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warp.py
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warp.py
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# -*- coding:utf-8 -*-
import os
import sys
import cv2
import numpy as np
from utils import tqdm
# from ...sphereface.matlab_cp2tform import get_similarity_transform_for_cv2
submodule_path = os.path.abspath('../../sphereface')
sys.path.append(submodule_path)
from matlab_cp2tform import get_similarity_transform_for_cv2
# 这里注意x代表的是横轴，y代表的是纵轴
imgSize = np.array([128, 128])
coord5points = {
'p': np.array([
(30.2946, 51.6963,), # Left eye
(65.5318, 51.5014,), # Right eye
(48.0252, 71.7366,), # Nose tip
(33.5493, 92.3655,), # Mouth left corner
(62.7299, 92.2041,), # Mouth right corner
]) + np.array([16, 8]), # [16, 8] is an offset, and i forget why i add it, just leave it alone
'c': np.array([
(30.2946, 51.6963,), # Left eye
(65.5318, 51.5014,), # Right eye
(48.5851, 79.0356,), # Nose tip
(31.4217, 98.5210,), # Mouth left corner
(66.2410, 98.4563,), # Mouth right corner
]) + np.array([16, 8]), # [16, 8] is an offset, and i forget why i add it, just leave it alone
}
imgSize *= 4
for key, value in coord5points.items():
coord5points[key] = value * 4
# change imgSize to tuple
imgSize = tuple(map(int, imgSize))
def calc_eye_point(landmark, is_right_eye=0):
offset = is_right_eye * 2
t = np.array([
landmark[8+offset],
landmark[9+offset],
])
return t.mean(axis=0)
def get_img5point(landmark):
return np.array([
calc_eye_point(landmark, is_right_eye=0), # Left eye
calc_eye_point(landmark, is_right_eye=1), # Right eye
landmark[12], # Nose tip
landmark[14], # Mouth left corner
landmark[16], # Mouth right corner
])
def warp_img(img, landmark, image_type):
img5point = get_img5point(landmark)
img = img.astype(np.uint8)
M = cv2.estimateRigidTransform(img5point, coord5points[image_type], fullAffine=True)
img = cv2.warpAffine(img, M, imgSize)
landmark = np.array(landmark)
landmark = np.concatenate((
landmark,
np.ones((landmark.shape[0], 1))
), axis=1)
M = np.concatenate((
M,
np.array(((0, 0, 1),)),
), axis=0)
landmark = np.dot(landmark, M.T)[:, :2]
landmark = [tuple(x) for x in landmark]
return img, landmark
def alignment(src_img, src_landmark, resize_factor=2):
offset = 2
ref_pts = [
[30.2946+offset, 51.6963+offset],
[65.5318+offset, 51.5014+offset],
[48.0252+offset, 71.7366+offset],
[33.5493+offset, 92.3655+offset],
[62.7299+offset, 92.2041+offset],
]
crop_size = (96+offset*2, 112+offset*2)
src_pts = get_img5point(src_landmark) # float64
src_pts = np.array(src_pts).reshape(5, 2)
s = np.array(src_pts).astype(np.float32)
r = np.array(ref_pts).astype(np.float32) * resize_factor
crop_size = (crop_size[0]*resize_factor, crop_size[1]*resize_factor)
tfm = get_similarity_transform_for_cv2(s, r) # (2,3)
face_img = cv2.warpAffine(src_img, tfm, crop_size) # 仿射变换，crop_size为变换后的大小，tfm是仿射矩阵
# face_img (232,200,3)
dst_landmark = np.array(src_landmark)
dst_landmark = np.concatenate((
dst_landmark,
np.ones((dst_landmark.shape[0], 1))
), axis=1) # (17,3)
tfm = np.concatenate((
tfm,
np.array(((0, 0, 1),)), # (1,3)
), axis=0) # (3,3)
dst_landmark = np.dot(dst_landmark, tfm.T)[:, :2]
dst_landmark = [tuple(x) for x in dst_landmark]
return face_img, dst_landmark
def generate_dataset_face_frontalization():
import config
from datas import get_dirs, get_image, get_overview
from utils import dataset_iterator, perpare_dataset_dir, im_str_to_np
# perpare_dataset_dir
new_dataset_names = ['frontalization_dataset']
new_dataset_dirs = []
for new_dataset_name in new_dataset_names:
new_dataset_dirs.append(perpare_dataset_dir(new_dataset_name, __file__))
new_images_dir = os.path.join(new_dataset_dirs[0], config.WC_original_images_dir_name)
new_landmarks_dir = os.path.join(new_dataset_dirs[0], config.WC_landmarks_dir_name)
# face_frontalization
for people_name, image_type, image_name, landmark in tqdm(dataset_iterator(config.WC_original_dataset_name)):
im_str = get_image(config.WC_original_dataset_name, people_name, image_name, show_landmark=0)
im = im_str_to_np(im_str)
im, landmark = alignment(im, landmark) # duiqi(Affine Transformation)
people_name = people_name.replace('_', ' ')
file_dir = os.path.join(new_images_dir, people_name)
os.makedirs(file_dir, exist_ok=True)
# os.chdir(file_dir)
if not cv2.imwrite(os.path.join(file_dir, image_name+'.jpg'), im):
assert 0, "cv2.imwrite failed"
file_dir = os.path.join(new_landmarks_dir, people_name)
os.makedirs(file_dir, exist_ok=True)
file_path = os.path.join(file_dir, image_name+'.txt')
with open(file_path, 'w') as file:
for ld in landmark:
file.write('%f %f\n' % ld)
# 转正却没有剔除
if __name__ == '__main__':
# from . import config
# from .utils import dataset_iterator, get_image
# def imshow(im):
# import matplotlib.pyplot as plt
# plt.imshow(im[:, :, ::-1])
# plt.show()
# dataset_name = config.WC_original_dataset_name
# for people_name, image_type, image_name, landmark in dataset_iterator(dataset_name):
# im_str = get_image(dataset_name, people_name, image_name, show_landmark=0)
# im = np.fromstring(im_str, np.uint8)
# im = cv2.imdecode(im, cv2.IMREAD_COLOR)
# im = warp_img(im, landmark)
# imshow(im)
generate_dataset_face_frontalization()