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Diffstat (limited to 'megapixels/commands/cv/face_vector_mt.py')
| -rw-r--r-- | megapixels/commands/cv/face_vector_mt.py | 118 |
1 files changed, 0 insertions, 118 deletions
diff --git a/megapixels/commands/cv/face_vector_mt.py b/megapixels/commands/cv/face_vector_mt.py deleted file mode 100644 index 412f9806..00000000 --- a/megapixels/commands/cv/face_vector_mt.py +++ /dev/null @@ -1,118 +0,0 @@ -""" -Converts ROIs to face vector -""" - -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('-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('-j', '--jitters', 'opt_jitters', default=cfg.DLIB_FACEREC_JITTERS, - help='Number of jitters') -@click.option('-p', '--padding', 'opt_padding', default=cfg.DLIB_FACEREC_PADDING, - help='Percentage padding') -@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('-g', '--gpu', 'opt_gpu', default=0, - help='GPU index') -@click.pass_context -def cli(ctx, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, opt_size, - opt_slice, opt_force, opt_gpu, opt_jitters, opt_padding): - """Converts face ROIs to vectors""" - - 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.models.data_store import DataStore - from app.utils import logger_utils, file_utils, im_utils - from app.processors import face_recognition - - - # ------------------------------------------------- - # 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_VECTOR) 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 - facerec = face_recognition.RecognitionDLIB() - - # load data - fp_record = data_store.metadata(types.Metadata.FILE_RECORD) - df_record = pd.read_csv(fp_record).set_index('index') - fp_roi = data_store.metadata(types.Metadata.FACE_ROI) - df_roi = pd.read_csv(fp_roi).set_index('index') - - if opt_slice: - df_roi = df_roi[opt_slice[0]:opt_slice[1]] - - # ------------------------------------------------- - # process here - df_img_groups = df_roi.groupby('record_index') - log.debug('processing {:,} groups'.format(len(df_img_groups))) - - vecs = [] - 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) - 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 - imw = df_img.image_width - imh = df_img.image_height - dim = im.shape[:2][::-1] - # get face vector - dim = (imw, imh) - bbox_dim = BBox.from_xywh(x, y, w, h).to_dim(dim) # convert to int real dimensions - # compute vec - # padding=opt_padding not yet implemented in 19.16 but merged in master - vec = facerec.vec(im, bbox_dim, jitters=opt_jitters) - vec_str = ','.join([repr(x) for x in vec]) # convert to string for CSV - vecs.append( {'roi_index': roi_index, 'record_index': record_index, 'vec': vec_str}) - - - # save date - df = pd.DataFrame.from_dict(vecs) - df.index.name = 'index' - file_utils.mkdirs(fp_out) - df.to_csv(fp_out)
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