def main():
args = parse_args()
det_json = args.det_json
save_dir = args.save_dir
model_dir = args.mtcnn_model_dir
gpu_id = args.gpu_id
aligner = FaceAligner(model_dir, gpu_id=gpu_id)
index = 0
with open(det_json, "r") as f:
for line in f:
index += 1
print("Processing img %d" % index)
line = json.loads(line.strip())
# 一个url对应一个pts
url = str(line["url"])
if not line['det']:
continue
pts = line['det'][0]['boundingBox']['pts']
# 图片以人名为前缀, 若无, 则为neg
name = str(url.split('/')[-2])
img_name = url.split('/')[-1]
sub_save_dir = os.path.join(save_dir, name)
if not os.path.exists(sub_save_dir):
os.makedirs(sub_save_dir)
img = _pull_img(url)
if img is None:
continue
# 只crop一张脸
face_chip = aligner.get_face_chips(img, [pts],
output_square=default_square)
save_name = os.path.join(sub_save_dir, img_name)
cv2.imwrite(save_name, face_chip[0])
def main(nsplits,
split_id,
list_file,
img_root_dir,
mtcnn_model_dir,
save_dir=None,
rects_fn=None):
if not save_dir:
save_dir = './facescrub_mtcnn_aligned'
if not osp.exists(save_dir):
print('mkdir for aligned root dir: ', save_dir)
os.makedirs(save_dir)
save_aligned_dir = osp.join(save_dir, 'aligned_imgs')
if not osp.exists(save_aligned_dir):
print('mkdir for aligned/cropped face imgs: ', save_dir)
os.makedirs(save_aligned_dir)
save_rects_dir = osp.join(save_dir, 'face_rects')
if not osp.exists(save_rects_dir):
print('mkdir for face rects/landmarks: ', save_rects_dir)
os.makedirs(save_rects_dir)
aligner = FaceAligner(mtcnn_model_dir)
#fp = open(list_file, 'r')
#all_lines = fp.readlines()
#fp.close()
rects_list = load_rect_list(rects_fn)
all_lines = rects_list
total_line_cnt = len(all_lines)
print('--->%d imgs in total' % total_line_cnt)
if nsplits < 2:
if split_id > 0:
print('===> Will only process first %d imgs' % split_id)
start_line = 0
end_line = split_id
else:
print('===> Will process all of the images')
start_line = 0
end_line = total_line_cnt
else:
assert (split_id < nsplits)
lines_per_split = float(total_line_cnt) / nsplits
start_line = int(lines_per_split * split_id)
end_line = int(lines_per_split * (split_id + 1))
if end_line + 1 >= total_line_cnt:
end_line = total_line_cnt
print('===> Will only process imgs in the range [%d, %d)]' %
(start_line, end_line))
count = start_line
fp_log = open(osp.join(save_dir, 'missing_imgs_split_%d.txt' % split_id),
'w')
for line in all_lines[start_line:end_line]:
#line = line.strip()
print count
count = count + 1
img_fn = osp.join(img_root_dir, line['image'])
print('===> Processing img: ' + img_fn)
img = cv2.imread(img_fn)
if img is None:
print 'falied to read image: ', img_fn
fp_log.write(img_fn + '\n')
continue
ht = img.shape[0]
wd = img.shape[1]
print 'image.shape:', img.shape
spl = osp.split(line['image'])
#sub_dir = osp.split(spl[0])[1]
sub_dir = spl[0]
print 'sub_dir: ', sub_dir
if CHINESE_2_PINYIN:
sub_dir = pinyin.get(sub_dir, format="strip")
# replace the dot sign in names
sub_dir = sub_dir.replace(u'\xb7', '-').encode('utf-8')
base_name = osp.splitext(spl[1])[0]
save_img_subdir = osp.join(save_aligned_dir, sub_dir)
if not osp.exists(save_img_subdir):
os.mkdir(save_img_subdir)
save_rect_subdir = osp.join(save_rects_dir, sub_dir)
if not osp.exists(save_rect_subdir):
os.mkdir(save_rect_subdir)
# print pts
save_rects_fn = osp.join(save_rect_subdir, base_name + '.txt')
fp_rect = open(save_rects_fn, 'w')
#rect = get_rects_for_image(rects_list, base_name)
rect = line['pts']
# boxes, points = aligner.align_face(img, [rect])
boxes, points = aligner.align_face(img, [rect])
nfaces = len(boxes)
fp_rect.write('%d\n' % nfaces)
for i in range(nfaces):
box = boxes[i]
pts = points[i]
if i:
save_img_fn = osp.join(save_img_subdir,
base_name + '_%d.jpg' % (i + 1))
else:
save_img_fn = osp.join(save_img_subdir, base_name + '.jpg')
facial5points = np.reshape(pts, (2, -1))
# dst_img = warp_and_crop_face(
# img, facial5points, reference_5pts, output_size)
dst_img = aligner.get_face_chips(img, [box], [pts])[0]
cv2.imwrite(save_img_fn, dst_img)
print 'aligend face saved into: ', save_img_fn
for it in box:
fp_rect.write('%5.2f\t' % it)
fp_rect.write('\n')
for i in range(5):
fp_rect.write('%5.2f\t%5.2f\n' %
(facial5points[0][i], facial5points[1][i]))
fp_rect.close()
fp_log.close()
# fp_rlt = open(osp.join(save_dir, save_json), 'w')
# results = []
img = cv2.imread(img_path)
aligner = FaceAligner(model_path)
t1 = time.clock()
# You can align the faces in two steps like this:
# bboxes, points = aligner.align_face(img, face_rects)
# face_chips = aligner.get_face_chips(img, bboxes, points)
# OR just align them in one step by calling the following function,
# which combine last two functions
face_chips = aligner.get_face_chips(img, face_rects)
t2 = time.clock()
for i, chip in enumerate(face_chips):
print('---> chip.shape: ', chip.shape)
save_name = osp.join(save_dir, 'face_chip_%s_%d' % (name, i) + ext)
cv2.imwrite(save_name, chip)
if show_img:
cv2.imshow('face_chip', chip)
cv2.waitKey(0)
n_boxes = len(face_rects)
print(
"-->Alignment cost %f seconds, processed %d face rects, avg time: %f seconds"
def main(img_list_file, root_dir, mtcnn_model_dir, save_dir=None):
if not save_dir:
save_dir = './aligned_images'
if not osp.exists(save_dir):
print('mkdir for aligned faces, aligned root dir: ', save_dir)
os.makedirs(save_dir)
aligned_save_dir = osp.join(save_dir, 'aligned_faces')
if not osp.exists(aligned_save_dir):
print('mkdir for aligned faces, aligned images dir: ',
aligned_save_dir)
os.makedirs(aligned_save_dir)
#aligner = MtcnnAligner(mtcnn_model_dir, False)
aligner = FaceAligner(mtcnn_model_dir)
fp = open(img_list_file, 'r')
fn_rlt = osp.join(save_dir, 'fd_rlt.json')
fp_rlt = open(fn_rlt, 'w')
fp_rlt.write('[\n')
count = 0
for line in fp:
print line
line_split = line.split()
img_fn = line_split[0]
id_num = line_split[1]
img_fn_split = img_fn.split('/')
img_fn = osp.join(root_dir, img_fn)
print 'process image: ', img_fn, " id_num: ", id_num
#for root,dirs,files in path_walk:
err_msg = ''
if not count:
fp_rlt.write(',\n')
count = count + 1
print 'count: ', count
overlap_thresh_0 = overlap_thresh
save_subdir = osp.join(aligned_save_dir, img_fn_split[-2])
save_img_fn = osp.join(save_subdir, img_fn_split[-1])
if not osp.exists(save_subdir):
os.makedirs(save_subdir)
image = cv2.imread(img_fn)
print image.shape
boxes, points = aligner.align_face(image, [GT_RECT])
box = boxes[0]
pts = points[0]
facial5points = np.reshape(points, (2, -1))
#dst_img = warp_and_crop_face(image, facial5points, reference_5pts, output_size)
dst_img = aligner.get_face_chips(image, [box], [pts])[0]
cv2.imwrite(save_img_fn, dst_img)
item = {}
tmp = {'rect': box[0:4], 'score': box[4], 'pts': pts, 'id': id_num}
item['faces'] = tmp
#item['id'] = data[u'url'].line_splitit('/')[-3]
item['shape'] = image.shape
json_str = json.dumps(item, indent=2)
fp_rlt.write(json_str + '\n')
fp_rlt.flush()
fp_rlt.write(']\n')
fp_rlt.close()
fp.close()
def main(nsplits,
split_id,
list_file,
img_root_dir,
mtcnn_model_dir,
save_dir=None):
if not save_dir:
save_dir = './aligned_root_dir'
if not osp.exists(save_dir):
print('mkdir for aligned root dir: ', save_dir)
os.makedirs(save_dir)
save_aligned_dir = osp.join(save_dir, 'aligned_imgs')
if not osp.exists(save_aligned_dir):
print('mkdir for aligned/cropped face imgs: ', save_dir)
os.makedirs(save_aligned_dir)
save_rects_dir = osp.join(save_dir, 'face_rects')
if not osp.exists(save_rects_dir):
print('mkdir for face rects/landmarks: ', save_rects_dir)
os.makedirs(save_rects_dir)
# aligner = MtcnnAligner(mtcnn_model_dir, False)
aligner = FaceAligner(mtcnn_model_dir, False)
fp = open(list_file, 'r')
all_lines = fp.readlines()
fp.close()
total_line_cnt = len(all_lines)
print('--->%d imgs in total' % total_line_cnt)
if nsplits < 2:
if split_id > 0:
print('===> Will only process first %d imgs' % split_id)
start_line = 0
end_line = split_id
else:
print('===> Will process all of the images')
start_line = 0
end_line = total_line_cnt
else:
assert (split_id < nsplits)
lines_per_split = float(total_line_cnt) / nsplits
start_line = int(lines_per_split * split_id)
end_line = int(lines_per_split * (split_id + 1))
if end_line + 1 >= total_line_cnt:
end_line = total_line_cnt
print('===> Will only process imgs in the range [%d, %d)]' %
(start_line, end_line))
count = start_line
for line in all_lines[start_line:end_line]:
line = line.strip()
print count
count = count + 1
img_fn = osp.join(img_root_dir, line)
print('===> Processing img: ' + img_fn)
img = cv2.imread(img_fn)
ht = img.shape[0]
wd = img.shape[1]
print 'image.shape:', img.shape
# GT_RECT = [0,0,img.shape[0],img.shape[1]]
GT_RECT = [
int(wd * 0.25),
int(ht * 0.25),
int(wd * 0.75),
int(ht * 0.72)
]
# print 'face rect: ', gt
# boxes, points = aligner.align_face(img, [GT_RECT])
boxes, points = aligner.align_face(img, [rect])
box = boxes[0]
pts = points[0]
spl = osp.split(line)
sub_dir = spl[0]
base_name = spl[1]
save_img_subdir = osp.join(save_aligned_dir, sub_dir)
if not osp.exists(save_img_subdir):
os.mkdir(save_img_subdir)
save_rect_subdir = osp.join(save_rects_dir, sub_dir)
if not osp.exists(save_rect_subdir):
os.mkdir(save_rect_subdir)
# print pts
save_img_fn = osp.join(save_img_subdir, base_name)
facial5points = np.reshape(pts, (2, -1))
# dst_img = warp_and_crop_face(
# img, facial5points, reference_5pts, output_size)
dst_img = aligner.get_face_chips(img, [box], [pts])[0]
cv2.imwrite(save_img_fn, dst_img)
save_rect_fn = osp.join(save_rect_subdir,
osp.splitext(base_name)[0] + '.txt')
fp_rect = open(save_rect_fn, 'w')
for it in box:
fp_rect.write('%5.2f\t' % it)
fp_rect.write('\n')
for i in range(5):
fp_rect.write('%5.2f\t%5.2f\n' %
(facial5points[0][i], facial5points[1][i]))
fp_rect.close()
def main(argv):
args = parse_arguments(argv)
print '===> args:\n', args
config = load_config(args.config)
print '===> config:\n', config
extractor_config = config['face_feature']
mtcnn_model_path = str(config['mtcnn_model_dir'])
do_detect = not args.no_detect
do_align = not args.no_align
save_dir = args.save_dir
if not osp.exists(save_dir):
os.makedirs(save_dir)
save_img = args.save_image
show_img = args.show_image
detector = None
aligner = None
if do_detect:
detector = MtcnnDetector(mtcnn_model_path)
if do_align:
if not do_detect:
aligner = FaceAligner(mtcnn_model_path)
else:
aligner = FaceAligner(None)
else:
aligner = None
feature_extractor = CaffeFeatureExtractor(extractor_config)
feat_layer = feature_extractor.get_feature_layers()[0]
fp = open(args.img_list_file, 'r')
fp_rlt = open(osp.join(save_dir, 'face_feature.json'), 'w')
fp_rlt.write('[\n')
write_comma_flag = False
# result_list = []
img_cnt = 0
faces_cnt = 0
ttl_det_time = 0.0
ttl_feat_time = 0.0
for line in fp:
img_path = line.strip()
print("\n===>" + img_path)
if img_path == '':
print 'empty line, not a file name, skip to next'
continue
if img_path[0] == '#':
print 'skip line starts with #, skip to next'
continue
# result_list.append(rlt)
if write_comma_flag:
fp_rlt.write(',\n')
else:
write_comma_flag = True
rlt = {}
rlt["filename"] = img_path
rlt["faces"] = []
rlt['face_count'] = 0
try:
if args.image_root_dir:
img = cv2.imread(osp.join(args.image_root_dir, img_path))
else:
img = cv2.imread(img_path)
print '\n---> img.shape: ', img.shape
except:
print('failed to load image: ' + img_path)
#rlt["message"] = "failed to load"
json_str = json.dumps(rlt, indent=2)
fp_rlt.write(json_str)
fp_rlt.flush()
continue
if img is None:
print('failed to load image: ' + img_path)
rlt["message"] = "failed to load"
# result_list.append(rlt)
json_str = json.dumps(rlt, indent=2)
fp_rlt.write(json_str)
fp_rlt.flush()
continue
img_cnt += 1
if do_detect:
t1 = time.clock()
bboxes, points = detector.detect_face(img)
t2 = time.clock()
ttl_det_time += t2 - t1
print("detect_face() costs %f seconds" % (t2 - t1))
else:
print '---> Will not do detection because of option "--no_detect"'
shp = img.shape
rect = [0, 0, shp[1] - 1, shp[0] - 1, 1.0]
bboxes = [rect]
points = [None]
n_faces = 0
if bboxes is not None:
n_faces = len(bboxes)
if n_faces > 0:
for (box, pts) in zip(bboxes, points):
# box = box.tolist()
# pts = pts.tolist()
tmp = {'rect': box[0:4], 'score': box[4], 'pts': pts}
rlt['faces'].append(tmp)
rlt['face_count'] = n_faces
# print('output bboxes: ' + str(bboxes))
# print('output points: ' + str(points))
# toc()
if do_detect:
print(
"\n===> Detect %d images, costs %f seconds, avg time: %f seconds"
% (img_cnt, ttl_det_time, ttl_det_time / img_cnt))
print "---> %d faces detected" % n_faces
if not n_faces:
continue
t1 = time.clock()
if do_align:
if points is None or points[0] is None:
face_chips = aligner.get_face_chips(img, bboxes, None)
else:
face_chips = aligner.get_face_chips(img, bboxes, points)
# face_chips = aligner.get_face_chips(img, bboxes, None)
# face_chips = [im.astype(np.float) for im in face_chips_ubyte]
else:
print '---> Will not do alignment because of option "--no_align"'
face_chips = [img.astype(np.float)]
features = feature_extractor.extract_features_batch(
face_chips)[feat_layer]
t2 = time.clock()
ttl_feat_time += t2 - t1
print("Cropping and extracting features for %d faces cost %f seconds" %
(n_faces, t2 - t1))
faces_cnt += n_faces
print(
"\n===> Extracting features for %d faces, costs %f seconds, avg time: %f seconds"
% (faces_cnt, ttl_feat_time, ttl_feat_time / faces_cnt))
for i, box in enumerate(bboxes):
# feat_file = '%s_%d_rect[%d_%d_%d_%d].npy' % (
# osp.basename(img_path), i, box[0], box[1], box[2], box[3])
# feat_file = osp.join(save_dir, feat_file)
# np.save(feat_file, features[i])
base_name = osp.basename(img_path)
face_fn_prefix = '%s_face_%d' % (osp.splitext(base_name)[0], i)
feat_file = face_fn_prefix + '.npy'
np.save(osp.join(save_dir, feat_file), features[i])
face_chip_fn = face_fn_prefix + '.jpg'
cv2.imwrite(osp.join(save_dir, face_chip_fn), face_chips[i])
rlt['faces'][i]['feat'] = feat_file
rlt['faces'][i]['face_chip'] = face_chip_fn
rlt['message'] = 'success'
# result_list.append(rlt)
json_str = json.dumps(rlt, indent=2)
fp_rlt.write(json_str)
fp_rlt.flush()
if save_img or show_img:
draw_faces(img, bboxes, points)
if save_img:
save_name = osp.join(save_dir, osp.basename(img_path))
cv2.imwrite(save_name, img)
if show_img:
cv2.imshow('img', img)
ch = cv2.waitKey(0) & 0xFF
if ch == 27:
break
#json.dump(result_list, fp_rlt, indent=4)
fp_rlt.write('\n]\n')
fp_rlt.close()
fp.close()
if show_img:
cv2.destroyAllWindows()
def main(json_file, save_dir=None, save_img=True, show_img=True):
if not osp.exists(json_file):
print 'Cannot find json file: ' + json_file
pass
if save_dir is None:
save_dir = './fa_facex_rlt'
save_json = 'mtcnn_align_rlt.json'
model_path = "../../model"
fp_json = open(json_file, 'r')
facex_response = json.load(fp_json)
fp_json.close()
if (not facex_response or not isinstance(facex_response, dict)
or 'facex_det' not in facex_response):
print 'Invalid json file: ' + json_file
pass
facex_det_response = facex_response['facex_det']
if not osp.exists(save_dir):
os.makedirs(save_dir)
fp_rlt = open(osp.join(save_dir, save_json), 'w')
results = []
for item in facex_det_response:
img_path = item['name']
print '===> Processing image: ' + img_path
if 'detections' not in item:
continue
face_rects = []
for face in item['detections']:
face_rects.append(face['pts'])
img = cv2.imread(img_path)
aligner = FaceAligner(model_path, False)
rlt = {}
rlt["filename"] = img_path
rlt["faces"] = []
rlt['face_count'] = 0
t1 = time.clock()
bboxes, points = aligner.align_face(img, face_rects)
t2 = time.clock()
n_boxes = len(face_rects)
print(
"-->Alignment cost %f seconds, processed %d face rects, avg time: %f seconds"
% ((t2 - t1), n_boxes, (t2 - t1) / n_boxes))
if bboxes is not None and len(bboxes) > 0:
for (box, pts) in zip(bboxes, points):
# box = box.tolist()
# pts = pts.tolist()
tmp = {'rect': box[0:4], 'score': box[4], 'pts': pts}
rlt['faces'].append(tmp)
rlt['face_count'] = len(bboxes)
rlt['message'] = 'success'
results.append(rlt)
spl = osp.split(img_path)
sub_dir = osp.split(spl[0])[1]
base_name = spl[1]
save_img_subdir = osp.join(save_dir, sub_dir)
if not osp.exists(save_img_subdir):
os.mkdir(save_img_subdir)
# save_rect_subdir = osp.join(save_dir, sub_dir)
# if not osp.exists(save_rect_subdir):
# os.mkdir(save_rect_subdir)
# print pts
save_img_fn = osp.join(save_img_subdir, base_name)
print 'save face chip into ', save_img_fn
# facial5points = np.reshape(pts, (2, -1))
# dst_img = warp_and_crop_face(
# img, facial5points, reference_5pts, output_size)
dst_img = aligner.get_face_chips(img, [box], [pts], True)[0]
cv2.imwrite(save_img_fn, dst_img)
json.dump(results, fp_rlt, indent=2)
fp_rlt.close()
