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"""
"""
import click
from app.settings import types
from app.utils import click_utils
from app.settings import app_cfg as cfg
color_filters = {'color': 1, 'gray': 2, 'all': 3}
@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('-d', '--detector', 'opt_detector_type',
type=cfg.FaceLandmark3D_68Var,
default=click_utils.get_default(types.FaceLandmark3D_68.FACE_ALIGNMENT),
help=click_utils.show_help(types.FaceLandmark3D_68))
@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_detector_type,
opt_size, opt_slice, opt_force, opt_display):
"""Generate 3D 68-point landmarks"""
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 cv2 as cv
import pandas as pd
from app.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils
from app.utils import plot_utils
from app.processors import face_landmarks
from app.models.data_store import DataStore
from app.models.bbox import BBox
# --------------------------------------------------------------------------
# init here
log = logger_utils.Logger.getLogger()
log.warn('not normalizing points')
# init filepaths
data_store = DataStore(opt_data_store, opt_dataset)
# set file output path
metadata_type = types.Metadata.FACE_LANDMARK_3D_68
fp_out = data_store.metadata(metadata_type) 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 landmark processors
if opt_detector_type == types.FaceLandmark3D_68.FACE_ALIGNMENT:
# use FaceAlignment 68 point 3D detector
landmark_detector = face_landmarks.FaceAlignment3D_68()
else:
log.error('{} not yet implemented'.format(opt_detector_type.name))
return
log.info(f'Using landmark detector: {opt_detector_type.name}')
# -------------------------------------------------------------------------
# load data
fp_record = data_store.metadata(types.Metadata.FILE_RECORD) # file_record.csv
df_record = pd.read_csv(fp_record).set_index('index')
fp_roi = data_store.metadata(types.Metadata.FACE_ROI) # face_roi.csv
df_roi = pd.read_csv(fp_roi).set_index('index')
if opt_slice:
df_roi = df_roi[opt_slice[0]:opt_slice[1]] # slice if you want
df_img_groups = df_roi.groupby('record_index') # groups by image index (load once)
log.debug('processing {:,} groups'.format(len(df_img_groups)))
# store landmarks in list
results = []
# iterate groups with file/record index as key
for record_index, df_img_group in tqdm(df_img_groups):
# acces file record
ds_record = df_record.iloc[record_index]
# load image
fp_im = data_store.face(ds_record.subdir, ds_record.fn, ds_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 = im_resized.shape[:2][::-1]
bbox = BBox.from_xywh(x, y, w, h).to_dim(dim)
# get landmark points
points = landmark_detector.landmarks(im_resized, bbox)
# NB can't really normalize these points, but are normalized against 3D space
#points_norm = landmark_detector.normalize(points, dim) # normalized using 200
points_flattenend = landmark_detector.flatten(points)
# display to screen if optioned
if opt_display:
draw_utils.draw_landmarks3D(im_resized, points)
draw_utils.draw_bbox(im_resized, bbox)
cv.imshow('', im_resized)
display_utils.handle_keyboard()
#plot_utils.generate_3d_landmark_anim(points, '/home/adam/Downloads/3d.gif')
results.append(points_flattenend)
# 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))
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