def crop_process(image, filename, folder_path, save_path, size=224):
if image is None:
print("Could not read input image")
return False
rects = face_detector(image, 1)
if len(rects) == 0:
suffix = get_suffix(filename) #suffix = '.jpg'
image_name = filename.replace(folder_path + '\\', '')
with open('./notfound.txt', 'a+') as f:
f.write(image_name + '\n')
print("face not found")
return False
rect = rects[0]
offset = 0
top = rect.top()
bottom = rect.bottom() - 0
left = rect.left() + offset
right = rect.right() - offset
faceBoxRectangleS = dlib.rectangle(left=left,
top=top,
right=right,
bottom=bottom)
# - use landmark for cropping
pts = face_regressor(image, faceBoxRectangleS).parts()
pts = np.array([[pt.x, pt.y] for pt in pts]).T
roi_box = parse_roi_box_from_landmark(pts)
height, width, _ = image.shape
########## left
if roi_box[0] < 0:
roi_box[0] = 0
########## right
if roi_box[1] < 0:
roi_box[1] = 0
########## width
if roi_box[2] > width:
roi_box[2] = width
########## height
if roi_box[3] > height:
roi_box[3] = height
cropped_image = crop_img(image, roi_box)
# forward: one step
cropped_image = cv2.resize(cropped_image,
dsize=(size, size),
interpolation=cv2.INTER_LINEAR)
save_img(cropped_image, filename, save_path, folder_path)
# print('saved')
return cropped_image
def main(args):
# 1. load pre-tained model
checkpoint_fp = 'models/phase1_wpdc_vdc.pth.tar'
arch = 'mobilenet_1'
checkpoint = torch.load(
checkpoint_fp, map_location=lambda storage, loc: storage)['state_dict']
model = getattr(mobilenet_v1, arch)(
num_classes=62) # 62 = 12(pose) + 40(shape) +10(expression)
model_dict = model.state_dict()
# because the model is trained by multiple gpus, prefix module should be removed
for k in checkpoint.keys():
model_dict[k.replace('module.', '')] = checkpoint[k]
model.load_state_dict(model_dict)
if args.mode == 'gpu':
cudnn.benchmark = True
model = model.cuda()
model.eval()
# 2. load dlib model for face detection and landmark used for face cropping
if args.dlib_landmark:
dlib_landmark_model = 'models/shape_predictor_68_face_landmarks.dat'
face_regressor = dlib.shape_predictor(dlib_landmark_model)
if args.dlib_bbox:
face_detector = dlib.get_frontal_face_detector()
# 3. forward
tri = sio.loadmat('visualize/tri.mat')['tri']
transform = transforms.Compose(
[ToTensorGjz(), NormalizeGjz(mean=127.5, std=128)])
for img_fp in args.files:
img_ori = cv2.imread(img_fp)
if args.dlib_bbox:
rects = face_detector(img_ori, 1)
else:
rects = []
if len(rects) == 0:
rects = dlib.rectangles()
rect_fp = img_fp + '.bbox'
try:
lines = open(rect_fp).read().strip().split('\n')[1:]
except FileNotFoundError:
print('Cannot load bbox file')
continue
for l in lines:
l, r, t, b = [int(_) for _ in l.split(' ')[1:]]
rect = dlib.rectangle(l, r, t, b)
rects.append(rect)
pts_res = []
Ps = [] # Camera matrix collection
poses = [] # pose collection, [todo: validate it]
vertices_lst = [] # store multiple face vertices
ind = 0
suffix = get_suffix(img_fp)
for rect in rects:
# whether use dlib landmark to crop image, if not, use only face bbox to calc roi bbox for cropping
if args.dlib_landmark:
# - use landmark for cropping
pts = face_regressor(img_ori, rect).parts()
pts = np.array([[pt.x, pt.y] for pt in pts]).T
roi_box = parse_roi_box_from_landmark(pts)
else:
# - use detected face bbox
bbox = [rect.left(), rect.top(), rect.right(), rect.bottom()]
roi_box = parse_roi_box_from_bbox(bbox)
img = crop_img(img_ori, roi_box)
# forward: one step
img = cv2.resize(img,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input = transform(img).unsqueeze(0)
with torch.no_grad():
if args.mode == 'gpu':
input = input.cuda()
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(
np.float32)
# 68 pts
pts68 = predict_68pts(param, roi_box)
# two-step for more accurate bbox to crop face
if args.bbox_init == 'two':
roi_box = parse_roi_box_from_landmark(pts68)
img_step2 = crop_img(img_ori, roi_box)
img_step2 = cv2.resize(img_step2,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input = transform(img_step2).unsqueeze(0)
with torch.no_grad():
if args.mode == 'gpu':
input = input.cuda()
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(
np.float32)
pts68 = predict_68pts(param, roi_box)
pts_res.append(pts68)
P, pose = parse_pose(param)
Ps.append(P)
poses.append(pose)
# dense face 3d vertices
if args.dump_ply or args.dump_vertex or args.dump_depth or args.dump_pncc or args.dump_obj:
vertices = predict_dense(param, roi_box)
vertices_lst.append(vertices)
if args.dump_ply:
dump_to_ply(
vertices, tri,
'{}_{}.ply'.format(img_fp.replace(suffix, ''), ind))
if args.dump_vertex:
dump_vertex(
vertices, '{}_{}.mat'.format(img_fp.replace(suffix, ''),
ind))
if args.dump_pts:
wfp = '{}_{}.txt'.format(img_fp.replace(suffix, ''), ind)
np.savetxt(wfp, pts68, fmt='%.3f')
print('Save 68 3d landmarks to {}'.format(wfp))
if args.dump_roi_box:
wfp = '{}_{}.roibox'.format(img_fp.replace(suffix, ''), ind)
np.savetxt(wfp, roi_box, fmt='%.3f')
print('Save roi box to {}'.format(wfp))
if args.dump_paf:
wfp_paf = '{}_{}_paf.jpg'.format(img_fp.replace(suffix, ''),
ind)
wfp_crop = '{}_{}_crop.jpg'.format(img_fp.replace(suffix, ''),
ind)
paf_feature = gen_img_paf(img_crop=img,
param=param,
kernel_size=args.paf_size)
cv2.imwrite(wfp_paf, paf_feature)
cv2.imwrite(wfp_crop, img)
print('Dump to {} and {}'.format(wfp_crop, wfp_paf))
if args.dump_obj:
wfp = '{}_{}.obj'.format(img_fp.replace(suffix, ''), ind)
colors = get_colors(img_ori, vertices)
write_obj_with_colors(wfp, vertices, tri, colors)
print('Dump obj with sampled texture to {}'.format(wfp))
ind += 1
if args.dump_pose:
# P, pose = parse_pose(param) # Camera matrix (without scale), and pose (yaw, pitch, roll, to verify)
img_pose = plot_pose_box(img_ori, Ps, pts_res)
wfp = img_fp.replace(suffix, '_pose.jpg')
cv2.imwrite(wfp, img_pose)
print('Dump to {}'.format(wfp))
if args.dump_depth:
wfp = img_fp.replace(suffix, '_depth.png')
# depths_img = get_depths_image(img_ori, vertices_lst, tri-1) # python version
depths_img = cget_depths_image(img_ori, vertices_lst,
tri - 1) # cython version
cv2.imwrite(wfp, depths_img)
print('Dump to {}'.format(wfp))
if args.dump_pncc:
wfp = img_fp.replace(suffix, '_pncc.png')
pncc_feature = cpncc(img_ori, vertices_lst,
tri - 1) # cython version
cv2.imwrite(
wfp,
pncc_feature[:, :, ::-1]) # cv2.imwrite will swap RGB -> BGR
print('Dump to {}'.format(wfp))
if args.dump_res:
draw_landmarks(img_ori,
pts_res,
wfp=img_fp.replace(suffix, '_3DDFA.jpg'),
show_flg=args.show_flg)
def main(args):
# 1. load pre-tained model
checkpoint_fp = 'models/phase1_wpdc_vdc.pth.tar'
arch = 'mobilenet_1'
checkpoint = torch.load(checkpoint_fp, map_location=lambda storage, loc: storage)['state_dict']
model = getattr(mobilenet_v1, arch)(num_classes=62) # 62 = 12(pose) + 40(shape) +10(expression)
model_dict = model.state_dict()
# because the model is trained by multiple gpus, prefix module should be removed
for k in checkpoint.keys():
model_dict[k.replace('module.', '')] = checkpoint[k]
model.load_state_dict(model_dict)
if args.mode == 'gpu':
cudnn.benchmark = True
model = model.cuda()
model.eval()
tri = sio.loadmat('visualize/tri.mat')['tri']
transform = transforms.Compose([ToTensorGjz(), NormalizeGjz(mean=127.5, std=128)])
# 2. parse images list
with open(args.img_list) as f:
img_list = [x.strip() for x in f.readlines()]
landmark_list = []
alignment_model = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False)
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
if not os.path.exists(args.save_lmk_dir):
os.mkdir(args.save_lmk_dir)
for img_idx, img_fp in enumerate(tqdm(img_list)):
img_ori = cv2.imread(os.path.join(args.img_prefix, img_fp))
pts_res = []
Ps = [] # Camera matrix collection
poses = [] # pose collection, [todo: validate it]
vertices_lst = [] # store multiple face vertices
ind = 0
suffix = get_suffix(img_fp)
# face alignment model use RGB as input, result is a tuple with landmarks and boxes, cv2读取图片的书序是 BGR
preds = alignment_model.get_landmarks(img_ori[:, :, ::-1])
pts_2d_68 = preds[0]
pts_2d_5 = get_5lmk_from_68lmk(pts_2d_68)
landmark_list.append(pts_2d_5)
roi_box = parse_roi_box_from_landmark(pts_2d_68.T) # 根据68个关键点，确定roi区域
img = crop_img(img_ori, roi_box)
# import pdb; pdb.set_trace()
# forward: one step
img = cv2.resize(img, dsize=(STD_SIZE, STD_SIZE), interpolation=cv2.INTER_LINEAR)
input = transform(img).unsqueeze(0)
with torch.no_grad():
if args.mode == 'gpu':
input = input.cuda()
param = model(input) # 人脸的RT矩阵，形状和表情的系数
param = param.squeeze().cpu().numpy().flatten().astype(np.float32)
# 68 pts
pts68 = predict_68pts(param, roi_box) # 此时pts68是一个68*3的顶点坐标列表
# two-step for more accurate bbox to crop face
if args.bbox_init == 'two':
roi_box = parse_roi_box_from_landmark(pts68)
img_step2 = crop_img(img_ori, roi_box)
img_step2 = cv2.resize(img_step2, dsize=(STD_SIZE, STD_SIZE), interpolation=cv2.INTER_LINEAR)
input = transform(img_step2).unsqueeze(0)
with torch.no_grad():
if args.mode == 'gpu':
input = input.cuda()
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(np.float32)
pts68 = predict_68pts(param, roi_box)
pts_res.append(pts68)
P, pose = parse_pose(param)
Ps.append(P)
poses.append(pose)
# dense face 3d vertices
vertices = predict_dense(param, roi_box)
if args.dump_2d_img: # 人脸区域的2d图像
wfp_2d_img = os.path.join(args.save_dir, os.path.basename(img_fp))
colors = get_colors(img_ori, vertices)
# aligned_param = get_aligned_param(param)
# vertices_aligned = predict_dense(aligned_param, roi_box)
# h, w, c = 120, 120, 3
h, w, c = img_ori.shape
img_2d = crender_colors(vertices.T, (tri - 1).T, colors[:, ::-1], h, w)
cv2.imwrite(wfp_2d_img, img_2d[:, :, ::-1])
if args.dump_param:
split = img_fp.split('/')
save_name = os.path.join(args.save_dir, '{}.txt'.format(os.path.splitext(split[-1])[0]))
this_param = param * param_std + param_mean
this_param = np.concatenate((this_param, roi_box))
this_param.tofile(save_name, sep=' ')
if args.dump_lmk: # 存储
save_path = os.path.join(args.save_lmk_dir, 'realign_lmk')
with open(save_path, 'w') as f:
for idx, (fname, land) in enumerate(zip(img_list, landmark_list)):
# f.write('{} {} {} {}')
land = land.astype(np.int)
land_str = ' '.join([str(x) for x in land])
msg = f'{fname} {idx} {land_str}\n'
f.write(msg)
def main(args):
# 1. load pre-tained model
checkpoint_fp = 'models/phase1_wpdc_vdc.pth.tar'
arch = 'mobilenet_1'
checkpoint = torch.load(
checkpoint_fp, map_location=lambda storage, loc: storage)['state_dict']
model = getattr(mobilenet_v1, arch)(
num_classes=62) # 62 = 12(pose) + 40(shape) +10(expression)
model_dict = model.state_dict()
# because the model is trained by multiple gpus, prefix module should be removed
for k in checkpoint.keys():
model_dict[k.replace('module.', '')] = checkpoint[k]
model.load_state_dict(model_dict)
if args.mode == 'gpu':
cudnn.benchmark = True
model = model.cuda()
model.eval()
tri = sio.loadmat('visualize/tri.mat')['tri']
transform = transforms.Compose(
[ToTensorGjz(), NormalizeGjz(mean=127.5, std=128)])
# 2. parse images list
img_list = listdir(args.img_prefix)
alignment_model = face_alignment.FaceAlignment(
face_alignment.LandmarksType._2D, flip_input=False, device=args.mode)
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
for img_idx, img_fp in enumerate(tqdm(img_list)):
img_ori = cv2.imread(os.path.join(args.img_prefix, img_fp))
pts_res = []
Ps = [] # Camera matrix collection
poses = [] # pose collection, [todo: validate it]
vertices_lst = [] # store multiple face vertices
ind = 0
suffix = get_suffix(img_fp)
# face alignment model use RGB as input, result is a tuple with landmarks and boxes
preds = alignment_model.get_landmarks(img_ori[:, :, ::-1])
try:
pts_2d_68 = preds[0]
except:
continue
roi_box = parse_roi_box_from_landmark(pts_2d_68.T)
img = crop_img(img_ori, roi_box)
# import pdb; pdb.set_trace()
# forward: one step
img = cv2.resize(img,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input = transform(img).unsqueeze(0)
with torch.no_grad():
if args.mode == 'gpu':
input = input.cuda()
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(np.float32)
# 68 pts
pts68 = predict_68pts(param, roi_box)
# two-step for more accurate bbox to crop face
if args.bbox_init == 'two':
roi_box = parse_roi_box_from_landmark(pts68)
img_step2 = crop_img(img_ori, roi_box)
img_step2 = cv2.resize(img_step2,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input = transform(img_step2).unsqueeze(0)
with torch.no_grad():
if args.mode == 'gpu':
input = input.cuda()
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(
np.float32)
pts68 = predict_68pts(param, roi_box)
pts_res.append(pts68)
P, pose = parse_pose(param)
Ps.append(P)
poses.append(pose)
# dense face 3d vertices
vertices = predict_dense(param, roi_box)
if args.dump_2d_img:
wfp_2d_img = os.path.join(args.save_dir, os.path.basename(img_fp))
colors = get_colors(img_ori, vertices)
# aligned_param = get_aligned_param(param)
# vertices_aligned = predict_dense(aligned_param, roi_box)
# h, w, c = 120, 120, 3
h, w, c = img_ori.shape
img_2d = crender_colors(vertices.T, (tri - 1).T, colors[:, ::-1],
h, w)
cv2.imwrite(wfp_2d_img, img_2d[:, :, ::-1])
del img_ori
del img
del img_2d
def crop_process(image, filename, folder_path, save_path, size=224):
if image is None:
print("Could not read input image")
return False
h, w, _ = image.shape
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), [104, 117, 123],
False, False)
net.setInput(blob)
# inference, find faces
detections = net.forward()
# postprocessing
faceBoxRectangleS = None
accuracy = 0.
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > conf_threshold:
x1 = int(detections[0, 0, i, 3] * w)
y1 = int(detections[0, 0, i, 4] * h)
x2 = int(detections[0, 0, i, 5] * w)
y2 = int(detections[0, 0, i, 6] * h)
#, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
accuracy = confidence * 100
faceBoxRectangleS = dlib.rectangle(left=x1,
top=y1,
right=x2,
bottom=y2)
if faceBoxRectangleS == None:
suffix = get_suffix(filename) #suffix = '.jpg'
image_name = filename.replace(folder_path + '\\', '')
with open('./notfound2.txt', 'a+') as f:
f.write(image_name + '\n')
return False
# - use landmark for cropping
pts = face_regressor(image, faceBoxRectangleS).parts()
pts = np.array([[pt.x, pt.y] for pt in pts]).T
roi_box = parse_roi_box_from_landmark(pts)
height, width, _ = image.shape
########## left
if roi_box[0] < 0:
roi_box[0] = 0
########## right
if roi_box[1] < 0:
roi_box[1] = 0
########## width
if roi_box[2] > width:
roi_box[2] = width
########## height
if roi_box[3] > height:
roi_box[3] = height
cropped_image = crop_img(image, roi_box)
# forward: one step
cropped_image = cv2.resize(cropped_image,
dsize=(size, size),
interpolation=cv2.INTER_LINEAR)
save_img(cropped_image, filename, save_path, folder_path)
# print('saved')
return cropped_image
def save_img(image, filename, save_path, folder_path):
suffix = get_suffix(filename) #suffix = '.jpg'
image_name = filename.replace(folder_path + '\\', '')
image_name = image_name.replace(suffix, '')
wfp_crop = save_path + '/{}.jpg'.format(image_name)
cv2.imwrite(wfp_crop, image)
def main(args):
# 1. load pre-tained model
checkpoint_fp = 'models/phase1_wpdc_vdc.pth.tar'
arch = 'mobilenet_1'
checkpoint = torch.load(
checkpoint_fp, map_location=lambda storage, loc: storage)['state_dict']
model = getattr(mobilenet_v1, arch)(
num_classes=62) # 62 = 12(pose) + 40(shape) +10(expression)
model_dict = model.state_dict()
# because the model is trained by multiple gpus, prefix module should be removed
for k in checkpoint.keys():
model_dict[k.replace('module.', '')] = checkpoint[k]
model.load_state_dict(model_dict)
if args.mode == 'gpu':
cudnn.benchmark = True
model = model.cuda()
model.eval()
# 2. load dlib model for face detection and landmark used for face cropping
if args.dlib_landmark:
dlib_landmark_model = 'models/shape_predictor_68_face_landmarks.dat'
face_regressor = dlib.shape_predictor(dlib_landmark_model)
if args.dlib_bbox:
face_detector = dlib.get_frontal_face_detector()
# 3. forward
tri = sio.loadmat('visualize/tri.mat')['tri']
transform = transforms.Compose(
[ToTensorGjz(), NormalizeGjz(mean=127.5, std=128)])
files = sorted(
glob.glob(os.path.join(args.folder, '*.jpg')) +
glob.glob(os.path.join(args.folder, '*.png')))
p_bar = tqdm(total=len(files))
for img_fp in files:
img_ori = cv2.imread(img_fp)
if args.dlib_bbox:
rects = face_detector(img_ori, 1)
else:
rects = []
if len(rects) == 0:
rects = dlib.rectangles()
rect_fp = img_fp + '.bbox'
lines = open(rect_fp).read().strip().split('\n')[1:]
for l in lines:
l, r, t, b = [int(_) for _ in l.split(' ')[1:]]
rect = dlib.rectangle(l, r, t, b)
rects.append(rect)
pts_res = []
Ps = [] # Camera matrix collection
poses = [] # pose collection, [todo: validate it]
vertices_lst = [] # store multiple face vertices
ind = 0
suffix = get_suffix(img_fp)
for rect in rects:
# whether use dlib landmark to crop image, if not, use only face bbox to calc roi bbox for cropping
if args.dlib_landmark:
# - use landmark for cropping
pts = face_regressor(img_ori, rect).parts()
pts = np.array([[pt.x, pt.y] for pt in pts]).T
roi_box = parse_roi_box_from_landmark(pts)
else:
# - use detected face bbox
bbox = [rect.left(), rect.top(), rect.right(), rect.bottom()]
roi_box = parse_roi_box_from_bbox(bbox)
img = crop_img(img_ori, roi_box)
# forward: one step
img = cv2.resize(img,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input_ = transform(img).unsqueeze(0)
with torch.no_grad():
if args.mode == 'gpu':
input_ = input_.cuda()
param = model(input_)
param = param.squeeze().cpu().numpy().flatten().astype(
np.float32)
# 68 pts
pts68 = predict_68pts(param, roi_box)
# two-step for more accurate bbox to crop face
if args.bbox_init == 'two':
roi_box = parse_roi_box_from_landmark(pts68)
img_step2 = crop_img(img_ori, roi_box)
img_step2 = cv2.resize(img_step2,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input_ = transform(img_step2).unsqueeze(0)
with torch.no_grad():
if args.mode == 'gpu':
input_ = input_.cuda()
param = model(input_)
param = param.squeeze().cpu().numpy().flatten().astype(
np.float32)
pts68 = predict_68pts(param, roi_box)
pts_res.append(pts68)
P, pose = parse_pose(param)
Ps.append(P)
poses.append(pose)
points = np.array(pts_res)[0].T
rotated = eulerAnglesToRotationMatrix(np.array([0., np.pi, 0.]))
points = points.dot(rotated)
scaler = MinMaxScaler(feature_range=(-1., 1))
scaled_points = scaler.fit_transform(points)
points = scaled_points
f_name = img_fp.replace(args.folder + '/',
'').replace('.png',
'').replace('.jpg', '')
np.save('./results/{}.npy'.format(f_name), points.reshape(-1))
if args.plot:
plot_face(points, img_fp)
if args.show_flg:
plt.show()
else:
plt.savefig('./results/{}.png'.format(f_name))
p_bar.update(1)
def process_one_frame(frame):
if frame is None: print("No frame, check your camera")
img_ori = frame
img_fp = "camera.png" # 随便给一个filename 反正之后用不到
rects = face_detector(img_ori, 1)
pts_res = []
Ps = [] # Camera matrix collection
poses = [] # pose collection, [todo: validate it]
vertices_lst = [] # store multiple face vertices
ind = 0
suffix = get_suffix(img_fp)
for i in range(len(rects)):
if i != 0: break # 这里只检测第一个脸
rect = rects[i]
# whether use dlib landmark to crop image, if not, use only face bbox to calc roi bbox for cropping
# - use landmark for cropping
pts = face_regressor(img_ori, rect).parts()
pts = np.array([[pt.x, pt.y] for pt in pts]).T
roi_box = parse_roi_box_from_landmark(pts)
img = crop_img(img_ori, roi_box)
# forward: one step
img = cv2.resize(img,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input = transform(img).unsqueeze(0)
with torch.no_grad():
if use_gpu:
input = input.cuda()
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(
np.float32)
# 68 pts
pts68 = predict_68pts(param, roi_box)
# two-step for more accurate bbox to crop face
if use_two_step_bbox_init:
roi_box = parse_roi_box_from_landmark(pts68)
img_step2 = crop_img(img_ori, roi_box)
img_step2 = cv2.resize(img_step2,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input = transform(img_step2).unsqueeze(0)
with torch.no_grad():
if use_gpu:
input = input.cuda()
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(
np.float32)
pts68 = predict_68pts(param, roi_box)
pts_res.append(pts68)
P, pose = parse_pose(param)
Ps.append(P)
poses.append(pose)
# dense face 3d vertices
vertices = predict_dense(param, roi_box)
vertices_lst.append(vertices)
if is_dump_to_ply:
dump_to_ply(
vertices, tri,
'{}_{}.ply'.format(img_fp.replace(suffix, ''), ind))
if is_dump_vertex:
dump_vertex(
vertices, '{}_{}.mat'.format(img_fp.replace(suffix, ''),
ind))
if is_dump_pts:
wfp = '{}_{}.txt'.format(img_fp.replace(suffix, ''), ind)
np.savetxt(wfp, pts68, fmt='%.3f')
print('Save 68 3d landmarks to {}'.format(wfp))
if is_dump_roi_box:
wfp = '{}_{}.roibox'.format(img_fp.replace(suffix, ''), ind)
np.savetxt(wfp, roi_box, fmt='%.3f')
print('Save roi box to {}'.format(wfp))
if is_dump_paf:
wfp_paf = '{}_{}_paf.jpg'.format(img_fp.replace(suffix, ''),
ind)
wfp_crop = '{}_{}_crop.jpg'.format(img_fp.replace(suffix, ''),
ind)
paf_feature = gen_img_paf(img_crop=img,
param=param,
kernel_size=paf_size)
cv2.imwrite(wfp_paf, paf_feature)
cv2.imwrite(wfp_crop, img)
print('Dump to {} and {}'.format(wfp_crop, wfp_paf))
if is_dump_obj:
wfp = '{}_{}.obj'.format(img_fp.replace(suffix, ''), ind)
colors = get_colors(img_ori, vertices)
write_obj_with_colors(wfp, vertices, tri, colors)
print('Dump obj with sampled texture to {}'.format(wfp))
ind += 1
if is_dump_pose:
# P, pose = parse_pose(param) # Camera matrix (without scale), and pose (yaw, pitch, roll, to verify)
img_pose = plot_pose_box(img_ori, Ps, pts_res)
wfp = img_fp.replace(suffix, '_pose.jpg')
cv2.imwrite(wfp, img_pose)
print('Dump to {}'.format(wfp))
if is_dump_depth:
wfp = img_fp.replace(suffix, '_depth.png')
# depths_img = get_depths_image(img_ori, vertices_lst, tri-1) # python version
depths_img = cget_depths_image(img_ori, vertices_lst,
tri - 1) # cython version
cv2.imwrite(wfp, depths_img)
print('Dump to {}'.format(wfp))
if is_dump_pncc:
wfp = img_fp.replace(suffix, '_pncc.png')
pncc_feature = cpncc(img_ori, vertices_lst,
tri - 1) # cython version
cv2.imwrite(
wfp,
pncc_feature[:, :, ::-1]) # cv2.imwrite will swap RGB -> BGR
print('Dump to {}'.format(wfp))
if is_dump_res:
draw_landmarks(img_ori,
pts_res,
wfp=img_fp.replace(suffix, '_3DDFA.jpg'),
show_flg=show_landmarks_fig)
# 下面这一句将原始图像关掉
img_ori = np.ones_like(img_ori) * 255
img_pose = plot_pose_box(img_ori, Ps, pts_res)
draw_landmarks_opencv(img_pose, pts_res)
cv2.imshow("pose", img_pose)
def main(args):
# 1. load pre-trained model
checkpoint_fp = 'models/phase1_wpdc_vdc.pth.tar'
arch = 'mobilenet_1'
checkpoint = torch.load(
checkpoint_fp, map_location=lambda storage, loc: storage)['state_dict']
model = getattr(mobilenet_v1, arch)(
num_classes=62) # 62 = 12(pose) + 40(shape) +10(expression)
model_dict = model.state_dict()
# because the model is trained by multiple gpus, prefix module should be removed
for k in checkpoint.keys():
model_dict[k.replace('module.', '')] = checkpoint[k]
model.load_state_dict(model_dict)
if args.mode == 'gpu':
cudnn.benchmark = True
model = model.cuda()
model.eval()
# 2. load pre-trained model uv-gan
if args.uvgan:
if args.checkpoint_uv_gan == "":
print("Specify the path to checkpoint uv_gan")
exit()
uvgan = infer_uv_gan.UV_GAN(args.checkpoint_uv_gan)
# 3. load dlib model for face detection and landmark used for face cropping
if args.dlib_landmark:
dlib_landmark_model = 'models/shape_predictor_68_face_landmarks.dat'
face_regressor = dlib.shape_predictor(dlib_landmark_model)
if args.dlib_bbox:
face_detector = dlib.get_frontal_face_detector()
# 4. forward
tri = sio.loadmat('visualize/tri.mat')['tri']
transform = transforms.Compose(
[ToTensorGjz(), NormalizeGjz(mean=127.5, std=128)])
for img_fp in args.files:
img_ori = cv2.imread(img_fp)
if args.dlib_bbox:
rects = face_detector(img_ori, 1)
else:
rects = []
if len(rects) == 0:
rects = dlib.rectangles()
rect_fp = img_fp + '.bbox'
lines = open(rect_fp).read().strip().split('\n')[1:]
for l in lines:
l, r, t, b = [int(_) for _ in l.split(' ')[1:]]
rect = dlib.rectangle(l, r, t, b)
rects.append(rect)
pts_res = []
Ps = [] # Camera matrix collection
poses = [] # pose collection, [todo: validate it]
vertices_lst = [] # store multiple face vertices
ind = 0
suffix = get_suffix(img_fp)
for rect in rects:
# whether use dlib landmark to crop image, if not, use only face bbox to calc roi bbox for cropping
if args.dlib_landmark:
# - use landmark for cropping
pts = face_regressor(img_ori, rect).parts()
pts = np.array([[pt.x, pt.y] for pt in pts]).T
roi_box = parse_roi_box_from_landmark(pts)
else:
# - use detected face bbox
bbox = [rect.left(), rect.top(), rect.right(), rect.bottom()]
roi_box = parse_roi_box_from_bbox(bbox)
img = crop_img(img_ori, roi_box)
# forward: one step
img = cv2.resize(img,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input = transform(img).unsqueeze(0)
with torch.no_grad():
if args.mode == 'gpu':
input = input.cuda()
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(
np.float32)
# 68 pts
pts68 = predict_68pts(param, roi_box)
# two-step for more accurate bbox to crop face
if args.bbox_init == 'two':
roi_box = parse_roi_box_from_landmark(pts68)
img_step2 = crop_img(img_ori, roi_box)
img_step2 = cv2.resize(img_step2,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input = transform(img_step2).unsqueeze(0)
with torch.no_grad():
if args.mode == 'gpu':
input = input.cuda()
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(
np.float32)
pts68 = predict_68pts(param, roi_box)
pts_res.append(pts68)
P, pose = parse_pose(param)
Ps.append(P)
poses.append(pose)
if args.dump_obj:
vertices = predict_dense(param, roi_box)
vertices_lst.append(vertices)
wfp = '{}_{}.obj'.format(img_fp.replace(suffix, ''), ind)
colors = get_colors(img_ori, vertices)
p, offset, alpha_shp, alpha_exp = _parse_param(param)
vertices = (u + w_shp @ alpha_shp + w_exp @ alpha_exp).reshape(
3, -1, order='F') + offset
vertices = vertices.T
tri = tri.T - 1
print('Dump obj with sampled texture to {}'.format(wfp))
unwraps = create_unwraps(vertices)
h, w = args.height, args.width
tcoords = process_uv(unwraps[:, :2], h, w)
texture = render_colors(tcoords, tri, colors, h, w,
c=3).astype('uint8')
scaled_tcoords = scale_tcoords(tcoords)
if args.uvgan:
texture = uvgan.infer(texture)
else:
texture = cv2.cvtColor(texture, cv2.COLOR_BGR2RGB)
vertices, colors, uv_coords = vertices.astype(
np.float32).copy(), colors.astype(
np.float32).copy(), scaled_tcoords.astype(
np.float32).copy()
write_obj_with_colors_texture(wfp, vertices, colors, tri,
texture * 255.0, uv_coords)
ind += 1
print('param: ' + str(param))
p, offset, alpha_shp, alpha_exp = _parse_param(param)
print('alpha_exp: ' + str(alpha_exp))
# 68 pts
# bbox = [0, 0, 120, 120]
# roi_box = parse_roi_box_from_bbox(bbox)
pts68 = predict_68pts(param, roi_box)
# print('pts68: ' + str(pts68))
print('pts68.shape: ' + str(pts68.shape))
P, pose = parse_pose(param)
# print('P: ' + str(P))
print('P.shape: ' + str(P.shape))
print('pose: ' + str(pose))
vertices = predict_dense(param, roi_box)
# print('vertices: ' + str(vertices))
print('vertices.shape: ' + str(vertices.shape))
ensure_folder('result')
suffix = get_suffix(filename)
print('suffix: ' + suffix)
tri = sio.loadmat('visualize/tri.mat')['tri']
dump_to_ply(vertices, tri, '{}.ply'.format(filename.replace(suffix, '')))
wfp = '{}.obj'.format(filename.replace(suffix, ''))
colors = get_colors(img_ori, vertices)
write_obj_with_colors(wfp, vertices, tri, colors)
print('Dump obj with sampled texture to {}'.format(wfp))
def run(image):
# 1. load pre-tained model
checkpoint_fp = 'models/phase1_wpdc_vdc.pth.tar'
arch = 'mobilenet_1'
checkpoint = torch.load(
checkpoint_fp, map_location=lambda storage, loc: storage)['state_dict']
model = getattr(mobilenet_v1, arch)(
num_classes=62) # 62 = 12(pose) + 40(shape) +10(expression)
model_dict = model.state_dict()
# because the model is trained by multiple gpus, prefix module should be removed
for k in checkpoint.keys():
model_dict[k.replace('module.', '')] = checkpoint[k]
model.load_state_dict(model_dict)
model.eval()
# 2. load dlib model for face detection and landmark used for face cropping
dlib_landmark_model = 'models/shape_predictor_68_face_landmarks.dat'
face_regressor = dlib.shape_predictor(dlib_landmark_model)
face_detector = dlib.get_frontal_face_detector()
# 3. forward
tri = sio.loadmat('tri.mat')['tri']
transform = transforms.Compose(
[ToTensorGjz(), NormalizeGjz(mean=127.5, std=128)])
img_ori = cv2.imread(image)
# img_ori = img_ori[:, :, [2, 1, 0]]
rects = face_detector(img_ori, 1)
# if len(rects) == 0:
# rects = dlib.rectangles()
# rect_fp = img_fp + '.bbox'
# lines = open(rect_fp).read().strip().split('\n')[1:]
# for l in lines:
# l, r, t, b = [int(_) for _ in l.split(' ')[1:]]
# rect = dlib.rectangle(l, r, t, b)
# rects.append(rect)
pts_res = []
Ps = [] # Camera matrix collection
poses = [] # pose collection, [todo: validate it]
vertices_lst = [] # store multiple face vertices
ind = 0
suffix = get_suffix(image)
for rect in rects:
# whether use dlib landmark to crop image, if not, use only face bbox to calc roi bbox for cropping
# - use landmark for cropping
pts = face_regressor(img_ori, rect).parts()
pts = np.array([[pt.x, pt.y] for pt in pts]).T
roi_box = parse_roi_box_from_landmark(pts)
img = crop_img(img_ori, roi_box)
# forward: one step
img = cv2.resize(img,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input = transform(img).unsqueeze(0)
with torch.no_grad():
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(np.float32)
# 68 pts
pts68 = predict_68pts(param, roi_box)
# two-step for more accurate bbox to crop face
pts_res.append(pts68)
P, pose = parse_pose(param)
Ps.append(P)
poses.append(pose)
vertices = predict_dense(param, roi_box)
vertices_lst.append(vertices)
# dense face 3d vertices
wfp = './output/obj_{}.obj'.format(image.split('/')[-1])
colors = get_colors(img_ori, vertices)
write_obj_with_colors(wfp, vertices, tri, colors)
print('Dump obj with sampled texture to {}'.format(wfp))
ind += 1
def classify(model, inputs):
in_img = inputs['photo']
img_ori = np.array(in_img)
img_fp = 'samples/test1.jpg'
face_detector = dlib.get_frontal_face_detector()
# 3. forward
tri = sio.loadmat('visualize/tri.mat')['tri']
transform = transforms.Compose(
[ToTensorGjz(), NormalizeGjz(mean=127.5, std=128)])
#print(transform)
rects = face_detector(img_ori, 1)
pts_res = []
Ps = [] # Camera matrix collection
poses = [] # pose collection, [todo: validate it]
vertices_lst = [] # store multiple face vertices
ind = 0
suffix = get_suffix(img_fp)
for rect in rects:
# - use detected face bbox
bbox = [rect.left(), rect.top(), rect.right(), rect.bottom()]
roi_box = parse_roi_box_from_bbox(bbox)
img = crop_img(img_ori, roi_box)
# forward: one step
img = cv2.resize(img,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input = transform(img).unsqueeze(0)
print(input)
with torch.no_grad():
if mode == 'gpu':
input = input.cuda()
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(np.float32)
# 68 pts
pts68 = predict_68pts(param, roi_box)
# two-step for more accurate bbox to crop face
if bbox_init == 'two':
roi_box = parse_roi_box_from_landmark(pts68)
img_step2 = crop_img(img_ori, roi_box)
img_step2 = cv2.resize(img_step2,
dsize=(STD_SIZE, STD_SIZE),
interpolation=cv2.INTER_LINEAR)
input = transform(img_step2).unsqueeze(0)
with torch.no_grad():
if mode == 'gpu':
input = input.cuda()
param = model(input)
param = param.squeeze().cpu().numpy().flatten().astype(
np.float32)
pts68 = predict_68pts(param, roi_box)
pts_res.append(pts68)
P, pose = parse_pose(param)
Ps.append(P)
poses.append(pose)
vertices = predict_dense(param, roi_box)
vertices_lst.append(vertices)
ind += 1
pncc_feature = cpncc(img_ori, vertices_lst, tri - 1)
output = pncc_feature[:, :, ::-1]
print(type(output))
pilImg = transforms.ToPILImage()(np.uint8(output))
return {"image": pilImg}
