def get_3d_pkl_lrw(
pkl,
root,
bbb=0
): # the first cell is video path the last cell is the key frame nnuumber
# ---- init PRN
# os.environ['CUDA_VISIBLE_DEVICES'] = '0' # GPU number, -1 for CPU
prn = PRN(is_dlib=True)
_file = open(pkl, "rb")
data = pickle.load(_file)
_file.close()
gg = len(data)
data = data[int(gg * 1 * (bbb)):int(gg * 1 * (bbb + 1))]
for kk, item in enumerate(data):
print(kk)
print(item)
if os.path.exists(item[0] + '_original.obj'):
continue
target_id = item[-1]
img_path = item[0] + '_%05d.png' % target_id
print(img_path)
target_frame = cv2.imread(img_path)
target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
image = target_frame
# read image
[h, w, c] = image.shape
pos = prn.process(image) # use dlib to detect face
image = image / 255.
if pos is None:
print('+++++')
continue
# landmark
kpt = prn.get_landmarks(pos)
kpt[:, 1] = h - kpt[:, 1]
np.save(item[0] + '_prnet.npy', kpt)
# 3D vertices
vertices = prn.get_vertices(pos)
save_vertices = vertices.copy()
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
# corresponding colors
colors = prn.get_colors(image, vertices)
# print (colors.shape)
# print ('=========')
# cv2.imwrite('./mask.png', colors * 255)
write_obj_with_colors(item[0] + '_original.obj', save_vertices,
prn.triangles,
colors) #save 3d face(can open with meshlab)
def get_3d_single(video_path=None, target_id=None, img_path=None):
# ---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = '0' # GPU number, -1 for CPU
prn = PRN(is_dlib=True)
if video_path != None:
if not os.path.exists(video_path):
print(video_path)
print('+++++')
if os.path.exists(video_path[:-4] + '.obj'):
print('-----')
cap = cv2.VideoCapture(video_path)
for i in range(target_id):
ret, frame = cap.read()
ret, target_frame = cap.read()
cv2.imwrite(video_path[:-4] + '_%05d.png' % target_id, target_frame)
elif img_path != None:
target_frame = cv2.imread(img_path)
target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
image = target_frame
# read image
[h, w, c] = image.shape
pos = prn.process(image) # use dlib to detect face
image = image / 255.
# landmark
kpt = prn.get_landmarks(pos)
kpt[:, 1] = h - kpt[:, 1]
if video_path != None:
np.save(video_path[:-4] + '_prnet.npy', kpt)
else:
np.save(img_path[:-4] + '_prnet.npy', kpt)
# 3D vertices
vertices = prn.get_vertices(pos)
#
save_vertices = vertices.copy()
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
# corresponding colors
colors = prn.get_colors(image, vertices)
if video_path != None:
write_obj_with_colors(video_path[:-4] + '_original.obj', save_vertices,
prn.triangles,
colors) #save 3d face(can open with meshlab)
print('The generated 3d mesh model is stored in ' + video_path[:-4] +
'_original.obj')
else:
write_obj_with_colors(img_path[:-4] + '_original.obj', save_vertices,
prn.triangles,
colors) #save 3d face(can open with meshlab)
print('The generated 3d mesh model is stored in ' + img_path[:-4] +
'_original.obj')
def get_3d_single_video(
img_path): # you need the image path of the most visible frame.
# root =
# ---- init PRN
# os.environ['CUDA_VISIBLE_DEVICES'] = '0' # GPU number, -1 for CPU
prn = PRN(is_dlib=True)
# _file = open(pkl, "rb")
# data = pickle.load(_file)
# _file.close()
# gg = len(data)
# data = data[int(gg * 0.2 *( bbb) ): int(gg * 0.2 * (bbb + 1) ) ]
# for kk ,item in enumerate(data) :
print(img_path)
target_frame = cv2.imread(img_path)
target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
image = target_frame
# read image
[h, w, c] = image.shape
pos = prn.process(image) # use dlib to detect face
image = image / 255.
if pos is None:
print('No pos')
# landmark
kpt = prn.get_landmarks(pos)
kpt[:, 1] = h - kpt[:, 1]
np.save(img_path[:-11] + '__prnet.npy', kpt)
# 3D vertices
vertices = prn.get_vertices(pos)
save_vertices = vertices.copy()
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
# corresponding colors
colors = prn.get_colors(image, vertices)
# print (colors.shape)
# print ('=========')
# cv2.imwrite('./mask.png', colors * 255)
write_obj_with_colors(img_path[:-11] + '__original.obj', save_vertices,
prn.triangles,
colors) #save 3d face(can open with meshlab)
def main(args):
# ---- 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
# vertices_dir = args.vertDir
#i.e. d:\source
base_dir = args.baseDir
#i.e. d:\characters
base_save_dir = args.baseSavedir
#i.e. source\raupach
scene = args.sceneDir
#i.e source\raupach\richardson (the target character)
character = args.characterDir
#i.e. source\rauapch\richardson\richardson_001
source_num = args.sourceNum
# targ_character = args.targChar
#i.e. richardson_targ_10
targ_num = args.targNum
# something like D:\source\raupach\richardson\raupach_richardson_001
image_folder = "%s\\%s\\%s\\%s_%s_%s" % (base_dir, scene, character, scene,
character, source_num)
print(image_folder)
#something like d:\character\richardson\vertices\richards_t10
vertices_dir = "%s\\%s\\vertices\\%s_t%s" % (base_save_dir, character,
character, targ_num)
print(vertices_dir)
#something like d:\character\raupach\src\align\raupach_richardson_t10_s001\\obj
save_folder = "%s\\%s\\src\\align\\%s_%s_s%s_t%s\\obj" % (
base_save_dir, character, scene, character, source_num, targ_num)
print(save_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
# image_path_list= []
# for root, dirs, files in os.walk('%s' % image_folder):
# for file in files:
# if file.endswith('.jpg'):
# image_path_list.append(file)
# print (image_path_list)
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)
image_path_list = sorted(image_path_list)
#print (image_path_list)
# #repeating the above logic for a vertices directory.
types = ('*.npy', '*.jpg')
vert_path_list = []
for files in types:
vert_path_list.extend(glob(os.path.join(vertices_dir, files)))
total_num_vert = len(vert_path_list)
# vert_path_list.reverse()
vert_path_list = sorted(vert_path_list)
#print (vert_path_list)
for i, image_path in enumerate(image_path_list):
name = image_path.strip().split('\\')[-1][:-4]
print("%s aligned with %s" % (image_path_list[i], vert_path_list[i]))
# 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 = 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
vertices = prn.get_vertices(pos)
#takes the nth file in the directory of the vertices to "frontalize" the source image.
can_vert = vert_path_list[i]
print(can_vert)
save_vertices = align(vertices, can_vert)
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
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)
def get_3d(bbb):
# ---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = '0' # GPU number, -1 for CPU
prn = PRN(is_dlib=True)
# ------------- load data
# frame_id = "test_video/id00419/3U0abyjM2Po/00024"
# mesh_file = os.path.join(root, frame_id + ".obj")
# rt_file = os.path.join(root, frame_id + "_sRT.npy")
# image_path
# _file = open(os.path.join(root, 'txt', "front_rt.pkl"), "rb")
# data = pickle._Unpickler(_file)
# data.encoding = 'latin1'
# data = data.load()
_file = open(os.path.join(root, 'txt', "front_rt.pkl"), "rb")
data = pickle.load(_file)
_file.close()
gg = len(data)
print(len(data))
data = data[int(gg * 0.1 * bbb):int(gg * 0.1 * (bbb + 1))]
for kk, item in enumerate(data):
print(kk)
target_id = item[-1]
video_path = os.path.join(root, 'unzip', item[0] + '.mp4')
if not os.path.exists(video_path):
print(video_path)
print('+++++')
continue
if os.path.exists(video_path[:-4] + '.obj'):
print('-----')
continue
cap = cv2.VideoCapture(video_path)
for i in range(target_id):
ret, frame = cap.read()
ret, target_frame = cap.read()
cv2.imwrite(video_path[:-4] + '_%05d.png' % target_id, target_frame)
target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
image = target_frame
# read image
[h, w, c] = image.shape
pos = prn.process(image) # use dlib to detect face
image = image / 255.
if pos is None:
continue
# landmark
kpt = prn.get_landmarks(pos)
kpt[:, 1] = 224 - kpt[:, 1]
np.save(video_path[:-4] + '_prnet.npy', kpt)
# 3D vertices
vertices = prn.get_vertices(pos)
# save_vertices, p = frontalize(vertices)
# np.save(video_path[:-4] + '_p.npy', p)
# if os.path.exists(video_path[:-4] + '.obj'):
# continue
save_vertices = vertices.copy()
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
# corresponding colors
colors = prn.get_colors(image, vertices)
# print (colors.shape)
# print ('=========')
# cv2.imwrite('./mask.png', colors * 255)
write_obj_with_colors(video_path[:-4] + '_original.obj', save_vertices,
prn.triangles,
colors) # save 3d face(can open with meshlab)
def get_3d_single(video_path=None,
target_id=None,
img_path=None,
device_id='3'):
# ---- init PRN
target_id = count_frames(video_path)
os.environ['CUDA_VISIBLE_DEVICES'] = device_id # GPU number, -1 for CPU
prn = PRN(is_dlib=True)
# if video_path != None:
# if not os.path.exists(video_path):
# print (video_path)
# print ('+++++')
# if os.path.exists(video_path[:-4] + '.obj'):
# print ('-----')
# cap = cv2.VideoCapture(video_path)
# for i in range(target_id):
# ret, frame = cap.read()
# ret, target_frame = cap.read()
# cv2.imwrite(video_path[:-4] + '_%05d.png'%target_id,target_frame)
# elif img_path != None:
# target_frame = cv2.imread(img_path)
# target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
cap = cv2.VideoCapture(video_path)
for i in range(target_id):
ret, frame = cap.read()
print(target_path[:-4] + '_%05d.png' % i)
cv2.imwrite(target_path[:-4] + '_%05d.png' % i, frame)
# tt = cv2.imread(target_path[:-4] + '_%05d.png' % i)
# target_frame = cv2.cvtColor(cv2.imread(target_path[:-4] + '_%05d.png' % i), cv2.COLOR_BGR2RGB)
target_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = target_frame
[h, w, c] = image.shape
pos = prn.process(image)
image = image / 255
kpt = prn.get_landmarks(pos)
kpt[:, 1] = 224 - kpt[:, 1]
if video_path is not None:
print(target_path[:-4] + '_%05d' % i + '_prnet.npy')
np.save(target_path[:-4] + '_%05d' % i + '_prnet.npy', kpt)
else:
np.save(img_path[:-4] + '_%05d' % i + '_prnet.npy', kpt)
vertices = prn.get_vertices(pos)
# save_vertices, p = frontalize(vertices)
# np.save(video_path[:-4] + '_p.npy', p)
# if os.path.exists(video_path[:-4] + '.obj'):
# continue
save_vertices = vertices.copy()
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
# corresponding colors
colors = prn.get_colors(image, vertices)
# print (colors.shape)
# print ('=========')
# cv2.imwrite('./mask.png', colors * 255)
if video_path != None:
write_obj_with_colors(
target_path[:-4] + '_%05d' % i + '_original.obj',
save_vertices, prn.triangles,
colors) # save 3d face(can open with meshlab)
else:
write_obj_with_colors(
img_path[:-4] + '_%05d' % i + '_original.obj', save_vertices,
prn.triangles, colors) # save 3d face(can open with meshlab)
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)
# ------------- 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 get_3d_folder(
pkl
): # the first cell is video path the last cell is the key frame nnuumber
# ---- init PRN
# os.environ['CUDA_VISIBLE_DEVICES'] = '0' # GPU number, -1 for CPU
prn = PRN(is_dlib=True)
_file = open(pkl, "rb")
data = pickle.load(_file)
_file.close()
gg = len(data)
print(len(data))
# data = data[int(gg * 0.1 *bbb ): int(gg * 0.1 * (bbb + 1) ) ]
for kk, item in enumerate(data):
print(kk)
target_id = item[-1]
video_path = os.path.join(root, 'unzip', item[0])
if not os.path.exists(video_path):
print(video_path)
print('+++++')
continue
if os.path.exists(video_path[:-4] + '.obj'):
print('-----')
continue
cap = cv2.VideoCapture(video_path)
for i in range(target_id):
ret, frame = cap.read()
ret, target_frame = cap.read()
cv2.imwrite(video_path[:-4] + '_%05d.png' % target_id, target_frame)
target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
image = target_frame
# read image
[h, w, c] = image.shape
pos = prn.process(image) # use dlib to detect face
image = image / 255.
if pos is None:
continue
# landmark
kpt = prn.get_landmarks(pos)
kpt[:, 1] = 224 - kpt[:, 1]
np.save(video_path[:-4] + '_prnet.npy', kpt)
# 3D vertices
vertices = prn.get_vertices(pos)
# save_vertices, p = frontalize(vertices)
# np.save(video_path[:-4] + '_p.npy', p)
# if os.path.exists(video_path[:-4] + '.obj'):
# continue
save_vertices = vertices.copy()
save_vertices[:, 1] = h - 1 - save_vertices[:, 1]
# corresponding colors
colors = prn.get_colors(image, vertices)
# print (colors.shape)
# print ('=========')
# cv2.imwrite('./mask.png', colors * 255)
write_obj_with_colors(video_path[:-4] + '_original.obj', save_vertices,
prn.triangles,
colors) #save 3d face(can open with meshlab)
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 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)
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):
# read image
image = imread(image_path)
# the core: regress position map
if 'AFLW2000' in image_path:
mat_path = image_path.replace('jpg', 'mat')
info = sio.loadmat(mat_path)
kpt = info['pt3d_68']
pos = prn.process(image, kpt) # kpt information is only used for detecting face and cropping image
else:
pos = prn.process(image) # use dlib to detect face
# -- Basic Applications
# get landmarks
kpt = prn.get_landmarks(pos)
# 3D vertices
vertices = prn.get_vertices(pos)
# corresponding colors
colors = prn.get_colors(image, vertices)
# -- save
name = image_path.strip().split('/')[-1][:-4]
np.savetxt(os.path.join(save_folder, name + '.txt'), kpt)
write_obj(os.path.join(save_folder, name + '.obj'), vertices, colors, prn.triangles) #save 3d face(can open with meshlab)
sio.savemat(os.path.join(save_folder, name + '_mesh.mat'), {'vertices': vertices, 'colors': colors, 'triangles': prn.triangles})
# the core: regress position map
if 'AFLW2000' in image_path:
mat_path = image_path.replace('jpg', 'mat')
info = sio.loadmat(mat_path)
kpt = info['pt3d_68']
pos = prn.process(
image, kpt
) # kpt information is only used for detecting face and cropping image
else:
pos = prn.process(image) # use dlib to detect face
# -- Basic Applications
# get landmarks
kpt = prn.get_landmarks(pos)
# 3D vertices
vertices = prn.get_vertices(pos)
# corresponding colors
colors = prn.get_colors(image, vertices)
# -- save
name = image_path.strip().split('/')[-1][:-4]
np.savetxt(os.path.join(save_folder, name + '.txt'), kpt)
write_obj(os.path.join(save_folder, name + '.obj'), vertices, colors,
prn.triangles) #save 3d face(can open with meshlab)
sio.savemat(os.path.join(save_folder, name + '_mesh.mat'), {
'vertices': vertices,
'colors': colors,
'triangles': prn.triangles
})
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):
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)
