path: root/megapixels/commands/demo/face_3ddfa.py

'''
Combines 3D face mode + rendering
https://github.com/cleardusk/3DDFA
https://github.com/YadiraF/face3d
'''
import click

from app.settings import types
from app.utils import click_utils
from app.settings import app_cfg as cfg


@click.command()
@click.option('-i', '--input', 'opt_fp_in', default=None, required=True,
  help='Image filepath')
@click.option('-o', '--output', 'opt_fp_out', default=None,
  help='GIF output path')
@click.option('--size', 'opt_size', 
  type=(int, int), default=(300, 300),
  help='Output image size')
@click.option('-g', '--gpu', 'opt_gpu', default=0,
  help='GPU index')
@click.option('-f', '--force', 'opt_force', is_flag=True,
  help='Force overwrite file')
@click.option('--bbox-init', 'opt_bbox_init', is_flag=True,
  help='Use landmarks for ROI instead of BBox')
@click.option('--size', 'opt_render_dim', 
  type=(int, int), default=(512, 512),
  help='2.5D render image size')
@click.option('--display/--no-display', 'opt_display', is_flag=True, default=False,
  help='Display detections to debug')
@click.pass_context
def cli(ctx, opt_fp_in, opt_fp_out, opt_gpu, opt_bbox_init, 
  opt_size, opt_render_dim, opt_force, opt_display):
  """3D face demo"""
  
  import sys
  import os
  from os.path import join
  from pathlib import Path
  import time
  
  from tqdm import tqdm
  import numpy as np
  import pandas as pd
  import cv2 as cv
  import dlib

  from app.models.bbox import BBox
  from app.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils
  from app.utils import plot_utils
  from app.processors import face_detector, face_landmarks
  from app.models.data_store import DataStore

  import torch
  import torchvision.transforms as transforms
  import torch.backends.cudnn as cudnn
  import scipy.io as sio

  sys.path.append(join(Path.cwd().parent, '3rdparty'))
  # change name of 3DDFA to d3DDFA because can't start with number
  from d3DDFA import mobilenet_v1
  from d3DDFA.utils.ddfa import ToTensorGjz, NormalizeGjz, str2bool
  from d3DDFA.utils import inference as d3dfa_utils
  from d3DDFA.utils.inference import parse_roi_box_from_landmark, crop_img, predict_68pts
  from d3DDFA.utils.inference import dump_to_ply, dump_vertex, draw_landmarks
  from d3DDFA.utils.inference import predict_dense, parse_roi_box_from_bbox, get_colors
  from d3DDFA.utils.inference import write_obj_with_colors
  from d3DDFA.utils.estimate_pose import parse_pose
  from d3DDFA.utils.render import get_depths_image, cget_depths_image, cpncc
  from d3DDFA.utils import paf as d3dfa_paf_utils

  # https://github.com/YadiraF/face3d
  # compile cython module in face3d/mesh/cython/ python setup.py build_ext -i
  from face3d.face3d import mesh as face3d_mesh
  

  log = logger_utils.Logger.getLogger()

  # -------------------------------------------------
  # load image

  fpp_in = Path(opt_fp_in)
  im = cv.imread(opt_fp_in)
  #im = im_utils.resize(im_orig, width=opt_size[0], height=opt_size[1])
  # im = im_orig.copy()

  # ----------------------------------------------------------------------------
  # detect face

  face_detector = face_detector.DetectorDLIBCNN(gpu=opt_gpu)  # -1 for CPU
  bboxes = face_detector.detect(im, largest=True)
  bbox = bboxes[0]
  dim = im.shape[:2][::-1]
  bbox_dim = bbox.to_dim(dim)
  if not bbox:
    log.error('no face detected')
    return
  else:
    log.info(f'face detected: {bbox_dim.to_xyxy()}')


  # -------------------------------------------------------------------------
  # landmarks

  landmark_predictor = face_landmarks.Dlib2D_68()
  lanmarks = landmark_predictor.landmarks(im, bbox_dim)


  # -------------------------------------------------------------------------
  # 3ddfa

  STD_SIZE = 120

  # load pre-tained model
  fp_ckpt = join(cfg.DIR_MODELS_PYTORCH, '3ddfa', 'phase1_wpdc_vdc_v2.pth.tar')
  arch = 'mobilenet_1'
  checkpoint = torch.load(fp_ckpt, map_location=lambda storage, loc: storage)['state_dict']
  model = getattr(mobilenet_v1, arch)(num_classes=62)  # 62 = 12(pose) + 40(shape) +10(expression)
  model_dict = model.state_dict()
  
  # because the model is trained by multiple gpus, prefix module should be removed
  for k in checkpoint.keys():
    model_dict[k.replace('module.', '')] = checkpoint[k]
  model.load_state_dict(model_dict, strict=False)
  if opt_gpu > -1:
    cudnn.benchmark = True
    model = model.cuda()
  model.eval()

  # forward
  st = time.time()
  fp_tri = join(cfg.DIR_MODELS_PYTORCH, '3ddfa', 'tri.mat')
  triangles = sio.loadmat(fp_tri)['tri']
  transform = transforms.Compose([ToTensorGjz(), NormalizeGjz(mean=127.5, std=128)])

  pts_res = []
  Ps = []  # Camera matrix collection
  poses = []  # pose collection, [todo: validate it]
  vertices_lst = []  # store multiple face vertices

  # use landmark as roi
  pts = np.array(lanmarks).T
  # roi_box = d3dfa_utils.parse_roi_box_from_landmark(pts)
  roi_box = parse_roi_box_from_bbox(bbox_dim.to_xyxy())
  im_crop = d3dfa_utils.crop_img(im, roi_box)
  im_crop = cv.resize(im_crop, dsize=(STD_SIZE, STD_SIZE), interpolation=cv.INTER_LINEAR)

  # forward
  torch_input = transform(im_crop).unsqueeze(0)
  with torch.no_grad():
    if opt_gpu > -1:
      torch_input = torch_input.cuda()
    param = model(torch_input)
    param = param.squeeze().cpu().numpy().flatten().astype(np.float32)

  # 68 pts
  pts68 = d3dfa_utils.predict_68pts(param, roi_box)

  pts_res.append(pts68)
  P, pose = parse_pose(param)
  Ps.append(P)
  poses.append(pose)

  # dense face 3d vertices
  vertices = d3dfa_utils.predict_dense(param, roi_box)
  vertices_lst.append(vertices)
    
  log.info(f'generated 3d data in: {(time.time() - st):.2f}s')

  # filepath helper function
  def to_fp(fpp, ext, suffix=None):
    if suffix:
      fp = join(fpp.parent, f'{fpp.stem}_{suffix}.{ext}')
    else:
      fp = join(fpp.parent, f'{fpp.stem}.{ext}')
    return fp
  
  # save .mat
  colors = d3dfa_utils.get_colors(im, vertices)
  vertices_orig = vertices.copy()
  fp_mat_3df = to_fp(fpp_in, 'mat', suffix='face3d')
  sio.savemat(fp_mat_3df, {'vertices': vertices, 'colors': colors, 'triangles': triangles})

  # save PAF
  #fp_paf = to_fp(fpp_in, 'jpg', suffix='paf')
  #opt_paf_size = 3  # PAF feature kernel size
  #im_paf = d3dfa_paf_utils.gen_img_paf(img_crop=im_crop, param=param, kernel_size=opt_paf_size)
  #cv.imwrite(fp_paf, im_paf)
  
  # save pose image
  # P, pose = parse_pose(param)  # Camera matrix (without scale), and pose (yaw, pitch, roll, to verify)

  img_pose = draw_utils.plot_pose_box(im, Ps, pts_res)
  fp_pose = to_fp(fpp_in, 'jpg', suffix='pose')
  cv.imwrite(fp_pose, img_pose)

  # save depth image
  fp_depth = to_fp(fpp_in, 'png', suffix='depth')
  # depths_img = get_depths_image(im, vertices_lst, tri-1)  # python version
  im_depth = cget_depths_image(im, vertices_lst, triangles - 1)  # cython version
  cv.imwrite(fp_depth, im_depth)

  # save pncc image
  fp_pose = to_fp(fpp_in, 'png', suffix='pncc')
  pncc_feature = cpncc(im, vertices_lst, triangles - 1)  # cython version
  cv.imwrite(fp_pose, pncc_feature[:, :, ::-1])  # cv.imwrite will swap RGB -> BGR

  # save .ply
  #fp_ply = to_fp(fpp_in, 'ply')
  #dump_to_ply(vertices, triangles, fp_ply)

  # skip: save .mat (3ddfa default not compatible with face3d utils)
  #fp_mat = to_fp(fpp_in, 'mat')
  #d3dfa_utils.dump_vertex(vertices, fp_mat)
  
  # save 68 points
  #fp_txt = to_fp(fpp_in, 'txt', suffix='68')
  #np.savetxt(to_fp(fpp_in, 'txt'), pts68, fmt='%.3f')

  # save roi
  #fp_txt = to_fp(fpp_in, 'txt', suffix='roi')
  #np.savetxt(fp_txt, roi_box, fmt='%.3f')a
  
  # save crop
  #fp_crop = to_fp(fpp_in, 'jpg', suffix='crop')
  #cv.imwrite(fp_crop, im_crop)

  # save obj
  colors = d3dfa_utils.get_colors(im, vertices_orig)
  fp_obj = to_fp(fpp_in, 'obj')
  write_obj_with_colors(fp_obj, vertices_orig, triangles, colors)

  #fp_landmarks = to_fp(fpp_in, 'jpg', suffix='3DDFA')
  # show_flg?
  #d3dfa_utils.draw_landmarks(im, pts_res, wfp=fp_landmarks, show_flg=False)

  # -------------------------------------------------------------------------
  # face3d

  # create 3D mesh photo face
  # if loading file
  # TODO find where vertices is being changed
  vertices = vertices_orig  # vertices changes somewhere, so keep copy

  # preprocess 3D data from 3DDFA for face3d rendering
  vertices = vertices.transpose()
  triangles = triangles.transpose()
  vertices = vertices.astype(np.float64)  # change data type
  # subtract 1 from triangle vertex indices (depends on your .mat file)
  triangles = np.array([np.array([t[0]-1, t[1]-1, t[2]-1]).astype(np.int32) for t in triangles])
  vertices -= np.array([abs(np.min(vertices[:,0])), np.min(abs(vertices[:,1])), np.min(abs(vertices[:,2]))])
  vertices -= np.array([np.mean(vertices[:,0]), np.mean(vertices[:,1]), np.mean(vertices[:,2])])
  # colors = np.array([c[::-1] for c in colors])  # BGR --> RGB
  colors = colors/np.max(colors)  # normalize color range

  # set max render size (about 75% of canvas size)
  max_render_size = int(max(opt_render_dim) * .75)
  s = max_render_size/(np.max(vertices[:,1]) - np.min(vertices[:,1]))
  
  # rotation matrix
  R = face3d_mesh.transform.angle2matrix([-180, -20, 0]) 
  
  # no translation. center of obj:[0,0]
  t = [0, 0, 0]
  vertices_trans = face3d_mesh.transform.similarity_transform(vertices, s, R, t)
  
  # lighting: add point lights, positions are defined in world space
  light_pos = np.array([[-128, -128, 512]])
  light_clr_amt = np.array([[1, 1, 1]])
  colors_lit = face3d_mesh.light.add_light(vertices_trans, triangles, colors, light_pos, light_clr_amt)

  # transform from world space to camera space (what the world is in the eye of observer)
  vertices_cam = face3d_mesh.transform.lookat_camera(vertices_trans, eye = [0, 0, 0], at = np.array([0, 0, 1]), up = None)
  # project from 3d world space into 2d image plane. orthographic or perspective projection
  vertices_proj = face3d_mesh.transform.orthographic_project(vertices_cam)

  # -------------------------------------------------------------------------
  # render 2D image

  w = h = max(opt_render_dim)
  vertices_im = face3d_mesh.transform.to_image(vertices_proj, h, w)
  rendering = face3d_mesh.render.render_colors(vertices_im, triangles, colors_lit, h, w)

  cv.imshow('', rendering)
  display_utils.handle_keyboard()

  # ----------------------------------------------------------------------------
  # save
  
  if opt_fp_out:
    # save pose only
    fpp_out = Path(opt_fp_out)
    
    fp_out = join(fpp_out.parent, f'{fpp_out.stem}_real{fpp_out.suffix}')
    cv.imwrite(fp_out, im_age_real)

    fp_out = join(fpp_out.parent, f'{fpp_out.stem}_apparent{fpp_out.suffix}')
    cv.imwrite(fp_out, im_age_apparent)

    fp_out = join(fpp_out.parent, f'{fpp_out.stem}_gender{fpp_out.suffix}')
    cv.imwrite(fp_out, im_age_apparent)


  # ----------------------------------------------------------------------------
  # display

  if opt_display:
    # show all images here
    cv.imshow('real', im_age_real)
    cv.imshow('apparent', im_age_apparent)
    cv.imshow('gender', im_gender)
    display_utils.handle_keyboard()