helper.py 文件源码

def skin_filter(cfg, vd):
    df = pd.read_csv(vd.photo_csv, index_col=0)
    numbers = df.number.tolist()
    notface = []
    for number in numbers:
        lower = np.array([0, 48, 80], dtype = "uint8")
        upper = np.array([13, 255, 255], dtype = "uint8")
        image = cv2.imread('%s/%d.png' % (vd.photo_dir, number), cv2.IMREAD_COLOR)
        converted = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
        skinMask = cv2.inRange(converted, lower, upper)

        # apply a series of erosions and dilations to the mask
        # using an elliptical kernel
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
        skinMask = cv2.erode(skinMask, kernel, iterations = 2)
        skinMask = cv2.dilate(skinMask, kernel, iterations = 2)

        # blur the mask to help remove noise, then apply the
        # mask to the frame
        skinMask = cv2.GaussianBlur(skinMask, (3, 3), 0)
        skin = cv2.bitwise_and(image, image, mask = skinMask)
        if len(skin.nonzero()[0]) < cfg.min_skin_pixels:
            notface.append(number)
    print '%d/%d are faces' % ( len(df) - len(notface), len(df) )
    df['face']= 1
    df.loc[df.number.isin(notface),'face'] = -99
    df.to_csv(vd.photo_csv)