diff options
Diffstat (limited to 'megapixels/commands/processor/face_landmark_3d_68.py')
| -rw-r--r-- | megapixels/commands/processor/face_landmark_3d_68.py | 147 |
1 files changed, 147 insertions, 0 deletions
diff --git a/megapixels/commands/processor/face_landmark_3d_68.py b/megapixels/commands/processor/face_landmark_3d_68.py new file mode 100644 index 00000000..a2d14d72 --- /dev/null +++ b/megapixels/commands/processor/face_landmark_3d_68.py @@ -0,0 +1,147 @@ +""" + +""" + +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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