RegionOfInterest.py 文件源码

def roiMask(image, boundaries):
    scale = max([1.0, np.average(np.array(image.shape)[0:2] / 400.0)])
    shape = (int(round(image.shape[1] / scale)), int(round(image.shape[0] / scale)))

    small_color = cv2.resize(image, shape, interpolation=cv2.INTER_LINEAR)

    # reduce details and remove noise for better edge detection
    small_color = cv2.bilateralFilter(small_color, 8, 64, 64)
    small_color = cv2.pyrMeanShiftFiltering(small_color, 8, 64, maxLevel=1)
    small = cv2.cvtColor(small_color, cv2.COLOR_BGR2HSV)

    hue = small[::, ::, 0]
    intensity = cv2.cvtColor(small_color, cv2.COLOR_BGR2GRAY)

    edges = extractEdges(hue, intensity)
    roi = roiFromEdges(edges)
    weight_map = weightMap(hue, intensity, edges, roi)

    _, final_mask = cv2.threshold(roi, 5, 255, cv2.THRESH_BINARY)
    small = cv2.bitwise_and(small, small, mask=final_mask)

    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (4, 4))

    for (lower, upper) in boundaries:
        lower = np.array([lower, 80, 50], dtype="uint8")
        upper = np.array([upper, 255, 255], dtype="uint8")

        # find the colors within the specified boundaries and apply
        # the mask
        mask = cv2.inRange(small, lower, upper)
        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=3)
        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
        final_mask = cv2.bitwise_and(final_mask, mask)

    # blur the mask for better contour extraction
    final_mask = cv2.GaussianBlur(final_mask, (5, 5), 0)
    return (final_mask, weight_map, scale)