From 9d3bc46e43de7ff1304bde2449380e9e64f4805c Mon Sep 17 00:00:00 2001 From: dengy Date: Wed, 8 Apr 2020 15:32:34 +0800 Subject: [PATCH] transfer reconstruction process from numpy to tensorflow, add rendering process, update image preprocessing method --- demo.py | 44 ++++--- face_decoder.py | 293 ++++++++++++++++++++++++++++++++++++++++++++ input/000002.jpg | Bin 0 -> 32197 bytes input/000002.txt | 5 + input/000006.jpg | Bin 0 -> 30244 bytes input/000006.txt | 5 + input/000007.jpg | Bin 0 -> 27917 bytes input/000007.txt | 5 + input/000031.jpg | Bin 0 -> 31058 bytes input/000031.txt | 5 + input/000033.jpg | Bin 0 -> 9588 bytes input/000033.txt | 5 + input/000037.jpg | Bin 0 -> 14528 bytes input/000037.txt | 5 + input/000050.jpg | Bin 0 -> 8941 bytes input/000050.txt | 5 + input/000055.jpg | Bin 0 -> 17170 bytes input/000055.txt | 5 + input/000114.jpg | Bin 0 -> 42950 bytes input/000114.txt | 5 + input/000125.jpg | Bin 0 -> 13011 bytes input/000125.txt | 5 + input/000126.jpg | Bin 0 -> 75159 bytes input/000126.txt | 5 + input/015259.jpg | Bin 0 -> 20068 bytes input/015259.txt | 5 + input/015270.jpg | Bin 0 -> 65841 bytes input/015270.txt | 5 + input/015309.jpg | Bin 0 -> 137028 bytes input/015309.txt | 5 + input/015310.jpg | Bin 0 -> 20661 bytes input/015310.txt | 5 + input/015316.jpg | Bin 0 -> 60644 bytes input/015316.txt | 5 + input/015384.jpg | Bin 0 -> 33478 bytes input/015384.txt | 5 + input/vd005.png | Bin 403978 -> 0 bytes input/vd005.txt | 5 - input/vd006.txt | 10 +- input/vd025.txt | 10 +- input/vd026.txt | 10 +- input/vd034.txt | 10 +- input/vd051.txt | 10 +- input/vd057.png | Bin 287661 -> 0 bytes input/vd057.txt | 5 - input/vd070.txt | 10 +- input/vd092.txt | 10 +- input/vd102.txt | 10 +- load_data.py | 14 --- preprocess_img.py | 20 ++- reconstruct_mesh.py | 204 ------------------------------ 51 files changed, 455 insertions(+), 295 deletions(-) create mode 100644 face_decoder.py create mode 100644 input/000002.jpg create mode 100644 input/000002.txt create mode 100644 input/000006.jpg create mode 100644 input/000006.txt create mode 100644 input/000007.jpg create mode 100644 input/000007.txt create mode 100644 input/000031.jpg create mode 100644 input/000031.txt create mode 100644 input/000033.jpg create mode 100644 input/000033.txt create mode 100644 input/000037.jpg create mode 100644 input/000037.txt create mode 100644 input/000050.jpg create mode 100644 input/000050.txt create mode 100644 input/000055.jpg create mode 100644 input/000055.txt create mode 100644 input/000114.jpg create mode 100644 input/000114.txt create mode 100644 input/000125.jpg create mode 100644 input/000125.txt create mode 100644 input/000126.jpg create mode 100644 input/000126.txt create mode 100644 input/015259.jpg create mode 100644 input/015259.txt create mode 100644 input/015270.jpg create mode 100644 input/015270.txt create mode 100644 input/015309.jpg create mode 100644 input/015309.txt create mode 100644 input/015310.jpg create mode 100644 input/015310.txt create mode 100644 input/015316.jpg create mode 100644 input/015316.txt create mode 100644 input/015384.jpg create mode 100644 input/015384.txt delete mode 100644 input/vd005.png delete mode 100644 input/vd005.txt delete mode 100644 input/vd057.png delete mode 100644 input/vd057.txt delete mode 100644 reconstruct_mesh.py diff --git a/demo.py b/demo.py index 6212fda..68d3439 100644 --- a/demo.py +++ b/demo.py @@ -8,7 +8,7 @@ from scipy.io import loadmat,savemat from preprocess_img import Preprocess from load_data import * -from reconstruct_mesh import Reconstruction +from face_decoder import Face3D def load_graph(graph_filename): with tf.gfile.GFile(graph_filename,'rb') as f: @@ -21,56 +21,68 @@ def demo(): # input and output folder image_path = 'input' save_path = 'output' + if not os.path.exists(save_path): + os.makedirs(save_path) img_list = glob.glob(image_path + '/' + '*.png') + img_list +=glob.glob(image_path + '/' + '*.jpg') # read BFM face model # transfer original BFM model to our model if not os.path.isfile('./BFM/BFM_model_front.mat'): transferBFM09() - # read face model - facemodel = BFM() # read standard landmarks for preprocessing images lm3D = load_lm3d() + batchsize = 1 n = 0 # build reconstruction model with tf.Graph().as_default() as graph,tf.device('/cpu:0'): - images = tf.placeholder(name = 'input_imgs', shape = [None,224,224,3], dtype = tf.float32) + FaceReconstructor = Face3D() + images = tf.placeholder(name = 'input_imgs', shape = [batchsize,224,224,3], dtype = tf.float32) graph_def = load_graph('network/FaceReconModel.pb') tf.import_graph_def(graph_def,name='resnet',input_map={'input_imgs:0': images}) # output coefficients of R-Net (dim = 257) coeff = graph.get_tensor_by_name('resnet/coeff:0') + # reconstructing faces + FaceReconstructor.Reconstruction_Block(coeff,batchsize) + face_shape = FaceReconstructor.face_shape_t + face_texture = FaceReconstructor.face_texture + face_color = FaceReconstructor.face_color + landmarks_2d = FaceReconstructor.landmark_p + recon_img = FaceReconstructor.render_imgs + tri = FaceReconstructor.facemodel.face_buf + + with tf.Session() as sess: print('reconstructing...') for file in img_list: n += 1 print(n) # load images and corresponding 5 facial landmarks - img,lm = load_img(file,file.replace('png','txt')) + img,lm = load_img(file,file.replace('png','txt').replace('jpg','txt')) # preprocess input image input_img,lm_new,transform_params = Preprocess(img,lm,lm3D) - coef = sess.run(coeff,feed_dict = {images: input_img}) + coeff_,face_shape_,face_texture_,face_color_,landmarks_2d_,recon_img_,tri_ = sess.run([coeff,\ + face_shape,face_texture,face_color,landmarks_2d,recon_img,tri],feed_dict = {images: input_img}) - # reconstruct 3D face with output coefficients and face model - face_shape,face_texture,face_color,tri,face_projection,z_buffer,landmarks_2d = Reconstruction(coef,facemodel) # reshape outputs input_img = np.squeeze(input_img) - shape = np.squeeze(face_shape, (0)) - color = np.squeeze(face_color, (0)) - landmarks_2d = np.squeeze(landmarks_2d, (0)) + face_shape_ = np.squeeze(face_shape_, (0)) + face_texture_ = np.squeeze(face_texture_, (0)) + face_color_ = np.squeeze(face_color_, (0)) + landmarks_2d_ = np.squeeze(landmarks_2d_, (0)) + recon_img_ = np.squeeze(recon_img_, (0)) # save output files - # cropped image, which is the direct input to our R-Net - # 257 dim output coefficients by R-Net - # 68 face landmarks of cropped image - savemat(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','.mat')),{'cropped_img':input_img[:,:,::-1],'coeff':coef,'landmarks_2d':landmarks_2d,'lm_5p':lm_new}) - save_obj(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','_mesh.obj')),shape,tri,np.clip(color,0,255)/255) # 3D reconstruction face (in canonical view) + savemat(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','.mat').replace('jpg','mat')),{'cropped_img':input_img[:,:,::-1],'recon_img':recon_img_,'coeff':coeff_,\ + 'face_shape':face_shape_,'face_texture':face_texture_,'face_color':face_color_,'lm_68p':landmarks_2d_,'lm_5p':lm_new}) + save_obj(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','_mesh.obj').replace('jpg','_mesh.obj')),face_shape_,tri_,np.clip(face_color_,0,255)/255) # 3D reconstruction face (in canonical view) if __name__ == '__main__': demo() diff --git a/face_decoder.py b/face_decoder.py new file mode 100644 index 0000000..ef22388 --- /dev/null +++ b/face_decoder.py @@ -0,0 +1,293 @@ +import tensorflow as tf +import math as m +import numpy as np +import mesh_renderer +from scipy.io import loadmat +############################################################################################### +# Reconstruct 3D face based on output coefficients and facemodel +############################################################################################### + +# BFM 3D face model +class BFM(): + def __init__(self,model_path = 'BFM/BFM_model_front.mat'): + model = loadmat(model_path) + self.meanshape = tf.constant(model['meanshape']) # mean face shape. [3*N,1] + self.idBase = tf.constant(model['idBase']) # identity basis. [3*N,80] + self.exBase = tf.constant(model['exBase'].astype(np.float32)) # expression basis. [3*N,64] + self.meantex = tf.constant(model['meantex']) # mean face texture. [3*N,1] (0-255) + self.texBase = tf.constant(model['texBase']) # texture basis. [3*N,80] + self.point_buf = tf.constant(model['point_buf']) # triangle indices for each vertex that lies in. starts from 1. [N,8] + self.face_buf = tf.constant(model['tri']) # vertex indices in each triangle. starts from 1. [F,3] + self.keypoints = tf.squeeze(tf.constant(model['keypoints'])) # vertex indices of 68 facial landmarks. starts from 1. [68,1] + +# Analytic 3D face reconstructor +class Face3D(): + def __init__(self): + facemodel = BFM() + self.facemodel = facemodel + + # analytic 3D face reconstructions with coefficients from R-Net + def Reconstruction_Block(self,coeff,batchsize): + #coeff: [batchsize,257] reconstruction coefficients + id_coeff,ex_coeff,tex_coeff,angles,translation,gamma = self.Split_coeff(coeff) + # [batchsize,N,3] canonical face shape in BFM space + face_shape = self.Shape_formation_block(id_coeff,ex_coeff,self.facemodel) + # [batchsize,N,3] vertex texture (in RGB order) + face_texture = self.Texture_formation_block(tex_coeff,self.facemodel) + self.face_texture = face_texture + # [batchsize,3,3] rotation matrix for face shape + rotation = self.Compute_rotation_matrix(angles) + # [batchsize,N,3] vertex normal + face_norm = self.Compute_norm(face_shape,self.facemodel) + norm_r = tf.matmul(face_norm,rotation) + + # do rigid transformation for face shape using predicted rotation and translation + face_shape_t = self.Rigid_transform_block(face_shape,rotation,translation) + self.face_shape_t = face_shape_t + # compute 2d landmark projections + # landmark_p: [batchsize,68,2] + face_landmark_t = self.Compute_landmark(face_shape_t,self.facemodel) + landmark_p = self.Projection_block(face_landmark_t) # 256*256 image + self.landmark_p = landmark_p + + # [batchsize,N,3] vertex color (in RGB order) + face_color = self.Illumination_block(face_texture, norm_r, gamma) + self.face_color = face_color + + # reconstruction images + render_imgs = self.Render_block(face_shape_t,norm_r,face_color,self.facemodel,batchsize) + render_imgs = tf.clip_by_value(render_imgs,0,255) + render_imgs = tf.cast(render_imgs,tf.float32) + self.render_imgs = render_imgs + + ###################################################################################################### + def Split_coeff(self,coeff): + + id_coeff = coeff[:,:80] #identity + ex_coeff = coeff[:,80:144] #expression + tex_coeff = coeff[:,144:224] #texture + angles = coeff[:,224:227] #euler angles for pose + gamma = coeff[:,227:254] #lighting + translation = coeff[:,254:257] #translation + + return id_coeff,ex_coeff,tex_coeff,angles,translation,gamma + + def Shape_formation_block(self,id_coeff,ex_coeff,facemodel): + face_shape = tf.einsum('ij,aj->ai',facemodel.idBase,id_coeff) + \ + tf.einsum('ij,aj->ai',facemodel.exBase,ex_coeff) + facemodel.meanshape + + # reshape face shape to [batchsize,N,3] + face_shape = tf.reshape(face_shape,[tf.shape(face_shape)[0],-1,3]) + # re-centering the face shape with mean shape + face_shape = face_shape - tf.reshape(tf.reduce_mean(tf.reshape(facemodel.meanshape,[-1,3]),0),[1,1,3]) + + return face_shape + + def Compute_norm(self,face_shape,facemodel): + shape = face_shape + face_id = facemodel.face_buf + point_id = facemodel.point_buf + + # face_id and point_id index starts from 1 + face_id = tf.cast(face_id - 1,tf.int32) + point_id = tf.cast(point_id - 1,tf.int32) + + #compute normal for each face + v1 = tf.gather(shape,face_id[:,0], axis = 1) + v2 = tf.gather(shape,face_id[:,1], axis = 1) + v3 = tf.gather(shape,face_id[:,2], axis = 1) + e1 = v1 - v2 + e2 = v2 - v3 + face_norm = tf.cross(e1,e2) + + face_norm = tf.nn.l2_normalize(face_norm, dim = 2) # normalized face_norm first + face_norm = tf.concat([face_norm,tf.zeros([tf.shape(face_shape)[0],1,3])], axis = 1) + + #compute normal for each vertex using one-ring neighborhood + v_norm = tf.reduce_sum(tf.gather(face_norm, point_id, axis = 1), axis = 2) + v_norm = tf.nn.l2_normalize(v_norm, dim = 2) + + return v_norm + + def Texture_formation_block(self,tex_coeff,facemodel): + face_texture = tf.einsum('ij,aj->ai',facemodel.texBase,tex_coeff) + facemodel.meantex + + # reshape face texture to [batchsize,N,3], note that texture is in RGB order + face_texture = tf.reshape(face_texture,[tf.shape(face_texture)[0],-1,3]) + + return face_texture + + def Compute_rotation_matrix(self,angles): + n_data = tf.shape(angles)[0] + + # compute rotation matrix for X-axis, Y-axis, Z-axis respectively + rotation_X = tf.concat([tf.ones([n_data,1]), + tf.zeros([n_data,3]), + tf.reshape(tf.cos(angles[:,0]),[n_data,1]), + -tf.reshape(tf.sin(angles[:,0]),[n_data,1]), + tf.zeros([n_data,1]), + tf.reshape(tf.sin(angles[:,0]),[n_data,1]), + tf.reshape(tf.cos(angles[:,0]),[n_data,1])], + axis = 1 + ) + + rotation_Y = tf.concat([tf.reshape(tf.cos(angles[:,1]),[n_data,1]), + tf.zeros([n_data,1]), + tf.reshape(tf.sin(angles[:,1]),[n_data,1]), + tf.zeros([n_data,1]), + tf.ones([n_data,1]), + tf.zeros([n_data,1]), + -tf.reshape(tf.sin(angles[:,1]),[n_data,1]), + tf.zeros([n_data,1]), + tf.reshape(tf.cos(angles[:,1]),[n_data,1])], + axis = 1 + ) + + rotation_Z = tf.concat([tf.reshape(tf.cos(angles[:,2]),[n_data,1]), + -tf.reshape(tf.sin(angles[:,2]),[n_data,1]), + tf.zeros([n_data,1]), + tf.reshape(tf.sin(angles[:,2]),[n_data,1]), + tf.reshape(tf.cos(angles[:,2]),[n_data,1]), + tf.zeros([n_data,3]), + tf.ones([n_data,1])], + axis = 1 + ) + + rotation_X = tf.reshape(rotation_X,[n_data,3,3]) + rotation_Y = tf.reshape(rotation_Y,[n_data,3,3]) + rotation_Z = tf.reshape(rotation_Z,[n_data,3,3]) + + # R = RzRyRx + rotation = tf.matmul(tf.matmul(rotation_Z,rotation_Y),rotation_X) + + # because our face shape is N*3, so compute the transpose of R, so that rotation shapes can be calculated as face_shape*R + rotation = tf.transpose(rotation, perm = [0,2,1]) + + return rotation + + def Projection_block(self,face_shape,focal=1015.0,half_image_width=112.): + + # pre-defined camera focal for pespective projection + focal = tf.constant(focal) + # focal = tf.constant(400.0) + focal = tf.reshape(focal,[-1,1]) + batchsize = tf.shape(face_shape)[0] + # center = tf.constant(112.0) + + # define camera position + camera_pos = tf.reshape(tf.constant([0.0,0.0,10.0]),[1,1,3]) + + # compute projection matrix + p_matrix = tf.concat([focal*tf.ones([batchsize,1]),tf.zeros([batchsize,1]),half_image_width*tf.ones([batchsize,1]),tf.zeros([batchsize,1]),\ + focal*tf.ones([batchsize,1]),half_image_width*tf.ones([batchsize,1]),tf.zeros([batchsize,2]),tf.ones([batchsize,1])],axis = 1) + # p_matrix = tf.tile(tf.reshape(p_matrix,[1,3,3]),[tf.shape(face_shape)[0],1,1]) + p_matrix = tf.reshape(p_matrix,[-1,3,3]) + + # convert z in canonical space to the distance to camera + reverse_z = tf.tile(tf.reshape(tf.constant([1.0,0,0,0,1,0,0,0,-1.0]),[1,3,3]),[tf.shape(face_shape)[0],1,1]) + face_shape = tf.matmul(face_shape,reverse_z) + camera_pos + aug_projection = tf.matmul(face_shape,tf.transpose(p_matrix,[0,2,1])) + + # [batchsize, N,2] 2d face projection + face_projection = aug_projection[:,:,0:2]/tf.reshape(aug_projection[:,:,2],[tf.shape(face_shape)[0],tf.shape(aug_projection)[1],1]) + + + return face_projection + + + def Compute_landmark(self,face_shape,facemodel): + + # compute 3D landmark postitions with pre-computed 3D face shape + keypoints_idx = facemodel.keypoints + keypoints_idx = tf.cast(keypoints_idx - 1,tf.int32) + face_landmark = tf.gather(face_shape,keypoints_idx,axis = 1) + + return face_landmark + + def Illumination_block(self,face_texture,norm_r,gamma): + n_data = tf.shape(gamma)[0] + n_point = tf.shape(norm_r)[1] + gamma = tf.reshape(gamma,[n_data,3,9]) + # set initial lighting with an ambient lighting + init_lit = tf.constant([0.8,0,0,0,0,0,0,0,0]) + gamma = gamma + tf.reshape(init_lit,[1,1,9]) + + # compute vertex color using SH function approximation + a0 = m.pi + a1 = 2*m.pi/tf.sqrt(3.0) + a2 = 2*m.pi/tf.sqrt(8.0) + c0 = 1/tf.sqrt(4*m.pi) + c1 = tf.sqrt(3.0)/tf.sqrt(4*m.pi) + c2 = 3*tf.sqrt(5.0)/tf.sqrt(12*m.pi) + + Y = tf.concat([tf.tile(tf.reshape(a0*c0,[1,1,1]),[n_data,n_point,1]), + tf.expand_dims(-a1*c1*norm_r[:,:,1],2), + tf.expand_dims(a1*c1*norm_r[:,:,2],2), + tf.expand_dims(-a1*c1*norm_r[:,:,0],2), + tf.expand_dims(a2*c2*norm_r[:,:,0]*norm_r[:,:,1],2), + tf.expand_dims(-a2*c2*norm_r[:,:,1]*norm_r[:,:,2],2), + tf.expand_dims(a2*c2*0.5/tf.sqrt(3.0)*(3*tf.square(norm_r[:,:,2])-1),2), + tf.expand_dims(-a2*c2*norm_r[:,:,0]*norm_r[:,:,2],2), + tf.expand_dims(a2*c2*0.5*(tf.square(norm_r[:,:,0])-tf.square(norm_r[:,:,1])),2)],axis = 2) + + color_r = tf.squeeze(tf.matmul(Y,tf.expand_dims(gamma[:,0,:],2)),axis = 2) + color_g = tf.squeeze(tf.matmul(Y,tf.expand_dims(gamma[:,1,:],2)),axis = 2) + color_b = tf.squeeze(tf.matmul(Y,tf.expand_dims(gamma[:,2,:],2)),axis = 2) + + #[batchsize,N,3] vertex color in RGB order + face_color = tf.stack([color_r*face_texture[:,:,0],color_g*face_texture[:,:,1],color_b*face_texture[:,:,2]],axis = 2) + + return face_color + + def Rigid_transform_block(self,face_shape,rotation,translation): + # do rigid transformation for 3D face shape + face_shape_r = tf.matmul(face_shape,rotation) + face_shape_t = face_shape_r + tf.reshape(translation,[tf.shape(face_shape)[0],1,3]) + + return face_shape_t + + def Render_block(self,face_shape,face_norm,face_color,facemodel,batchsize): + # render reconstruction images + n_vex = int(facemodel.idBase.shape[0].value/3) + fov_y = 2*tf.atan(112/(1015.))*180./m.pi + tf.zeros([batchsize]) + + # full face region + face_shape = tf.reshape(face_shape,[batchsize,n_vex,3]) + face_norm = tf.reshape(face_norm,[batchsize,n_vex,3]) + face_color = tf.reshape(face_color,[batchsize,n_vex,3]) + + #cammera settings + # same as in Projection_block + camera_position = tf.constant([[0,0,10.0]]) + tf.zeros([batchsize,3]) + camera_lookat = tf.constant([[0,0,0.0]]) + tf.zeros([batchsize,3]) + camera_up = tf.constant([[0,1.0,0]]) + tf.zeros([batchsize,3]) + + # setting light source position(intensities are set to 0 because we have already computed the vertex color) + light_positions = tf.reshape(tf.constant([0,0,1e5]),[1,1,3]) + tf.zeros([batchsize,1,3]) + light_intensities = tf.reshape(tf.constant([0.0,0.0,0.0]),[1,1,3])+tf.zeros([batchsize,1,3]) + ambient_color = tf.reshape(tf.constant([1.0,1,1]),[1,3])+ tf.zeros([batchsize,3]) + + near_clip = 0.01*tf.ones([batchsize]) + far_clip = 50*tf.ones([batchsize]) + #using tf_mesh_renderer for rasterization (https://github.com/google/tf_mesh_renderer) + # img: [batchsize,224,224,4] images in RGBA order (0-255) + with tf.device('/cpu:0'): + img = mesh_renderer.mesh_renderer(face_shape, + tf.cast(facemodel.face_buf-1,tf.int32), + face_norm, + face_color, + camera_position = camera_position, + camera_lookat = camera_lookat, + camera_up = camera_up, + light_positions = light_positions, + light_intensities = light_intensities, + image_width = 224, + image_height = 224, + fov_y = fov_y, #12.5936 + ambient_color = ambient_color, + near_clip = near_clip, + far_clip = far_clip) + + return img + + diff --git a/input/000002.jpg b/input/000002.jpg new file mode 100644 index 0000000000000000000000000000000000000000..dc7ebcbf49817c0240db5b23ae3ef91c3dd20a1e GIT binary patch literal 32197 zcmbTdWl&sC^sqSu4FM8l2oAv|xVyU#Fu))Q4uiXg00F`T*DzRcoxwfi2Z6x{cLukh z2_7Jj&HsJ3cB}T&?w(t>>sH;au72*VBi-lp-^IThfEQY7nrZ+nEC2xO-v#))0#F7J 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b/input/vd092.txt @@ -1,5 +1,5 @@ -112 150 -160 148 -136 182 -123 202 -159 201 +108.59 149.07 +157.35 143.85 +134.4 173.2 +117.88 200.79 +159.56 196.36 diff --git a/input/vd102.txt b/input/vd102.txt index c28fd59..6fa2643 100644 --- a/input/vd102.txt +++ b/input/vd102.txt @@ -1,5 +1,5 @@ -124 227 -192 220 -164 274 -136 306 -187 298 +121.62 225.96 +186.73 223.07 +162.99 269.82 +132.12 302.62 +186.42 299.21 diff --git a/load_data.py b/load_data.py index 2f85b64..793de07 100644 --- a/load_data.py +++ b/load_data.py @@ -3,20 +3,6 @@ from PIL import Image from scipy.io import loadmat,savemat from array import array -# define facemodel for reconstruction -class BFM(): - def __init__(self): - model_path = './BFM/BFM_model_front.mat' - model = loadmat(model_path) - self.meanshape = model['meanshape'] # mean face shape - self.idBase = model['idBase'] # identity basis - self.exBase = model['exBase'] # expression basis - self.meantex = model['meantex'] # mean face texture - self.texBase = model['texBase'] # texture basis - self.point_buf = model['point_buf'] # adjacent face index for each vertex, starts from 1 (only used for calculating face normal) - self.tri = model['tri'] # vertex index for each triangle face, starts from 1 - self.keypoints = np.squeeze(model['keypoints']).astype(np.int32) - 1 # 68 face landmark index, starts from 0 - # load expression basis def LoadExpBasis(): n_vertex = 53215 diff --git a/preprocess_img.py b/preprocess_img.py index 28ff6db..76a2b4d 100644 --- a/preprocess_img.py +++ b/preprocess_img.py @@ -27,25 +27,23 @@ def POS(xp,x): return t,s -def process_img(img,lm,t,s): +def process_img(img,lm,t,s,target_size = 224.): w0,h0 = img.size - img = img.transform(img.size, Image.AFFINE, (1, 0, t[0] - w0/2, 0, 1, h0/2 - t[1])) w = (w0/s*102).astype(np.int32) h = (h0/s*102).astype(np.int32) - img = img.resize((w,h),resample = Image.BILINEAR) - lm = np.stack([lm[:,0] - t[0] + w0/2,lm[:,1] - t[1] + h0/2],axis = 1)/s*102 + img = img.resize((w,h),resample = Image.BICUBIC) - # crop the image to 224*224 from image center - left = (w/2 - 112).astype(np.int32) - right = left + 224 - up = (h/2 - 112).astype(np.int32) - below = up + 224 + left = (w/2 - target_size/2 + float((t[0] - w0/2)*102/s)).astype(np.int32) + right = left + target_size + up = (h/2 - target_size/2 + float((h0/2 - t[1])*102/s)).astype(np.int32) + below = up + target_size img = img.crop((left,up,right,below)) img = np.array(img) - img = img[:,:,::-1] + img = img[:,:,::-1] #RGBtoBGR img = np.expand_dims(img,0) - lm = lm - np.reshape(np.array([(w/2 - 112),(h/2-112)]),[1,2]) + lm = np.stack([lm[:,0] - t[0] + w0/2,lm[:,1] - t[1] + h0/2],axis = 1)/s*102 + lm = lm - np.reshape(np.array([(w/2 - target_size/2),(h/2-target_size/2)]),[1,2]) return img,lm diff --git a/reconstruct_mesh.py b/reconstruct_mesh.py deleted file mode 100644 index 1a646de..0000000 --- a/reconstruct_mesh.py +++ /dev/null @@ -1,204 +0,0 @@ -import numpy as np - -# input: coeff with shape [1,257] -def Split_coeff(coeff): - id_coeff = coeff[:,:80] # identity(shape) coeff of dim 80 - ex_coeff = coeff[:,80:144] # expression coeff of dim 64 - tex_coeff = coeff[:,144:224] # texture(albedo) coeff of dim 80 - angles = coeff[:,224:227] # ruler angles(x,y,z) for rotation of dim 3 - gamma = coeff[:,227:254] # lighting coeff for 3 channel SH function of dim 27 - translation = coeff[:,254:] # translation coeff of dim 3 - - return id_coeff,ex_coeff,tex_coeff,angles,gamma,translation - - -# compute face shape with identity and expression coeff, based on BFM model -# input: id_coeff with shape [1,80] -# ex_coeff with shape [1,64] -# output: face_shape with shape [1,N,3], N is number of vertices -def Shape_formation(id_coeff,ex_coeff,facemodel): - face_shape = np.einsum('ij,aj->ai',facemodel.idBase,id_coeff) + \ - np.einsum('ij,aj->ai',facemodel.exBase,ex_coeff) + \ - facemodel.meanshape - - face_shape = np.reshape(face_shape,[1,-1,3]) - # re-center face shape - face_shape = face_shape - np.mean(np.reshape(facemodel.meanshape,[1,-1,3]), axis = 1, keepdims = True) - - return face_shape - -# compute vertex normal using one-ring neighborhood -# input: face_shape with shape [1,N,3] -# output: v_norm with shape [1,N,3] -def Compute_norm(face_shape,facemodel): - - face_id = facemodel.tri # vertex index for each triangle face, with shape [F,3], F is number of faces - point_id = facemodel.point_buf # adjacent face index for each vertex, with shape [N,8], N is number of vertex - shape = face_shape - face_id = (face_id - 1).astype(np.int32) - point_id = (point_id - 1).astype(np.int32) - v1 = shape[:,face_id[:,0],:] - v2 = shape[:,face_id[:,1],:] - v3 = shape[:,face_id[:,2],:] - e1 = v1 - v2 - e2 = v2 - v3 - face_norm = np.cross(e1,e2) # compute normal for each face - face_norm = np.concatenate([face_norm,np.zeros([1,1,3])], axis = 1) # concat face_normal with a zero vector at the end - v_norm = np.sum(face_norm[:,point_id,:], axis = 2) # compute vertex normal using one-ring neighborhood - v_norm = v_norm/np.expand_dims(np.linalg.norm(v_norm,axis = 2),2) # normalize normal vectors - - return v_norm - -# compute vertex texture(albedo) with tex_coeff -# input: tex_coeff with shape [1,N,3] -# output: face_texture with shape [1,N,3], RGB order, range from 0-255 -def Texture_formation(tex_coeff,facemodel): - - face_texture = np.einsum('ij,aj->ai',facemodel.texBase,tex_coeff) + facemodel.meantex - face_texture = np.reshape(face_texture,[1,-1,3]) - - return face_texture - -# compute rotation matrix based on 3 ruler angles -# input: angles with shape [1,3] -# output: rotation matrix with shape [1,3,3] -def Compute_rotation_matrix(angles): - - angle_x = angles[:,0][0] - angle_y = angles[:,1][0] - angle_z = angles[:,2][0] - - # compute rotation matrix for X,Y,Z axis respectively - rotation_X = np.array([1.0,0,0,\ - 0,np.cos(angle_x),-np.sin(angle_x),\ - 0,np.sin(angle_x),np.cos(angle_x)]) - rotation_Y = np.array([np.cos(angle_y),0,np.sin(angle_y),\ - 0,1,0,\ - -np.sin(angle_y),0,np.cos(angle_y)]) - rotation_Z = np.array([np.cos(angle_z),-np.sin(angle_z),0,\ - np.sin(angle_z),np.cos(angle_z),0,\ - 0,0,1]) - - rotation_X = np.reshape(rotation_X,[1,3,3]) - rotation_Y = np.reshape(rotation_Y,[1,3,3]) - rotation_Z = np.reshape(rotation_Z,[1,3,3]) - - rotation = np.matmul(np.matmul(rotation_Z,rotation_Y),rotation_X) - rotation = np.transpose(rotation, axes = [0,2,1]) #transpose row and column (dimension 1 and 2) - - return rotation - -# project 3D face onto image plane -# input: face_shape with shape [1,N,3] -# rotation with shape [1,3,3] -# translation with shape [1,3] -# output: face_projection with shape [1,N,2] -# z_buffer with shape [1,N,1] -def Projection_layer(face_shape,rotation,translation,focal=1015.0,center=112.0): # we choose the focal length and camera position empirically - - camera_pos = np.reshape(np.array([0.0,0.0,10.0]),[1,1,3]) # camera position - reverse_z = np.reshape(np.array([1.0,0,0,0,1,0,0,0,-1.0]),[1,3,3]) - - - p_matrix = np.concatenate([[focal],[0.0],[center],[0.0],[focal],[center],[0.0],[0.0],[1.0]],axis = 0) # projection matrix - p_matrix = np.reshape(p_matrix,[1,3,3]) - - # calculate face position in camera space - face_shape_r = np.matmul(face_shape,rotation) - face_shape_t = face_shape_r + np.reshape(translation,[1,1,3]) - face_shape_t = np.matmul(face_shape_t,reverse_z) + camera_pos - - # calculate projection of face vertex using perspective projection - aug_projection = np.matmul(face_shape_t,np.transpose(p_matrix,[0,2,1])) - face_projection = aug_projection[:,:,0:2]/np.reshape(aug_projection[:,:,2],[1,np.shape(aug_projection)[1],1]) - z_buffer = np.reshape(aug_projection[:,:,2],[1,-1,1]) - - return face_projection,z_buffer - -# compute vertex color using face_texture and SH function lighting approximation -# input: face_texture with shape [1,N,3] -# norm with shape [1,N,3] -# gamma with shape [1,27] -# output: face_color with shape [1,N,3], RGB order, range from 0-255 -# lighting with shape [1,N,3], color under uniform texture -def Illumination_layer(face_texture,norm,gamma): - - num_vertex = np.shape(face_texture)[1] - - init_lit = np.array([0.8,0,0,0,0,0,0,0,0]) - gamma = np.reshape(gamma,[-1,3,9]) - gamma = gamma + np.reshape(init_lit,[1,1,9]) - - # parameter of 9 SH function - a0 = np.pi - a1 = 2*np.pi/np.sqrt(3.0) - a2 = 2*np.pi/np.sqrt(8.0) - c0 = 1/np.sqrt(4*np.pi) - c1 = np.sqrt(3.0)/np.sqrt(4*np.pi) - c2 = 3*np.sqrt(5.0)/np.sqrt(12*np.pi) - - Y0 = np.tile(np.reshape(a0*c0,[1,1,1]),[1,num_vertex,1]) - Y1 = np.reshape(-a1*c1*norm[:,:,1],[1,num_vertex,1]) - Y2 = np.reshape(a1*c1*norm[:,:,2],[1,num_vertex,1]) - Y3 = np.reshape(-a1*c1*norm[:,:,0],[1,num_vertex,1]) - Y4 = np.reshape(a2*c2*norm[:,:,0]*norm[:,:,1],[1,num_vertex,1]) - Y5 = np.reshape(-a2*c2*norm[:,:,1]*norm[:,:,2],[1,num_vertex,1]) - Y6 = np.reshape(a2*c2*0.5/np.sqrt(3.0)*(3*np.square(norm[:,:,2])-1),[1,num_vertex,1]) - Y7 = np.reshape(-a2*c2*norm[:,:,0]*norm[:,:,2],[1,num_vertex,1]) - Y8 = np.reshape(a2*c2*0.5*(np.square(norm[:,:,0])-np.square(norm[:,:,1])),[1,num_vertex,1]) - - Y = np.concatenate([Y0,Y1,Y2,Y3,Y4,Y5,Y6,Y7,Y8],axis=2) - - # Y shape:[batch,N,9]. - - lit_r = np.squeeze(np.matmul(Y,np.expand_dims(gamma[:,0,:],2)),2) #[batch,N,9] * [batch,9,1] = [batch,N] - lit_g = np.squeeze(np.matmul(Y,np.expand_dims(gamma[:,1,:],2)),2) - lit_b = np.squeeze(np.matmul(Y,np.expand_dims(gamma[:,2,:],2)),2) - - # shape:[batch,N,3] - face_color = np.stack([lit_r*face_texture[:,:,0],lit_g*face_texture[:,:,1],lit_b*face_texture[:,:,2]],axis = 2) - lighting = np.stack([lit_r,lit_g,lit_b],axis = 2)*128 - - return face_color,lighting - -# face reconstruction with coeff and BFM model -def Reconstruction(coeff,facemodel): - id_coeff,ex_coeff,tex_coeff,angles,gamma,translation = Split_coeff(coeff) - # compute face shape - face_shape = Shape_formation(id_coeff, ex_coeff, facemodel) - # compute vertex texture(albedo) - face_texture = Texture_formation(tex_coeff, facemodel) - # vertex normal - face_norm = Compute_norm(face_shape,facemodel) - # rotation matrix - rotation = Compute_rotation_matrix(angles) - face_norm_r = np.matmul(face_norm,rotation) - - # compute vertex projection on image plane (with image sized 224*224) - face_projection,z_buffer = Projection_layer(face_shape,rotation,translation) - face_projection = np.stack([face_projection[:,:,0],224 - face_projection[:,:,1]], axis = 2) - - # compute 68 landmark on image plane - landmarks_2d = face_projection[:,facemodel.keypoints,:] - - # compute vertex color using SH function lighting approximation - face_color,lighting = Illumination_layer(face_texture, face_norm_r, gamma) - - # vertex index for each face of BFM model - tri = facemodel.tri - - return face_shape,face_texture,face_color,tri,face_projection,z_buffer,landmarks_2d - -# def Reconstruction_for_render(coeff,facemodel): -# id_coeff,ex_coeff,tex_coeff,angles,gamma,translation = Split_coeff(coeff) -# face_shape = Shape_formation(id_coeff, ex_coeff, facemodel) -# face_texture = Texture_formation(tex_coeff, facemodel) -# face_norm = Compute_norm(face_shape,facemodel) -# rotation = Compute_rotation_matrix(angles) -# face_shape_r = np.matmul(face_shape,rotation) -# face_shape_r = face_shape_r + np.reshape(translation,[1,1,3]) -# face_norm_r = np.matmul(face_norm,rotation) -# face_color,lighting = Illumination_layer(face_texture, face_norm_r, gamma) -# tri = facemodel.face_buf - -# return face_shape_r,face_norm_r,face_color,tri \ No newline at end of file

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