def get_face_chips(self,
img,
face_rects,
facial_points=None,
output_square=False):
if facial_points is None:
if self.aligner is None:
raise Exception('FaceAligner.aligner is not initialized')
rects, facial_points = self.aligner.align_face(img, face_rects)
reference_5pts = None
output_size = (96, 112) # (w, h) not (h,w)
if output_square:
output_size = (112, 112)
reference_5pts = get_reference_facial_points(output_size)
face_chips = []
for facial_5pts in facial_points:
facial_5pts = np.reshape(facial_5pts, (2, -1))
dst_img = warp_and_crop_face(img, facial_5pts, reference_5pts,
output_size)
face_chips.append(dst_img)
return face_chips
def get_aligned_face_chips(self,
img_list,
facial_points_list,
output_square=True):
"""Get aligned face chips in a image list.
Params:
img_list: a list of input images, each image is a numpy array
facial_points_list: a list of face landmarks, has the same length as img_list, each one is for one image
output_square: whether to output square face chips
Return:
a list of aligned face roi chips (eacho one is a numpy array),
the output list has the same length of input pts_with_angles
"""
face_chips = []
output_size = (96, 112) # (w, h) not (h,w)
if output_square:
output_size = (112, 112)
reference_5pts = get_reference_facial_points(output_size)
for img, facial_points in zip(img_list, facial_points_list):
facial_5pts = np.reshape(facial_points, (5, -1))
dst_img = warp_and_crop_face(img, facial_5pts, reference_5pts,
output_size)
face_chips.append(dst_img)
return face_chips
#GT_RECT = [68, 68, 182, 182]
#GT_AREA = (GT_RECT[2] - GT_RECT[0] + 1) * (GT_RECT[3] - GT_RECT[1] + 1)
overlap_thresh = 0.3
only_align_missed = False
do_align = True
# crop settings, set the region of cropped faces
default_square = True
padding_factor = 0.25
outer_padding = (0, 0)
output_size = (224, 224)
# get the referenced 5 landmarks position in the crop settings
reference_5pts = get_reference_facial_points(output_size, padding_factor,
outer_padding, default_square)
aligned_save_dir = './gender_crop_data'
file_root_path = '../../../mtcnn-caffe-good/mtcnn_aligner/result_after_align/'
if not osp.exists(file_root_path):
print('ERROR: webface root dir not found!!!')
else:
if not osp.exists(aligned_save_dir):
print('mkdir for aligned faces, aligned root dir: ', aligned_save_dir)
os.makedirs(aligned_save_dir)
count = 30049
for root, dirs, files in os.walk(file_root_path):
for file in files[30049:]:
import cv2
import json
import os
import os.path as osp
#from matplotlib import pyplot as plt
from fx_warp_and_crop_face import get_reference_facial_points, warp_and_crop_face
# crop settings, set the region of cropped faces
output_square = True
padding_factor = 0.25
output_padding = (0, 0)
output_size = (224, 224)
# get the referenced 5 landmarks position in the crop settings
referenced_5pts = get_reference_facial_points(output_size, padding_factor,
output_padding, output_square)
img_root_dir = ''
landmark_fn = r'./mtcnn_fd_rlt_test_imgs.json'
aligned_save_dir = './faces' + '-mtcnn-aligned-224x224'
log_fn1 = 'align_succeeded_list.txt'
log_fn2 = 'align_failed_list.txt'
log_align_params = 'align_params.txt'
fp_in = open(landmark_fn, 'r')
img_list = json.load(fp_in)
fp_in.close()
if not osp.exists(aligned_save_dir):
