1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
|
"""
NB: This only works with the DLIB 68-point landmarks.
Converts ROIs to pose: yaw, roll, pitch
pitch: looking down or up in yes gesture
roll: tilting head towards shoulder
yaw: twisting head left to right in no gesture
"""
"""
TODO
- check compatibility with MTCNN 68 point detector
- improve accuracy by using MTCNN 5-point
- refer to https://github.com/jerryhouuu/Face-Yaw-Roll-Pitch-from-Pose-Estimation-using-OpenCV/
"""
import click
from app.settings import types
from app.utils import click_utils
from app.settings import app_cfg as cfg
@click.command()
@click.option('-i', '--input', 'opt_fp_in', default=None,
help='Override enum input filename CSV')
@click.option('-o', '--output', 'opt_fp_out', default=None,
help='Override enum output filename CSV')
@click.option('-m', '--media', 'opt_dir_media', default=None,
help='Override enum media directory')
@click.option('--store', 'opt_data_store',
type=cfg.DataStoreVar,
default=click_utils.get_default(types.DataStore.HDD),
show_default=True,
help=click_utils.show_help(types.Dataset))
@click.option('--dataset', 'opt_dataset',
type=cfg.DatasetVar,
required=True,
show_default=True,
help=click_utils.show_help(types.Dataset))
@click.option('--size', 'opt_size',
type=(int, int), default=(300, 300),
help='Output image size')
@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
help='Slice list of files')
@click.option('-f', '--force', 'opt_force', is_flag=True,
help='Force overwrite file')
@click.option('-d', '--display', 'opt_display', is_flag=True,
help='Display image for debugging')
@click.pass_context
def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, opt_size,
opt_slice, opt_force, opt_display):
"""Converts ROIs to pose: roll, yaw, pitch"""
import sys
import os
from os.path import join
from pathlib import Path
from glob import glob
from tqdm import tqdm
import numpy as np
import dlib # must keep a local reference for dlib
import cv2 as cv
import pandas as pd
from app.models.bbox import BBox
from app.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils
from app.processors.face_landmarks import Dlib2D_68
from app.processors.face_pose import FacePoseDLIB
from app.models.data_store import DataStore
# -------------------------------------------------
# init here
log = logger_utils.Logger.getLogger()
# set data_store
data_store = DataStore(opt_data_store, opt_dataset)
# get filepath out
fp_out = data_store.metadata(types.Metadata.FACE_POSE) if opt_fp_out is None else opt_fp_out
if not opt_force and Path(fp_out).exists():
log.error('File exists. Use "-f / --force" to overwite')
return
# init face processors
face_pose = FacePoseDLIB()
face_landmarks = Dlib2D_68()
# -------------------------------------------------
# load data
fp_record = data_store.metadata(types.Metadata.FILE_RECORD)
df_record = pd.read_csv(fp_record, dtype=cfg.FILE_RECORD_DTYPES).set_index('index')
# load ROI data
fp_roi = data_store.metadata(types.Metadata.FACE_ROI)
df_roi = pd.read_csv(fp_roi).set_index('index')
# slice if you want
if opt_slice:
df_roi = df_roi[opt_slice[0]:opt_slice[1]]
# group by image index (speedup if multiple faces per image)
df_img_groups = df_roi.groupby('record_index')
log.debug('processing {:,} groups'.format(len(df_img_groups)))
# store poses and convert to DataFrame
results = []
# -------------------------------------------------
# iterate groups with file/record index as key
for record_index, df_img_group in tqdm(df_img_groups):
# access the file_record
file_record = df_record.iloc[record_index] # pands.DataSeries
# load image
fp_im = data_store.face(file_record.subdir, file_record.fn, file_record.ext)
im = cv.imread(fp_im)
im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
# iterate image group dataframe with roi index as key
for roi_index, df_img in df_img_group.iterrows():
# get bbox
x, y, w, h = df_img.x, df_img.y, df_img.w, df_img.h
#dim = (file_record.width, file_record.height)
dim = im_resized.shape[:2][::-1]
bbox_norm = BBox.from_xywh(x, y, w, h)
bbox_dim = bbox_norm.to_dim(dim)
# get pose
landmarks = face_landmarks.landmarks(im_resized, bbox_norm)
pose_data = face_pose.pose(landmarks, dim)
#pose_degrees = pose_data['degrees'] # only keep the degrees data
#pose_degrees['points_nose'] = pose_data
# draw landmarks if optioned
if opt_display:
draw_utils.draw_pose(im_resized, pose_data['point_nose'], pose_data['points'])
draw_utils.draw_degrees(im_resized, pose_data)
cv.imshow('', im_resized)
display_utils.handle_keyboard()
# add image index and append to result CSV data
pose_data['roi_index'] = roi_index
for k, v in pose_data['points'].items():
pose_data[f'point_{k}_x'] = v[0] / dim[0]
pose_data[f'point_{k}_y'] = v[1] / dim[1]
# rearrange data structure for DataFrame
pose_data.pop('points')
pose_data['point_nose_x'] = pose_data['point_nose'][0] / dim[0]
pose_data['point_nose_y'] = pose_data['point_nose'][1] / dim[1]
pose_data.pop('point_nose')
results.append(pose_data)
# create DataFrame and save to CSV
file_utils.mkdirs(fp_out)
df = pd.DataFrame.from_dict(results)
df.index.name = 'index'
df.to_csv(fp_out)
# save script
file_utils.write_text(' '.join(sys.argv), '{}.sh'.format(fp_out))
|
