def make_posmaps(num_to_read=10, folder='300W_LP'):
uv_h = uv_w = 256
image_h = image_w = 256
save_folder = 'data_input'
if folder == 'AFLW2000':
save_folder = 'posmap_output'
global uv_coords
uv_coords = face3d.morphable_model.load.load_uv_coords(
'BFM/Out/BFM_UV.mat')
uv_coords = process_uv(uv_coords, uv_h, uv_w)
bfm = MorphabelModel('BFM/Out/BFM.mat')
img_paths = glob.glob(folder + '/*.jpg')
mat_paths = glob.glob(folder + '/*.mat')
img_mat_comb = []
for i in range(len(img_paths)):
img_paths[i] = img_paths[i].replace('\\', '/')
mat_paths[i] = mat_paths[i].replace('\\', '/')
img_mat_comb.append([img_paths[i], mat_paths[i]])
random.shuffle(img_mat_comb)
for i in range(num_to_read):
run_posmap_300W_LP(bfm, img_mat_comb[i][0], img_mat_comb[i][1],
save_folder)
def generate_batch_sample(input_dir, save_folder='./300WLP'):
if not os.path.exists(save_folder):
os.mkdir(save_folder)
# set para
uv_h = uv_w = 256
# load uv coords
global uv_coords
uv_coords = face3d.morphable_model.load.load_uv_coords('BFM/BFM_UV.mat') #
uv_coords = process_uv(uv_coords, uv_h, uv_w)
# load bfm
bfm = MorphabelModel('BFM/BFM.mat')
# Batch generating uv_map Dataset
"""
@date: 2019/07/19
Train Dataset:
AFW. 10413.
HELEN. 75351.
LFPW. 33111.
Test Dataset:
IBUG. 3571.
"""
base = 0
for idx, item in enumerate(os.listdir(input_dir)):
if 'jpg' in item:
ab_path = os.path.join(input_dir, item)
img_path = ab_path
mat_path = ab_path.replace('jpg', 'mat')
run_posmap_300W_LP(bfm, img_path, mat_path, save_folder, idx + base)
print("Number {} uv_pos_map was generated!".format(idx))
def generate_batch_sample(input_dir, save_folder):
uv_h = uv_w = 256
# load uv coords
global uv_coords
uv_coords = face3d.morphable_model.load.load_uv_coords(
'./examples/Data/BFM/Out/BFM_UV.mat') #
uv_coords = process_uv(uv_coords, uv_h, uv_w)
# load bfm
bfm = MorphabelModel('./examples/Data/BFM/Out/BFM.mat')
base = 0
path_w = open(
'/media/weepies/Seagate Backup Plus Drive/3DMM/train_path_afw.txt',
'w')
print('input: ', input_dir)
for idx, item in enumerate(os.listdir(input_dir)):
print('dealing')
if 'jpg' in item:
ab_path = os.path.join(input_dir, item)
img_path = ab_path
mat_path = ab_path.replace('jpg', 'mat')
run_posmap_300W_LP(bfm, img_path, mat_path, save_folder,
idx + base)
name_p = image_name = img_path.strip().split('/')[-1]
num_name = name_p.split('.jpg')
path_w.write(
'/media/weepies/Seagate Backup Plus Drive/3DMM/posnet/300W_HELEN/'
+ name_p + '*' +
'/media/weepies/Seagate Backup Plus Drive/3DMM/posnet/300W_HELEN/'
+ num_name[0] + '.txt' + '\n')
# print("Number {} uv_pos_map was generated!".format(idx))
path_w.close()
def generate_bfm_base_obj():
bfm = MorphabelModel('../Data/BFM/Out/BFM.mat')
sp = bfm.get_shape_para('zero')
ep = bfm.get_exp_para('zero')
tp = bfm.get_tex_para('random')
vertices = bfm.generate_vertices(sp, ep)
colors = bfm.generate_colors(tp)
colors = np.minimum(np.maximum(colors, 0), 255)
write_obj_with_colors('head_fit.obj', vertices, bfm.triangles, colors)
def get_depth_image(image_path):
im = cv2.imread(image_path)
h, w, c = im.shape
landmarks = pd.read_pickle(image_path.replace('.jpg', '.pkl'))
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
x = mesh.transform.from_image(landmarks, h, w)
X_ind = bfm.kpt_ind
fitted_sp, fitted_ep, fitted_s, fitted_angles, fitted_t = bfm.fit(
x, X_ind, max_iter=200, isShow=False)
colors = bfm.generate_colors(np.random.rand(bfm.n_tex_para, 1))
colors = np.minimum(np.maximum(colors, 0), 1)
fitted_vertices = bfm.generate_vertices(fitted_sp, fitted_ep)
transformed_vertices = bfm.transform(fitted_vertices, fitted_s,
fitted_angles, fitted_t)
image_vertices = mesh.transform.to_image(transformed_vertices, h, w)
triangles = bfm.triangles
z = image_vertices[:, 2:]
z = z - np.min(z)
z = z / np.max(z)
attribute = z
depth_image = mesh.render.render_colors(image_vertices,
triangles,
attribute,
h,
w,
c=1)
depth_image = (depth_image * 255).astype('uint8')
savename = image_path.replace('.jpg', '-depth.jpg')
io.imsave(savename, np.squeeze(depth_image))
def generate_prnet_trainset(root_300wlp, save_trainset):
'''
Input：
root_300wlp. The root path of your 300w_lp.
save_trainset. The path you want to save.
(All train set totally about 80G)
'''
root_300wlp = os.path.abspath(root_300wlp).replace('\\',
'/') # For windows
save_trainset = os.path.abspath(save_trainset).replace('\\',
'/') # For windows
# set para
uv_h = uv_w = 256
# load uv coords
global uv_coords
uv_coords = face3d.morphable_model.load.load_uv_coords(
'Data/BFM/Out/BFM_UV.mat')
uv_coords = process_uv(uv_coords, uv_h, uv_w)
# load bfm
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
# run
fp_label = open('trainDataLabel.txt', "w")
sub_dir_list = os.listdir(root_300wlp)
for item in sub_dir_list:
save_folder = os.path.join(save_trainset, item)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
img_list_rep = os.path.join(root_300wlp, item, "*.jpg")
img_list = glob.glob(img_list_rep)
for img_path in img_list:
img_path = img_path.replace('\\', '/') # For windows
mat_path = img_path.replace('jpg', 'mat')
if not os.path.exists(mat_path):
continue
save_img_path = img_path.replace(root_300wlp, save_trainset)
save_npy_path = save_img_path.replace('jpg', 'npy')
if os.path.exists(save_img_path) and os.path.exists(save_npy_path):
fp_label.writelines(save_img_path + ' ' + save_npy_path + '\n')
print("Passing ...")
continue
run_posmap_300W_LP(bfm, img_path, mat_path, save_folder)
print(save_img_path)
fp_label.writelines(save_img_path + ' ' + save_npy_path + '\n')
fp_label.close()
def __init__(self, model_dir, render_only=False):
self.bfm = MorphabelModel(osp.join(model_dir, 'BFM.mat'))
self.index_ind = self.bfm.kpt_ind
uv_coords = face3d.morphable_model.load.load_uv_coords(
osp.join(model_dir, 'BFM_UV.mat'))
self.uv_size = (224, 224)
self.mask_stxr = 0.1
self.mask_styr = 0.33
self.mask_etxr = 0.9
self.mask_etyr = 0.7
self.tex_h, self.tex_w, self.tex_c = self.uv_size[1], self.uv_size[
0], 3
texcoord = np.zeros_like(uv_coords)
texcoord[:, 0] = uv_coords[:, 0] * (self.tex_h - 1)
texcoord[:, 1] = uv_coords[:, 1] * (self.tex_w - 1)
texcoord[:, 1] = self.tex_w - texcoord[:, 1] - 1
self.texcoord = np.hstack((texcoord, np.zeros((texcoord.shape[0], 1))))
self.X_ind = self.bfm.kpt_ind
if not render_only:
from image_3d68 import Handler
self.if3d68_handler = Handler(osp.join(model_dir, 'if1k3d68'),
0,
192,
ctx_id=0)
def demo():
save_folder = 'results/posmap_300WLP'
if not os.path.exists(save_folder):
os.mkdir(save_folder)
# set para
uv_h = uv_w = 256
# load uv coords
global uv_coords
uv_coords = face3d.morphable_model.load.load_uv_coords('Data/BFM/Out/BFM_UV.mat')
uv_coords = process_uv(uv_coords, uv_h, uv_w)
# load bfm
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
# run
image_path = 'Data/IBUG_image_008_1_0.jpg'
mat_path = 'Data/IBUG_image_008_1_0.mat'
run_posmap_300W_LP(bfm, image_path, mat_path, save_folder)
import numpy as np
import scipy.io as sio
from skimage import io
from time import time
import matplotlib.pyplot as plt
import glob
sys.path.append('..')
import face3d
from face3d import mesh
from face3d import mesh_cython
from face3d.morphable_model import MorphabelModel
# --------------------- Forward: parameters(shape, expression, pose) --> 3D obj --> 2D image ---------------
# --- 1. load model
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
print('init bfm model success')
save_folder = 'results/faces/output'
if not os.path.exists(save_folder):
os.mkdir(save_folder)
h = w = 200; c = 3
for filename in glob.glob('{}/predicted_*.npy'.format(save_folder)):
i = os.path.splitext(os.path.basename(filename))[0].split('_')[-1]
params = np.load(filename)
params = np.float32(params)
class MaskRenderer:
def __init__(self, model_dir, render_only=False):
self.bfm = MorphabelModel(osp.join(model_dir, 'BFM.mat'))
self.index_ind = self.bfm.kpt_ind
uv_coords = face3d.morphable_model.load.load_uv_coords(
osp.join(model_dir, 'BFM_UV.mat'))
self.uv_size = (224, 224)
self.mask_stxr = 0.1
self.mask_styr = 0.33
self.mask_etxr = 0.9
self.mask_etyr = 0.7
self.tex_h, self.tex_w, self.tex_c = self.uv_size[1], self.uv_size[
0], 3
texcoord = np.zeros_like(uv_coords)
texcoord[:, 0] = uv_coords[:, 0] * (self.tex_h - 1)
texcoord[:, 1] = uv_coords[:, 1] * (self.tex_w - 1)
texcoord[:, 1] = self.tex_w - texcoord[:, 1] - 1
self.texcoord = np.hstack((texcoord, np.zeros((texcoord.shape[0], 1))))
self.X_ind = self.bfm.kpt_ind
if not render_only:
from image_3d68 import Handler
self.if3d68_handler = Handler(osp.join(model_dir, 'if1k3d68'),
0,
192,
ctx_id=0)
def transform(self, shape3D, R):
s = 1.0
shape3D[:2, :] = shape3D[:2, :]
shape3D = s * np.dot(R, shape3D)
return shape3D
def preprocess(self, vertices, w, h):
R1 = mesh.transform.angle2matrix([0, 180, 180])
t = np.array([-w // 2, -h // 2, 0])
vertices = vertices.T
vertices += t
vertices = self.transform(vertices.T, R1).T
return vertices
def project_to_2d(self, vertices, s, angles, t):
transformed_vertices = self.bfm.transform(vertices, s, angles, t)
projected_vertices = transformed_vertices.copy(
) # using stantard camera & orth projection
return projected_vertices[self.bfm.kpt_ind, :2]
def params_to_vertices(self, params, H, W):
fitted_sp, fitted_ep, fitted_s, fitted_angles, fitted_t = params
fitted_vertices = self.bfm.generate_vertices(fitted_sp, fitted_ep)
transformed_vertices = self.bfm.transform(fitted_vertices, fitted_s,
fitted_angles, fitted_t)
transformed_vertices = self.preprocess(transformed_vertices.T, W, H)
image_vertices = mesh.transform.to_image(transformed_vertices, H, W)
return image_vertices
def build_params(self, face_image):
landmark = self.if3d68_handler.get(face_image)[:, :2]
#print(landmark.shape, landmark.dtype)
if landmark is None:
return None #face not found
fitted_sp, fitted_ep, fitted_s, fitted_angles, fitted_t = self.bfm.fit(
landmark, self.X_ind, max_iter=3)
return [fitted_sp, fitted_ep, fitted_s, fitted_angles, fitted_t]
def generate_mask_uv(self, mask, positions):
uv_size = (self.uv_size[1], self.uv_size[0], 3)
h, w, c = uv_size
uv = np.zeros(shape=(self.uv_size[1], self.uv_size[0], 3),
dtype=np.uint8)
stxr, styr = positions[0], positions[1]
etxr, etyr = positions[2], positions[3]
stx, sty = int(w * stxr), int(h * styr)
etx, ety = int(w * etxr), int(h * etyr)
height = ety - sty
width = etx - stx
mask = cv2.resize(mask, (width, height))
uv[sty:ety, stx:etx] = mask
return uv
def render_mask(self,
face_image,
mask_image,
params,
auto_blend=True,
positions=[0.1, 0.33, 0.9, 0.7]):
uv_mask_image = self.generate_mask_uv(mask_image, positions)
h, w, c = face_image.shape
image_vertices = self.params_to_vertices(params, h, w)
output = (1 - mesh.render.render_texture(
image_vertices, self.bfm.full_triangles, uv_mask_image,
self.texcoord, self.bfm.full_triangles, h, w)) * 255
output = output.astype(np.uint8)
if auto_blend:
mask_bd = (output == 255).astype(np.uint8)
final = face_image * mask_bd + (1 - mask_bd) * output
return final
return output
print(sys.version_info)
import scipy.io as sio
#from skimage import io
from time import time
#import matplotlib.pyplot as plt
sys.path.append('..')
import face3d
from face3d import mesh
from face3d.morphable_model import MorphabelModel
# --------------------- Forward: parameters(shape, expression, pose) --> 3D obj ---------------
# --- 1. load model
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
print('init bfm model success')
# --- 2. generate face mesh: vertices(represent shape) & colors(represent texture)
sp = bfm.get_shape_para('random')
ep = bfm.get_exp_para('random')
vertices = bfm.generate_vertices(sp, ep)
tp = bfm.get_tex_para('random')
colors = bfm.generate_colors(tp)
colors = np.minimum(np.maximum(colors, 0), 1)
# --- 3. create a mesh in blender and load 3dmm vertices and faces
mesh = bpy.data.meshes.new("3dmm_face") # add the new mesh
obj = bpy.data.objects.new(mesh.name, mesh)
col = bpy.data.collections.get("Collection")
import os
import sys
import cv2
import numpy as np
import scipy.io as sci
import matplotlib.pyplot as plt
sys.path.append("..")
import face3d
from face3d import mesh
from face3d.morphable_model import MorphabelModel
import dlib
from skimage import io
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
predictor_path = "./shape_predictor_68_face_landmarks.dat"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
def detect_landmark(imgpath):
img = cv2.imread(imgpath)
dlib_img = io.imread(imgpath)
dets = detector(img, 1)
if len(dets) == 0:
return 1, None
x1 = dets[0].left()
y1 = dets[0].top()
x2 = dets[0].right()
save_folder = '300W_LP_out'
if not os.path.exists(save_folder):
os.mkdir(save_folder)
# set para
uv_h = uv_w = 450
image_h = image_w = 450
# load uv coords
global uv_coords
uv_coords = face3d.morphable_model.load.load_uv_coords(
'./BFM/BFM_UV.mat') #
uv_coords = process_uv(uv_coords, uv_h, uv_w)
# load bfm
bfm = MorphabelModel('./BFM/BFM.mat')
# run
folders = [
"AFW", "AFW_Flip", "HELEN", "HELEN_Flip", "IBUG", "IBUG_Flip", "LFPW",
"LFPW_Flip"
]
folders = ["AFW", "IBUG"]
for i in folders:
folder_path = os.path.join(".", "300W_LP", i)
files = [j[:-4] for j in os.listdir(folder_path) if j[-4:] == ".jpg"]
for k in tqdm(files):
image_path = os.path.join(folder_path, k + ".jpg")
mat_path = os.path.join(folder_path, k + ".mat")
save_path = os.path.join(save_folder, i + "_" + k)
if not os.path.exists(save_path):
def main(args):
with open(args.img_list) as f:
img_list = [x.strip() for x in f.readlines()]
landmark_list = []
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
if not os.path.exists(args.save_lmk_dir):
os.mkdir(args.save_lmk_dir)
for img_idx, img_fp in enumerate(tqdm(img_list)):
im = cv2.imread(os.path.join(args.img_prefix, img_fp), 1)
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
h, w, c = im.shape
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(
"face3d/models/shape_predictor_68_face_landmarks.dat")
rects = detector(gray, 1)
shape = predictor(gray, rects[0])
tl_corner_x = rects[0].center().x - rects[0].width() / 2
tl_corner_y = rects[0].center().y - rects[0].height() / 2
br_corner_x = rects[0].center().x + rects[0].width() / 2
br_corner_y = rects[0].center().y + rects[0].height() / 2
rects = [(tl_corner_x, tl_corner_y), (br_corner_x, br_corner_y)]
landmarks = np.zeros((68, 2))
for i, p in enumerate(shape.parts()):
landmarks[i] = [p.x, p.y]
im = cv2.circle(im, (p.x, p.y),
radius=3,
color=(0, 0, 255),
thickness=5)
bfm = MorphabelModel('face3d/Data/BFM/Out/BFM.mat')
x = mesh_numpy.transform.from_image(landmarks, h, w)
X_ind = bfm.kpt_ind
fitted_sp, fitted_ep, fitted_s, fitted_angles, fitted_t = bfm.fit(
x, X_ind, max_iter=200, isShow=False)
colors = bfm.generate_colors(np.random.rand(bfm.n_tex_para, 1))
colors = np.minimum(np.maximum(colors, 0), 1)
fitted_vertices = bfm.generate_vertices(fitted_sp, fitted_ep)
transformed_vertices = bfm.transform(fitted_vertices, fitted_s,
fitted_angles, fitted_t)
image_vertices = mesh_numpy.transform.to_image(transformed_vertices, h,
w)
triangles = bfm.triangles
colors = colors / np.max(colors)
attribute = colors
color_image = mesh_numpy.render.render_colors(image_vertices,
triangles,
attribute,
h,
w,
c=3)
io.imsave(os.path.join(args.save_lmk_dir,
str(img_idx) + ".png"),
img_as_ubyte(color_image))
import scipy.io as sio
from skimage import io
from time import time
import matplotlib.pyplot as plt
sys.path.append('..')
import face3d
from face3d import mesh
from face3d.morphable_model import MorphabelModel
import warnings
warnings.filterwarnings("ignore")
# --------------------- Forward: parameters(shape, expression, pose) --> 3D obj --> 2D image ---------------
# --- 1. load model
bfm = MorphabelModel('../examples/Data/BFM/Out/BFM.mat')
# save folder
save_folder = '3dmm_transform_angle'
if not os.path.exists(save_folder):
os.mkdir(save_folder)
# --- 2. generate face mesh: vertices(represent shape) & colors(represent texture)
sp = bfm.get_shape_para('random')
ep = bfm.get_exp_para('random')
vertices = bfm.generate_vertices(sp, ep)
tp = bfm.get_tex_para('random')
colors = bfm.generate_colors(tp)
colors = np.minimum(np.maximum(colors, 0), 1)
from face3d import mesh
from face3d import mesh_cython
from face3d.morphable_model import MorphabelModel
import cv2
from skimage import img_as_float
import glob as glob
# --------------------- Forward: parameters(shape, expression, pose) --> 3D obj --> 2D image ---------------
samples = 768
std = 1.2
h = w = 200
c = 3
# --- 1. load model
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
pncc = face3d.morphable_model.load.load_pncc_code('Data/BFM/Out/pncc_code.mat')
print('init bfm model success')
# load BG images
imgfiles = glob.glob(os.path.join('/home/karim/Documents/Data/Random',
'*.jpg'))
save_folder = 'results/faces/validation'
if not os.path.exists(save_folder):
os.mkdir(save_folder)
for i in range(samples):
b, g, r = cv2.split(cv2.imread(imgfiles[np.random.randint(len(imgfiles))]))
BG = cv2.merge([r, g, b])
BG = cv2.resize(BG, (w, h))
import os, sys
import subprocess
import numpy as np
import scipy.io as sio
from skimage import io
from time import time
import matplotlib.pyplot as plt
sys.path.append('..')
import face3d
from face3d import mesh
from face3d.morphable_model import MorphabelModel
# --------------------- Forward: parameters(shape, expression, pose) --> 3D obj --> 2D image ---------------
# --- 1. load model
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
print('init bfm model success')
# --- 2. generate face mesh: vertices(represent shape) & colors(represent texture)
sp = bfm.get_shape_para('random')
ep = bfm.get_exp_para('random')
vertices = bfm.generate_vertices(sp, ep)
tp = bfm.get_tex_para('random')
colors = bfm.generate_colors(tp)
colors = np.minimum(np.maximum(colors, 0), 1)
# --- 3. transform vertices to proper position
s = 8e-04
angles = [10, 30, 20]
t = [0, 0, 0]
import dlib
import glob
import subprocess
import numpy as np
import scipy.io as sio
from skimage import io
from time import time
import matplotlib.pyplot as plt
sys.path.append('..')
import face3d
from face3d import mesh
from face3d.morphable_model import MorphabelModel
# load BFM model
bfm = MorphabelModel('examples/Data/BFM/Out/BFM.mat')
print('init bfm model success')
uv_coords = face3d.morphable_model.load.load_uv_coords(
'examples/Data/BFM/Out/BFM_UV.mat')
t = [0, 0, 0]
c = 3
#random texture and colors
tp = bfm.get_tex_para('random')
colors = bfm.generate_colors(tp)
colors = np.minimum(np.maximum(colors, 0), 1)
predictor_path = "shape_predictor_68_face_landmarks.dat"
faces_folder_path = sys.argv[1]
result_folder_path = sys.argv[2]
if __name__ == '__main__':
if not os.path.exists(FLAGS["save_uv_path"]):
os.mkdir(FLAGS["save_uv_path"])
if not os.path.exists(FLAGS["save_im_path"]):
os.mkdir(FLAGS["save_im_path"])
# load uv coords
global uv_coords
uv_coords = face3d.morphable_model.load.load_uv_coords(
FLAGS["bfm_uv_path"])
uv_coords = process_uv(uv_coords, FLAGS["uv_h"], FLAGS["uv_w"])
# load bfm
bfm = MorphabelModel(FLAGS["bfm_path"])
img_names_list = Path(
FLAGS["list_path"]).read_text().strip().split('\n')[:128]
if FLAGS["is62Param"]:
param_62d = pickle.load(open(FLAGS["param_path"], 'rb'))
index = 0
for img_name in tqdm(img_names_list):
file_name = os.path.splitext(img_name)[0]
image_path = os.path.join(FLAGS["data_path"], file_name + ".jpg")
param = param_62d[index]
generate_posmap_lb_62params(bfm, image_path, param,
FLAGS["save_uv_path"],
FLAGS["save_im_path"])
index = index + 1
else:
for img_name in tqdm(img_names_list):
import subprocess
import numpy as np
import scipy.io as sio
from skimage import io
from time import time
import matplotlib.pyplot as plt
sys.path.append('..')
import face3d
from face3d import mesh
from face3d import mesh_cython
from face3d.morphable_model import MorphabelModel
# --------------------- Forward: parameters(shape, expression, pose) --> 3D obj --> 2D image ---------------
# --- 1. load model
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
print('init bfm model success')
# --- 2. generate face mesh: vertices(represent shape) & colors(represent texture)
sp = bfm.get_shape_para('random')
ep = bfm.get_exp_para('random')
vertices = bfm.generate_vertices(sp, ep)
tp = bfm.get_tex_para('random')
colors = bfm.generate_colors(tp)
colors = np.minimum(np.maximum(colors, 0), 1)
# --- 3. transform vertices to proper position
s = 8e-04
angles = [10, 30, 20]
t = [0, 0, 0]
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
rects = detector(gray, 1)
shape = predictor(gray, rects[0])
tl_corner_x = rects[0].center().x - rects[0].width()/2 # added
tl_corner_y = rects[0].center().y - rects[0].height()/2 # added
br_corner_x = rects[0].center().x + rects[0].width()/2 # added
br_corner_y = rects[0].center().y + rects[0].height()/2 # added
rects = [(tl_corner_x, tl_corner_y), (br_corner_x, br_corner_y)]
landmarks = np.zeros((68, 2))
for i, p in enumerate(shape.parts()):
landmarks[i] = [p.x, p.y]
im = cv2.circle(im, (p.x, p.y), radius=3, color=(0, 0, 255), thickness=5)
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
x = mesh.transform.from_image(landmarks, h, w)
X_ind = bfm.kpt_ind
global colors
global triangles
fitted_sp, fitted_ep, fitted_s, fitted_angles, fitted_t = bfm.fit(x, X_ind, max_iter=200, isShow=False)
colors = bfm.generate_colors(np.random.rand(bfm.n_tex_para, 1))
colors = np.minimum(np.maximum(colors, 0), 1)
fitted_vertices = bfm.generate_vertices(fitted_sp, fitted_ep)
transformed_vertices = bfm.transform(fitted_vertices, fitted_s, fitted_angles, fitted_t)
image_vertices = mesh.transform.to_image(transformed_vertices, h, w)
vertices = image_vertices
triangles = bfm.triangles
3dmm parameters --> mesh
fitting: 2d image + 3dmm -> 3d face
'''
import os
import sys
import numpy as np
from skimage import io
sys.path.append('..')
from face3d import mesh
from face3d.morphable_model import MorphabelModel
# --------------------- Forward: parameters(shape, expression, pose) --> 3D obj --> 2D image ---------------
# --- 1. load model
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
print('init bfm model success')
# print(bfm.n_tex_para)
# --- 2. generate face mesh: vertices(represent shape) & colors(represent texture)
# sp = bfm.get_shape_para('random')
sp = bfm.get_shape_para('zero')
# print(sp_sigma)
# print(sp_sigma.shape)
ep = bfm.get_exp_para('zero')
print(ep.shape)
ep[:10] = np.array([[-2.4658144 ],
[ 1.5232908 ],
[-0.58274204],
[ 1.1200945 ],