, dtype=np.uint8)
        stat_for_label = stats[i]
        stat_left = stat_for_label[cv2.CC_STAT_LEFT]
        stat_top = stat_for_label[cv2.CC_STAT_TOP]
        stat_width = stat_for_label[cv2.CC_STAT_WIDTH]
        stat_height = stat_for_label[cv2.CC_STAT_HEIGHT]
        rect = Rect(stat_left, stat_top, stat_width, stat_height)
        centr = centroids[i]
        area = _get_area_rect(rect)
        num = int(np.sum(original_mask[mask == 1]))
        if area > labels.shape[0] * labels.shape[1] / 16:
            log.debug("_convert_connection_components: i = {}".format(i))
            log.debug("_convert_connection_components: rect = {}".format(rect))
            log.debug("_convert_connection_components: centr = {}".format(centr))
            log.debug("_convert_connection_components: area = {}".format(area))
            log.debug("_convert_connection_components: num = {}".format(num))

        component = ConnectedComponent(label_id=i, mask=mask, centroid=centr, rect=rect,
                                       area=area, num=num)
        connected_components[i] = component

    return connected_components
Example #2
Source File: craft_utils.py    From CRAFT-pytorch with MIT License 4 votes vote down vote up 
def getDetBoxes_core(textmap, linkmap, text_threshold, link_threshold, low_text):
    # prepare data
    linkmap = linkmap.copy()
    textmap = textmap.copy()
    img_h, img_w = textmap.shape

    """ labeling method """
    ret, text_score = cv2.threshold(textmap, low_text, 1, 0)
    ret, link_score = cv2.threshold(linkmap, link_threshold, 1, 0)

    text_score_comb = np.clip(text_score + link_score, 0, 1)
    nLabels, labels, stats, centroids = cv2.connectedComponentsWithStats(text_score_comb.astype(np.uint8), connectivity=4)

    det = []
    mapper = []
    for k in range(1,nLabels):
        # size filtering
        size = stats[k, cv2.CC_STAT_AREA]
        if size < 10: continue

        # thresholding
        if np.max(textmap[labels==k]) < text_threshold: continue

        # make segmentation map
        segmap = np.zeros(textmap.shape, dtype=np.uint8)
        segmap[labels==k] = 255
        segmap[np.logical_and(link_score==1, text_score==0)] = 0   # remove link area
        x, y = stats[k, cv2.CC_STAT_LEFT], stats[k, cv2.CC_STAT_TOP]
        w, h = stats[k, cv2.CC_STAT_WIDTH], stats[k, cv2.CC_STAT_HEIGHT]
        niter = int(math.sqrt(size * min(w, h) / (w * h)) * 2)
        sx, ex, sy, ey = x - niter, x + w + niter + 1, y - niter, y + h + niter + 1
        # boundary check
        if sx < 0 : sx = 0
        if sy < 0 : sy = 0
        if ex >= img_w: ex = img_w
        if ey >= img_h: ey = img_h
        kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(1 + niter, 1 + niter))
        segmap[sy:ey, sx:ex] = cv2.dilate(segmap[sy:ey, sx:ex], kernel)

        # make box
        np_contours = np.roll(np.array(np.where(segmap!=0)),1,axis=0).transpose().reshape(-1,2)
        rectangle = cv2.minAreaRect(np_contours)
        box = cv2.boxPoints(rectangle)

        # align diamond-shape
        w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2])
        box_ratio = max(w, h) / (min(w, h) + 1e-5)
        if abs(1 - box_ratio) <= 0.1:
            l, r = min(np_contours[:,0]), max(np_contours[:,0])
            t, b = min(np_contours[:,1]), max(np_contours[:,1])
            box = np.array([[l, t], [r, t], [r, b], [l, b]], dtype=np.float32)

        # make clock-wise order
        startidx = box.sum(axis=1).argmin()
        box = np.roll(box, 4-startidx, 0)
        box = np.array(box)

        det.append(box)
        mapper.append(k)

    return det, labels, mapper