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face_image.py
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face_image.py
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from easydict import EasyDict as edict
import os
import json
import numpy as np
def load_property(data_dir):
prop = edict()
for line in open(os.path.join(data_dir, 'property')):
vec = line.strip().split(',')
assert len(vec)==3
prop.num_classes = int(vec[0])
prop.image_size = [int(vec[1]), int(vec[2])]
return prop
def get_dataset_webface(input_dir):
clean_list_file = input_dir+"_clean_list.txt"
ret = []
for line in open(clean_list_file, 'r'):
vec = line.strip().split()
assert len(vec)==2
fimage = edict()
fimage.id = vec[0].replace("\\", '/')
fimage.classname = vec[1]
fimage.image_path = os.path.join(input_dir, fimage.id)
ret.append(fimage)
return ret
def get_dataset_celeb(input_dir):
clean_list_file = input_dir+"_clean_list.txt"
ret = []
dir2label = {}
for line in open(clean_list_file, 'r'):
line = line.strip()
if not line.startswith('./m.'):
continue
line = line[2:]
vec = line.split('/')
assert len(vec)==2
if vec[0] in dir2label:
label = dir2label[vec[0]]
else:
label = len(dir2label)
dir2label[vec[0]] = label
fimage = edict()
fimage.id = line
fimage.classname = str(label)
fimage.image_path = os.path.join(input_dir, fimage.id)
ret.append(fimage)
return ret
def _get_dataset_celeb(input_dir):
list_file = input_dir+"_original_list.txt"
ret = []
for line in open(list_file, 'r'):
vec = line.strip().split()
assert len(vec)==2
fimage = edict()
fimage.id = vec[0]
fimage.classname = vec[1]
fimage.image_path = os.path.join(input_dir, fimage.id)
ret.append(fimage)
return ret
def get_dataset_facescrub(input_dir):
ret = []
label = 0
person_names = []
for person_name in os.listdir(input_dir):
person_names.append(person_name)
person_names = sorted(person_names)
for person_name in person_names:
subdir = os.path.join(input_dir, person_name)
if not os.path.isdir(subdir):
continue
for _img in os.listdir(subdir):
fimage = edict()
fimage.id = os.path.join(person_name, _img)
fimage.classname = str(label)
fimage.image_path = os.path.join(subdir, _img)
fimage.landmark = None
fimage.bbox = None
ret.append(fimage)
label += 1
return ret
def get_dataset_megaface(input_dir):
ret = []
label = 0
for prefixdir in os.listdir(input_dir):
_prefixdir = os.path.join(input_dir, prefixdir)
for subdir in os.listdir(_prefixdir):
_subdir = os.path.join(_prefixdir, subdir)
if not os.path.isdir(_subdir):
continue
for img in os.listdir(_subdir):
if not img.endswith('.jpg.jpg') and img.endswith('.jpg'):
fimage = edict()
fimage.id = os.path.join(prefixdir, subdir, img)
fimage.classname = str(label)
fimage.image_path = os.path.join(_subdir, img)
json_file = fimage.image_path+".json"
data = None
fimage.bbox = None
fimage.landmark = None
if os.path.exists(json_file):
with open(json_file, 'r') as f:
data = f.read()
data = json.loads(data)
assert data is not None
if 'bounding_box' in data:
fimage.bbox = np.zeros( (4,), dtype=np.float32 )
bb = data['bounding_box']
fimage.bbox[0] = bb['x']
fimage.bbox[1] = bb['y']
fimage.bbox[2] = bb['x']+bb['width']
fimage.bbox[3] = bb['y']+bb['height']
#print('bb')
if 'landmarks' in data:
landmarks = data['landmarks']
if '1' in landmarks and '0' in landmarks and '2' in landmarks:
fimage.landmark = np.zeros( (3,2), dtype=np.float32 )
fimage.landmark[0][0] = landmarks['1']['x']
fimage.landmark[0][1] = landmarks['1']['y']
fimage.landmark[1][0] = landmarks['0']['x']
fimage.landmark[1][1] = landmarks['0']['y']
fimage.landmark[2][0] = landmarks['2']['x']
fimage.landmark[2][1] = landmarks['2']['y']
#print('lm')
ret.append(fimage)
label+=1
return ret
def get_dataset_fgnet(input_dir):
ret = []
label = 0
for subdir in os.listdir(input_dir):
_subdir = os.path.join(input_dir, subdir)
if not os.path.isdir(_subdir):
continue
for img in os.listdir(_subdir):
if img.endswith('.JPG'):
fimage = edict()
fimage.id = os.path.join(_subdir, img)
fimage.classname = str(label)
fimage.image_path = os.path.join(_subdir, img)
json_file = fimage.image_path+".json"
data = None
fimage.bbox = None
fimage.landmark = None
if os.path.exists(json_file):
with open(json_file, 'r') as f:
data = f.read()
data = json.loads(data)
assert data is not None
if 'bounding_box' in data:
fimage.bbox = np.zeros( (4,), dtype=np.float32 )
bb = data['bounding_box']
fimage.bbox[0] = bb['x']
fimage.bbox[1] = bb['y']
fimage.bbox[2] = bb['x']+bb['width']
fimage.bbox[3] = bb['y']+bb['height']
#print('bb')
if 'landmarks' in data:
landmarks = data['landmarks']
if '1' in landmarks and '0' in landmarks and '2' in landmarks:
fimage.landmark = np.zeros( (3,2), dtype=np.float32 )
fimage.landmark[0][0] = landmarks['1']['x']
fimage.landmark[0][1] = landmarks['1']['y']
fimage.landmark[1][0] = landmarks['0']['x']
fimage.landmark[1][1] = landmarks['0']['y']
fimage.landmark[2][0] = landmarks['2']['x']
fimage.landmark[2][1] = landmarks['2']['y']
#print('lm')
#fimage.landmark = None
ret.append(fimage)
label+=1
return ret
def get_dataset_ytf(input_dir):
ret = []
label = 0
person_names = []
for person_name in os.listdir(input_dir):
person_names.append(person_name)
person_names = sorted(person_names)
for person_name in person_names:
_subdir = os.path.join(input_dir, person_name)
if not os.path.isdir(_subdir):
continue
for _subdir2 in os.listdir(_subdir):
_subdir2 = os.path.join(_subdir, _subdir2)
if not os.path.isdir(_subdir2):
continue
_ret = []
for img in os.listdir(_subdir2):
fimage = edict()
fimage.id = os.path.join(_subdir2, img)
fimage.classname = str(label)
fimage.image_path = os.path.join(_subdir2, img)
fimage.bbox = None
fimage.landmark = None
_ret.append(fimage)
ret += _ret
label+=1
return ret
def get_dataset_clfw(input_dir):
ret = []
label = 0
for img in os.listdir(input_dir):
fimage = edict()
fimage.id = img
fimage.classname = str(0)
fimage.image_path = os.path.join(input_dir, img)
fimage.bbox = None
fimage.landmark = None
ret.append(fimage)
return ret
def get_dataset_common(input_dir, min_images = 1):
ret = []
label = 0
person_names = []
for person_name in os.listdir(input_dir):
person_names.append(person_name)
person_names = sorted(person_names)
for person_name in person_names:
_subdir = os.path.join(input_dir, person_name)
if not os.path.isdir(_subdir):
continue
_ret = []
for img in os.listdir(_subdir):
fimage = edict()
fimage.id = os.path.join(person_name, img)
fimage.classname = str(label)
fimage.image_path = os.path.join(_subdir, img)
fimage.bbox = None
fimage.landmark = None
_ret.append(fimage)
if len(_ret)>=min_images:
ret += _ret
label+=1
return ret
def get_dataset(name, input_dir):
if name=='webface' or name=='lfw' or name=='vgg':
return get_dataset_common(input_dir)
if name=='celeb':
return get_dataset_celeb(input_dir)
if name=='facescrub':
return get_dataset_facescrub(input_dir)
if name=='megaface':
return get_dataset_megaface(input_dir)
if name=='fgnet':
return get_dataset_fgnet(input_dir)
if name=='ytf':
return get_dataset_ytf(input_dir)
if name=='clfw':
return get_dataset_clfw(input_dir)
return None