#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'Will Brennan'
import logging
import cv2
import numpy
from . import scripts
logger = logging.getLogger('main')
def get_hsv_mask(img, debug=False):
assert isinstance(img, numpy.ndarray), 'image must be a numpy array'
assert img.ndim == 3, 'skin detection can only work on color images'
logger.debug('getting hsv mask')
lower_thresh = numpy.array([0, 50, 0], dtype=numpy.uint8)
upper_thresh = numpy.array([120, 150, 255], dtype=numpy.uint8)
img_hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
msk_hsv = cv2.inRange(img_hsv, lower_thresh, upper_thresh)
msk_hsv[msk_hsv < 128] = 0
msk_hsv[msk_hsv >= 128] = 1
if debug:
scripts.display('input', img)
scripts.display('mask_hsv', msk_hsv)
return msk_hsv.astype(float)
def get_rgb_mask(img, debug=False):
assert isinstance(img, numpy.ndarray), 'image must be a numpy array'
assert img.ndim == 3, 'skin detection can only work on color images'
logger.debug('getting rgb mask')
lower_thresh = numpy.array([45, 52, 108], dtype=numpy.uint8)
upper_thresh = numpy.array([255, 255, 255], dtype=numpy.uint8)
mask_a = cv2.inRange(img, lower_thresh, upper_thresh)
mask_b = 255 * ((img[:, :, 2] - img[:, :, 1]) / 20)
mask_c = 255 * ((numpy.max(img, axis=2) - numpy.min(img, axis=2)) / 20)
# msk_rgb = cv2.bitwise_and(mask_c, cv2.bitwise_and(mask_a, mask_b))
mask_d = numpy.bitwise_and(numpy.uint64(mask_a), numpy.uint64(mask_b))
msk_rgb = numpy.bitwise_and(numpy.uint64(mask_c), numpy.uint64(mask_d))
msk_rgb[msk_rgb < 128] = 0
msk_rgb[msk_rgb >= 128] = 1
if debug:
scripts.display('input', img)
scripts.display('mask_rgb', msk_rgb)
return msk_rgb.astype(float)
def get_ycrcb_mask(img, debug=False):
assert isinstance(img, numpy.ndarray), 'image must be a numpy array'
assert img.ndim == 3, 'skin detection can only work on color images'
logger.debug('getting ycrcb mask')
lower_thresh = numpy.array([90, 100, 130], dtype=numpy.uint8)
upper_thresh = numpy.array([230, 120, 180], dtype=numpy.uint8)
img_ycrcb = cv2.cvtColor(img, cv2.COLOR_RGB2YCR_CB)
msk_ycrcb = cv2.inRange(img_ycrcb, lower_thresh, upper_thresh)
msk_ycrcb[msk_ycrcb < 128] = 0
msk_ycrcb[msk_ycrcb >= 128] = 1
if debug:
scripts.display('input', img)
scripts.display('mask_ycrcb', msk_ycrcb)
return msk_ycrcb.astype(float)
def grab_cut_mask(img_col, mask, debug=False):
assert isinstance(img_col, numpy.ndarray), 'image must be a numpy array'
assert isinstance(mask, numpy.ndarray), 'mask must be a numpy array'
assert img_col.ndim == 3, 'skin detection can only work on color images'
assert mask.ndim == 2, 'mask must be 2D'
kernel = numpy.ones((50, 50), numpy.float32) / (50 * 50)
dst = cv2.filter2D(mask, -1, kernel)
dst[dst != 0] = 255
free = numpy.array(cv2.bitwise_not(dst), dtype=numpy.uint8)
if debug:
scripts.display('not skin', free)
scripts.display('grabcut input', mask)
grab_mask = numpy.zeros(mask.shape, dtype=numpy.uint8)
grab_mask[:, :] = 2
grab_mask[mask == 255] = 1
grab_mask[free == 255] = 0
if numpy.unique(grab_mask).tolist() == [0, 1]:
logger.debug('conducting grabcut')
bgdModel = numpy.zeros((1, 65), numpy.float64)
fgdModel = numpy.zeros((1, 65), numpy.float64)
if img_col.size != 0:
mask, bgdModel, fgdModel = cv2.grabCut(img_col, grab_mask, None, bgdModel, fgdModel, 5,
cv2.GC_INIT_WITH_MASK)
mask = numpy.where((mask == 2) | (mask == 0), 0, 1).astype(numpy.uint8)
else:
logger.warning('img_col is empty')
return mask
def closing(mask):
assert isinstance(mask, numpy.ndarray), 'mask must be a numpy array'
assert mask.ndim == 2, 'mask must be a greyscale image'
logger.debug("closing mask of shape {0}".format(mask.shape))
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=2)
return mask
def process(img, thresh=0.5, debug=False):
assert isinstance(img, numpy.ndarray), 'image must be a numpy array'
assert img.ndim == 3, 'skin detection can only work on color images'
logger.debug("processing image of shape {0}".format(img.shape))
mask_hsv = get_hsv_mask(img, debug=debug)
mask_rgb = get_rgb_mask(img, debug=debug)
mask_ycrcb = get_ycrcb_mask(img, debug=debug)
n_masks = 3.0
mask = (mask_hsv + mask_rgb + mask_ycrcb) / n_masks
mask[mask < thresh] = 0.0
mask[mask >= thresh] = 255.0
logger.debug('{0}% of the image is skin'.format(int((100.0 / 255.0) * numpy.sum(mask) / mask.size)))
mask = mask.astype(numpy.uint8)
mask = closing(mask)
mask = grab_cut_mask(img, mask, debug=debug)
return mask
