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"""
Crop images to prepare for training
"""
import click
# from PIL import Image, ImageOps, ImageFilter, ImageDraw
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('-f', '--files', 'opt_fp_files', required=True,
help='Input ROI CSV')
@click.option('-r', '--rois', 'opt_fp_rois', required=True,
help='Input ROI CSV')
@click.option('-m', '--media', 'opt_dir_media', required=True,
help='Input media directory')
@click.option('-o', '--output', 'opt_fp_out', required=True,
help='Output CSV')
@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.pass_context
def cli(ctx, opt_fp_files, opt_fp_rois, opt_dir_media, opt_fp_out, opt_size,
opt_slice, opt_force):
"""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
# -------------------------------------------------
# init here
log = logger_utils.Logger.getLogger()
# init face processors
face_pose = FacePoseDLIB()
face_landmarks = LandmarksDLIB()
df_files = pd.read_csv(opt_fp_files)
df_rois = pd.read_csv(opt_fp_rois)
if not opt_force and Path(opt_fp_out).exists():
log.error('File exists. Use "-f / --force" to overwite')
return
if opt_slice:
df_rois = df_rois[opt_slice[0]:opt_slice[1]]
# -------------------------------------------------
# process here
df_roi_groups = df_rois.groupby('index')
log.debug('processing {:,} groups'.format(len(df_roi_groups)))
poses = []
#for df_roi_group in tqdm(df_roi_groups.itertuples(), total=len(df_roi_groups)):
for df_roi_group_idx, df_roi_group in tqdm(df_roi_groups):
# make fp
image_index = df_roi_group.image_index.values[0]
pds_file = df_files.iloc[image_index]
fp_im = join(opt_dir_media, pds_file.subdir, '{}.{}'.format(pds_file.fn, pds_file.ext))
im = cv.imread(fp_im)
# get bbox
x = df_roi_group.x.values[0]
y = df_roi_group.y.values[0]
w = df_roi_group.w.values[0]
h = df_roi_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 = face_pose.pose(landmarks, dim)
pose['image_index'] = image_index
poses.append(pose)
# save date
file_utils.mkdirs(opt_fp_out)
df = pd.DataFrame.from_dict(poses)
df.index.name = 'index'
df.to_csv(opt_fp_out)
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