tslsr.py 文件源码

def __bound_contours(roi):
    """
        returns modified roi(non-destructive) and rectangles that founded by the algorithm.
        @roi region of interest to find contours
        @return (roi, rects)
    """

    roi_copy = roi.copy()
    roi_hsv = cv2.cvtColor(roi, cv2.COLOR_RGB2HSV)
    # filter black color
    mask1 = cv2.inRange(roi_hsv, np.array([0, 0, 0]), np.array([180, 255, 125]))
    mask1 = cv2.morphologyEx(mask1, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
    mask1 = cv2.Canny(mask1, 100, 300)
    mask1 = cv2.GaussianBlur(mask1, (1, 1), 0)
    mask1 = cv2.Canny(mask1, 100, 300)

    # mask1 = cv2.morphologyEx(mask1, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)))

    # Find contours for detected portion of the image
    im2, cnts, hierarchy = cv2.findContours(mask1.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5] # get largest five contour area
    rects = []
    for c in cnts:
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.02 * peri, True)
        x, y, w, h = cv2.boundingRect(approx)
        if h >= 15:
            # if height is enough
            # create rectangle for bounding
            rect = (x, y, w, h)
            rects.append(rect)
            cv2.rectangle(roi_copy, (x, y), (x+w, y+h), (0, 255, 0), 1);

    return (roi_copy, rects)