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");
## For loop over the input images
count = 0
listImgs = glob("tmp_ims/*.png")
for image_path in listImgs:
start_each_image = time.time()
count = count + 1
fig_name = ntpath.basename(image_path)
outFile = data_out + "/" + fig_name[:-4]
print '> Processing image: ', image_path, ' ', fig_name, ' ', str(count) + '/' + str(len(listImgs))
net.blobs['data'].reshape(1,3,trg_size,trg_size)
im = caffe.io.load_image(image_path)
## Transforming the image into the right format
net.blobs['data'].data[...] = transformer.preprocess('data', im)
## Forward pass into the CNN
net_output = net.forward()
## Getting the output
features = np.hstack( [net.blobs[layer_name].data[0].flatten()] )
## Writing the regressed 3DMM parameters
np.savetxt(outFile + '.ply.alpha', features[0:99])
np.savetxt(outFile + '.ply.beta', features[99:198])
#################################
## Mapping back the regressed 3DMM into the original
## Basel Face Model (Shape)
##################################
S,T = utils.projectBackBFM(model,features)
print '> Writing 3D file in: ', outFile + '.ply' + 'cost {0}s.'.format(time.time()-start_each_image)
utils.write_ply(outFile + '.ply', S, T, faces)
end_time = time.time()
print "Costtime{0}s".format(end_time - start_time)
def cnn_3dmm( img, outputPath ):
"""
use 3dmm cnn to deal with
"""
# load net
try:
caffe.set_mode_gpu()
caffe.set_device(GPU_ID)
except Exception as ex:
print '> Could not setup Caffe in GPU ' +str(GPU_ID) + ' - Error: ' + ex
print '> Reverting into CPU mode'
caffe.set_mode_cpu()
## Opening mean average image
proto_data = open(FLAGS.mean_path, "rb").read()
a = caffe.io.caffe_pb2.BlobProto.FromString(proto_data)
mean = caffe.io.blobproto_to_array(a)[0]
## Loading the CNN
net = caffe.Classifier(FLAGS.deploy_path, FLAGS.model_path)
## Setting up the right transformer for an input image
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
transformer.set_channel_swap('data', (2,1,0))
transformer.set_raw_scale('data', 255.0)
transformer.set_mean('data',mean)
print '> CNN Model loaded to regress 3D Shape and Texture!'
## Loading the Basel Face Model to write the 3D output
model = scipy.io.loadmat(FLAGS.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!'
net.blobs['data'].reshape(1,3,trg_size,trg_size)
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# im = img / 255
imname = ntpath.basename(FLAGS.imagePath)
imname = imname.split(imname.split('.')[-1])[0][0:-1] # 记录图片basename
im = caffe.io.load_image(os.path.join(FLAGS.tmp_ims, imname + '.png'))
## Transforming the image into the right format
net.blobs['data'].data[...] = transformer.preprocess('data', im)
## Forward pass into the CNN
net_output = net.forward()
## Getting the output
features = np.hstack( [net.blobs[layer_name].data[0].flatten()] )
#imname = ntpath.basename(FLAGS.imagePath)
#imname = imname.split(imname.split('.')[-1])[0][0:-1] # 记录图片basename
## Writing the regressed 3DMM parameters
np.savetxt(os.path.join( outputPath, imname+ '.ply.alpha'), features[0:99])
np.savetxt(os.path.join( outputPath, imname + '.ply.beta'), features[99:198])
#################################
## Mapping back the regressed 3DMM into the original
## Basel Face Model (Shape)
##################################
S,T = utils.projectBackBFM(model, features)
print '> Writing 3D file in: ', os.path.join( outputPath, imname+ '.ply')
utils.write_ply(os.path.join( outputPath, imname+ '.ply'), S, T, faces)