def gener3DFace():
print('create 3d face ...........')
name = "screen"
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
prn = PRN.PRN(is_dlib=True, prefix='PRNet/')
# read image
image = cv2.imread(facepath)
[h, w, _] = image.shape
max_size = max(image.shape[0], image.shape[1])
if max_size > 1000:
image = cv2.rescale(image, 1000. / max_size)
image = (image * 255).astype(np.uint8)
pos = prn.process(image) # use dlib to detect face
image = image / 255.
# 3D vertices
vertices = prn.get_vertices(pos)
save_vertices = vertices.copy()
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
colors = prn.get_colors(image, vertices)
texture = cv2.remap(image,
pos[:, :, :2].astype(np.float32),
None,
interpolation=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
vertices_vis = get_visibility(vertices, prn.triangles, h, w)
uv_mask = get_uv_mask(vertices_vis, prn.triangles, prn.uv_coords, h, w,
prn.resolution_op)
texture = texture * uv_mask[:, :, np.newaxis]
write_obj_with_texture(
os.path.join(save_folder, name + '.obj'), save_vertices, colors,
prn.triangles, texture, prn.uv_coords /
prn.resolution_op) # save 3d face with texture(can open with meshlab)
sio.savemat(os.path.join(save_folder, name + '_mesh.mat'), {
'vertices': vertices,
'colors': colors,
'triangles': prn.triangles
})
kpt = prn.get_landmarks(pos)
np.savetxt(os.path.join(save_folder, name + '_kpt.txt'), kpt)
camera_matrix, pose = estimate_pose(vertices)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
np.savetxt(os.path.join(save_folder, name + '_camera_matrix.txt'),
camera_matrix)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
#image_pose = plot_pose_box(image, camera_matrix, kpt)
#cv2.imshow('sparse alignment', plot_kpt(image, kpt))
#cv2.imshow('dense alignment', image)
#cv2.imshow('pose', plot_pose_box(image, camera_matrix, kpt))
#cv2.waitKey(0)
image = (image * 255).astype(np.uint8) # 将背景图片部分数值还原
image = plot_vertices(image, vertices)
return image
def save_information(img, output):
pos = prn.process(img)
vertices = prn.get_vertices(pos)
cam_mat, pose, R = estimate_pose(vertices)
dictionary = {
'img': img,
'pos': pos,
'vertices': vertices,
'cam_mat': cam_mat,
'pose': pose,
'R': R
}
pickle.dump(dictionary, open(output, 'wb'))
def main(args):
image_folder = args.inputDir
save_folder = args.outputDir
if not os.path.exists(save_folder):
os.mkdir(save_folder)
types = ('*.jpg', '*.png')
image_path_list = []
for files in types:
image_path_list.extend(glob(os.path.join(image_folder, files)))
total_num = len(image_path_list)
resolution = args.resolution
face_ind = np.loadtxt('./Data/uv-data/face_ind.txt').astype(
np.int32) # get valid vertices in the pos map
uv_kpt_ind = np.loadtxt('./Data/uv-data/uv_kpt_ind.txt').astype(
np.int32) # 2 x 68 get kpt
for i, image_path in enumerate(image_path_list):
name = image_path.strip().split('/')[-1][:-4]
# read image
image = imread(image_path)
[h, w, _] = image.shape
tform = np.loadtxt(os.path.join(save_folder, 'tform.txt'),
delimiter=',')
pos = get_pos(image, tform, resolution)
if args.isPose:
# 3D vertices
vertices = get_vertices(pos, resolution,
face_ind) # get valid points
if args.isFront:
save_vertices = frontalize(
vertices) # affine matrix to frontalize
else:
save_vertices = vertices.copy() # 68 * 3
save_vertices[:,
1] = h - 1 - save_vertices[:, 1] # control the scope
if args.isKpt or args.isShow:
# get landmarks
kpt = get_landmarks(pos, uv_kpt_ind)
np.savetxt(os.path.join(save_folder, name + '_kpt.txt'), kpt)
if args.isPose: # camera matrix
# estimate pose
camera_matrix, pose = estimate_pose(vertices)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
np.savetxt(os.path.join(save_folder, name + '_camera_matrix.txt'),
camera_matrix)
def head_pose(frame):
global prn
try:
pos = prn.process(frame)
kpt = prn.get_landmarks(pos)
vertices = prn.get_vertices(pos)
camera_matrix, pose = estimate_pose(vertices)
if pose[1] > config.SPEED_STEP:
color = COLOR_UP
elif pose[1] < -config.SPEED_STEP:
color = COLOR_DOWN
else:
color = COLOR_STAY
frame = plot_pose_box(frame, camera_matrix, kpt, color=color)
except:
pose = (0.0, 0.0, 0.0)
return frame, pose
def main(args):
if args.isShow or args.isTexture:
import cv2
from utils.cv_plot import plot_kpt, plot_vertices, plot_pose_box
# ---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # GPU number, -1 for CPU
prn = PRN(is_dlib=args.isDlib)
# ------------- load data
image_folder = args.inputDir
save_folder = args.outputDir
if not os.path.exists(save_folder):
os.mkdir(save_folder)
types = ('*.jpg', '*.png')
image_path_list = []
if os.path.isfile(image_folder):
image_path_list.append(image_folder)
for files in types:
image_path_list.extend(glob(os.path.join(image_folder, files)))
total_num = len(image_path_list)
for i, image_path in enumerate(image_path_list):
name = image_path.strip().split('/')[-1][:-4]
# read image
image = imread(image_path)
[h, w, _] = image.shape
# the core: regress position map
if args.isDlib:
max_size = max(image.shape[0], image.shape[1])
if max_size > 1000:
image = rescale(image, 1000. / max_size)
image = (image * 255).astype(np.uint8)
pos, crop_image = prn.process(image) # use dlib to detect face
else:
if image.shape[1] == image.shape[2]:
image = resize(image, (256, 256))
pos = prn.net_forward(
image / 255.) # input image has been cropped to 256x256
crop_image = None
else:
box = np.array([0, image.shape[1] - 1, 0, image.shape[0] - 1
]) # cropped with bounding box
pos, crop_image = prn.process(image, box)
image = image / 255.
if pos is None:
continue
if args.is3d or args.isMat or args.isPose or args.isShow:
# 3D vertices
vertices = prn.get_vertices(pos)
if args.isFront:
save_vertices = frontalize(vertices)
else:
save_vertices = vertices.copy()
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
if args.isImage and crop_image is not None:
imsave(os.path.join(save_folder, name + '_crop.jpg'), crop_image)
imsave(os.path.join(save_folder, name + '_orig.jpg'), image)
if args.is3d:
# corresponding colors
colors = prn.get_colors(image, vertices)
if args.isTexture:
texture = cv2.remap(image,
pos[:, :, :2].astype(np.float32),
None,
interpolation=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
if args.isMask:
vertices_vis = get_visibility(vertices, prn.triangles, h,
w)
uv_mask = get_uv_mask(vertices_vis, prn.triangles,
prn.uv_coords, h, w,
prn.resolution_op)
texture = texture * uv_mask[:, :, np.newaxis]
write_obj_with_texture(
os.path.join(save_folder,
name + '.obj'), save_vertices, colors,
prn.triangles, texture, prn.uv_coords / prn.resolution_op
) #save 3d face with texture(can open with meshlab)
else:
write_obj(os.path.join(save_folder,
name + '.obj'), save_vertices, colors,
prn.triangles) #save 3d face(can open with meshlab)
if args.isDepth:
depth_image = get_depth_image(vertices, prn.triangles, h, w, True)
depth = get_depth_image(vertices, prn.triangles, h, w)
imsave(os.path.join(save_folder, name + '_depth.jpg'), depth_image)
sio.savemat(os.path.join(save_folder, name + '_depth.mat'),
{'depth': depth})
if args.isMat:
sio.savemat(os.path.join(save_folder, name + '_mesh.mat'), {
'vertices': vertices,
'colors': colors,
'triangles': prn.triangles
})
if args.isKpt or args.isShow:
# get landmarks
kpt = prn.get_landmarks(pos)
np.savetxt(os.path.join(save_folder, name + '_kpt.txt'), kpt)
if args.isPose or args.isShow:
# estimate pose
camera_matrix, pose = estimate_pose(vertices)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
np.savetxt(os.path.join(save_folder, name + '_camera_matrix.txt'),
camera_matrix)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
if args.isShow:
# ---------- Plot
image_pose = plot_pose_box(image, camera_matrix, kpt)
cv2.imshow('sparse alignment', plot_kpt(image, kpt))
cv2.imshow('dense alignment', plot_vertices(image, vertices))
cv2.imshow('pose', plot_pose_box(image, camera_matrix, kpt))
if crop_image is not None:
cv2.imshow('crop', crop_image)
cv2.waitKey(0)
def main(args):
#---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # GPU number, -1 for CPU
prn = PRN(is_dlib = args.isDlib)
if mode == FAKE:
dataset_folder_path = "/home/wukong/librealsense/examples/realsense-dataset/attack_dataset"
elif mode == REAL:
dataset_folder_path = "/home/wukong/librealsense/examples/realsense-dataset/all_dataset"
elif mode == PRINT:
dataset_folder_path = "/home/wukong/librealsense/examples/realsense-dataset/all_dataset"
dataset_folder_list = os.walk(dataset_folder_path).next()[1]
train_folder_path = "/home/wukong/anaconda3/dataset/phase7/train"
train_folder_list = os.walk(train_folder_path).next()[1]
for fname in dataset_folder_list:
source_path = os.path.join(dataset_folder_path, fname)
hs_warp_folder = os.path.join(source_path, "hs_raw_warp")
rs_color_folder = os.path.join(source_path, "rs_raw_color")
rs_depth_folder = os.path.join(source_path, "rs_raw_depth")
hs_warp_list = sorted(glob(os.path.join(hs_warp_folder, '*.jpg')))
rs_color_list = sorted(glob(os.path.join(rs_color_folder, '*.jpg')))
rs_depth_list = sorted(glob(os.path.join(rs_depth_folder, '*.jpg')))
if not hs_warp_list or not rs_color_list or not rs_depth_list:
print("skip ", source_path)
continue
elif len(hs_warp_list) != len(rs_color_list) or len(hs_warp_list) != len(rs_depth_list):
print("skip ", source_path)
continue
else:
pass
if mode == FAKE:
new_fname = "1_" + fname[1:] + "_3_1_4"
elif mode == REAL:
new_fname = "1_" + fname[1:] + "_1_1_1"
elif mode == PRINT:
new_fname = "1_" + fname[1:] + "_0_0_0"
if new_fname not in train_folder_list:
new_path = os.path.join(train_folder_path, new_fname)
os.mkdir(new_path)
new_depth_path = os.path.join(new_path, "depth")
os.mkdir(new_depth_path)
new_profile_path = os.path.join(new_path, "profile")
os.mkdir(new_profile_path)
new_rs_path = os.path.join(new_path, "rs")
os.mkdir(new_rs_path)
print("create new folder: {}".format(fname))
else:
print("skip {}".format(new_fname))
continue
spatial_coordinate_idx = index.Index(properties=p)
count_num = 1
total_num = len(hs_warp_list)
for j in range(total_num):
if j % 10 == 0:
print("has processed {} of {} images".format(j, total_num))
hs_warp_image = imread(hs_warp_list[j])
[h, w, c] = hs_warp_image.shape
if c>3:
hs_warp_image = hs_warp_image[:,:,:3]
# the core: regress position map
if args.isDlib:
max_size = max(hs_warp_image.shape[0], hs_warp_image.shape[1])
if max_size> 1000:
hs_warp_image = rescale(hs_warp_image, 1000./max_size)
hs_warp_image = (hs_warp_image*255).astype(np.uint8)
hs_pos = prn.process(hs_warp_image) # use dlib to detect face
else:
if hs_warp_image.shape[0] == hs_warp_image.shape[1]:
hs_warp_image = resize(hs_warp_image, (256,256))
hs_pos = prn.net_forward(hs_warp_image/255.) # input hs_warp_image has been cropped to 256x256
else:
box = np.array([0, hs_warp_image.shape[1]-1, 0, hs_warp_image.shape[0]-1]) # cropped with bounding box
hs_pos = prn.process(hs_warp_image, box)
hs_warp_image = hs_warp_image/255.
if hs_pos is None:
continue
hs_vertices = prn.get_vertices(hs_pos)
camera_matrix, euler_pose = estimate_pose(hs_vertices)
# check similarity with previous pose
hit = spatial_coordinate_idx.nearest((euler_pose[0], euler_pose[1], euler_pose[2], euler_pose[0], euler_pose[1], euler_pose[2]), 1, objects=True)
hit = [i for i in hit]
if hit:
nearest_euler_pose = np.array(hit[0].bbox[:3])
current_euler_pose = np.array(euler_pose)
dist = np.linalg.norm(current_euler_pose - nearest_euler_pose)
if dist > SPATIAL_THRESHOLD_DEGREE:
print("Get a new euler pose {}".format(euler_pose))
spatial_coordinate_idx.insert(0,(euler_pose[0], euler_pose[1], euler_pose[2], euler_pose[0], euler_pose[1], euler_pose[2]))
else:
continue
else:
print("First euler_pose: {}".format(euler_pose))
spatial_coordinate_idx.insert(0,(euler_pose[0], euler_pose[1], euler_pose[2], euler_pose[0], euler_pose[1], euler_pose[2]))
##############################################
#
##############################################
if mode == FAKE:
imsave(os.path.join(new_profile_path, ('%04d' % count_num) + '.jpg'), plot_crop(hs_warp_image, hs_vertices))
rs_depth_image = imread(rs_depth_list[j])
imsave(os.path.join(new_depth_path, ('%04d' % count_num) + '.jpg'), plot_crop(rs_depth_image, hs_vertices))
elif mode == PRINT:
rs_color_image = imread(rs_color_list[j])
imsave(os.path.join(new_rs_path, ('%04d' % count_num) + '.jpg'), rs_color_image)
elif mode == REAL:
rs_color_image = imread(rs_color_list[j])
[h, w, c] = rs_color_image.shape
if c>3:
rs_color_image = rs_color_image[:,:,:3]
# the core: regress position map
if args.isDlib:
max_size = max(rs_color_image.shape[0], rs_color_image.shape[1])
if max_size> 1000:
rs_color_image = rescale(rs_color_image, 1000./max_size)
rs_color_image = (rs_color_image*255).astype(np.uint8)
rs_pos = prn.process(rs_color_image) # use dlib to detect face
else:
if rs_color_image.shape[0] == rs_color_image.shape[1]:
rs_color_image = resize(rs_color_image, (256,256))
rs_pos = prn.net_forward(rs_color_image/255.) # input rs_color_image has been cropped to 256x256
else:
box = np.array([0, rs_color_image.shape[1]-1, 0, rs_color_image.shape[0]-1]) # cropped with bounding box
rs_pos = prn.process(rs_color_image, box)
rs_color_image = rs_color_image/255.
if rs_pos is None:
continue
rs_vertices = prn.get_vertices(rs_pos)
rs_depth_image = imread(rs_depth_list[j])
imsave(os.path.join(new_profile_path, ('%04d' % count_num) + '.jpg'), plot_crop(hs_warp_image, rs_vertices))
imsave(os.path.join(new_depth_path, ('%04d' % count_num) + '.jpg'), plot_crop(rs_depth_image, rs_vertices))
imsave(os.path.join(new_rs_path, ('%04d' % count_num) + '.jpg'), plot_crop(rs_color_image, rs_vertices))
count_num += 1
depth = get_depth_image(vertices, prn.triangles, h, w)
# imsave(os.path.join(save_folder, name + '_depth.jpg'), depth_image)
# sio.savemat(os.path.join(save_folder, name + '_depth.mat'), {'depth':depth})
if args.isMat:
# sio.savemat(os.path.join(save_folder, name + '_mesh.mat'), {'vertices': vertices, 'colors': colors, 'triangles': prn.triangles})
pass
if args.isKpt or args.isShow:
# get landmarks
kpt = prn.get_landmarks(pos)
# np.savetxt(os.path.join(save_folder, name + '_kpt.txt'), kpt)
if args.isPose or args.isShow:
# estimate pose
camera_matrix, pose = estimate_pose(vertices)
# np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
# np.savetxt(os.path.join(save_folder, name + '_camera_matrix.txt'), camera_matrix)
# np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
if args.isShow:
# ---------- Plot
image_pose = plot_pose_box(image, camera_matrix, kpt)
cv2.imshow('sparse alignment', plot_kpt(image, kpt))
cv2.imshow('dense alignment', plot_vertices(image, vertices))
cv2.imshow('pose', plot_pose_box(image, camera_matrix, kpt))
#cv2.waitKey(0)
frame_count += 1
if cv2.waitKey(1) & 0xFF == ord('q'):
def main(args):
if args.isShow:
args.isOpencv = True
from utils.cv_plot import plot_kpt, plot_vertices, plot_pose_box
if args.isObj:
from utils.write import write_obj
if args.isPose:
from utils.estimate_pose import estimate_pose
# ---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # GPU number, -1 for CPU
prn = PRN(is_dlib=args.isDlib, is_opencv=args.isOpencv)
# ------------- load data
image_folder = args.inputFolder
save_folder = args.outputFolder
if not os.path.exists(save_folder):
os.mkdir(save_folder)
types = ('*.jpg', '*.png')
image_path_list = []
for files in types:
image_path_list.extend(glob(os.path.join(image_folder, files)))
total_num = len(image_path_list)
for i, image_path in enumerate(image_path_list):
name = image_path.strip().split('/')[-1][:-4]
# read image
image = imread(image_path)
# the core: regress position map
pos = prn.process(image) # use dlib to detect face
if args.isObj or args.isShow:
# 3D vertices
vertices = prn.get_vertices(pos)
# corresponding colors
colors = prn.get_colors(image, vertices)
write_obj(os.path.join(save_folder,
name + '.obj'), vertices, colors,
prn.triangles) #save 3d face(can open with meshlab)
if args.isKpt or args.isShow:
# get landmarks
kpt = prn.get_landmarks(pos)
np.savetxt(os.path.join(save_folder, name + '_kpt.txt'), kpt)
if args.isPose or args.isShow:
# estimate pose
camera_matrix, pose = estimate_pose(vertices)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
if args.isShow:
# ---------- Plot
image_pose = plot_pose_box(image, camera_matrix, kpt)
cv2.imshow('sparse alignment', plot_kpt(image, kpt))
cv2.imshow('dense alignment', plot_vertices(image, vertices))
cv2.imshow('pose', plot_pose_box(image, camera_matrix, kpt))
cv2.waitKey(0)
def main(args):
if args.isShow or args.isTexture:
import cv2
from utils.cv_plot import plot_kpt, plot_vertices, plot_pose_box
# ---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # GPU number, -1 for CPU
prn = PRN(is_dlib=args.isDlib, is_faceboxes=args.isFaceBoxes)
# ---- load data
image_folder = args.inputDir
save_folder = args.outputDir
if not os.path.exists(save_folder):
os.mkdir(save_folder)
types = ('*.jpg', '*.png')
image_path_list = []
for files in types:
image_path_list.extend(glob(os.path.join(image_folder, files)))
total_num = len(image_path_list)
for i, image_path in enumerate(image_path_list):
name = image_path.strip().split('/')[-1][:-4]
# read image
image = imread(image_path)
[h, w, c] = image.shape
if c > 3: image = image[:, :, :3] # RGBA图中，去除A通道
# the core: regress position map
if args.isDlib:
max_size = max(image.shape[0], image.shape[1])
if max_size > 1000:
image = rescale(image, 1000. / max_size)
image = (image * 255).astype(np.uint8)
pos = prn.process(image) # use dlib to detect face
elif args.isFaceBoxes:
pos, cropped_img = prn.process(
image) # use faceboxes to detect face
else:
if image.shape[0] == image.shape[1]:
image = resize(image, (256, 256))
pos = prn.net_forward(
image / 255.) # input image has been cropped to 256x256
else:
box = np.array([0, image.shape[1] - 1, 0, image.shape[0] - 1
]) # cropped with bounding box
pos = prn.process(image, box)
image = image / 255.
if pos is None: continue
if args.is3d or args.isMat or args.isPose or args.isShow:
# 3D vertices
vertices = prn.get_vertices(pos)
if args.isFront:
save_vertices = frontalize(vertices)
else:
save_vertices = vertices.copy()
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
# 三维人脸旋转对齐方法
# if args.isImage:
# vertices = prn.get_vertices(pos)
# scale_init = 180 / (np.max(vertices[:, 1]) - np.min(vertices[:, 1]))
# colors = prn.get_colors(image, vertices)
# triangles = prn.triangles
# camera_matrix, pose = estimate_pose(vertices)
# yaw, pitch, roll = pos * ANGULAR
# vertices1 = vertices - np.mean(vertices, 0)[np.newaxis, :]
#
# obj = {'s': scale_init, 'angles': [-pitch, yaw, -roll + 180], 't': [0, 0, 0]}
# camera = {'eye':[0, 0, 256], 'proj_type':'perspective', 'at':[0, 0, 0],
# 'near': 1000, 'far':-100, 'fovy':30, 'up':[0,1,0]}
#
# image1 = transform_test(vertices1, obj, camera, triangles, colors, h=256, w=256) * 255
# image1 = image1.astype(np.uint8)
# imsave(os.path.join(save_folder, name + '.jpg'), image1)
if args.is3d:
# corresponding colors
colors = prn.get_colors(image, vertices)
if args.isTexture:
if args.texture_size != 256:
pos_interpolated = resize(
pos, (args.texture_size, args.texture_size),
preserve_range=True)
else:
pos_interpolated = pos.copy()
texture = cv2.remap(image,
pos_interpolated[:, :, :2].astype(
np.float32),
None,
interpolation=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
if args.isMask:
vertices_vis = get_visibility(vertices, prn.triangles, h,
w)
uv_mask = get_uv_mask(vertices_vis, prn.triangles,
prn.uv_coords, h, w,
prn.resolution_op)
uv_mask = resize(uv_mask,
(args.texture_size, args.texture_size),
preserve_range=True)
texture = texture * uv_mask[:, :, np.newaxis]
write_obj_with_texture(
os.path.join(save_folder, name + '.obj'), save_vertices,
prn.triangles, texture, prn.uv_coords / prn.resolution_op
) #save 3d face with texture(can open with meshlab)
else:
write_obj_with_colors(
os.path.join(save_folder,
name + '.obj'), save_vertices, prn.triangles,
colors) #save 3d face(can open with meshlab)
if args.isDepth:
depth_image = get_depth_image(vertices, prn.triangles, h, w, True)
depth = get_depth_image(vertices, prn.triangles, h, w)
imsave(os.path.join(save_folder, name + '_depth.jpg'), depth_image)
sio.savemat(os.path.join(save_folder, name + '_depth.mat'),
{'depth': depth})
if args.isMat:
sio.savemat(os.path.join(save_folder, name + '_mesh.mat'), {
'vertices': vertices,
'colors': colors,
'triangles': prn.triangles
})
if args.isKpt:
# get landmarks
kpt = prn.get_landmarks(pos)
np.savetxt(os.path.join(save_folder, name + '_kpt.txt'), kpt)
if args.is2dKpt and args.is68Align:
ori_kpt = prn.get_landmarks_2d(pos)
dlib_aligner = DlibAlign()
dst_img = dlib_aligner.dlib_68_align(image, ori_kpt, 256, 0.5)
imsave(os.path.join(save_folder, name + '.jpg'), dst_img)
if args.isPose:
# estimate pose
camera_matrix, pose, rot = estimate_pose(vertices)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'),
np.array(pose) * ANGULAR)
np.savetxt(os.path.join(save_folder, name + '_camera_matrix.txt'),
camera_matrix)
if args.isShow:
kpt = prn.get_landmarks(pos)
cv2.imshow('sparse alignment', plot_kpt(image, kpt))
# cv2.imshow('dense alignment', plot_vertices(image, vertices))
# cv2.imshow('pose', plot_pose_box(image, camera_matrix, kpt))
cv2.waitKey(1)
def texture_editing(prn, args):
# read image
image = imread(args.image_path)
if len(image.shape) > 2:
[h, w, c] = image.shape
if c > 3:
image = image[:, :, :3]
else:
[h, w] = image.shape
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
#[h, w, _] = image.shape
#-- 1. 3d reconstruction -> get texture.
pos = prn.process(image)
print("Pose shape: ", pos.shape)
vertices = prn.get_vertices(pos)
print("Vertice shape: ", vertices.shape)
image = image / 255.
texture = cv2.remap(image,
pos[:, :, :2].astype(np.float32),
None,
interpolation=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
#filename, _ = os.path.splitext(args.image_path)
#cv2.imwrite(filename + '_tex.jpg', texture[:, :, ::-1] * 255)
#-- 2. Texture Editing
Mode = args.mode
# change part of texture(for data augumentation/selfie editing. Here modify eyes for example)
if Mode == 0:
# load eye mask
uv_face_eye = imread('Data/uv-data/uv_face_eyes.png',
as_grey=True) / 255.
uv_face = imread('Data/uv-data/uv_face.png', as_grey=True) / 255.
eye_mask = (abs(uv_face_eye - uv_face) > 0).astype(np.float32)
# texture from another image or a processed texture
ref_image = imread(args.ref_path)
ref_pos = prn.process(ref_image)
ref_image = ref_image / 255.
ref_texture = cv2.remap(ref_image,
ref_pos[:, :, :2].astype(np.float32),
None,
interpolation=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
# modify texture
new_texture = texture * (1 - eye_mask[:, :, np.newaxis]
) + ref_texture * eye_mask[:, :, np.newaxis]
# change whole face(face swap)
elif Mode == 1:
# texture from another image or a processed texture
ref_image = imread(args.ref_path)
if len(ref_image.shape) > 2:
#[h, w, c] = ref_image.shape
if c > 3:
ref_image = ref_image[:, :, :3]
else:
#[h, w] = ref_image.shape
ref_image = cv2.cvtColor(ref_image, cv2.COLOR_GRAY2BGR)
ref_pos = prn.process(ref_image)
ref_image = ref_image / 255.
ref_texture = cv2.remap(ref_image,
ref_pos[:, :, :2].astype(np.float32),
None,
interpolation=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
filename, _ = os.path.splitext(args.ref_path)
cv2.imwrite(filename + '_tex.jpg', ref_texture[:, :, ::-1] * 255)
ref_vertices = prn.get_vertices(ref_pos)
new_texture = ref_texture #(texture + ref_texture)/2.
else:
print('Wrong Mode! Mode should be 0 or 1.')
exit()
#-- 3. remap to input image.(render)
vis_colors = np.ones((vertices.shape[0], 1))
face_mask = render_texture(vertices.T,
vis_colors.T,
prn.triangles.T,
h,
w,
c=1)
face_mask = np.squeeze(face_mask > 0).astype(np.float32)
new_colors = prn.get_colors_from_texture(new_texture)
new_image = render_texture(vertices.T,
new_colors.T,
prn.triangles.T,
h,
w,
c=3)
new_image = image * (1 - face_mask[:, :, np.newaxis]
) + new_image * face_mask[:, :, np.newaxis]
# Possion Editing for blending image
vis_ind = np.argwhere(face_mask > 0)
vis_min = np.min(vis_ind, 0)
vis_max = np.max(vis_ind, 0)
center = (int((vis_min[1] + vis_max[1]) / 2 + 0.5),
int((vis_min[0] + vis_max[0]) / 2 + 0.5))
output = cv2.seamlessClone(
(new_image * 255).astype(np.uint8), (image * 255).astype(np.uint8),
(face_mask * 255).astype(np.uint8), center, cv2.NORMAL_CLONE)
# save output
print("Vertice:", vertices)
camera_matrix, pose, rotation_matrix = estimate_pose(vertices)
# pose, rotation_matrix = estimate_pose(vertices)
center_pt = np.mean(vertices, axis=0)
vertices_trans = vertices - center_pt
save_vertices = frontalize(vertices_trans, rotation_matrix)
save_vertices = save_vertices + center_pt
if not os.path.exists(os.path.dirname(args.output_path)):
os.makedirs(os.path.dirname(args.output_path))
sio.savemat(
os.path.join(os.path.dirname(args.output_path),
os.path.basename(args.output_path) + '_mesh.mat'), {
'vertices': save_vertices,
'colors': new_colors,
'triangles': prn.triangles
})
imsave(args.output_path, output)
print('Done.')
def main(args):
if args.isShow or args.isTexture:
import cv2
from utils.cv_plot import plot_kpt, plot_vertices, plot_pose_box
# ---- transform
transform_img = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(FLAGS["normalize_mean"], FLAGS["normalize_std"])
])
# ---- init PRN
prn = PRN(args.model)
# ------------- load data
image_folder = args.inputDir
save_folder = args.outputDir
if not os.path.exists(save_folder):
os.mkdir(save_folder)
types = ('*.jpg', '*.png')
image_path_list = []
for files in types:
image_path_list.extend(glob(os.path.join(image_folder, files)))
total_num = len(image_path_list)
print("#" * 25)
print("[PRNet Inference] {} picture were under processing~".format(
total_num))
print("#" * 25)
for i, image_path in enumerate(image_path_list):
name = image_path.strip().split('/')[-1][:-4]
# read image
image = cv2.imread(image_path)
[h, w, c] = image.shape
# the core: regress position map
image = cv2.resize(image, (256, 256))
image_t = transform_img(image)
image_t = image_t.unsqueeze(0)
pos = prn.net_forward(
image_t) # input image has been cropped to 256x256
out = pos.cpu().detach().numpy()
pos = np.squeeze(out)
cropped_pos = pos * 255
pos = cropped_pos.transpose(1, 2, 0)
if pos is None:
continue
if args.is3d or args.isMat or args.isPose or args.isShow:
# 3D vertices
vertices = prn.get_vertices(pos)
if args.isFront:
save_vertices = frontalize(vertices)
else:
save_vertices = vertices.copy()
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
if args.isImage:
cv2.imwrite(os.path.join(save_folder, name + '.jpg'), image)
if args.is3d:
# corresponding colors
colors = prn.get_colors(image, vertices)
if args.isTexture:
if args.texture_size != 256:
pos_interpolated = cv2.resize(
pos, (args.texture_size, args.texture_size),
preserve_range=True)
else:
pos_interpolated = pos.copy()
texture = cv2.remap(image,
pos_interpolated[:, :, :2].astype(
np.float32),
None,
interpolation=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
if args.isMask:
vertices_vis = get_visibility(vertices, prn.triangles, h,
w)
uv_mask = get_uv_mask(vertices_vis, prn.triangles,
prn.uv_coords, h, w,
prn.resolution_op)
uv_mask = cv2.resize(
uv_mask, (args.texture_size, args.texture_size),
preserve_range=True)
texture = texture * uv_mask[:, :, np.newaxis]
write_obj_with_texture(
os.path.join(save_folder, name + '.obj'), save_vertices,
prn.triangles, texture, prn.uv_coords / prn.resolution_op
) # save 3d face with texture(can open with meshlab)
else:
write_obj_with_colors(
os.path.join(save_folder,
name + '.obj'), save_vertices, prn.triangles,
colors) # save 3d face(can open with meshlab)
# if args.isDepth:
# depth_image = get_depth_image(vertices, prn.triangles, h, w, True)
# depth = get_depth_image(vertices, prn.triangles, h, w)
# cv2.imwrite(os.path.join(save_folder, name + '_depth.jpg'), depth_image)
# sio.savemat(os.path.join(save_folder, name + '_depth.mat'), {'depth': depth})
if args.isKpt or args.isShow:
# get landmarks
kpt = prn.get_landmarks(pos)
np.savetxt(os.path.join(save_folder, name + '_kpt.txt'), kpt)
if args.isPose or args.isShow:
# estimate pose
camera_matrix, pose = estimate_pose(vertices)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
np.savetxt(os.path.join(save_folder, name + '_camera_matrix.txt'),
camera_matrix)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
if args.isShow:
# ---------- Plot
image_pose = plot_pose_box(image, camera_matrix, kpt)
cv2.imshow('sparse alignment', plot_kpt(image, kpt))
cv2.imshow('dense alignment', plot_vertices(image, vertices))
cv2.imshow('pose', plot_pose_box(image, camera_matrix, kpt))
cv2.waitKey(0)
def main(args):
if args.isShow or args.isTexture:
import cv2
from utils.cv_plot import plot_kpt, plot_vertices, plot_pose_box
# ---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # GPU number, -1 for CPU
prn = PRN(is_dlib = args.isDlib)
# ------------- load data
image_folder = args.inputDir
save_folder = args.outputDir
if not os.path.exists(save_folder):
os.mkdir(save_folder)
# types = ('*.jpg', '*.png')
# image_path_list= []
# for files in types:
# image_path_list.extend(glob(os.path.join(image_folder, files)))
# total_num = len(image_path_list)
for dir, dirs, files in sorted(os.walk(image_folder)):
for file in files:
image_path = os.path.join(dir, file)
dir = dir.replace("\\", "/")
new_dir = dir.replace(image_folder, save_folder)
if not os.path.isdir(new_dir):
os.mkdir(new_dir)
name = image_path.replace(image_folder, save_folder)
print('data path:', name)
# read image
image = imread(image_path)
[h, w, c] = image.shape
if c>3:
image = image[:,:,:3]
# the core: regress position map
if args.isDlib:
max_size = max(image.shape[0], image.shape[1])
if max_size> 1000:
image = rescale(image, 1000./max_size)
image = (image*255).astype(np.uint8)
pos = prn.process(image) # use dlib to detect face
else:
# if image.shape[0] == image.shape[1]:
# image = resize(image, (256,256))
# pos = prn.net_forward(image/255.) # input image has been cropped to 256x256
# else:
box = np.array([0, image.shape[1]-1, 0, image.shape[0]-1]) # cropped with bounding box
pos = prn.process(image, box)
image = image/255.
if pos is None:
continue
if args.is3d or args.isMat or args.isPose or args.isShow:
# 3D vertices
vertices = prn.get_vertices(pos)
if args.isFront:
save_vertices = frontalize(vertices)
else:
save_vertices = vertices.copy()
save_vertices[:,1] = h - 1 - save_vertices[:,1]
if args.isImage:
imsave(name, image)
if args.is3d:
# corresponding colors
colors = prn.get_colors(image, vertices)
if args.isTexture:
if args.texture_size != 256:
pos_interpolated = resize(pos, (args.texture_size, args.texture_size), preserve_range = True)
else:
pos_interpolated = pos.copy()
texture = cv2.remap(image, pos_interpolated[:,:,:2].astype(np.float32), None, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT,borderValue=(0))
if args.isMask:
vertices_vis = get_visibility(vertices, prn.triangles, h, w)
uv_mask = get_uv_mask(vertices_vis, prn.triangles, prn.uv_coords, h, w, prn.resolution_op)
uv_mask = resize(uv_mask, (args.texture_size, args.texture_size), preserve_range = True)
texture = texture*uv_mask[:,:,np.newaxis]
write_obj_with_texture(name.replace('.jpg', '.obj'), save_vertices, prn.triangles, texture, prn.uv_coords/prn.resolution_op)#save 3d face with texture(can open with meshlab)
else:
write_obj_with_colors(name.replace('.jpg', '.obj'), save_vertices, prn.triangles, colors) #save 3d face(can open with meshlab)
filepath = name.replace('.jpg', '.obj')
filepath = filepath.replace("\\", "/")
print('filepath:', filepath)
new_dir = dir.replace(args.inputDir, args.renderDir)
# print(new_dir + '/' + file)
if not os.path.isdir(new_dir):
os.mkdir(new_dir)
color_image1, _ = render_scene(filepath, 4.0, 0.0, 3.0)
color_image2, _ = render_scene(filepath, 4.0, np.pi / 18.0, 3.0)
color_image3, _ = render_scene(filepath, 4.0, np.pi / 9.0, 3.0)
if color_image1 is None or color_image2 is None:
continue
new_path = filepath.replace(args.outputDir, args.renderDir)
# print('new_path:', new_path)
save_image(new_path, '_40_', color_image1)
save_image(new_path, '_50_', color_image2)
save_image(new_path, '_60_', color_image3)
os.remove(name.replace('.jpg', '.obj'))
if args.isDepth:
depth_image = get_depth_image(vertices, prn.triangles, h, w, True)
depth = get_depth_image(vertices, prn.triangles, h, w)
imsave(os.path.join(name.replace('.jpg', '_depth.jpg')), depth_image)
sio.savemat(name.replace('.jpg', '_depth.mat'), {'depth': depth})
if args.isMat:
sio.savemat(name.replace('.jpg', '_mesh.mat'),
{'vertices': vertices, 'colors': colors, 'triangles': prn.triangles})
if args.isKpt or args.isShow:
# get landmarks
kpt = prn.get_landmarks(pos)
np.savetxt(name.replace('.jpg', '_kpt.txt'), kpt)
if args.isPose or args.isShow:
# estimate pose
camera_matrix, pose = estimate_pose(vertices)
np.savetxt(name.replace('.jpg', '_pose.txt'), pose)
np.savetxt(name.replace('.jpg', '_camera_matrix.txt'), camera_matrix)
np.savetxt(name.replace('.jpg', '_pose.txt'), pose)
if args.isShow:
# ---------- Plot
image_pose = plot_pose_box(image, camera_matrix, kpt)
cv2.imshow('sparse alignment', plot_kpt(image, kpt))
cv2.imshow('dense alignment', plot_vertices(image, vertices))
cv2.imshow('pose', plot_pose_box(image, camera_matrix, kpt))
cv2.waitKey(0)
def main(args):
if args.isShow or args.isTexture:
import cv2
from utils.cv_plot import plot_kpt, plot_vertices, plot_pose_box
# ---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # GPU number, -1 for CPU
prn = PRN(is_dlib = args.isDlib)
# ------------- load data
image_folder = args.inputDir
save_folder = args.outputDir
if not os.path.exists(save_folder):
os.mkdir(save_folder)
types = ('*.jpg', '*.png')
image_path_list= []
for files in types:
image_path_list.extend(glob(os.path.join(image_folder, files)))
total_num = len(image_path_list)
for i, image_path in enumerate(image_path_list):
name = image_path.strip().split('/')[-1][:-4]
# read image
image = imread(image_path)
[h, w, _] = image.shape
# the core: regress position map
if args.isDlib:
max_size = max(image.shape[0], image.shape[1])
if max_size> 1000:
image = rescale(image, 1000./max_size)
pos = prn.process(image) # use dlib to detect face
else:
if image.shape[1] == image.shape[2]:
image = resize(image, (256,256))
pos = prn.net_forward(image/255.) # input image has been cropped to 256x256
else:
box = np.array([0, image.shape[1]-1, 0, image.shape[0]-1]) # cropped with bounding box
pos = prn.process(image, box)
image = image/255.
if pos is None:
continue
if args.is3d or args.isMat or args.isPose or args.isShow:
# 3D vertices
vertices = prn.get_vertices(pos)
if args.isFront:
save_vertices = frontalize(vertices)
else:
save_vertices = vertices
if args.isImage:
imsave(os.path.join(save_folder, name + '.jpg'), image)
if args.is3d:
# corresponding colors
colors = prn.get_colors(image, vertices)
if args.isTexture:
texture = cv2.remap(image, pos[:,:,:2].astype(np.float32), None, interpolation=cv2.INTER_NEAREST, borderMode=cv2.BORDER_CONSTANT,borderValue=(0))
if args.isMask:
vertices_vis = get_visibility(vertices, prn.triangles, h, w)
uv_mask = get_uv_mask(vertices_vis, prn.triangles, prn.uv_coords, h, w, prn.resolution_op)
texture = texture*uv_mask[:,:,np.newaxis]
write_obj_with_texture(os.path.join(save_folder, name + '.obj'), save_vertices, colors, prn.triangles, texture, prn.uv_coords/prn.resolution_op)#save 3d face with texture(can open with meshlab)
else:
write_obj(os.path.join(save_folder, name + '.obj'), save_vertices, colors, prn.triangles) #save 3d face(can open with meshlab)
if args.isDepth:
depth_image = get_depth_image(vertices, prn.triangles, h, w)
imsave(os.path.join(save_folder, name + '_depth.jpg'), depth_image)
if args.isMat:
sio.savemat(os.path.join(save_folder, name + '_mesh.mat'), {'vertices': save_vertices, 'colors': colors, 'triangles': prn.triangles})
if args.isKpt or args.isShow:
# get landmarks
kpt = prn.get_landmarks(pos)
np.savetxt(os.path.join(save_folder, name + '_kpt.txt'), kpt)
if args.isPose or args.isShow:
# estimate pose
camera_matrix, pose = estimate_pose(vertices)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
if args.isShow:
# ---------- Plot
image_pose = plot_pose_box(image, camera_matrix, kpt)
cv2.imshow('sparse alignment', plot_kpt(image, kpt))
cv2.imshow('dense alignment', plot_vertices(image, vertices))
cv2.imshow('pose', plot_pose_box(image, camera_matrix, kpt))
cv2.waitKey(0)
def main(args):
if args.isShow or args.isTexture:
import cv2
from utils.cv_plot import plot_kpt, plot_vertices, plot_pose_box
# ---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # GPU number, -1 for CPU
prn = PRN(is_dlib = args.isDlib)
# ------------- load data
image_folder = args.inputDir
print(image_folder)
save_folder = args.outputDir
print(save_folder)
if not os.path.exists(save_folder):
os.mkdir(save_folder)
meta_save_folder = os.path.join(save_folder, 'meta')
if not os.path.exists(meta_save_folder):
os.mkdir(meta_save_folder)
types = ('*.jpg', '*.png', '*,JPG')
image_path_list= find_files(image_folder, ('.jpg', '.png', '.JPG'))
total_num = len(image_path_list)
print(image_path_list)
for i, image_path in enumerate(image_path_list):
name = image_path.strip().split('/')[-1][:-4]
print(image_path)
# read image
image = imread(image_path)
[h, w, c] = image.shape
if c>3:
image = image[:,:,:3]
# the core: regress position map
if args.isDlib:
max_size = max(image.shape[0], image.shape[1])
if max_size> 1000:
image = rescale(image, 1000./max_size)
image = (image*255).astype(np.uint8)
pos = prn.process(image) # use dlib to detect face
else:
if image.shape[1] == image.shape[2]:
image = resize(image, (256,256))
pos = prn.net_forward(image/255.) # input image has been cropped to 256x256
else:
box = np.array([0, image.shape[1]-1, 0, image.shape[0]-1]) # cropped with bounding box
pos = prn.process(image, box)
image = image/255.
if pos is None:
continue
if args.is3d or args.isMat or args.isPose or args.isShow:
# 3D vertices
vertices = prn.get_vertices(pos)
if args.isFront:
save_vertices = frontalize(vertices)
else:
save_vertices = vertices.copy()
save_vertices[:,1] = h - 1 - save_vertices[:,1]
if args.isImage:
imsave(os.path.join(save_folder, name + '.jpg'), image)
if args.is3d:
# corresponding colors
colors = prn.get_colors(image, vertices)
if args.isTexture:
if args.texture_size != 256:
pos_interpolated = resize(pos, (args.texture_size, args.texture_size), preserve_range = True)
else:
pos_interpolated = pos.copy()
texture = cv2.remap(image, pos_interpolated[:,:,:2].astype(np.float32), None, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT,borderValue=(0))
if args.isMask:
vertices_vis = get_visibility(vertices, prn.triangles, h, w)
uv_mask = get_uv_mask(vertices_vis, prn.triangles, prn.uv_coords, h, w, prn.resolution_op)
uv_mask = resize(uv_mask, (args.texture_size, args.texture_size), preserve_range = True)
texture = texture*uv_mask[:,:,np.newaxis]
write_obj_with_texture(os.path.join(save_folder, name + '.obj'), save_vertices, prn.triangles, texture, prn.uv_coords/prn.resolution_op)#save 3d face with texture(can open with meshlab)
else:
write_obj_with_colors(os.path.join(save_folder, name + '.obj'), save_vertices, prn.triangles, colors) #save 3d face(can open with meshlab)
if args.isDepth:
depth_image = get_depth_image(vertices, prn.triangles, h, w, True)
depth = get_depth_image(vertices, prn.triangles, h, w)
imsave(os.path.join(save_folder, name + '_depth.jpg'), depth_image)
sio.savemat(os.path.join(meta_save_folder, name + '_depth.mat'), {'depth':depth})
if args.isMat:
sio.savemat(os.path.join(meta_save_folder, name + '_mesh.mat'), {'vertices': vertices, 'colors': colors, 'triangles': prn.triangles})
if args.isKpt or args.isShow:
# get landmarks
kpt = prn.get_landmarks(pos)
# pdb.set_trace()
np.save(os.path.join(meta_save_folder, name + '_kpt.npy'), kpt)
# cv2.imwrite(os.path.join(save_folder, name + '_skpt.jpg'), plot_kpt(image, kpt))
if args.isPose or args.isShow:
# estimate pose
camera_matrix, pose = estimate_pose(vertices)
np.savetxt(os.path.join(meta_save_folder, name + '_pose.txt'), pose)
np.savetxt(os.path.join(meta_save_folder, name + '_camera_matrix.txt'), camera_matrix)
if args.isShow:
# ---------- Plot
image = imread(os.path.join(save_folder, name + '.jpg'))
image_pose = plot_pose_box(image, camera_matrix, kpt)
#cv2.imwrite(os.path.join(save_folder, name + '_pose.jpg'), plot_kpt(image, kpt))
#cv2.imwrite(os.path.join(save_folder, name + '_camera_matrix.jpg'), plot_vertices(image, vertices))
#cv2.imwrite(os.path.join(save_folder, name + '_pose.jpg'), plot_pose_box(image, camera_matrix, kpt))
image = imread(os.path.join(save_folder, name + '.jpg'))
b, g, r = cv2.split(image)
image = cv2.merge([r,g,b])
def main(args):
if args.isShow or args.isTexture or args.isCamera:
import cv2
from utils.cv_plot import plot_kpt, plot_vertices, plot_pose_box
# ---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # GPU number, -1 for CPU
prn = PRN(is_dlib=args.isDlib)
# ------------- load data
image_folder = args.inputDir
save_folder = args.outputDir
if not os.path.exists(save_folder):
os.mkdir(save_folder)
types = ('*.jpg', '*.png')
image_path_list = []
for files in types:
image_path_list.extend(glob(os.path.join(image_folder, files)))
total_num = len(image_path_list)
if args.isCamera:
# Create a VideoCapture object and read from input file
# If the input is the camera, pass 0 instead of the video file name
cap = cv2.VideoCapture(0)
# Check if camera opened successfully
if (cap.isOpened() == False):
print("Error opening video stream or file")
# Read until video is completed
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
if ret == True:
if args.isDlib:
max_size = max(frame.shape[0], frame.shape[1])
if max_size > 1000:
frame = rescale(frame, 1000. / max_size)
frame = (frame * 255).astype(np.uint8)
pos = prn.process(frame) # use dlib to detect face
else:
if frame.shape[0] == frame.shape[1]:
frame = resize(frame, (256, 256))
pos = prn.net_forward(
frame /
255.) # input frame has been cropped to 256x256
else:
box = np.array(
[0, frame.shape[1] - 1, 0,
frame.shape[0] - 1]) # cropped with bounding box
pos = prn.process(frame, box)
# Normalizing the frame and skiping if there was no one in the frame
frame = frame / 255.
if pos is None:
continue
# Get landmarks in frame
kpt = prn.get_landmarks(pos)
# Display the resulting frame
cv2.imshow('sparse alignment', plot_kpt(frame, kpt))
# Press Q on keyboard to exit
if cv2.waitKey(25) & 0xFF == ord('q'):
break
# Break the loop
else:
break
# When everything done, release the video capture object
cap.release()
# Closes all the frames
cv2.destroyAllWindows()
else:
for i, image_path in enumerate(image_path_list):
name = image_path.strip().split('/')[-1][:-4]
# read image
image = imread(image_path)
[h, w, c] = image.shape
if c > 3:
image = image[:, :, :3]
# the core: regress position map
if args.isDlib:
max_size = max(image.shape[0], image.shape[1])
if max_size > 1000:
image = rescale(image, 1000. / max_size)
image = (image * 255).astype(np.uint8)
pos = prn.process(image) # use dlib to detect face
else:
if image.shape[0] == image.shape[1]:
image = resize(image, (256, 256))
pos = prn.net_forward(
image /
255.) # input image has been cropped to 256x256
else:
box = np.array(
[0, image.shape[1] - 1, 0,
image.shape[0] - 1]) # cropped with bounding box
pos = prn.process(image, box)
image = image / 255.
if pos is None:
continue
if args.is3d or args.isMat or args.isPose or args.isShow:
# 3D vertices
vertices = prn.get_vertices(pos)
if args.isFront:
save_vertices = frontalize(vertices)
else:
save_vertices = vertices.copy()
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
if args.isImage:
imsave(os.path.join(save_folder, name + '.jpg'), image)
if args.is3d:
# corresponding colors
colors = prn.get_colors(image, vertices)
if args.isTexture:
if args.texture_size != 256:
pos_interpolated = resize(
pos, (args.texture_size, args.texture_size),
preserve_range=True)
else:
pos_interpolated = pos.copy()
texture = cv2.remap(image,
pos_interpolated[:, :, :2].astype(
np.float32),
None,
interpolation=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
if args.isMask:
vertices_vis = get_visibility(vertices, prn.triangles,
h, w)
uv_mask = get_uv_mask(vertices_vis, prn.triangles,
prn.uv_coords, h, w,
prn.resolution_op)
uv_mask = resize(
uv_mask, (args.texture_size, args.texture_size),
preserve_range=True)
texture = texture * uv_mask[:, :, np.newaxis]
write_obj_with_texture(
os.path.join(save_folder, name + '.obj'),
save_vertices, prn.triangles, texture,
prn.uv_coords / prn.resolution_op
) #save 3d face with texture(can open with meshlab)
else:
write_obj_with_colors(
os.path.join(save_folder, name + '.obj'),
save_vertices, prn.triangles,
colors) #save 3d face(can open with meshlab)
if args.isDepth:
depth_image = get_depth_image(vertices, prn.triangles, h, w,
True)
depth = get_depth_image(vertices, prn.triangles, h, w)
imsave(os.path.join(save_folder, name + '_depth.jpg'),
depth_image)
sio.savemat(os.path.join(save_folder, name + '_depth.mat'),
{'depth': depth})
if args.isMat:
sio.savemat(
os.path.join(save_folder, name + '_mesh.mat'), {
'vertices': vertices,
'colors': colors,
'triangles': prn.triangles
})
if args.isKpt or args.isShow:
# get landmarks
kpt = prn.get_landmarks(pos)
np.savetxt(os.path.join(save_folder, name + '_kpt.txt'), kpt)
if args.isPose or args.isShow:
# estimate pose
camera_matrix, pose = estimate_pose(vertices)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
np.savetxt(
os.path.join(save_folder, name + '_camera_matrix.txt'),
camera_matrix)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
if args.isShow:
# ---------- Plot
image_pose = plot_pose_box(image, camera_matrix, kpt)
cv2.imshow(
'sparse alignment',
cv2.cvtColor(np.float32(plot_kpt(image, kpt)),
cv2.COLOR_RGB2BGR))
cv2.imshow(
'dense alignment',
cv2.cvtColor(np.float32(plot_vertices(image, vertices)),
cv2.COLOR_RGB2BGR))
cv2.imshow(
'pose',
cv2.cvtColor(
np.float32(plot_pose_box(image, camera_matrix, kpt)),
cv2.COLOR_RGB2BGR))
cv2.waitKey(0)
