# https://gist.github.com/parosky/7890436
# https://github.com/nagadomi/lbpcascade_animeface
import cv2
import numpy as np
GC_UNKNOWN = 5
def unique(a):
""" remove duplicate columns and rows
from http://stackoverflow.com/questions/8560440 """
order = np.lexsort(a.T)
a = a[order]
diff = np.diff(a, axis=0)
ui = np.ones(len(a), 'bool')
ui[1:] = (diff != 0).any(axis=1)
return a[ui]
def detect_faces(im):
hc = cv2.CascadeClassifier('lbpcascade_animeface.xml')
faces = hc.detectMultiScale(im, minSize=(30, 30))
if len(faces) == 0:
raise Exception('no faces')
return faces
def canny_edges(im):
edge_im = cv2.Canny(im, 100, 200)
kernel = np.ones((3, 3), np.uint8)
opening_im = cv2.morphologyEx(edge_im, cv2.MORPH_CLOSE, kernel)
return opening_im
def detect_contours(im):
opening_im = canny_edges(im)
ret, thresh = cv2.threshold(opening_im, 127, 255, 0)
height, width = thresh.shape[:2]
thresh[0:3, 0:width-1] = 255
thresh[height-3:height-1, 0:width-1] = 255
thresh[0:height-1, 0:3] = 255
thresh[0:height-1, width-3:width-1] = 255
_, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
im_size = width * height
ret = []
for contour in contours:
area = cv2.contourArea(contour)
if area < im_size/4:
ret.append(contour)
return ret
def mask_skin_detection(image, flood_diff=3, min_face_size=(30,30),
num_iter=3, verbose=False, step=1):
faces = detect_faces(image)
if len(faces) == 0:
raise Exception('no faces')
image_original = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image2 = np.copy(image_original)
image2[image2==[0,0,0]] = 1
skin_color = np.zeros((len(faces), 3))
for i, face in enumerate(faces):
image_face = image2[face[1]:face[1]+face[3],face[0]:face[0]+face[2]]
skin_color[i] = np.array([image_face[image_face.shape[0]/2, image_face.shape[1]/2]])
mask = np.zeros(image_original.shape)
for i in range(num_iter):
# for each pixel, call floodFill(image2) if it is close to skin_color
for y in range(0, image2.shape[0], step):
if verbose:
print('iter: %d, y:%d' % (i, y))
for x in range(0, image2.shape[1], step):
color = image2[y,x]
if (color!=(0,0,0)).any():
if any((np.abs(skin_color-color)<=(flood_diff,)*3).all(1)):
cv2.floodFill(image2, None, (x,y), (0, 0, 0),
loDiff=(flood_diff,)*3, upDiff=(flood_diff,)*3)
# update mask image and skin_color
mask[image2==(0,0,0)] = 255
skin_color = image_original[mask.nonzero()]
skin_color.shape = (skin_color.shape[0]/3, 3)
skin_color = unique(skin_color)
mask = np.bool_(mask)
return mask
def calc_face_histgram_normalized(im, faces, padding=0):
im_hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
hsv_histgrams = []
for i, face in enumerate(faces):
origin_x, origin_y, width, height = face
area = (height - padding*2)*(width - padding*2)
roi_image = im_hsv[(origin_y + padding):(origin_y + height - padding), (origin_x + padding):(origin_x + width - padding)]
cv2.imshow('win', roi_image)
cv2.waitKey(1000)
h_h = cv2.calcHist([roi_image], [0], None, [180], [0, 180], None, 0)
h_s = cv2.calcHist([roi_image], [1], None, [256], [0, 256], None, 0)
h_v = cv2.calcHist([roi_image], [2], None, [256], [0, 256], None, 0)
# normalization and append
if area == 0:
area = 1
hsv_histgrams.append([h_h/area, h_s/area, h_v/area])
return hsv_histgrams
def mask_from_contour(im_shape, contour):
mask = np.zeros(im_shape, np.uint8)
cv2.drawContours(mask, [contour], 0, 255, -1)
return mask
def mask_from_contours(im_shape, fg_contours, bg_contours):
mask = np.zeros(im_shape, np.uint8)
mask.fill(cv2.GC_PR_BGD)
for contour in fg_contours:
cv2.drawContours(mask, [contour], 0, cv2.GC_FGD, -1)
for contour in bg_contours:
cv2.drawContours(mask, [contour], 0, cv2.GC_BGD, -1)
return mask
def mask_from_point_and_contour(im_shape, mask_skin_point, fg_contours, bg_contours):
mask = np.zeros(im_shape, np.uint8)
mask.fill(GC_UNKNOWN)
for contour in fg_contours:
cv2.drawContours(mask, [contour], 0, cv2.GC_PR_FGD, -1)
for contour in bg_contours:
cv2.drawContours(mask, [contour], 0, cv2.GC_PR_BGD, -1)
height, width = im_shape
for y in range(height):
for x in range(width):
gc_label = mask[y][x]
if mask_skin_point[y, x, 0]:
if gc_label == cv2.GC_PR_FGD:
mask[y][x] = cv2.GC_FGD
else:
mask[y][x] = cv2.GC_FGD
else:
if gc_label == cv2.GC_PR_BGD:
mask[y][x] = cv2.GC_BGD
else:
mask[y][x] = cv2.GC_PR_BGD
return mask
def calc_contour_histgram_normalized(im, contour):
mask = mask_from_contour(im.shape, contour)
area = cv2.contourArea(contour)
im_hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
im_mask = cv2.inRange(mask, 10, 255)
h_h = cv2.calcHist([im_hsv], [0], im_mask, [180], [0, 180], None, 0)
h_s = cv2.calcHist([im_hsv], [1], im_mask, [256], [0, 256], None, 0)
h_v = cv2.calcHist([im_hsv], [2], im_mask, [256], [0, 256], None, 0)
# normalization and return
if area == 0:
area = 1
return [h_h/area, h_s/area, h_v/area]
def blown_skin_mask(im, mask, diff_color):
height, width = im.shape[:2]
hsv_im = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
for y in range(height):
for x in range(width):
if (mask[y][x]):
h = hsv_im[y, x, 0] + diff_color[0]
s = hsv_im[y, x, 1] + diff_color[1]
v = hsv_im[y, x, 2] + diff_color[2]
if h < 0:
hsv_im[y, x, 0] = h + 180
elif h > 180:
hsv_im[y, x, 0] = h - 180
else:
hsv_im[y, x, 0] = h
if s < 0:
hsv_im[y, x, 1] = 0
elif s > 255:
hsv_im[y, x, 1] = 255
else:
hsv_im[y, x, 1] = s
if v < 0:
hsv_im[y, x, 2] = 0
elif v > 255:
hsv_im[y, x, 2] = 255
else:
hsv_im[y, x, 2] = v
return cv2.cvtColor(hsv_im, cv2.COLOR_HSV2BGR)
def calc_hsv_diff(skin_hist):
blown_color = np.array([13, 110, 220])
return blown_color - skin_color
def max_bin_histgram(hist):
idx, _ = np.unravel_index(hist.argmax(), hist.shape)
return idx
if __name__ == '__main__':
thresh = 2.1
thresh_ng = 0.6
filename = 'test.jpg'
filename_out = (''.join(filename.split('.')[:-1])
+ 'gc_out%d.' + filename.split('.')[-1])
image = cv2.imread(filename)
im_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# detect face
faces = detect_faces(image)
face_hists = calc_face_histgram_normalized(image, faces, padding=40)
contours = detect_contours(image)
skin_contours = []
skin_hists = []
bg_contours = []
# detect contour
for iter in range(1):
face_hists.extend(skin_hists)
skin_hists = []
for index, contour in enumerate(contours):
hists = calc_contour_histgram_normalized(image, contour)
for face_hist in face_hists:
h_dist = cv2.compareHist(face_hist[0], hists[0], 0)
s_dist = cv2.compareHist(face_hist[1], hists[1], 0)
v_dist = cv2.compareHist(face_hist[2], hists[2], 0)
if (h_dist + s_dist + v_dist) > thresh:
skin_contours.append(contour)
skin_hists.append(hists)
elif (h_dist + s_dist + v_dist) < thresh_ng:
bg_contours.append(contour)
skin_color = np.array([max_bin_histgram(face_hists[0][0]), max_bin_histgram(face_hists[0][1]), max_bin_histgram(face_hists[0][2])])
diff_color = calc_hsv_diff(skin_color)
# graph cut
mask_skin = mask_skin_detection(image, num_iter=2, verbose=True)
mask = mask_from_point_and_contour(image.shape[:2], mask_skin, skin_contours, bg_contours)
bgd_model = np.zeros((1, 65), np.float64)
fgd_model = np.zeros((1, 65), np.float64)
mask, bgd_model, fgd_model = cv2.grabCut(image, mask, None, bgd_model, fgd_model, 5, cv2.GC_INIT_WITH_MASK)
gc_mask = np.where((mask==2)|(mask==0), 0, 1).astype('uint8')
im_masked = image*gc_mask[:, :, np.newaxis]
im_blown = blown_skin_mask(image, gc_mask, diff_color)
# output image
cv2.imwrite(filename_out % 1, im_blown)
cv2.imwrite(filename_out % 2, im_masked)
im_concat = np.concatenate((image, im_blown), axis=1)
cv2.imwrite(filename_out % 3, im_concat)
Created
February 21, 2015 18:41
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点と面の肌色領域推定結果を用いたグラフカットによる肌色検出
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