Evaluation on 3DFAW

[JesseZhang92]

Hi Wu,
Thanks for releasing your impressive work. Recently I am trying to reproduce the results of Table 5 and have got the 3DFAW dataset. I wonder know how you perform the data processing and do the evaluation. I guess that you refer to the depth-net repo https://github.com/joelmoniz/DepthNets/tree/master/depthnet-pytorch, but it is still confusing that how to crop the image, preserve the key points location and calculate the metrics. Is it possible for you to release this part of code or show more details?

[elliottwu]

Hi,
I have not cleaned the code for the keypoint depth evaluation yet, but I can share the snippets for cropping and evaluation.

Also, the current released celeba model crops too much, and keypoints land outside of the image. For the evaluation, we used another model that was trained on slightly larger faces (8/7 of the height and width of the current crops provided), for which you might need to redo the cropping using the provided bounding boxes. I hope this helps for now.

crop_3DFAW.py

import os
from glob import glob
import numpy as np
import shutil
import cv2


split = np.loadtxt('/users/szwu/DepthNets/depthnet-pytorch/data/3DFAW/list_valid_test.txt', dtype=str, delimiter=',')

rootin_im = '/users/szwu/DepthNets/depthnet-pytorch/data/3DFAW/valid_img/'
rootin_kpt = '/users/szwu/DepthNets/depthnet-pytorch/data/3DFAW/valid_lm/'
rootout_ori_im = '/users/szwu/DepthNets/depthnet-pytorch/data/test_depthcorr/ori/img/'
rootout_ori_kpt = '/users/szwu/DepthNets/depthnet-pytorch/data/test_depthcorr/ori/kpts/'
rootout_crop_im = '/users/szwu/DepthNets/depthnet-pytorch/data/test_depthcorr/cropped/img/'
rootout_crop_kpt = '/users/szwu/DepthNets/depthnet-pytorch/data/test_depthcorr/cropped/kpts/'

oriens = ['left','center','right']
for ori in oriens:
    for rootout in [rootout_ori_im, rootout_ori_kpt, rootout_crop_im, rootout_crop_kpt]:
        dirout = os.path.join(rootout, ori)
        if not os.path.isdir(dirout):
            os.makedirs(dirout)

for fid, sp, ori in split:
    if ori in oriens:
        im_fname = fid + '.jpg'
        im_fpath = os.path.join(rootin_im, im_fname)
        shutil.copyfile(im_fpath, os.path.join(rootout_ori_im, ori, im_fname))

        kpt_fname = fid + '_lm.csv'
        kpt_fpath = os.path.join(rootin_kpt, kpt_fname)
        shutil.copyfile(kpt_fpath, os.path.join(rootout_ori_kpt, ori, kpt_fname))

        kpt = np.loadtxt(kpt_fpath, delimiter=',')

        im = cv2.imread(im_fpath)
        a2 = int((kpt.max(0)-kpt.min(0))[:2].mean()*1)
        im = cv2.copyMakeBorder(im,a2,a2,a2,a2,cv2.BORDER_REPLICATE)

        leye = kpt[36:42,:2].mean(0)
        reye = kpt[42:48,:2].mean(0)
        lmouth = kpt[48,:2]
        rmouth = kpt[54,:2]
        nose = kpt[30,:2]

        c1 = (leye + reye + lmouth + rmouth)/4
        c2 = nose - 2*(nose - c1)
        xc, yc = (c2 + 0.5*((leye + reye)/2 - c1)).astype(int)

        x1 = xc-a2 + a2
        x2 = x1+a2*2
        y1 = yc-a2 +int(0.1*a2) + a2
        y2 = y1+a2*2

        crop = im[y1:y2, x1:x2, :]
        crop = cv2.resize(crop, (256,256))
        cv2.imwrite(os.path.join(rootout_crop_im, ori, im_fname), crop)

        s = 256/(a2*2)
        kpt_xy = kpt[:,:2] - np.array([[x1, y1]]) +a2
        kpt_xy = kpt_xy *s
        kpt_z = kpt[:,2:] *s
        np.savetxt(os.path.join(rootout_crop_kpt, ori, kpt_fname), np.concatenate([kpt_xy, kpt_z], 1), delimiter=',')

eval_kpt_depth.py

import cv2
import numpy as np
import torch
import matplotlib.pyplot as plt
import os
from glob import glob


# with border (reported in paper)
result_dir = '/scratch/shared/nfs1/szwu/all_results/finetuned_celeba_test_depthcorr_080_db0.7_crop1.4/'
# result_dir = '/scratch/shared/nfs1/szwu/all_results/3dfaw_test_depthcorr_100_db0.7_crop1.4/'
source_dir = '/scratch/shared/nfs1/szwu/data/test_depthcorr'

kpts_fpaths = sorted(glob(os.path.join(source_dir, 'cropped/kpts/center/*.csv')))
kpts = np.array([np.loadtxt(kpts_fpath, delimiter=',') for kpts_fpath in kpts_fpaths])

crop = 1.4
kpts_xy = kpts[:,:,:2]
s = 256/(64*crop)
kpts_xy = kpts_xy / s
p = 64*(crop-1)/2
kpts_xy = kpts_xy - p
kpts_xy[:,:,1] -= 4

ori_kpts_fpaths = sorted(glob(os.path.join(source_dir, 'ori/kpts/center/*.csv')))
ori_kpts = np.array([np.loadtxt(kpts_fpath, delimiter=',') for kpts_fpath in ori_kpts_fpaths])

im_fpaths = sorted(glob(os.path.join(result_dir, 'input/*.png')))[:75]
depth_fpaths = sorted(glob(os.path.join(result_dir, 'depth/*.png')))[:75]
depths = np.array([cv2.imread(depth_fpath, -1)/65535. for depth_fpath in depth_fpaths])
ori_im_fpaths = sorted(glob(os.path.join(source_dir, 'ori/img/center/*.jpg')))
ori_ims = np.array([cv2.imread(im_fpath)[:,:,::-1] for im_fpath in ori_im_fpaths])


def compute_covar(preds, actuals, n_kps=66):
    return np.sum(np.diag(np.abs(np.corrcoef(preds, actuals, rowvar=0)[:n_kps,n_kps:])))

avg_szs = np.array([sum(im.shape[:2])/2 for im in ori_ims])
gt_z_rescaled = ori_kpts[:,:,2] / avg_szs[:,None]

depths_t = torch.FloatTensor(depths).unsqueeze(1)
kpts_xy_t = torch.FloatTensor(kpts_xy).unsqueeze(1)/32-1

depths_t = torch.nn.functional.pad(depths_t, (32,32,32,32), mode='replicate')
z_sampled = torch.nn.functional.grid_sample(depths_t, kpts_xy_t/2)[:,0,0,:]
z_sampled = z_sampled - z_sampled.mean(1,keepdim=True)

score = compute_covar(z_sampled, gt_z_rescaled)
print(score)

[dafuny]

Hi Wu,
Thanks for releasing your impressive work. Recently I am trying to reproduce the results of Table 5 and have got the 3DFAW dataset. I wonder know how you perform the data processing and do the evaluation. I guess that you refer to the depth-net repo https://github.com/joelmoniz/DepthNets/tree/master/depthnet-pytorch, but it is still confusing that how to crop the image, preserve the key points location and calculate the metrics. Is it possible for you to release this part of code or show more details?

Hi @JesseZhang92,I'm also very interested in the 3DFAW valuation experienment,but follow Wu's instructions,I got a bad result,and I wonder if the cropped size is wrong or other steps .And if you have produced the result,could you share your code details to me?Very appreciated if you could give me a reply.

[Heng14]

Hi, I am also working on the evaluation of the 3DFAW dataset. Did you guys figure it out?
Thank you so much!