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import os.path as osp
from PIL import Image
import numpy as np
from additional_modules.deep3dfacerecon.util.load_mats import load_lm3d
from additional_modules.deep3dfacerecon.util.preprocess import align_img
TARGET_SIZE = 1024.
RESCALE_FACTOR = 300
CENTER_CROP_SIZE = 700
OUTPUT_SIZE = 512
class ImageCropper:
def __init__(self, lm3d=None):
bfm_folder = osp.join('additional_modules/deep3dfacerecon/BFM')
if lm3d is None:
self.lm3d_std = load_lm3d(bfm_folder)
else:
# calculate 5 facial landmarks using 68 landmarks
lm_idx = np.array([31, 37, 40, 43, 46, 49, 55]) - 1
lm3d_5p = np.stack([lm3d[lm_idx[0], :], np.mean(lm3d[lm_idx[[1, 2]], :], 0), np.mean(
lm3d[lm_idx[[3, 4]], :], 0), lm3d[lm_idx[5], :], lm3d[lm_idx[6], :]], axis=0)
lm3d_5p = lm3d_5p[[1, 2, 0, 3, 4], :]
self.lm3d_std = lm3d_5p
def __call__(self, im, lm):
_, H = im.size
lm = lm.copy()
lm_orig = lm.copy()
lm[:, -1] = H - 1 - lm[:, -1]
_, im_high, _, lm_high, _, _ = align_img(
im, lm, lm_orig, self.lm3d_std, target_size=TARGET_SIZE, rescale_factor=RESCALE_FACTOR
)
left = int(im_high.size[0]/2 - CENTER_CROP_SIZE/2)
upper = int(im_high.size[1]/2 - CENTER_CROP_SIZE/2)
right = left + CENTER_CROP_SIZE
lower = upper + CENTER_CROP_SIZE
im_cropped = im_high.crop((left, upper, right, lower))
lm_cropped = lm_high.copy()
lm_cropped[..., 0] -= left
lm_cropped[..., 1] -= upper
lm_cropped[..., 0] *= OUTPUT_SIZE / im_cropped.size[0]
lm_cropped[..., 1] *= OUTPUT_SIZE / im_cropped.size[1]
im_cropped = im_cropped.resize((OUTPUT_SIZE, OUTPUT_SIZE), resample=Image.LANCZOS)
return im_cropped, lm_cropped
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