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"""
Converts ROIs to pose: yaw, roll, pitch
"""
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('--data_store', 'opt_data_store',
type=cfg.DataStoreVar,
default=click_utils.get_default(types.DataStore.SSD),
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
from app.processors.face_landmarks import LandmarksDLIB
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 = LandmarksDLIB()
# load filepath data
fp_record = data_store.metadata(types.Metadata.FILE_RECORD)
df_record = pd.read_csv(fp_record).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
poses = []
# iterate
for record_index, df_img_group in tqdm(df_img_groups):
# make fp
ds_record = df_record.iloc[record_index]
fp_im = data_store.face(ds_record.subdir, ds_record.fn, ds_record.ext)
im = cv.imread(fp_im)
# get bbox
x = df_img_group.x.values[0]
y = df_img_group.y.values[0]
w = df_img_group.w.values[0]
h = df_img_group.h.values[0]
dim = im.shape[:2][::-1]
bbox = BBox.from_xywh(x, y, w, h).to_dim(dim)
# get pose
landmarks = face_landmarks.landmarks(im, bbox)
pose_data = face_pose.pose(landmarks, dim, project_points=opt_display)
pose_degrees = pose_data['degrees'] # only keep the degrees data
# use the project point data if display flag set
if opt_display:
pts_im = pose_data['points_image']
pts_model = pose_data['points_model']
pt_nose = pose_data['point_nose']
dst = im.copy()
face_pose.draw_pose(dst, pts_im, pts_model, pt_nose)
face_pose.draw_degrees(dst, pose_degrees)
# display to cv window
cv.imshow('', dst)
while True:
k = cv.waitKey(1) & 0xFF
if k == 27 or k == ord('q'): # ESC
cv.destroyAllWindows()
sys.exit()
elif k != 255:
# any key to continue
break
# add image index and append to result CSV data
pose_degrees['record_index'] = record_index
poses.append(pose_degrees)
# save date
file_utils.mkdirs(fp_out)
df = pd.DataFrame.from_dict(poses)
df.index.name = 'index'
df.to_csv(fp_out)
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