def demo_checkill():
from detect.dlib_face import cv_get_frontal_face_shape_detector, draw_shapes, cv_get_frontal_face_detector
predictor_path = "./detect/models/shape_predictor_68_face_landmarks.dat"
predictor = cv_get_frontal_face_shape_detector(predictor_path)
# img_path = gb.glob(r"Y:/maxiaofang/data/Uface_register_face/*g")
img_path = "D:/data/tt/reg/"
personNum = 0
lightcount =1
for path in img_path:
# print path
img = cv2.imread(path)
img = cv2.imread(r"./image/0.93_267.jpg")
h,w = img.shape[:2]
maskw = np.zeros([h, w], dtype = np.uint8)
# for some reason some too big picture may could not find face,so the shape will devided
shapes, rects = predictor(img)
for shape in shapes:
if True:
lightcount+=1
if lightcount%100==0:
print "lightcount = ",lightcount
whole =[]
for pt in shape[:17]:
whole.append(pt)
for i in range(22, 27):
whole.append(shape[22 + 26 - i])
for i in range(17, 21):
whole.append(shape[17 + 21 - i])
wholeface = np.array([[whole]], dtype=np.int32)
cv2.fillPoly(maskw, wholeface, 255)
wholemask = cv2.bitwise_and(img, img, mask=maskw)
wholemask_hsv = cv2.cvtColor(wholemask, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(wholemask_hsv)
facearea = np.sum(maskw)/255.
mean_v = np.sum(v) * 1.0 / facearea/255.
mean_b = np.sum(wholemask[:,:,0]) * 1.0 / facearea
mean_g = np.sum(wholemask[:,:,1]) * 1.0 / facearea
mean_r = np.sum(wholemask[:,:,2]) * 1.0 / facearea
print mean_b,mean_g,mean_r
# print "wholemask_v= ",mean_v,mean_v*255
lightcount+=1
# cv2.imshow("wholemask",wholemask)
# cv2.imshow("wholemask_hsv",wholemask_hsv)
info = "%.f_%.f_%.f.jpg"%(mean_b,mean_g,mean_r)
min_light = min(mean_b,mean_g,mean_r)
# if min_light<50:
# newPath = os.path.join(save_dir,"dark")
# elif min_light>200:
# newPath = os.path.join(save_dir,"light")
# else:
# continue
# if (not os.path.exists(newPath)):
# os.mkdir(newPath)
# cv2.imwrite(os.path.join(newPath,info),img)
cv2.imshow("img", img)
cv2.waitKey(0)
personNum +=1
# cv2.waitKey(0)
print "lightcount",lightcount
def firstDemo():
from detect.dlib_face import cv_get_frontal_face_shape_detector, draw_shapes, cv_get_frontal_face_detector
predictor_path = "./detect/models/shape_predictor_68_face_landmarks.dat"
predictor = cv_get_frontal_face_shape_detector(predictor_path)
detector = cv_get_frontal_face_detector()
img = cv2.imread(r"./image/test-12.jpg")
h,w = img.shape[:2]
mask = np.ones([h, w,3], dtype = np.uint8)
# for some reason some too big picture may could not find face,so the shape will devided
shapes, rects = predictor(img)
for shape in shapes:
keypoint_68= []
x1,y1 = np.min(shape, axis=0)
x2,y2 = np.max(shape, axis=0)
mean_pixels = (127.5)
# _mean_pixels = np.array(mean_pixels).reshape((1, 2))
img_var = img[y1:y2,x1:x2].copy()
img_var = cv2.cvtColor(img_var, cv2.COLOR_BGR2HSV)
img_varH, img_varS, img_varV = cv2.split(img_var)
chip = img[y1:y2,x1:x2]
img = img.astype("float32")
mask_t = mask.astype("float32")
mask_t[y1:y2, x1:x2] = 0.5
img_t = np.multiply(img,mask_t)
img_t = img_t.astype("uint8")
cv2.imshow("img",img)
cv2.imshow("mask",mask_t)
cv2.imshow("img_t",img_t)
cv2.waitKey(0)
def demo_keypoint68_txt():
from detect.dlib_face import cv_get_frontal_face_shape_detector, draw_shapes, cv_get_frontal_face_detector
predictor_path = "./detect/models/shape_predictor_68_face_landmarks.dat"
predictor = cv_get_frontal_face_shape_detector(predictor_path)
img_path = gb.glob(r"D:\data\face\uface_for_register\images\*g")
# img_path = gb.glob(r"./image\*g")
histLength = len(img_path)
yinyangScore = np.zeros(histLength)
personNum = 0
with open("yinyang.txt","w") as fin:
for path in img_path:
img = cv2.imread(path)
h, w = img.shape[:2]
mask = np.zeros([h, w], dtype=np.uint8)
mask2 = np.zeros([h, w], dtype=np.uint8)
# for some reason some too big picture may could not find face,so the shape will devided
shapes, rects = predictor(img)
for shape in shapes:
left = []
for pt in shape[:9]:
left.append(pt)
left.append(shape[28])
left.append(shape[27])
for i in range(17, 21):
left.append(shape[17 + 21 - i])
leftface = np.array([[left]], dtype=np.int32)
cv2.fillPoly(mask, leftface, 255)
leftmask = cv2.bitwise_and(img, img, mask=mask)
right = []
for pt in shape[8:16]:
right.append(pt)
right.append(shape[26])
right.append(shape[24])
right.append(shape[22])
for pt in shape[27:30]:
right.append(pt)
right = np.array([[right]], dtype=np.int32)
cv2.fillPoly(mask2, right, 255)
rightmask = cv2.bitwise_and(img, img, mask=mask2)
leftmask_hsv = cv2.cvtColor(leftmask, cv2.COLOR_BGR2HSV)
rightmask_hsv = cv2.cvtColor(rightmask, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(leftmask_hsv)
h2, s2, v2 = cv2.split(rightmask_hsv)
leftarea = np.sum(mask) / 255.
rightarea = np.sum(mask2) / 255.
leftvrate = np.sum(v) * 1.0 / leftarea
rightRate = np.sum(v2) * 1.0 / rightarea
# print leftarea, rightarea, "leftRate==", leftvrate, "rightRate==", rightRate
minusrate = abs(leftvrate - rightRate)
rate = abs(leftvrate / (leftvrate + rightRate) - 0.5)
if personNum%100==0:
print personNum
yinyangScore[personNum] = rate
personNum += 1
fin.write("%s_%.3f_%.3f\n"%(path,rate,minusrate))
drawHist(yinyangScore)
cv2.waitKey(0)
def demo():
from detect.dlib_face import cv_get_frontal_face_shape_detector, draw_shapes, cv_get_frontal_face_detector
predictor_path = "./detect/models/shape_predictor_68_face_landmarks.dat"
predictor = cv_get_frontal_face_shape_detector(predictor_path)
detector = cv_get_frontal_face_detector()
# img = cv2.imread("../image/3.jpg")
# img_path = gb.glob(r"Y:\maxiaofang\data\Uface_register_face\*g")
# img_path = gb.glob(r"Y:\maxiaofang\data\10000\tight\suc\*g")
img_path = gb.glob(r"./reg/0.2-0.8/*g")
histLength = len(img_path)
for path in img_path:
print path
img = cv2.imread(path)
img = cv2.imread(r"./image/test-12.jpg")
h, w = img.shape[:2]
mask = np.ones([h, w, 3], dtype=np.uint8)
# for some reason some too big picture may could not find face,so the shape will devided
shapes, rects = predictor(img)
for shape in shapes:
keypoint_68 = []
x1, y1 = np.min(shape, axis=0)
x2, y2 = np.max(shape, axis=0)
mean_pixels = (127.5)
# _mean_pixels = np.array(mean_pixels).reshape((1, 2))
img_var = img[y1:y2, x1:x2].copy()
img_var = cv2.cvtColor(img_var, cv2.COLOR_BGR2HSV)
img_varH, img_varS, img_varV = cv2.split(img_var)
chip = img[y1:y2, x1:x2]
# mask_t = np.multiply(mask,1)
# mask_tmp =
# cv2.imshow("chip",chip)
img = img.astype("float32")
mask_t = mask.astype("float32")
mask_t[y1:y2, x1:x2] = 0.5
img_t = np.multiply(img, mask_t)
img_t = img_t.astype("uint8")
# hsv = cv2.cvtColor(chip, cv2.COLOR_BGR2HSV) # convert it to hsv
# h, s, v = cv2.split(hsv)
# v += 25
# final_hsv = cv2.merge((h, s, v))
# chip_r = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
# cv2.imwrite("image_processed.jpg", img)
# cv2.imshow("chip_r",chip_r)
cv2.imshow("img", img)
cv2.imshow("mask", mask_t)
cv2.imshow("img_t", img_t)
cv2.waitKey(0)
def demo_keypoint68():
from detect.dlib_face import cv_get_frontal_face_shape_detector, draw_shapes, cv_get_frontal_face_detector
predictor_path = "./detect/models/shape_predictor_68_face_landmarks.dat"
predictor = cv_get_frontal_face_shape_detector(predictor_path)
detector = cv_get_frontal_face_detector()
# img = cv2.imread("../image/3.jpg")
# img_path = gb.glob(r"Y:\maxiaofang\data\Uface_register_face\*g")
img_path = gb.glob(r"Y:\maxiaofang\data\10000\tight\suc\*g")
img_path = gb.glob(r"./reg/0.2-0.8/light/*g")
# img_path = gb.glob(r"./image/*g")
# img_path = gb.glob(r"D:\face_lib\CelebA\Img\img_align_celeba_png.7z\img_align_celeba_png/*g")
histLength = len(img_path)
yinyangScore = np.zeros(histLength)
personNum = 0
lightcount =1
for path in img_path:
# print path
img = cv2.imread(path)
# img = cv2.imread(r"Y:\maxiaofang\data\all\light\05B1C54C697240228A6C62111FF6AB0D.jpg")
# img = cv2.imread(r"./image/0.93_267.jpg")
h,w = img.shape[:2]
mask = np.zeros([h, w], dtype = np.uint8)
mask2 = np.zeros([h, w], dtype = np.uint8)
maskw = np.zeros([h, w], dtype = np.uint8)
# for some reason some too big picture may could not find face,so the shape will devided
shapes, rects = predictor(img)
for shape in shapes:
keypoint_68= []
x1,y1 = np.min(shape, axis=0)
x2,y2 = np.max(shape, axis=0)
mean_pixels = (127.5)
# # _mean_pixels = np.array(mean_pixels).reshape((1, 2))
# img_var = img[y1:y2,x1:x2].copy()
# img_var = cv2.cvtColor(img_var, cv2.COLOR_BGR2HSV)
# img_varH, img_varS, img_varV = cv2.split(img_var)
# data = img_varV.astype('float32')
# _data = (data - np.array(mean_pixels)) * 1 / 127.5
# cv2.imshow("var",img[y1:y2,x1:x2])
# print "np.var = ",np.var(_data)
keypointFive = estimate_5points(shape)
if True:
whole =[]
for pt in shape[:17]:
whole.append(pt)
for i in range(22, 27):
whole.append(shape[22 + 26 - i])
for i in range(17, 21):
whole.append(shape[17 + 21 - i])
wholeface = np.array([[whole]], dtype=np.int32)
cv2.fillPoly(maskw, wholeface, 255)
wholemask = cv2.bitwise_and(img, img, mask=maskw)
wholemask_hsv = cv2.cvtColor(wholemask, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(wholemask_hsv)
facearea = np.sum(maskw)/255.
mean_v = np.sum(v) * 1.0 / facearea/255.
mean_b = np.sum(wholemask[:,:,0]) * 1.0 / facearea
mean_g = np.sum(wholemask[:,:,1]) * 1.0 / facearea
mean_r = np.sum(wholemask[:,:,2]) * 1.0 / facearea
print mean_b,mean_g,mean_r
# vs= v.astype("float32")
# v_tmp = vs*0.2
# v_tmp2 = v_tmp.astype("uint8")
print "wholemask_v= ",mean_v,mean_v*255
# img2 = cv2.merge([h,s,v_tmp2])
# if mean_v<0.88 and mean_v>0.2:
# if mean_v>0.4:
# continue
# cv2.imwrite("./reg/%.2f_%d.jpg"%(mean_v,lightcount),img)
lightcount+=1
# draw point cloud
# cv2.circle(wholemask, (50,50), 20, (170,220,255), thickness=-1)
cv2.imshow("wholemask",wholemask)
cv2.imshow("wholemask_hsv",wholemask_hsv)
# cv2.imshow("v",v)
# cv2.imshow("v_tmp",v_tmp2)
# cv2.imshow("img2",img2)
# for partshape in whole:
# cv2.circle(img, tuple(partshape), 2, color, thickness=2)
for i in xrange(5):
x1= keypointFive[2*i]
y1= keypointFive[2*i+1]
# cv2.circle(img, ((int)(x1),(int)(y1)), 2, color, thickness=2)
# cv2.imshow("img", img)
# # cv2.imshow("mask", leftmask)
# # cv2.imshow("mask2", rightmask)
# cv2.waitKey(0)
left = []
for pt in shape[:9]:
left.append(pt)
left.append(shape[28])
left.append(shape[27])
for i in range(17,21):
left.append(shape[17+21-i])
leftface = np.array([[left]],dtype=np.int32)
cv2.fillPoly(mask, leftface, 255)
leftmask = cv2.bitwise_and(img, img, mask=mask)
right =[]
for pt in shape[8:16]:
right.append(pt)
right.append(shape[26])
right.append(shape[24])
right.append(shape[22])
for pt in shape[27:30]:
right.append(pt)
# right.append(shape[9])
right = np.array([[right]],dtype=np.int32)
cv2.fillPoly(mask2, right, 255)
rightmask = cv2.bitwise_and(img, img, mask=mask2)
leftmask_hsv = cv2.cvtColor(leftmask, cv2.COLOR_BGR2HSV)
rightmask_hsv = cv2.cvtColor(rightmask, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(leftmask_hsv)
h2, s2, v2 = cv2.split(rightmask_hsv)
leftarea = np.sum(mask) / 255.
# print " np.sum(mask) ",np.sum(v),"_leftarea:",leftarea
rightarea = np.sum(mask2) / 255.
leftvrate = np.sum(v) * 1.0 / leftarea
rightRate = np.sum(v2) * 1.0 / rightarea
# print "avr =",(np.sum(v) * 1.0 +np.sum(v2) * 1.0)/ (leftarea+ rightarea)
# print leftarea, rightarea,"leftRate==",leftvrate,"rightRate==",rightRate
# print abs(leftvrate-rightRate)
rate = abs(leftvrate / (leftvrate + rightRate) - 0.5)
# if rate<0.1:
# continue
# rate = abs(np.sum(v) * 1.0 / (np.sum(v2)+ np.sum(v))-0.5)
# print rate
# draw point cloud
for shape in shapes:
count = 0
for pt in shape:
# cv2.circle(img, tuple(pt), 2, color, thickness=2)
# cv2.putText(img,str(count), tuple(pt), font, 0.3, (255, 0, 0), 1)
count += 1
# get average of light
cv2.imshow("img", img)
# cv2.imshow("mask", v)
# cv2.imshow("mask2", v2)
cv2.waitKey(0)
yinyangScore[personNum] = rate
personNum +=1
drawHist(yinyangScore)
cv2.waitKey(0)
print "lightcount",lightcount
fpath = os.path.join(root, fname)
suffix = os.path.splitext(fname)[1].lower()
if os.path.isfile(fpath):
yield (i, os.path.relpath(fpath, root), 0)
i += 1
if __name__ == '__main__':
# fid = open("img.pkl","rb")
# container = pkl.load(fid)
# print container.__len__()
# for i,value in enumerate(container):
# print i,value
from detect.dlib_face import cv_get_frontal_face_shape_detector
predictor_path = "./detect/models/shape_predictor_68_face_landmarks.dat"
predictor = cv_get_frontal_face_shape_detector(predictor_path)
working_dir = r"Y:\maxiaofang\data\10000\tight"
# working_dir = r"./image/"
imgList = list_image(working_dir, recursive=True)
imgList = list(imgList)
container = []
count = 0
for i in xrange(len(imgList)):
imgName = str(imgList[i][1])
path = os.path.join(working_dir, imgName)
img = cv2.imread(path)
h, w = img.shape[:2]
maskw = np.zeros([h, w], dtype=np.uint8)
det_result = predictor(img)
shapes, rects = det_result
newContainerPart = {}