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"""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
# bounding box for 68 landmark detection
def BBRegression(points, params):
w1 = params['W1']
b1 = params['B1']
w2 = params['W2']
b2 = params['B2']
data = points.copy()
data = data.reshape([5, 2])
data_mean = np.mean(data, axis=0)
x_mean = data_mean[0]
y_mean = data_mean[1]
data[:, 0] = data[:, 0] - x_mean
data[:, 1] = data[:, 1] - y_mean
rms = np.sqrt(np.sum(data ** 2)/5)
data = data / rms
data = data.reshape([1, 10])
data = np.transpose(data)
inputs = np.matmul(w1, data) + b1
inputs = 2 / (1 + np.exp(-2 * inputs)) - 1
inputs = np.matmul(w2, inputs) + b2
inputs = np.transpose(inputs)
x = inputs[:, 0] * rms + x_mean
y = inputs[:, 1] * rms + y_mean
w = 224/inputs[:, 2] * rms
rects = [x, y, w, w]
return np.array(rects).reshape([4])
# utils for landmark detection
def img_padding(img, box):
success = True
bbox = box.copy()
res = np.zeros([2*img.shape[0], 2*img.shape[1], 3])
res[img.shape[0] // 2: img.shape[0] + img.shape[0] //
2, img.shape[1] // 2: img.shape[1] + img.shape[1]//2] = img
bbox[0] = bbox[0] + img.shape[1] // 2
bbox[1] = bbox[1] + img.shape[0] // 2
if bbox[0] < 0 or bbox[1] < 0:
success = False
return res, bbox, success
# utils for landmark detection
def crop(img, bbox):
padded_img, padded_bbox, flag = img_padding(img, bbox)
if flag:
crop_img = padded_img[padded_bbox[1]: padded_bbox[1] +
padded_bbox[3], padded_bbox[0]: padded_bbox[0] + padded_bbox[2]]
crop_img = cv2.resize(crop_img.astype(np.uint8),
(224, 224), interpolation=cv2.INTER_CUBIC)
scale = 224 / padded_bbox[3]
return crop_img, scale
else:
return padded_img, 0
# utils for landmark detection
def scale_trans(img, lm, t, s):
imgw = img.shape[1]
imgh = img.shape[0]
M_s = np.array([[1, 0, -t[0] + imgw//2 + 0.5], [0, 1, -imgh//2 + t[1]]],
dtype=np.float32)
img = cv2.warpAffine(img, M_s, (imgw, imgh))
w = int(imgw / s * 100)
h = int(imgh / s * 100)
img = cv2.resize(img, (w, h))
lm = np.stack([lm[:, 0] - t[0] + imgw // 2, lm[:, 1] -
t[1] + imgh // 2], axis=1) / s * 100
left = w//2 - 112
up = h//2 - 112
bbox = [left, up, 224, 224]
cropped_img, scale2 = crop(img, bbox)
assert(scale2!=0)
t1 = np.array([bbox[0], bbox[1]])
# back to raw img s * crop + s * t1 + t2
t1 = np.array([w//2 - 112, h//2 - 112])
scale = s / 100
t2 = np.array([t[0] - imgw/2, t[1] - imgh / 2])
inv = (scale/scale2, scale * t1 + t2.reshape([2]))
return cropped_img, inv
# utils for landmark detection
def align_for_lm(img, five_points):
five_points = np.array(five_points).reshape([1, 10])
params = loadmat('util/BBRegressorParam_r.mat')
bbox = BBRegression(five_points, params)
assert(bbox[2] != 0)
bbox = np.round(bbox).astype(np.int32)
crop_img, scale = crop(img, bbox)
return crop_img, scale, bbox
# resize and crop images for face reconstruction
def resize_n_crop_img(img, lm, t, s, target_size=1024., mask=None):
#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.save("/home/koki/Projects/Deep3DFaceRecon_pytorch/checkpoints/pretrained/results/iphone/epoch_20_000000/img_debug.jpg")
img = img.resize((w, h), resample=Image.LANCZOS)
# img = np.asarray(img)
# cx = int(0.5 * left + 0.5 * right)
# cy = int(0.5 * up + 0.5 * below)
# img = cv2.circle(img, (cx, cy), 3, (255,0,0), 3)
# img = Image.fromarray(img)
# print(str(cx/s) + " " + str(cy/s))
img = img.crop((left, up, right, below))
if mask is not None:
mask = mask.resize((w, h), resample=Image.LANCZOS)
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])
#img.save("/home/koki/Projects/Deep3DFaceRecon_pytorch/checkpoints/pretrained/results/iphone/epoch_20_000000/crop_low.jpg")
# mask.save("/home/koki/Projects/Deep3DFaceRecon_pytorch/checkpoints/pretrained/results/iphone/epoch_20_000000/mask.jpg")
#print(lm)
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=1024., rescale_factor=466.285):
#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 = img.resize((1024,1024),resample=Image.LANCZOS)
#lm_new = lm*1024.0/512.0
# mask_new=None
img_new, lm_new, mask_new = resize_n_crop_img(img, lm, t, s, target_size=target_size, mask=mask)
# img.save("/home/koki/Projects/Deep3DFaceRecon_pytorch/checkpoints/pretrained/results/iphone/epoch_20_000000/img_new.jpg")
print(w0, h0, s, t[0][0], t[1][0])
trans_params = np.array([w0, h0, s, t[0][0], t[1][0]])
lm_new *= 224/1024.0
img_new_low = img_new.resize((224, 224), resample=Image.LANCZOS)
return trans_params, img_new_low, lm_new, mask_new, img_new
# utils for face recognition model
def estimate_norm(lm_68p, H):
# from https://github.com/deepinsight/insightface/blob/c61d3cd208a603dfa4a338bd743b320ce3e94730/recognition/common/face_align.py#L68
"""
Return:
trans_m --numpy.array (2, 3)
Parameters:
lm --numpy.array (68, 2), y direction is opposite to v direction
H --int/float , image height
"""
lm = extract_5p(lm_68p)
lm[:, -1] = H - 1 - lm[:, -1]
tform = trans.SimilarityTransform()
src = np.array(
[[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
[41.5493, 92.3655], [70.7299, 92.2041]],
dtype=np.float32)
tform.estimate(lm, src)
M = tform.params
if np.linalg.det(M) == 0:
M = np.eye(3)
return M[0:2, :]
def estimate_norm_torch(lm_68p, H):
lm_68p_ = lm_68p.detach().cpu().numpy()
M = []
for i in range(lm_68p_.shape[0]):
M.append(estimate_norm(lm_68p_[i], H))
M = torch.tensor(np.array(M), dtype=torch.float32).to(lm_68p.device)
return M
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