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A10G
| """This script contains the image preprocessing code for Deep3DFaceRecon_pytorch | |
| """ | |
| import numpy as np | |
| from scipy.io import loadmat | |
| from PIL import Image | |
| import cv2 | |
| import os | |
| from skimage import transform as trans | |
| import torch | |
| import warnings | |
| warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning) | |
| warnings.filterwarnings("ignore", category=FutureWarning) | |
| # calculating least square problem for image alignment | |
| def POS(xp, x): | |
| npts = xp.shape[1] | |
| A = np.zeros([2*npts, 8]) | |
| A[0:2*npts-1:2, 0:3] = x.transpose() | |
| A[0:2*npts-1:2, 3] = 1 | |
| A[1:2*npts:2, 4:7] = x.transpose() | |
| A[1:2*npts:2, 7] = 1 | |
| b = np.reshape(xp.transpose(), [2*npts, 1]) | |
| k, _, _, _ = np.linalg.lstsq(A, b) | |
| R1 = k[0:3] | |
| R2 = k[4:7] | |
| sTx = k[3] | |
| sTy = k[7] | |
| s = (np.linalg.norm(R1) + np.linalg.norm(R2))/2 | |
| t = np.stack([sTx, sTy], axis=0) | |
| return t, s | |
| # resize and crop images for face reconstruction | |
| def resize_n_crop_img(img, lm, t, s, target_size=224., mask=None): | |
| w0, h0 = img.size | |
| w = (w0*s).astype(np.int32) | |
| h = (h0*s).astype(np.int32) | |
| left = (w/2 - target_size/2 + float((t[0] - w0/2)*s)).astype(np.int32) | |
| right = left + target_size | |
| up = (h/2 - target_size/2 + float((h0/2 - t[1])*s)).astype(np.int32) | |
| below = up + target_size | |
| img = img.resize((w, h), resample=Image.BICUBIC) | |
| img = img.crop((left, up, right, below)) | |
| if mask is not None: | |
| mask = mask.resize((w, h), resample=Image.BICUBIC) | |
| mask = mask.crop((left, up, right, below)) | |
| lm = np.stack([lm[:, 0] - t[0] + w0/2, lm[:, 1] - | |
| t[1] + h0/2], axis=1)*s | |
| lm = lm - np.reshape( | |
| np.array([(w/2 - target_size/2), (h/2-target_size/2)]), [1, 2]) | |
| return img, lm, mask | |
| # utils for face reconstruction | |
| def extract_5p(lm): | |
| lm_idx = np.array([31, 37, 40, 43, 46, 49, 55]) - 1 | |
| lm5p = np.stack([lm[lm_idx[0], :], np.mean(lm[lm_idx[[1, 2]], :], 0), np.mean( | |
| lm[lm_idx[[3, 4]], :], 0), lm[lm_idx[5], :], lm[lm_idx[6], :]], axis=0) | |
| lm5p = lm5p[[1, 2, 0, 3, 4], :] | |
| return lm5p | |
| # utils for face reconstruction | |
| def align_img(img, lm, lm3D, mask=None, target_size=224., rescale_factor=102.): | |
| """ | |
| Return: | |
| transparams --numpy.array (raw_W, raw_H, scale, tx, ty) | |
| img_new --PIL.Image (target_size, target_size, 3) | |
| lm_new --numpy.array (68, 2), y direction is opposite to v direction | |
| mask_new --PIL.Image (target_size, target_size) | |
| Parameters: | |
| img --PIL.Image (raw_H, raw_W, 3) | |
| lm --numpy.array (68, 2), y direction is opposite to v direction | |
| lm3D --numpy.array (5, 3) | |
| mask --PIL.Image (raw_H, raw_W, 3) | |
| """ | |
| w0, h0 = img.size | |
| if lm.shape[0] != 5: | |
| lm5p = extract_5p(lm) | |
| else: | |
| lm5p = lm | |
| # calculate translation and scale factors using 5 facial landmarks and standard landmarks of a 3D face | |
| t, s = POS(lm5p.transpose(), lm3D.transpose()) | |
| s = rescale_factor/s | |
| # processing the image | |
| img_new, lm_new, mask_new = resize_n_crop_img(img, lm, t, s, target_size=target_size, mask=mask) | |
| trans_params = np.array([w0, h0, s, t[0], t[1]]) | |
| return trans_params, img_new, lm_new, mask_new | |