def prepareImage(imagePath):
""" Prepare images before use it.
"""
print("> Prepare image "+imagePath + ":")
imname = ntpath.basename(imagePath)
imname = imname.split(imname.split('.')[-1])[0][0:-1] # 记录图片basename
img = cv2.imread( imagePath ) # 加载图片
if needCrop:
dlib_img = io.imread(imagePath)
img2 = cv2.copyMakeBorder(img,0,0,0,0,cv2.BORDER_REPLICATE)
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(FLAGS.predictor_path) # 加载 dlib 检测
dets = detector(img, 1)
print("> Number of faces detected: {}".format(len(dets)))
if len(dets) == 0:
print '> Could not detect the face, skipping the image...' + image_path
return None
if len(dets) > 1:
print "> Process only the first detected face!"
# 标记第一个人脸
detected_face = dets[0]
cv2.rectangle(img2, (detected_face.left(),detected_face.top()), \
(detected_face.right(),detected_face.bottom()), (0,0,255),2)
fileout = open(os.path.join(FLAGS.tmp_detect , imname + ".bbox"),"w")
fileout.write("%f %f %f %f\n" % (detected_face.left(),detected_face.top(), \
detected_face.right(),detected_face.bottom()))
fileout.close()
## If we are using landmarks to crop
if useLM:
shape = predictor(dlib_img, detected_face)
nLM = shape.num_parts
fileout = open(os.path.join(FLAGS.tmp_detect, imname + ".pts" ), "w")
for i in range(0, nLM):
cv2.circle( img2, (shape.part(i).x, shape.part(i).y), 5, (255,0,0))
fileout.write("%f %f\n" % (shape.part(i).x, shape.part(i).y))
fileout.close()
img = utils.cropByLM(img, shape, img2)
else:
print "> cropByFaceDet "
img = utils.cropByFaceDet(img, detected_face, img2)
cv2.imwrite(os.path.join( FLAGS.tmp_detect, imname+"_detect.png"), img2)
img = cv2.resize(img, (trg_size, trg_size))
cv2.imwrite(os.path.join(FLAGS.tmp_ims, imname + '.png'), img)
return img
cv2.rectangle(img2, (detected_face.left(),detected_face.top()), \
(detected_face.right(),detected_face.bottom()), (0,0,255),2)
fileout = open("tmp_detect/"+imname+".bbox","w")
fileout.write("%f %f %f %f\n" % (detected_face.left(),detected_face.top(), \
detected_face.right(),detected_face.bottom()))
fileout.close()
## If we are using landmarks to crop
if useLM:
shape = predictor(dlib_img, detected_face)
nLM = shape.num_parts
fileout = open("tmp_detect/"+imname+".pts","w")
for i in range(0,nLM):
cv2.circle(img2, (shape.part(i).x, shape.part(i).y), 5, (255,0,0))
fileout.write("%f %f\n" % (shape.part(i).x, shape.part(i).y))
fileout.close()
img = utils.cropByLM(img, shape, img2)
else:
print "> cropByFaceDet "
img = utils.cropByFaceDet(img, detected_face, img2)
cv2.imwrite("tmp_detect/"+imname+"_detect.png",img2)
img = cv2.resize(img,(trg_size, trg_size))
cv2.imwrite("tmp_ims/" + imname + ".png",img)
#####CNN fitting ##############################
start_time = time.time()
# load net
try:
caffe.set_mode_gpu()
caffe.set_device(GPU_ID)
except Exception as ex:
dlib_img = io.imread(image_path)
img2 = cv2.copyMakeBorder(img,0,0,0,0,cv2.BORDER_REPLICATE)
dets = detector(img, 1)
print("> Number of faces detected: {}".format(len(dets)))
if len(dets) == 0:
print('> Could not detect the face, skipping the image...' + image_path)
continue
if len(dets) > 1:
print("> Process only the first detected face!")
detected_face = dets[0]
## If we are using landmarks to crop
shape = predictor(dlib_img, detected_face)
nLM = shape.num_parts
for i in range(0,nLM):
cv2.circle(img2, (shape.part(i).x, shape.part(i).y), 5, (255,0,0))
img, lms = utils.cropByLM(img, shape, img2)
cv2.imwrite(data_out + "/imgs/"+imname+"_detect.png",img2)
lms = lms * 500.0/img.shape[0]
fileout = open(data_out + "/imgs/"+imname + ".pts","w")
for i in range(0,lms.shape[0]):
fileout.write("%f %f\n" % (lms[i,0], lms[i,1]))
fileout.close()
img = cv2.resize(img,(500, 500))
cv2.imwrite(data_out + "/imgs/"+imname+ ".png",img)
outs.write("%s\n" % (data_out + "/imgs/"+imname+ ".png"))
countIms = countIms + 1
##################################################
##### Shape fitting ##############################
# load net
def run():
###################################################
##### Prepare images ##############################
countIms = 0
with open(fileList, "r") as ins, open(data_out + "/imList.txt","w") as outs:
for image_path in ins:
# image_path = image_path[:-1]
print("> Prepare image "+image_path + ":")
imname = ntpath.basename(image_path)
#imname = imname[:-4]
imname = imname.split(imname.split('.')[-1])[0][0:-1]
img = cv2.imread(image_path)
## If we have input landmarks
if len(landmarkDir) > 0:
lms = np.loadtxt(landmarkDir + '/' + imname + '.pts')
img2 = cv2.copyMakeBorder(img,0,0,0,0,cv2.BORDER_REPLICATE)
nLM = lms.shape[0]
for i in range(0,nLM):
cv2.circle(img2, (lms[i,0], lms[i,1]), 5, (255,0,0))
img, lms = utils.cropByInputLM(img, lms, img2)
else:
dlib_img = io.imread(image_path)
img2 = cv2.copyMakeBorder(img,0,0,0,0,cv2.BORDER_REPLICATE)
dets = detector(img, 1)
print("> Number of faces detected: {}".format(len(dets)))
if len(dets) == 0:
print('> Could not detect the face, skipping the image...' + image_path)
continue
if len(dets) > 1:
print("> Process only the first detected face!")
detected_face = dets[0]
## If we are using landmarks to crop
shape = predictor(dlib_img, detected_face)
nLM = shape.num_parts
for i in range(0,nLM):
cv2.circle(img2, (shape.part(i).x, shape.part(i).y), 5, (255,0,0))
img, lms = utils.cropByLM(img, shape, img2)
cv2.imwrite(data_out + "/imgs/"+imname+"_detect.png",img2)
lms = lms * 500.0/img.shape[0]
fileout = open(data_out + "/imgs/"+imname + ".pts","w")
for i in range(0,lms.shape[0]):
fileout.write("%f %f\n" % (lms[i,0], lms[i,1]))
fileout.close()
img = cv2.resize(img,(500, 500))
cv2.imwrite(data_out + "/imgs/"+imname+ ".png",img)
outs.write("%s\n" % (data_out + "/imgs/"+imname+ ".png"))
countIms = countIms + 1
###################################################
##### Shape fitting ##############################
# load net
MainModel = imp.load_source('MainModel', "../CNN/shape_model.py")
net = torch.load(model_path)
net.eval()
mean0 = np.load(mean_path, encoding='latin1')
mean = mean0['arr_0']
net.cuda()
print('> CNN Model loaded to regress 3D Shape and Texture!')
model = scipy.io.loadmat(BFM_path,squeeze_me=True,struct_as_record=False)
model = model["BFM"]
faces = model.faces-1
print('> Loaded the Basel Face Model to write the 3D output!')
## For loop over the input images
count = 0
with open(data_out + "/imList.txt", "r") as ins:
for image_path in ins:
if len(image_path) < 3:
continue
image_path = image_path[:-1]
count = count + 1
fig_name = ntpath.basename(image_path)
outFile = data_out + "/shape/" + fig_name[:-4]
print('> Processing image: ' + image_path)
im = cv2.imread(image_path)
im = cv2.resize(im, (224, 224)).astype(float).transpose((2,0,1))
im = im - mean
#im = im/255
im = Variable(torch.from_numpy(im).unsqueeze(0).float().cuda())
features = net(im).data.cpu().numpy()
## Writing the regressed 3DMM parameters
np.savetxt(outFile + '.ply.alpha', features[0,0:99])
S,T = utils.projectBackBFM(model,features[0,:])
print('> Writing 3D file in: ', outFile + '.ply')
utils.write_ply(outFile + '.ply', S, T, faces)
##################################################
##### Bump map regression ########################
print("Regress bump maps")
bumpMapRegressor.estimateBump(bumpModel_path, data_out + "/imList.txt", data_out + "/bump/")
##################################################
##### Recover the 3D models ##################
print("Recover the 3D models")
print("./TestBump -batch " + data_out + "/imList.txt " + data_out + "/3D/ " + data_out + "/shape " + data_out + "/bump " + data_out + "/bump ../3DMM_model/BaselFaceModel_mod.h5 ../dlib_model/shape_predictor_68_face_landmarks.dat " + data_out + "/imgs " + data_out + "/imgs/ 1");
os.system("./TestBump -batch " + data_out + "/imList.txt " + data_out + "/3D/ " + data_out + "/shape " + data_out + "/bump " + data_out + "/bump ../3DMM_model/BaselFaceModel_mod.h5 ../dlib_model/shape_predictor_68_face_landmarks.dat " + data_out + "/imgs " + data_out + "/imgs/ 1");