diff options
Diffstat (limited to 'megapixels/commands')
| -rw-r--r-- | megapixels/commands/datasets/megaface_age_from_orig.py | 62 | ||||
| -rw-r--r-- | megapixels/commands/demo/face_search.py | 2 | ||||
| -rw-r--r-- | megapixels/commands/processor/_old_files_to_face_rois.py | 2 | ||||
| -rw-r--r-- | megapixels/commands/processor/face_roi_from_annos.py | 187 | ||||
| -rw-r--r-- | megapixels/commands/processor/file_record.py (renamed from megapixels/commands/datasets/file_record.py) | 2 | ||||
| -rw-r--r-- | megapixels/commands/site/age_gender_to_site.py | 100 |
6 files changed, 352 insertions, 3 deletions
diff --git a/megapixels/commands/datasets/megaface_age_from_orig.py b/megapixels/commands/datasets/megaface_age_from_orig.py new file mode 100644 index 00000000..489bebf3 --- /dev/null +++ b/megapixels/commands/datasets/megaface_age_from_orig.py @@ -0,0 +1,62 @@ +import click + +@click.command() +@click.option('-i', '--input', 'opt_fp_in', required=True, + help='Input path to metadata directory') +@click.option('-o', '--output', 'opt_fp_out', + help='Output path to age CSV') +@click.pass_context +def cli(ctx, opt_fp_in, opt_fp_out): + """Creates CSV of MegaFace ages from original BBoxes""" + + import os + from os.path import join + from pathlib import Path + from glob import glob + + import dlib + import pandas as pd + from tqdm import tqdm + + from app.settings import types + from app.utils import click_utils + from app.settings import app_cfg + + from PIL import Image, ImageOps, ImageFilter + from app.utils import file_utils, im_utils, logger_utils + + log = logger_utils.Logger.getLogger() + + # ------------------------------------------------- + # process + fp_im_dirs = glob(join(opt_fp_in, '**/'), recursive=True) + + log.info('Found {} directories'.format(len(fp_im_dirs))) + + identities = {} + + for fp_im_dir in tqdm(fp_im_dirs): + # 1234567@N05_identity_1 + try: + dir_id_name = Path(fp_im_dir).name + nsid = dir_id_name.split('_')[0] + identity_num = dir_id_name.split('_')[2] + id_key = '{}_{}'.format(nsid, identity_num) + num_images = len(glob(join(fp_im_dir, '*.jpg'))) + if not id_key in identities.keys(): + identities[id_key] = {'nsid': nsid, 'identity': identity_num, 'images': num_images} + else: + identities[id_key]['images'] += num_images + except Exception as e: + continue + + # convert to dict + identities_list = [v for k, v in identities.items()] + df = pd.DataFrame.from_dict(identities_list) + + file_utils.mkdirs(opt_fp_out) + + log.info('Wrote {} lines to {}'.format(len(df), opt_fp_out)) + df.to_csv(opt_fp_out, index=False) + + diff --git a/megapixels/commands/demo/face_search.py b/megapixels/commands/demo/face_search.py index 4c7036f4..5218d501 100644 --- a/megapixels/commands/demo/face_search.py +++ b/megapixels/commands/demo/face_search.py @@ -10,7 +10,7 @@ log = Logger.getLogger() @click.command() @click.option('-i', '--input', 'opt_fp_in', required=True, - help='File to lookup') + help='Face image file to lookup') @click.option('--data_store', 'opt_data_store', type=cfg.DataStoreVar, default=click_utils.get_default(types.DataStore.HDD), diff --git a/megapixels/commands/processor/_old_files_to_face_rois.py b/megapixels/commands/processor/_old_files_to_face_rois.py index 895f4718..d92cbd74 100644 --- a/megapixels/commands/processor/_old_files_to_face_rois.py +++ b/megapixels/commands/processor/_old_files_to_face_rois.py @@ -1,4 +1,4 @@ - """ +""" Crop images to prepare for training """ diff --git a/megapixels/commands/processor/face_roi_from_annos.py b/megapixels/commands/processor/face_roi_from_annos.py new file mode 100644 index 00000000..fc933049 --- /dev/null +++ b/megapixels/commands/processor/face_roi_from_annos.py @@ -0,0 +1,187 @@ +""" +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('-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=(480, 480), + help='Output image size') +@click.option('-d', '--detector', 'opt_detector_type', + type=cfg.FaceDetectNetVar, + default=click_utils.get_default(types.FaceDetectNet.CVDNN), + help=click_utils.show_help(types.FaceDetectNet)) +@click.option('-g', '--gpu', 'opt_gpu', default=0, + help='GPU index') +@click.option('--conf', 'opt_conf_thresh', default=0.85, type=click.FloatRange(0,1), + help='Confidence minimum threshold') +@click.option('-p', '--pyramids', 'opt_pyramids', default=0, type=click.IntRange(0,4), + help='Number pyramids to upscale for DLIB detectors') +@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None), + help='Slice list of files') +@click.option('--display/--no-display', 'opt_display', is_flag=True, default=False, + help='Display detections to debug') +@click.option('-f', '--force', 'opt_force', is_flag=True, + help='Force overwrite file') +@click.option('--color', 'opt_color_filter', + type=click.Choice(color_filters.keys()), default='color', + help='Filter to keep color or grayscale images (color = keep color') +@click.option('--keep', 'opt_largest', type=click.Choice(['largest', 'all']), default='largest', + help='Only keep largest face') +@click.option('--zone', 'opt_zone', default=(0.0, 0.0), type=(float, float), + help='Face center must be located within zone region (0.5 = half width/height)') +@click.pass_context +def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_data_store, opt_dataset, opt_size, opt_detector_type, + opt_gpu, opt_conf_thresh, opt_pyramids, opt_slice, opt_display, opt_force, opt_color_filter, + opt_largest, opt_zone): + """Converts frames with faces to CSV of ROIs""" + + 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.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils + from app.processors import face_detector + 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_ROI) 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 + + # set detector + if opt_detector_type == types.FaceDetectNet.CVDNN: + detector = face_detector.DetectorCVDNN() + elif opt_detector_type == types.FaceDetectNet.DLIB_CNN: + detector = face_detector.DetectorDLIBCNN(gpu=opt_gpu) + elif opt_detector_type == types.FaceDetectNet.DLIB_HOG: + detector = face_detector.DetectorDLIBHOG() + elif opt_detector_type == types.FaceDetectNet.MTCNN_TF: + detector = face_detector.DetectorMTCNN_TF(gpu=opt_gpu) + elif opt_detector_type == types.FaceDetectNet.HAAR: + log.error('{} not yet implemented'.format(opt_detector_type.name)) + return + + + # get list of files to process + fp_record = data_store.metadata(types.Metadata.FILE_RECORD) if opt_fp_in is None else opt_fp_in + df_record = pd.read_csv(fp_record, dtype=cfg.FILE_RECORD_DTYPES).set_index('index') + if opt_slice: + df_record = df_record[opt_slice[0]:opt_slice[1]] + log.debug('processing {:,} files'.format(len(df_record))) + + # filter out grayscale + color_filter = color_filters[opt_color_filter] + # set largest flag, to keep all or only largest + opt_largest = (opt_largest == 'largest') + + data = [] + skipped_files = [] + processed_files = [] + + for df_record in tqdm(df_record.itertuples(), total=len(df_record)): + fp_im = data_store.face(str(df_record.subdir), str(df_record.fn), str(df_record.ext)) + try: + im = cv.imread(fp_im) + im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1]) + except Exception as e: + log.debug(f'could not read: {fp_im}') + return + # filter out color or grayscale iamges + if color_filter != color_filters['all']: + try: + is_gray = im_utils.is_grayscale(im) + if is_gray and color_filter != color_filters['gray']: + log.debug('Skipping grayscale image: {}'.format(fp_im)) + continue + except Exception as e: + log.error('Could not check grayscale: {}'.format(fp_im)) + continue + + try: + bboxes_norm = detector.detect(im_resized, pyramids=opt_pyramids, largest=opt_largest, + zone=opt_zone, conf_thresh=opt_conf_thresh) + except Exception as e: + log.error('could not detect: {}'.format(fp_im)) + log.error('{}'.format(e)) + continue + + if len(bboxes_norm) == 0: + skipped_files.append(fp_im) + log.warn(f'no faces in: {fp_im}') + log.warn(f'skipped: {len(skipped_files)}. found:{len(processed_files)} files') + else: + processed_files.append(fp_im) + for bbox in bboxes_norm: + roi = { + 'record_index': int(df_record.Index), + 'x': bbox.x, + 'y': bbox.y, + 'w': bbox.w, + 'h': bbox.h + } + data.append(roi) + + # if display optined + if opt_display and len(bboxes_norm): + # draw each box + for bbox_norm in bboxes_norm: + dim = im_resized.shape[:2][::-1] + bbox_dim = bbox.to_dim(dim) + if dim[0] > 1000: + im_resized = im_utils.resize(im_resized, width=1000) + im_resized = draw_utils.draw_bbox(im_resized, bbox_norm) + + # display and wait + cv.imshow('', im_resized) + display_utils.handle_keyboard() + + # create DataFrame and save to CSV + file_utils.mkdirs(fp_out) + df = pd.DataFrame.from_dict(data) + df.index.name = 'index' + df.to_csv(fp_out) + + # save script + file_utils.write_text(' '.join(sys.argv), '{}.sh'.format(fp_out))
\ No newline at end of file diff --git a/megapixels/commands/datasets/file_record.py b/megapixels/commands/processor/file_record.py index 41a5df28..6403c768 100644 --- a/megapixels/commands/datasets/file_record.py +++ b/megapixels/commands/processor/file_record.py @@ -78,7 +78,7 @@ def cli(ctx, opt_fp_in, opt_fp_out, opt_dataset, opt_data_store, opt_dir_media, fp_out = data_store.metadata(types.Metadata.FILE_RECORD) if opt_fp_out is None else opt_fp_out # exit if exists if not opt_force and Path(fp_out).exists(): - log.error('File exists. Use "-f / --force" to overwite') + log.error(f'File {fp_out} exists. Use "-f / --force" to overwite') return # ---------------------------------------------------------------- diff --git a/megapixels/commands/site/age_gender_to_site.py b/megapixels/commands/site/age_gender_to_site.py new file mode 100644 index 00000000..3ad24a8d --- /dev/null +++ b/megapixels/commands/site/age_gender_to_site.py @@ -0,0 +1,100 @@ +""" + +""" + +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('-f', '--force', 'opt_force', is_flag=True, + help='Force overwrite file') +@click.pass_context +def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, opt_force): + """Converts age/gender to CSV for pie chartgs""" + + 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 + from app.models.data_store import DataStore + + # ------------------------------------------------------------------------- + # init here + + log = logger_utils.Logger.getLogger() + + # init filepaths + data_store = DataStore(opt_data_store, opt_dataset) + # set file output path + metadata_type = types.Metadata.FACE_ATTRIBUTES + fp_in = data_store.metadata(metadata_type) if opt_fp_out is None else opt_fp_in + dk = opt_dataset.name.lower() + log.debug(f'dk: {dk}') + fp_out_age = f'../site/content/pages/datasets/{dk}/assets/age.csv' + fp_out_gender = f'../site/content/pages/datasets/{dk}/assets/gender.csv' + + if not opt_force and (Path(fp_out_age).exists() or Path(fp_out_gender).exists()): + log.error('File exists. Use "-f / --force" to overwite') + return + + # ------------------------------------------------------------------------- + # Age + + df = pd.read_csv(fp_in) + + results = [] + brackets = [(0, 12), (13, 18), (19,24), (25, 34), (35, 44), (45, 54), (55, 64), (64, 75), (75, 100)] + df_age = df['age_real'] + + for a1, a2 in brackets: + n = len(df_age.loc[((df_age >= a1) & (df_age <= a2))]) + results.append({'age': f'{a1} - {a2}', 'faces': n}) + + df_out = pd.DataFrame.from_dict(results) + df_out = df_out[['age','faces']] + df_out.to_csv(fp_out_age, index=False) + + # Gender + results = [] + + df_f = df['f'] + nm = len(df_f.loc[((df_f < 0.33))]) + nnb = len(df_f.loc[((df_f >= 0.33) & (df_f <= 0.66))]) + nf = len(df_f.loc[((df_f > 0.66))]) + + results = [] + results.append({'gender': 'Male', 'faces':nm}) + results.append({'gender': 'Female', 'faces': nf}) + results.append({'gender': 'They', 'faces': nnb}) + + df_out = pd.DataFrame.from_dict(results) + df_out = df_out[['gender','faces']] + df_out.to_csv(fp_out_gender, index=False)
\ No newline at end of file |