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300Wgen.py
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# coding: UTF-8
'''
Generate uv position map of 300W_LP.
'''
import os, sys
sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
import cv2 as cv
import numpy as np
import scipy.io as sio
from skimage import io
import skimage.transform
import argparse
from time import time
import matplotlib.pyplot as plt
import dlib
sys.path.append('..')
import face3d
from face3d import mesh
from face3d.morphable_model import MorphabelModel
def process_uv(uv_coords, uv_h=256, uv_w=256):
uv_coords[:, 0] = uv_coords[:, 0] * (uv_w - 1)
uv_coords[:, 1] = uv_coords[:, 1] * (uv_h - 1)
uv_coords[:, 1] = uv_h - uv_coords[:, 1] - 1
uv_coords = np.hstack((uv_coords, np.zeros((uv_coords.shape[0], 1)))) # add z
return uv_coords
def get_colors(image, vertices):
'''
Args:
pos: the 3D position map. shape = (256, 256, 3).
Returns:
colors: the corresponding colors of vertices. shape = (num of points, 3). n is 45128 here.
'''
[h, w, _] = image.shape
vertices[:,0] = np.minimum(np.maximum(vertices[:,0], 0), w -1) # x
vertices[:,1] = np.minimum(np.maximum(vertices[:,1], 0), h -1) # y
ind = np.round(vertices).astype(np.int32)
colors = image[ind[:,1], ind[:,0], :] # n x 3
return colors
def isPointInTri(point, tri_points):
''' Judge whether the point is in the triangle
Method:
http://blackpawn.com/texts/pointinpoly/
Args:
point: [u, v] or [x, y]
tri_points: three vertices(2d points) of a triangle. 2 coords x 3 vertices
Returns:
bool: true for in triangle
'''
tp = tri_points
# vectors
v0 = tp[:,2] - tp[:,0]
v1 = tp[:,1] - tp[:,0]
v2 = point - tp[:,0]
# dot products
dot00 = np.dot(v0.T, v0)
dot01 = np.dot(v0.T, v1)
dot02 = np.dot(v0.T, v2)
dot11 = np.dot(v1.T, v1)
dot12 = np.dot(v1.T, v2)
# barycentric coordinates
if dot00*dot11 - dot01*dot01 == 0:
inverDeno = 0
else:
inverDeno = 1/(dot00*dot11 - dot01*dot01)
u = (dot11*dot02 - dot01*dot12)*inverDeno
v = (dot00*dot12 - dot01*dot02)*inverDeno
# check if point in triangle
return (u >= 0) & (v >= 0) & (u + v < 1)
def render_texture_simple(vertices, colors, triangles, h, w, c = 3, BG = None):
''' render mesh by z buffer
Args:
vertices: 3 x nver
colors: 3 x nver
triangles: 3 x ntri
h: height
w: width
'''
# initial
if BG is None:
image = np.zeros((h, w, c))
else:
image = np.array(BG)
depth_buffer = np.zeros([h, w]) - 999999.
# triangle depth: approximate the depth to the average value of z in each vertex(v0, v1, v2), since the vertices are closed to each other
tri_depth = (vertices[2, triangles[0,:]] + vertices[2,triangles[1,:]] + vertices[2, triangles[2,:]])/3.
tri_tex = (colors[:, triangles[0,:]] + colors[:,triangles[1,:]] + colors[:, triangles[2,:]])/3.
for i in range(triangles.shape[1]):
tri = triangles[:, i] # 3 vertex indices
# the inner bounding box
umin = max(int(np.ceil(np.min(vertices[0,tri]))), 0)
umax = min(int(np.floor(np.max(vertices[0,tri]))), w-1)
vmin = max(int(np.ceil(np.min(vertices[1,tri]))), 0)
vmax = min(int(np.floor(np.max(vertices[1,tri]))), h-1)
if umax<umin or vmax<vmin:
continue
for u in range(umin, umax+1):
for v in range(vmin, vmax+1):
if tri_depth[i] > depth_buffer[v, u] and isPointInTri([u,v], vertices[:2, tri]):
depth_buffer[v, u] = tri_depth[i]
image[v, u, :] = tri_tex[:, i]
return image
def write_obj_with_colors(obj_name, vertices, triangles):
''' Save 3D face model with texture represented by colors.
Args:
obj_name: str
vertices: shape = (nver, 3)
colors: shape = (nver, 3)
triangles: shape = (ntri, 3)
'''
triangles = triangles.copy()
triangles += 1 # meshlab start with 1
if obj_name.split('.')[-1] != 'obj':
obj_name = obj_name + '.obj'
# write obj
with open(obj_name, 'w') as f:
# write vertices & colors
for i in range(vertices.shape[0]):
# s = 'v {} {} {} \n'.format(vertices[0,i], vertices[1,i], vertices[2,i])
s = 'v {} {} {}\n'.format(vertices[i, 0], vertices[i, 1], vertices[i, 2])
f.write(s)
# write f: ver ind/ uv ind
[k, ntri] = triangles.shape
for i in range(triangles.shape[0]):
# s = 'f {} {} {}\n'.format(triangles[i, 0], triangles[i, 1], triangles[i, 2])
s = 'f {} {} {}\n'.format(triangles[i, 2], triangles[i, 1], triangles[i, 0])
f.write(s)
def transform_vertices(M, vertices):
v_size = vertices.size()
R = M[:, :2]
t = M[:, 2]
vertices2 = vertices.clone()
vertices2 = vertices2.float()
vertices2[:2, :] = R.mm(vertices2[:2, :]) + t.repeat(v_size[1], 1).t()
return vertices2
def run_posmap_300W_LP(bfm, image_path, mat_path, save_folder, idx=0, uv_h=256, uv_w=256, image_h=256, image_w=256):
# 1. load image and fitted parameters
image_name = image_path.strip().split('/')[-1]
image_ori = io.imread(image_path)
image = image_ori/255.
[h, w, c] = image.shape
#cv.imshow('ori',image_ori)
info = sio.loadmat(mat_path)
shape_para = info['Shape_Para'].astype(np.float32)
exp_para = info['Exp_Para'].astype(np.float32)
pose_para = info['new_pose'].T.astype(np.float32)
tp = bfm.get_tex_para('random')
colors = bfm.generate_colors(tp)
# print('colb: ',colors)
colors = np.minimum(np.maximum(colors, 0), 1)
# ----------------------------if use dlib
# dlib_landmark_model = './models/shape_predictor_68_face_landmarks.dat'
# face_regressor = dlib.shape_predictor(dlib_landmark_model)
# face_detector = dlib.get_frontal_face_detector()
# rects = face_detector(image_ori, 1)
# pts = face_regressor(image_ori, rects[0]).parts()
# pts = np.array([[pt.x, pt.y] for pt in pts]).T
# x = pts.T
# fitted_sp, fitted_ep, s, angles, t = bfm.fit(x, bfm.kpt_ind, max_iter = 100, isShow = False)
# ---------------------------------------
# generate shape
vertices = bfm.generate_vertices(shape_para, exp_para)
# # transform mesh
s = pose_para[-1, 0]
angles = pose_para[:3, 0]
t = pose_para[3:6, 0]
#print('angle: ',angles)
transformed_vertices = bfm.transform_3ddfa(vertices, s, angles, t)
projected_vertices = transformed_vertices.copy() # using stantard camera & orth projection as in 3DDFA
image_vertices = projected_vertices.copy()
image_vertices[:, 1] = h - image_vertices[:, 1] - 1
# --------------show render pic
# colors = get_colors(image_ori,image_vertices)
# print('colorso: ',colors)
# ccc = []
# for i in range(len(colors)):
# #print('aa ',float(colors[i][0]) / float(255.0))
# a = float(colors[i][0]) / float(255.0)
# b = float(colors[i][1]) / float(255.0)
# c = float(colors[i][2]) / float(255.0)
# ccc.append([a,b,c])
# #colors = np.minimum(np.maximum(colors, 0), 1)
# c_color = np.array(ccc)
# fitted_image = mesh.render.render_colors(image_vertices, bfm.triangles, c_color, h, w)
# cv.imshow('fitti',fitted_image)
# cv.waitKey(0)
#-----------------------
# 3. crop image with key points
kpt = image_vertices[bfm.kpt_ind, :].astype(np.int32)
left = np.min(kpt[:, 0])
right = np.max(kpt[:, 0])
top = np.min(kpt[:, 1])
bottom = np.max(kpt[:, 1])
center = np.array([right - (right - left) / 2.0,
bottom - (bottom - top) / 2.0])
old_size = (right - left + bottom - top) / 2
size = int(old_size * 1.5)
# random pertube. you can change the numbers
marg = old_size * 0.1
t_x = np.random.rand() * marg * 2 - marg
t_y = np.random.rand() * marg * 2 - marg
center[0] = center[0] + t_x
center[1] = center[1] + t_y
size = size * (np.random.rand() * 0.2 + 0.9)
# crop and record the transform parameters
src_pts = np.array([[center[0] - size / 2, center[1] - size / 2], [center[0] - size / 2, center[1] + size / 2],
[center[0] + size / 2, center[1] - size / 2]])
DST_PTS = np.array([[0, 0], [0, image_h - 1], [image_w - 1, 0]])
tform = skimage.transform.estimate_transform('similarity', src_pts, DST_PTS)
cropped_image = skimage.transform.warp(image, tform.inverse, output_shape=(image_h, image_w))
# transform face position(image vertices) along with 2d facial image
position = image_vertices.copy()
position[:, 2] = 1
position = np.dot(position, tform.params.T)
position[:, 2] = image_vertices[:, 2] * tform.params[0, 0] # scale z
position[:, 2] = position[:, 2] - np.min(position[:, 2]) # translate z
#write_obj_with_colors('./300w.obj',position,bfm.triangles)
# 4. uv position map: render position in uv space
uv_position_map = mesh.render.render_colors(uv_coords, bfm.full_triangles, position, uv_h, uv_w, c=3)
####only for verify
# uv_texture_map_rec = cv.remap(cropped_image, uv_position_map[:,:,:2].astype(np.float32), None, interpolation=cv.INTER_LINEAR, borderMode=cv.BORDER_CONSTANT,borderValue=(0))
# all_colors = np.reshape(uv_texture_map_rec, [256**2, -1])
# face_ind = np.loadtxt('./data/face_ind.txt').astype(np.int32)
# triangles = np.loadtxt('./data/triangles.txt').astype(np.int32)
# all_vertices_gt = np.reshape(uv_position_map, [256**2, -1])
# vertices_gt = all_vertices_gt[face_ind, :]
# text_c = all_colors[face_ind, :]
# print('vtx: ',vertices_gt.shape)
# print('text_c: ',text_c.shape)
# print('triangles: ',triangles.shape)
# pic = render_texture_simple(vertices_gt.T,text_c.T,triangles.T,256,256)
# cv.imshow('fittia',pic)
# cv.imshow('cropped_image',cropped_image)
# cv.waitKey(0)
## -----------------------
io.imsave('{}/{}'.format(save_folder, image_name), np.squeeze(cropped_image))
np.save('{}/{}'.format(save_folder, image_name.replace('jpg', 'npy')), uv_position_map)
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()
if __name__ == '__main__':
save_folder = '/media/weepies/Seagate Backup Plus Drive/3DMM/posnet/300W_HELEN/'
input_dir = '/media/weepies/Seagate Backup Plus Drive/3DMM/posnet/synthesize/'
generate_batch_sample(input_dir,save_folder)