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Diffstat (limited to 'megapixels/commands/cv/face_attributes.py')
| -rw-r--r-- | megapixels/commands/cv/face_attributes.py | 136 |
1 files changed, 0 insertions, 136 deletions
diff --git a/megapixels/commands/cv/face_attributes.py b/megapixels/commands/cv/face_attributes.py deleted file mode 100644 index 01fe3bd1..00000000 --- a/megapixels/commands/cv/face_attributes.py +++ /dev/null @@ -1,136 +0,0 @@ -""" - -""" - -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=cfg.DEFAULT_SIZE_FACE_DETECT, - help='Processing size for detection') -@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): - """Creates 2D 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.processors import face_age_gender - from app.models.data_store import DataStore - from app.models.bbox import BBox - - # ------------------------------------------------------------------------- - # init here - - log = logger_utils.Logger.getLogger() - # init face processors - age_estimator_apnt = face_age_gender.FaceAgeApparent() - age_estimator_real = face_age_gender.FaceAgeReal() - gender_estimator = face_age_gender.FaceGender() - - # init filepaths - data_store = DataStore(opt_data_store, opt_dataset) - # set file output path - metadata_type = types.Metadata.FACE_ATTRIBUTES - 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 - - # ------------------------------------------------------------------------- - # load filepath 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 landmarks in list - results = [] - - # ------------------------------------------------------------------------- - # iterate groups with file/record index as key - - for record_index, df_img_group in tqdm(df_img_groups): - - # access file_record DataSeries - file_record = df_record.iloc[record_index] - - # 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]) - dim = im_resized.shape[:2][::-1] - - # iterate ROIs in this image - for roi_index, df_img in df_img_group.iterrows(): - - # find landmarks - bbox_norm = BBox.from_xywh(df_img.x, df_img.y, df_img.w, df_img.h) - bbox_dim = bbox_norm.to_dim(dim) - - age_apnt = age_estimator_apnt.predict(im_resized, bbox_norm) - age_real = age_estimator_real.predict(im_resized, bbox_norm) - gender = gender_estimator.predict(im_resized, bbox_norm) - - attr_obj = { - 'age_real':float(f'{age_real:.2f}'), - 'age_apparent': float(f'{age_apnt:.2f}'), - 'm': float(f'{gender["m"]:.4f}'), - 'f': float(f'{gender["f"]:.4f}'), - 'roi_index': roi_index - } - results.append(attr_obj) - - - # 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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