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make_json.py
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make_json.py
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import os
import random
import numpy as np
import json
import traceback
from tqdm import tqdm
'''
i decide to merge more data from CelebA, the data anns will be complex, so json maybe a better way.
'''
data_dir = 'H:/datasets/300W_All_Orig' ########points to your director,300w
train_json = 'train.json'
val_json = 'val.json'
img_size = 160
eye_close_thres = 0.02
mouth_close_thres = 0.02
big_mouth_open_thres = 0.08
def GetFileList(dir, fileList):
newDir = dir
if os.path.isfile(dir):
fileList.append(dir)
elif os.path.isdir(dir):
for s in os.listdir(dir):
# if s == "pts":
# continue
newDir = os.path.join(dir, s)
GetFileList(newDir, fileList)
return fileList
pic_list = []
GetFileList(data_dir, pic_list)
pic_list = [x for x in pic_list if '.jpg' in x or 'png' in x or 'jpeg' in x]
random.shuffle(pic_list)
ratio = 0.95
train_list = pic_list[:int(ratio * len(pic_list))]
val_list = pic_list[int(ratio * len(pic_list)):]
train_json_list = []
for pic in tqdm(train_list):
one_image_ann = {}
one_image_ann['image_path'] = pic
pts = pic.rsplit('.', 1)[0] + '.pts'
if os.access(pic, os.F_OK) and os.access(pts, os.F_OK):
try:
tmp = []
with open(pts) as p_f:
labels = p_f.readlines()[3:-1]
for _one_p in labels:
xy = _one_p.rstrip().split(' ')
tmp.append([float(xy[0]), float(xy[1])])
one_image_ann['keypoints'] = tmp
label = np.array(tmp).reshape((-1, 2))
bbox = [float(np.min(label[:, 0])), float(np.min(label[:, 1])), float(np.max(label[:, 0])),
float(np.max(label[:, 1]))]
bbox_width = bbox[2] - bbox[0]
bbox_height = bbox[3] - bbox[1]
left_eye_close = np.sqrt(
np.square(label[37, 0] - label[41, 0]) +
np.square(label[37, 1] - label[41, 1])) / bbox_height < eye_close_thres \
or np.sqrt(np.square(label[38, 0] - label[40, 0]) +
np.square(label[38, 1] - label[40, 1])) / bbox_height < eye_close_thres
right_eye_close = np.sqrt(
np.square(label[43, 0] - label[47, 0]) +
np.square(label[43, 1] - label[47, 1])) / bbox_height < eye_close_thres \
or np.sqrt(np.square(label[44, 0] - label[46, 0]) +
np.square(
label[44, 1] - label[46, 1])) / bbox_height < eye_close_thres
small_eye_distance = np.sqrt(np.square(label[36, 0] - label[45, 0]) +
np.square(label[36, 1] - label[45, 1])) / bbox_width < 0.5
small_mouth_open = np.sqrt(np.square(label[62, 0] - label[66, 0]) +
np.square(label[62, 1] - label[66, 1])) / bbox_height > 0.15
big_mouth_open = np.sqrt(np.square(label[62, 0] - label[66, 0]) +
np.square(label[62, 1] - label[66, 1])) / img_size > big_mouth_open_thres
mouth_close = np.sqrt(np.square(label[61, 0] - label[67, 0]) +
np.square(label[61, 1] - label[67, 1])) / img_size < mouth_close_thres \
or np.sqrt(np.square(label[62, 0] - label[66, 0]) +
np.square(label[62, 1] - label[66, 1])) / img_size < mouth_close_thres \
or np.sqrt(np.square(label[63, 0] - label[65, 0]) +
np.square(label[63, 1] - label[65, 1])) / img_size < mouth_close_thres
one_image_ann['left_eye_close'] = bool(left_eye_close)
one_image_ann['right_eye_close'] = bool(right_eye_close)
one_image_ann['small_eye_distance'] = bool(small_eye_distance)
one_image_ann['small_mouth_open'] = bool(small_mouth_open)
one_image_ann['big_mouth_open'] = bool(big_mouth_open)
one_image_ann['mouth_close'] = bool(mouth_close)
one_image_ann['bbox'] = bbox
one_image_ann['attr'] = None
train_json_list.append(one_image_ann)
except:
print(pic)
traceback.print_exc()
with open(train_json, 'w') as f:
json.dump(train_json_list, f, indent=2)
val_json_list = []
for pic in tqdm(val_list):
one_image_ann = {}
### image_path
one_image_ann['image_path'] = pic
#### keypoints
pts = pic.rsplit('.', 1)[0] + '.pts'
if os.access(pic, os.F_OK) and os.access(pts, os.F_OK):
try:
tmp = []
with open(pts) as p_f:
labels = p_f.readlines()[3:-1]
for _one_p in labels:
xy = _one_p.rstrip().split(' ')
tmp.append([float(xy[0]), float(xy[1])])
one_image_ann['keypoints'] = tmp
label = np.array(tmp).reshape((-1, 2))
bbox = [float(np.min(label[:, 0])), float(np.min(label[:, 1])), float(np.max(label[:, 0])),
float(np.max(label[:, 1]))]
bbox_width = bbox[2] - bbox[0]
bbox_height = bbox[3] - bbox[1]
left_eye_close = np.sqrt(
np.square(label[37, 0] - label[41, 0]) +
np.square(label[37, 1] - label[41, 1])) / bbox_height < eye_close_thres \
or np.sqrt(np.square(label[38, 0] - label[40, 0]) +
np.square(label[38, 1] - label[40, 1])) / bbox_height < eye_close_thres
right_eye_close = np.sqrt(
np.square(label[43, 0] - label[47, 0]) +
np.square(label[43, 1] - label[47, 1])) / bbox_height < eye_close_thres \
or np.sqrt(np.square(label[44, 0] - label[46, 0]) +
np.square(
label[44, 1] - label[46, 1])) / bbox_height < eye_close_thres
small_eye_distance = np.sqrt(np.square(label[36, 0] - label[45, 0]) +
np.square(label[36, 1] - label[45, 1])) / bbox_width < 0.5
small_mouth_open = np.sqrt(np.square(label[62, 0] - label[66, 0]) +
np.square(label[62, 1] - label[66, 1])) / bbox_height > 0.15
big_mouth_open = np.sqrt(np.square(label[62, 0] - label[66, 0]) +
np.square(label[62, 1] - label[66, 1])) / img_size > big_mouth_open_thres
mouth_close = np.sqrt(np.square(label[61, 0] - label[67, 0]) +
np.square(label[61, 1] - label[67, 1])) / img_size < mouth_close_thres \
or np.sqrt(np.square(label[62, 0] - label[66, 0]) +
np.square(label[62, 1] - label[66, 1])) / img_size < mouth_close_thres \
or np.sqrt(np.square(label[63, 0] - label[65, 0]) +
np.square(label[63, 1] - label[65, 1])) / img_size < mouth_close_thres
one_image_ann['left_eye_close'] = bool(left_eye_close)
one_image_ann['right_eye_close'] = bool(right_eye_close)
one_image_ann['small_eye_distance'] = bool(small_eye_distance)
one_image_ann['small_mouth_open'] = bool(small_mouth_open)
one_image_ann['big_mouth_open'] = bool(big_mouth_open)
one_image_ann['mouth_close'] = bool(mouth_close)
one_image_ann['bbox'] = bbox
### placeholder
one_image_ann['attr'] = None
val_json_list.append(one_image_ann)
except:
print(pic)
with open(val_json, 'w') as f:
json.dump(val_json_list, f, indent=2)