change to cli_proc

18 files changed, 2434 insertions, 0 deletions

diff --git a/megapixels/commands/processor/_old_files_to_face_rois.py b/megapixels/commands/processor/_old_files_to_face_rois.py
new file mode 100644
index 00000000..d92cbd74
--- /dev/null
+++ b/megapixels/commands/processor/_old_files_to_face_rois.py
@@ -0,0 +1,168 @@
+"""
+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_files', required=True,
+  help='Input file meta CSV')
+@click.option('-o', '--output', 'opt_fp_out', required=True,
+  help='Output CSV')
+@click.option('-e', '--ext', 'opt_ext',
+  default='jpg', type=click.Choice(['jpg', 'png']),
+  help='File glob ext')
+@click.option('--size', 'opt_size', 
+  type=(int, int), default=(300, 300),
+  help='Output image size')
+@click.option('-t', '--detector-type', 'opt_detector_type',
+  type=cfg.FaceDetectNetVar,
+  default=click_utils.get_default(types.FaceDetectNet.DLIB_CNN),
+  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('--recursive/--no-recursive', 'opt_recursive', is_flag=True, default=False,
+  help='Use glob recursion (slower)')
+@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.pass_context
+def cli(ctx, opt_dirs_in, opt_fp_out, opt_ext, opt_size, opt_detector_type, 
+  opt_gpu, opt_conf_thresh, opt_pyramids, opt_slice, opt_display, opt_recursive, opt_force, opt_color_filter):
+  """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
+  from app.processors import face_detector 
+
+  # -------------------------------------------------
+  # init here
+
+  log = logger_utils.Logger.getLogger()
+
+  if not opt_force and Path(opt_fp_out).exists():
+    log.error('File exists. Use "-f / --force" to overwite')
+    return
+  
+  if opt_detector_type == types.FaceDetectNet.CVDNN:
+    detector = face_detector.DetectorCVDNN()
+  elif opt_detector_type == types.FaceDetectNet.DLIB_CNN:
+    detector = face_detector.DetectorDLIBCNN(opt_gpu)
+  elif opt_detector_type == types.FaceDetectNet.DLIB_HOG:
+    detector = face_detector.DetectorDLIBHOG()
+  elif opt_detector_type == types.FaceDetectNet.MTCNN:
+    detector = face_detector.DetectorMTCNN()
+  elif opt_detector_type == types.FaceDetectNet.HAAR:
+    log.error('{} not yet implemented'.format(opt_detector_type.name))
+    return
+
+  
+  # -------------------------------------------------
+  # process here
+  color_filter = color_filters[opt_color_filter]
+  
+  # get list of files to process
+  fp_ims = []
+  for opt_dir_in in opt_dirs_in:
+    if opt_recursive:
+      fp_glob = join(opt_dir_in, '**/*.{}'.format(opt_ext))
+      fp_ims += glob(fp_glob, recursive=True)
+    else:
+      fp_glob = join(opt_dir_in, '*.{}'.format(opt_ext))
+      fp_ims += glob(fp_glob)
+    log.debug(fp_glob)
+
+
+  if opt_slice:
+    fp_ims = fp_ims[opt_slice[0]:opt_slice[1]]
+  log.debug('processing {:,} files'.format(len(fp_ims)))
+
+
+  data = []
+
+  for fp_im in tqdm(fp_ims):
+    im = cv.imread(fp_im)
+
+    # 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 = detector.detect(im, opt_size=opt_size, opt_pyramids=opt_pyramids)
+    except Exception as e:
+      log.error('could not detect: {}'.format(fp_im))
+      log.error('{}'.format(e))
+    fpp_im = Path(fp_im)
+    subdir = str(fpp_im.parent.relative_to(opt_dir_in))
+
+    for bbox in bboxes:
+      # log.debug('is square: {}'.format(bbox.w == bbox.h))
+      nw,nh = int(bbox.w * im.shape[1]),  int(bbox.h * im.shape[0])
+      roi = {
+        'fn': fpp_im.stem, 
+        'ext': fpp_im.suffix.replace('.',''), 
+        'x': bbox.x, 
+        'y': bbox.y, 
+        'w': bbox.w, 
+        'h': bbox.h,
+        'image_height': im.shape[0],
+        'image_width': im.shape[1],
+        'subdir': subdir}
+      bbox_dim = bbox.to_dim(im.shape[:2][::-1])  # w,h
+      data.append(roi)
+    
+    # debug display
+    if opt_display and len(bboxes):
+      im_md = im_utils.resize(im, width=min(1200, opt_size[0]))
+      for bbox in bboxes:
+        bbox_dim = bbox.to_dim(im_md.shape[:2][::-1])
+        cv.rectangle(im_md, bbox_dim.pt_tl, bbox_dim.pt_br, (0,255,0), 3)
+      cv.imshow('', im_md)
+      while True:
+        k = cv.waitKey(1) & 0xFF
+        if k == 27 or k == ord('q'):  # ESC
+          cv.destroyAllWindows()
+          sys.exit()
+        elif k != 255:
+          # any key to continue
+          break
+
+  # save date
+  file_utils.mkdirs(opt_fp_out)
+  df = pd.DataFrame.from_dict(data)
+  df.to_csv(opt_fp_out, index=False)
\ No newline at end of file
diff --git a/megapixels/commands/processor/cluster.py b/megapixels/commands/processor/cluster.py
new file mode 100644
index 00000000..419091a0
--- /dev/null
+++ b/megapixels/commands/processor/cluster.py
@@ -0,0 +1,47 @@
+import click
+
+from app.settings import types
+from app.utils import click_utils
+from app.settings import app_cfg as cfg
+from app.utils.logger_utils import Logger
+
+@click.command()
+@click.option('--data_store', 'opt_data_store',
+  type=cfg.DataStoreVar,
+  default=click_utils.get_default(types.DataStore.NAS),
+  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('--metadata', 'opt_metadata', required=True,
+  type=cfg.MetadataVar,
+  show_default=True,
+  help=click_utils.show_help(types.Metadata))
+@click.pass_context
+def cli(ctx, opt_data_store, opt_dataset, opt_metadata):
+  """Display image info"""
+    
+    # cluster the embeddings
+  print("[INFO] clustering...")
+  clt = DBSCAN(metric="euclidean", n_jobs=args["jobs"])
+  clt.fit(encodings)
+   
+  # determine the total number of unique faces found in the dataset
+  labelIDs = np.unique(clt.labels_)
+  numUniqueFaces = len(np.where(labelIDs > -1)[0])
+  print("[INFO] # unique faces: {}".format(numUniqueFaces))
+  # load and display image
+  im = cv.imread(fp_im)
+  cv.imshow('', im)
+
+  while True:
+    k = cv.waitKey(1) & 0xFF
+    if k == 27 or k == ord('q'):  # ESC
+      cv.destroyAllWindows()
+      sys.exit()
+    elif k != 255:
+      # any key to continue
+      break  
\ No newline at end of file
diff --git a/megapixels/commands/processor/crop.py b/megapixels/commands/processor/crop.py
new file mode 100644
index 00000000..778be0c4
--- /dev/null
+++ b/megapixels/commands/processor/crop.py
@@ -0,0 +1,104 @@
+"""
+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
+
+@click.command()
+@click.option('-i', '--input', 'opt_dir_in', required=True,
+  help='Input directory')
+@click.option('-o', '--output', 'opt_dir_out', required=True,
+  help='Output directory')
+@click.option('-e', '--ext', 'opt_ext',
+  default='jpg', type=click.Choice(['jpg', 'png']),
+  help='File glob ext')
+@click.option('--size', 'opt_size', 
+  type=(int, int), default=(256, 256),
+  help='Output image size')
+@click.option('-t', '--crop-type', 'opt_crop_type',
+  default='center', type=click.Choice(['center', 'mirror', 'face', 'person', 'none']),
+  help='Force fit image center location')
+@click.pass_context
+def cli(ctx, opt_dir_in, opt_dir_out, opt_ext, opt_size, opt_crop_type):
+  """Crop, mirror images"""
+  
+  import os
+  from os.path import join
+  from pathlib import Path
+  from glob import glob
+  from tqdm import tqdm
+  
+
+  from app.utils import logger_utils, file_utils, im_utils
+  
+  # -------------------------------------------------
+  # process here
+
+  log = logger_utils.Logger.getLogger()
+  log.info('crop images')
+
+  # get list of files to process
+  fp_ims = glob(join(opt_dir_in, '*.{}'.format(opt_ext)))
+  log.debug('files: {}'.format(len(fp_ims)))
+  
+  # ensure output dir exists
+  file_utils.mkdirs(opt_dir_out)
+
+  for fp_im in tqdm(fp_ims):
+    im = process_crop(fp_im, opt_size, opt_crop_type)
+    fp_out = join(opt_dir_out, Path(fp_im).name)
+    im.save(fp_out)
+
+  
+def process_crop(fp_im, opt_size, crop_type):
+  im  = Image.open(fp_im)
+  if crop_type == 'center':
+    im = crop_square_fit(im, opt_size)
+  elif crop_type == 'mirror':
+    im = mirror_crop_square(im, opt_size)
+  return im
+
+def crop_square_fit(im, size, center=(0.5, 0.5)):
+  return ImageOps.fit(im, size, method=Image.BICUBIC, centering=center)
+
+def mirror_crop_square(im, size):
+  # force to even dims
+  if im.size[0] % 2 or im.size[1] % 2:
+    im = ImageOps.fit(im, ((im.size[0] // 2) * 2, (im.size[1] // 2) * 2))
+
+  # create new square image
+  min_size, max_size = (min(im.size), max(im.size))
+  orig_w, orig_h = im.size
+  margin = (max_size - min_size) // 2
+  w, h = (max_size, max_size)
+  im_new = Image.new('RGB', (w, h), color=(0, 0, 0))
+  
+  #crop (l, t, r, b)
+  if orig_w > orig_h:
+    # landscape, mirror expand T/B
+    im_top = ImageOps.mirror(im.crop((0, 0, margin, w)))
+    im_bot = ImageOps.mirror(im.crop((orig_h - margin, 0, orig_h, w)))
+    im_new.paste(im_top, (0, 0))
+    im_new.paste(im, (margin, 0, orig_h + margin, w))
+    im_new.paste(im_bot, (h - margin, 0))
+  elif orig_h > orig_w:
+    # portrait, mirror expand L/R
+    im_left = ImageOps.mirror(im.crop((0, 0, margin, h)))
+    im_right = ImageOps.mirror(im.crop((orig_w - margin, 0, orig_w, h)))
+    im_new.paste(im_left, (0, 0))
+    im_new.paste(im, (margin, 0, orig_w + margin, h))
+    im_new.paste(im_right, (w - margin, 0))
+
+  return im_new.resize(size)
+
+  
+def center_crop_face():
+  pass
+
+def center_crop_person():
+  pass
\ No newline at end of file
diff --git a/megapixels/commands/processor/csv_to_faces.py b/megapixels/commands/processor/csv_to_faces.py
new file mode 100644
index 00000000..64c8b965
--- /dev/null
+++ b/megapixels/commands/processor/csv_to_faces.py
@@ -0,0 +1,105 @@
+"""
+Reads in CSV of ROIs and extracts facial regions with padding
+"""
+
+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', required=True,
+  help='Input CSV')
+@click.option('-m', '--media', 'opt_dir_media', required=True,
+  help='Input image/video directory')
+@click.option('-o', '--output', 'opt_dir_out', required=True,
+  help='Output directory for extracted ROI images')
+@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
+  help='Slice list of files')
+@click.option('--padding', 'opt_padding', default=0.25,
+  help='Facial padding as percentage of face width')
+@click.option('--ext', 'opt_ext_out', default='png', type=click.Choice(['jpg', 'png']),
+  help='Output image type')
+@click.option('--min', 'opt_min', default=(60, 60),
+  help='Minimum original face size')
+@click.pass_context
+def cli(ctx, opt_fp_in, opt_dir_media, opt_dir_out, opt_slice, 
+  opt_padding, opt_ext_out, opt_min):
+  """Converts ROIs to images"""
+  
+  import os
+  from os.path import join
+  from pathlib import Path
+  from glob import glob
+  
+  from tqdm import tqdm
+  import numpy as np
+  from PIL import Image, ImageOps, ImageFilter, ImageDraw
+  import cv2 as cv
+  import pandas as pd
+
+  from app.utils import logger_utils, file_utils, im_utils
+  from app.models.bbox import BBox
+
+  # -------------------------------------------------
+  # process here
+  log = logger_utils.Logger.getLogger()
+  
+  df_rois = pd.read_csv(opt_fp_in, dtype={'subdir': str, 'fn': str})
+  if opt_slice:
+    df_rois = df_rois[opt_slice[0]:opt_slice[1]]
+  
+  log.info('Processing {:,} rows'.format(len(df_rois)))
+
+  file_utils.mkdirs(opt_dir_out)
+
+  df_rois_grouped = df_rois.groupby(['fn'])  # group by fn/filename
+  groups = df_rois_grouped.groups
+  skipped = []
+
+  for group in tqdm(groups):
+    # get image
+    group_rows = df_rois_grouped.get_group(group)
+
+    row = group_rows.iloc[0]
+    fp_im = join(opt_dir_media, str(row['subdir']), '{fn}.{ext}'.format(**row))  # TODO change to ext
+    try:
+      im = Image.open(fp_im).convert('RGB')
+      im.verify()
+    except Exception as e:
+      log.warn('Could not open: {}'.format(fp_im))
+      log.error(e)
+      continue
+
+    for idx, roi in group_rows.iterrows():
+      # get bbox to im dimensions
+      xywh = [roi['x'], roi['y'], roi['w'] , roi['h']]
+      bbox = BBox.from_xywh(*xywh)
+      dim = im.size
+      bbox_dim = bbox.to_dim(dim)
+      # expand
+      opt_padding_px = int(opt_padding * bbox_dim.width)
+      bbox_dim_exp = bbox_dim.expand_dim(opt_padding_px, dim)
+      # crop
+      x1y2 = bbox_dim_exp.pt_tl + bbox_dim_exp.pt_br
+      im_crop = im.crop(box=x1y2)
+
+      # strip exif, create new image and paste data
+      im_crop_data = list(im_crop.getdata())
+      im_crop_no_exif = Image.new(im_crop.mode, im_crop.size)
+      im_crop_no_exif.putdata(im_crop_data)
+
+      # save
+      idx_zpad = file_utils.zpad(idx, zeros=3)
+      subdir = '' if roi['subdir'] == '.' else '{}_'.format(roi['subdir'])
+      subdir = subdir.replace('/', '_')
+      fp_im_out = join(opt_dir_out, '{}{}{}.{}'.format(subdir, roi['fn'], idx_zpad, opt_ext_out))
+      # threshold size and save
+      if im_crop_no_exif.size[0] < opt_min[0] or im_crop_no_exif.size[1] < opt_min[1]:
+        skipped.append(fp_im_out)
+        log.info('Face too small: {}, idx: {}'.format(fp_im, idx))
+      else:
+        im_crop_no_exif.save(fp_im_out)
+
+  log.info('Skipped {:,} images'.format(len(skipped)))
diff --git a/megapixels/commands/processor/csv_to_faces_mt.py b/megapixels/commands/processor/csv_to_faces_mt.py
new file mode 100644
index 00000000..64c8b965
--- /dev/null
+++ b/megapixels/commands/processor/csv_to_faces_mt.py
@@ -0,0 +1,105 @@
+"""
+Reads in CSV of ROIs and extracts facial regions with padding
+"""
+
+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', required=True,
+  help='Input CSV')
+@click.option('-m', '--media', 'opt_dir_media', required=True,
+  help='Input image/video directory')
+@click.option('-o', '--output', 'opt_dir_out', required=True,
+  help='Output directory for extracted ROI images')
+@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
+  help='Slice list of files')
+@click.option('--padding', 'opt_padding', default=0.25,
+  help='Facial padding as percentage of face width')
+@click.option('--ext', 'opt_ext_out', default='png', type=click.Choice(['jpg', 'png']),
+  help='Output image type')
+@click.option('--min', 'opt_min', default=(60, 60),
+  help='Minimum original face size')
+@click.pass_context
+def cli(ctx, opt_fp_in, opt_dir_media, opt_dir_out, opt_slice, 
+  opt_padding, opt_ext_out, opt_min):
+  """Converts ROIs to images"""
+  
+  import os
+  from os.path import join
+  from pathlib import Path
+  from glob import glob
+  
+  from tqdm import tqdm
+  import numpy as np
+  from PIL import Image, ImageOps, ImageFilter, ImageDraw
+  import cv2 as cv
+  import pandas as pd
+
+  from app.utils import logger_utils, file_utils, im_utils
+  from app.models.bbox import BBox
+
+  # -------------------------------------------------
+  # process here
+  log = logger_utils.Logger.getLogger()
+  
+  df_rois = pd.read_csv(opt_fp_in, dtype={'subdir': str, 'fn': str})
+  if opt_slice:
+    df_rois = df_rois[opt_slice[0]:opt_slice[1]]
+  
+  log.info('Processing {:,} rows'.format(len(df_rois)))
+
+  file_utils.mkdirs(opt_dir_out)
+
+  df_rois_grouped = df_rois.groupby(['fn'])  # group by fn/filename
+  groups = df_rois_grouped.groups
+  skipped = []
+
+  for group in tqdm(groups):
+    # get image
+    group_rows = df_rois_grouped.get_group(group)
+
+    row = group_rows.iloc[0]
+    fp_im = join(opt_dir_media, str(row['subdir']), '{fn}.{ext}'.format(**row))  # TODO change to ext
+    try:
+      im = Image.open(fp_im).convert('RGB')
+      im.verify()
+    except Exception as e:
+      log.warn('Could not open: {}'.format(fp_im))
+      log.error(e)
+      continue
+
+    for idx, roi in group_rows.iterrows():
+      # get bbox to im dimensions
+      xywh = [roi['x'], roi['y'], roi['w'] , roi['h']]
+      bbox = BBox.from_xywh(*xywh)
+      dim = im.size
+      bbox_dim = bbox.to_dim(dim)
+      # expand
+      opt_padding_px = int(opt_padding * bbox_dim.width)
+      bbox_dim_exp = bbox_dim.expand_dim(opt_padding_px, dim)
+      # crop
+      x1y2 = bbox_dim_exp.pt_tl + bbox_dim_exp.pt_br
+      im_crop = im.crop(box=x1y2)
+
+      # strip exif, create new image and paste data
+      im_crop_data = list(im_crop.getdata())
+      im_crop_no_exif = Image.new(im_crop.mode, im_crop.size)
+      im_crop_no_exif.putdata(im_crop_data)
+
+      # save
+      idx_zpad = file_utils.zpad(idx, zeros=3)
+      subdir = '' if roi['subdir'] == '.' else '{}_'.format(roi['subdir'])
+      subdir = subdir.replace('/', '_')
+      fp_im_out = join(opt_dir_out, '{}{}{}.{}'.format(subdir, roi['fn'], idx_zpad, opt_ext_out))
+      # threshold size and save
+      if im_crop_no_exif.size[0] < opt_min[0] or im_crop_no_exif.size[1] < opt_min[1]:
+        skipped.append(fp_im_out)
+        log.info('Face too small: {}, idx: {}'.format(fp_im, idx))
+      else:
+        im_crop_no_exif.save(fp_im_out)
+
+  log.info('Skipped {:,} images'.format(len(skipped)))
diff --git a/megapixels/commands/processor/face_3ddfa.py b/megapixels/commands/processor/face_3ddfa.py
new file mode 100644
index 00000000..ffc74180
--- /dev/null
+++ b/megapixels/commands/processor/face_3ddfa.py
@@ -0,0 +1,331 @@
+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, required=True,
+  help='Image filepath')
+@click.option('-o', '--output', 'opt_fp_out', default=None,
+  help='GIF output path')
+@click.option('--size', 'opt_size', 
+  type=(int, int), default=(300, 300),
+  help='Output image size')
+@click.option('-g', '--gpu', 'opt_gpu', default=0,
+  help='GPU index')
+@click.option('-f', '--force', 'opt_force', is_flag=True,
+  help='Force overwrite file')
+@click.option('--display/--no-display', 'opt_display', is_flag=True, default=False,
+  help='Display detections to debug')
+@click.pass_context
+def cli(ctx, opt_fp_in, opt_fp_out, opt_gpu, opt_size, opt_force, opt_display):
+  """Face detector demo"""
+  
+  import sys
+  import os
+  from os.path import join
+  from pathlib import Path
+  import time
+  
+  from tqdm import tqdm
+  import numpy as np
+  import pandas as pd
+  import cv2 as cv
+  import dlib
+
+  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_detector, face_age
+  from app.models.data_store import DataStore
+  
+  # 3DDFA
+  # git clone https://github.com/cleardusk/3DDFA/ 3rdparty/
+  
+  import torch
+  import torchvision.transforms as transforms
+  import mobilenet_v1
+  from utils.ddfa import ToTensorGjz, NormalizeGjz, str2bool
+  import scipy.io as sio
+  from utils.inference import get_suffix, parse_roi_box_from_landmark, crop_img, predict_68pts, dump_to_ply, dump_vertex, \
+      draw_landmarks, predict_dense, parse_roi_box_from_bbox, get_colors, write_obj_with_colors
+  from utils.cv_plot import plot_pose_box
+  from utils.estimate_pose import parse_pose
+  from utils.render import get_depths_image, cget_depths_image, cpncc
+  from utils.paf import gen_img_paf
+  import argparse
+  import torch.backends.cudnn as cudnn
+
+
+  log = logger_utils.Logger.getLogger()
+
+  
+  # -------------------------------------------------
+  # load image
+
+  im = cv.imread(opt_fp_in)
+  im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
+
+  # ----------------------------------------------------------------------------
+  # detect face
+
+  face_detector = face_detector.DetectorDLIBCNN(gpu=opt_gpu)  # -1 for CPU
+  bboxes = face_detector.detect(im_resized, largest=True)
+  bbox = bboxes[0]
+  dim = im_resized.shape[:2][::-1]
+  bbox_dim = bbox.to_dim(dim)
+  if not bbox:
+    log.error('no face detected')
+    return
+  else:
+    log.info(f'face detected: {bbox_dim.to_xyxy()}')
+
+
+  # ----------------------------------------------------------------------------
+  # age
+
+  age_apparent_predictor = face_age.FaceAgeApparent()
+  age_real_predictor = face_age.FaceAgeReal()
+
+  st = time.time()
+  age_real = age_real_predictor.age(im_resized, bbox_dim)
+  log.info(f'age real took: {(time.time()-st)/1000:.5f}s')
+  st = time.time()
+  age_apparent = age_apparent_predictor.age(im_resized, bbox_dim)
+  log.info(f'age apparent took: {(time.time()-st)/1000:.5f}s')
+
+
+  # ----------------------------------------------------------------------------
+  # output
+  
+  log.info(f'Face coords: {bbox_dim} face')
+  log.info(f'Age (real): {(age_real):.2f}')
+  log.info(f'Age (apparent): {(age_apparent):.2f}')
+
+
+  # ----------------------------------------------------------------------------
+  # draw
+
+  # draw real age
+  im_age_real = im_resized.copy()
+  draw_utils.draw_bbox(im_age_real, bbox_dim)
+  txt = f'{(age_real):.2f}'
+  draw_utils.draw_text(im_age_real, bbox_dim.pt_tl, txt)
+
+  # apparent
+  im_age_apparent = im_resized.copy()
+  draw_utils.draw_bbox(im_age_apparent, bbox_dim)
+  txt = f'{(age_apparent):.2f}'
+  draw_utils.draw_text(im_age_apparent, bbox_dim.pt_tl, txt)
+
+
+  # ----------------------------------------------------------------------------
+  # save
+  
+  if opt_fp_out:
+    # save pose only
+    fpp_out = Path(opt_fp_out)
+    
+    fp_out = join(fpp_out.parent, f'{fpp_out.stem}_real{fpp_out.suffix}')
+    cv.imwrite(fp_out, im_age_real)
+
+    fp_out = join(fpp_out.parent, f'{fpp_out.stem}_apparent{fpp_out.suffix}')
+    cv.imwrite(fp_out, im_age_apparent)
+
+
+  # ----------------------------------------------------------------------------
+  # display
+
+  if opt_display:
+    # show all images here
+    cv.imshow('real', im_age_real)
+    cv.imshow('apparent', im_age_apparent)
+    display_utils.handle_keyboard()
+
+
+
+
+
+STD_SIZE = 120
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='3DDFA inference pipeline')
+    parser.add_argument('-f', '--files', nargs='+',
+                        help='image files paths fed into network, single or multiple images')
+    parser.add_argument('-m', '--mode', default='cpu', type=str, help='gpu or cpu mode')
+    parser.add_argument('--show_flg', default='true', type=str2bool, help='whether show the visualization result')
+    parser.add_argument('--bbox_init', default='one', type=str,
+                        help='one|two: one-step bbox initialization or two-step')
+    parser.add_argument('--dump_res', default='true', type=str2bool, help='whether write out the visualization image')
+    parser.add_argument('--dump_vertex', default='true', type=str2bool,
+                        help='whether write out the dense face vertices to mat')
+    parser.add_argument('--dump_ply', default='true', type=str2bool)
+    parser.add_argument('--dump_pts', default='true', type=str2bool)
+    parser.add_argument('--dump_roi_box', default='true', type=str2bool)
+    parser.add_argument('--dump_pose', default='true', type=str2bool)
+    parser.add_argument('--dump_depth', default='true', type=str2bool)
+    parser.add_argument('--dump_pncc', default='true', type=str2bool)
+    parser.add_argument('--dump_paf', default='true', type=str2bool)
+    parser.add_argument('--paf_size', default=3, type=int, help='PAF feature kernel size')
+    parser.add_argument('--dump_obj', default='true', type=str2bool)
+    parser.add_argument('--dlib_bbox', default='true', type=str2bool, help='whether use dlib to predict bbox')
+    parser.add_argument('--dlib_landmark', default='true', type=str2bool,
+                        help='whether use dlib landmark to crop image')
+
+    args = parser.parse_args()
+    main(args)
+
+
+
+def main(args):
+    # 1. load pre-tained model
+    checkpoint_fp = 'models/phase1_wpdc_vdc_v2.pth.tar'
+    arch = 'mobilenet_1'
+
+    checkpoint = torch.load(checkpoint_fp, map_location=lambda storage, loc: storage)['state_dict']
+    model = getattr(mobilenet_v1, arch)(num_classes=62)  # 62 = 12(pose) + 40(shape) +10(expression)
+    model_dict = model.state_dict()
+    # because the model is trained by multiple gpus, prefix module should be removed
+    for k in checkpoint.keys():
+        model_dict[k.replace('module.', '')] = checkpoint[k]
+    model.load_state_dict(model_dict, strict=False)
+    if args.mode == 'gpu':
+        cudnn.benchmark = True
+        model = model.cuda()
+    model.eval()
+
+    # 2. load dlib model for face detection and landmark used for face cropping
+    if args.dlib_landmark:
+        dlib_landmark_model = 'models/shape_predictor_68_face_landmarks.dat'
+        face_regressor = dlib.shape_predictor(dlib_landmark_model)
+    if args.dlib_bbox:
+        face_detector = dlib.get_frontal_face_detector()
+
+    # 3. forward
+    tri = sio.loadmat('visualize/tri.mat')['tri']
+    transform = transforms.Compose([ToTensorGjz(), NormalizeGjz(mean=127.5, std=128)])
+    for img_fp in args.files:
+        img_ori = cv2.imread(img_fp)
+        if args.dlib_bbox:
+            rects = face_detector(img_ori, 1)
+        else:
+            rects = []
+
+        if len(rects) == 0:
+            rects = dlib.rectangles()
+            rect_fp = img_fp + '.bbox'
+            lines = open(rect_fp).read().strip().split('\n')[1:]
+            for l in lines:
+                l, r, t, b = [int(_) for _ in l.split(' ')[1:]]
+                rect = dlib.rectangle(l, r, t, b)
+                rects.append(rect)
+
+        pts_res = []
+        Ps = []  # Camera matrix collection
+        poses = []  # pose collection, [todo: validate it]
+        vertices_lst = []  # store multiple face vertices
+        ind = 0
+        suffix = get_suffix(img_fp)
+        for rect in rects:
+            # whether use dlib landmark to crop image, if not, use only face bbox to calc roi bbox for cropping
+            if args.dlib_landmark:
+                # - use landmark for cropping
+                pts = face_regressor(img_ori, rect).parts()
+                pts = np.array([[pt.x, pt.y] for pt in pts]).T
+                roi_box = parse_roi_box_from_landmark(pts)
+            else:
+                # - use detected face bbox
+                bbox = [rect.left(), rect.top(), rect.right(), rect.bottom()]
+                roi_box = parse_roi_box_from_bbox(bbox)
+
+            img = crop_img(img_ori, roi_box)
+
+            # forward: one step
+            img = cv2.resize(img, dsize=(STD_SIZE, STD_SIZE), interpolation=cv2.INTER_LINEAR)
+            input = transform(img).unsqueeze(0)
+            with torch.no_grad():
+                if args.mode == 'gpu':
+                    input = input.cuda()
+                param = model(input)
+                param = param.squeeze().cpu().numpy().flatten().astype(np.float32)
+
+            # 68 pts
+            pts68 = predict_68pts(param, roi_box)
+
+            # two-step for more accurate bbox to crop face
+            if args.bbox_init == 'two':
+                roi_box = parse_roi_box_from_landmark(pts68)
+                img_step2 = crop_img(img_ori, roi_box)
+                img_step2 = cv2.resize(img_step2, dsize=(STD_SIZE, STD_SIZE), interpolation=cv2.INTER_LINEAR)
+                input = transform(img_step2).unsqueeze(0)
+                with torch.no_grad():
+                    if args.mode == 'gpu':
+                        input = input.cuda()
+                    param = model(input)
+                    param = param.squeeze().cpu().numpy().flatten().astype(np.float32)
+
+                pts68 = predict_68pts(param, roi_box)
+
+            pts_res.append(pts68)
+            P, pose = parse_pose(param)
+            Ps.append(P)
+            poses.append(pose)
+
+            # dense face 3d vertices
+            if args.dump_ply or args.dump_vertex or args.dump_depth or args.dump_pncc or args.dump_obj:
+                vertices = predict_dense(param, roi_box)
+                vertices_lst.append(vertices)
+            if args.dump_ply:
+                dump_to_ply(vertices, tri, '{}_{}.ply'.format(img_fp.replace(suffix, ''), ind))
+            if args.dump_vertex:
+                dump_vertex(vertices, '{}_{}.mat'.format(img_fp.replace(suffix, ''), ind))
+
+                # save .mat for 3d Face
+                wfp = '{}_{}_face3d.mat'.format(img_fp.replace(suffix, ''), ind) 
+                colors = get_colors(img_ori, vertices)
+                sio.savemat(wfp, {'vertices': vertices, 'colors': colors, 'triangles': tri})
+
+            if args.dump_pts:
+                wfp = '{}_{}.txt'.format(img_fp.replace(suffix, ''), ind)
+                np.savetxt(wfp, pts68, fmt='%.3f')
+                print('Save 68 3d landmarks to {}'.format(wfp))
+            if args.dump_roi_box:
+                wfp = '{}_{}.roibox'.format(img_fp.replace(suffix, ''), ind)
+                np.savetxt(wfp, roi_box, fmt='%.3f')
+                print('Save roi box to {}'.format(wfp))
+            if args.dump_paf:
+                wfp_paf = '{}_{}_paf.jpg'.format(img_fp.replace(suffix, ''), ind)
+                wfp_crop = '{}_{}_crop.jpg'.format(img_fp.replace(suffix, ''), ind)
+                paf_feature = gen_img_paf(img_crop=img, param=param, kernel_size=args.paf_size)
+
+                cv2.imwrite(wfp_paf, paf_feature)
+                cv2.imwrite(wfp_crop, img)
+                print('Dump to {} and {}'.format(wfp_crop, wfp_paf))
+            if args.dump_obj:
+                wfp = '{}_{}.obj'.format(img_fp.replace(suffix, ''), ind)
+                colors = get_colors(img_ori, vertices)
+                write_obj_with_colors(wfp, vertices, tri, colors)
+                print('Dump obj with sampled texture to {}'.format(wfp))
+            ind += 1
+
+        if args.dump_pose:
+            # P, pose = parse_pose(param)  # Camera matrix (without scale), and pose (yaw, pitch, roll, to verify)
+            img_pose = plot_pose_box(img_ori, Ps, pts_res)
+            wfp = img_fp.replace(suffix, '_pose.jpg')
+            cv2.imwrite(wfp, img_pose)
+            print('Dump to {}'.format(wfp))
+        if args.dump_depth:
+            wfp = img_fp.replace(suffix, '_depth.png')
+            # depths_img = get_depths_image(img_ori, vertices_lst, tri-1)  # python version
+            depths_img = cget_depths_image(img_ori, vertices_lst, tri - 1)  # cython version
+            cv2.imwrite(wfp, depths_img)
+            print('Dump to {}'.format(wfp))
+        if args.dump_pncc:
+            wfp = img_fp.replace(suffix, '_pncc.png')
+            pncc_feature = cpncc(img_ori, vertices_lst, tri - 1)  # cython version
+            cv2.imwrite(wfp, pncc_feature[:, :, ::-1])  # cv2.imwrite will swap RGB -> BGR
+            print('Dump to {}'.format(wfp))
+        if args.dump_res:
+            draw_landmarks(img_ori, pts_res, wfp=img_fp.replace(suffix, '_3DDFA.jpg'), show_flg=args.show_flg)
diff --git a/megapixels/commands/processor/face_attributes.py b/megapixels/commands/processor/face_attributes.py
new file mode 100644
index 00000000..01fe3bd1
--- /dev/null
+++ b/megapixels/commands/processor/face_attributes.py
@@ -0,0 +1,136 @@
+"""
+
+"""
+
+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))
\ No newline at end of file
diff --git a/megapixels/commands/processor/face_frames.py b/megapixels/commands/processor/face_frames.py
new file mode 100644
index 00000000..76f23af1
--- /dev/null
+++ b/megapixels/commands/processor/face_frames.py
@@ -0,0 +1,82 @@
+from glob import glob
+import os
+from os.path import join
+from pathlib import Path
+
+import click
+
+
+
+
+@click.command()
+@click.option('-i', '--input', 'opt_fp_in', required=True,
+  help='Input directory to glob')
+@click.option('-o', '--output', 'opt_fp_out', required=True,
+  help='Output directory for face frames')
+@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.pass_context
+def cli(ctx, opt_fp_in, opt_fp_out, opt_size, opt_slice):
+  """Split video to face frames"""
+
+  from tqdm import tqdm
+  import dlib
+  import pandas as pd
+  from PIL import Image, ImageOps, ImageFilter
+  import cv2 as cv
+  import numpy as np
+
+  from app.processors import face_detector
+  from app.utils import logger_utils, file_utils, im_utils
+  from app.settings import types
+  from app.utils import click_utils
+  from app.settings import app_cfg as cfg
+  from app.models.bbox import BBox
+
+  log = logger_utils.Logger.getLogger()
+
+  # -------------------------------------------------
+  # process  
+   
+  detector = face_detector.DetectorDLIBCNN()
+
+  # get file list
+  fp_videos = glob(join(opt_fp_in, '*.mp4'))
+  fp_videos += glob(join(opt_fp_in, '*.webm'))
+  fp_videos += glob(join(opt_fp_in, '*.mkv'))
+
+  min_distance_per = .025  # minimum distance percentage to save new face image
+  face_interval = 5
+  frame_interval_count = 0
+  frame_count = 0
+  bbox_prev = BBox(0,0,0,0)
+  file_utils.mkdirs(opt_fp_out)
+  dnn_size = opt_size
+  max_dim = max(dnn_size)
+  px_thresh = int(max_dim * min_distance_per)
+
+  for fp_video in tqdm(fp_videos):
+    # load video  
+    video = cv.VideoCapture(fp_video)
+    # iterate through frames
+    while video.isOpened():
+      res, frame = video.read()
+      if not res:
+        break
+      # increment frames, save frame if interval has passed
+      frame_count += 1  # for naming
+      frame_interval_count += 1  # for interval
+      bboxes = detector.detect(frame, opt_size=dnn_size, opt_pyramids=0)
+      if len(bboxes) > 0 and frame_interval_count >= face_interval:
+        dim = frame.shape[:2][::-1]
+        d = bboxes[0].to_dim(dim).distance(bbox_prev)
+        if d > px_thresh:
+          # save frame
+          zfc = file_utils.zpad(frame_count)
+          fp_frame = join(opt_fp_out, '{}_{}.jpg'.format(Path(fp_video).stem, zfc))
+          cv.imwrite(fp_frame, frame)
+          frame_interval_count = 0
+          bbox_prev = bboxes[0]
diff --git a/megapixels/commands/processor/face_landmark_2d_5.py b/megapixels/commands/processor/face_landmark_2d_5.py
new file mode 100644
index 00000000..40ec6f41
--- /dev/null
+++ b/megapixels/commands/processor/face_landmark_2d_5.py
@@ -0,0 +1,146 @@
+"""
+
+"""
+
+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.FaceLandmark2D_5Var,
+  default=click_utils.get_default(types.FaceLandmark2D_5.DLIB),
+  help=click_utils.show_help(types.FaceLandmark2D_5))
+@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):
+  """Creates 2D 5-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_landmarks
+  from app.models.data_store import DataStore
+  from app.models.bbox import BBox
+
+  # -------------------------------------------------
+  # 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_LANDMARK_2D_5
+  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.FaceLandmark2D_5.DLIB:
+    # use dlib 68 point detector
+    landmark_detector = face_landmarks.Dlib2D_5()
+  elif opt_detector_type == types.FaceLandmark2D_5.MTCNN:
+    # use dlib 5 point detector
+    landmark_detector = face_landmarks.MTCNN2D_5()
+  else:
+    log.error('{} not yet implemented'.format(opt_detector_type.name))
+    return
+
+  log.info(f'Using landmark detector: {opt_detector_type.name}')  
+
+  # load filepath data
+  fp_record = data_store.metadata(types.Metadata.FILE_RECORD)
+  df_record = pd.read_csv(fp_record).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):
+    
+    # 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)
+      points_norm = landmark_detector.normalize(points, dim)
+      points_flat = landmark_detector.flatten(points_norm)
+
+      # display to screen if optioned
+      if opt_display:
+        draw_utils.draw_landmarks2D(im_resized, points)
+        draw_utils.draw_bbox(im_resized, bbox)
+        cv.imshow('', im_resized)
+        display_utils.handle_keyboard()
+
+      results.append(points_flat)
+
+  # 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))
\ No newline at end of file
diff --git a/megapixels/commands/processor/face_landmark_2d_68.py b/megapixels/commands/processor/face_landmark_2d_68.py
new file mode 100644
index 00000000..c6978a40
--- /dev/null
+++ b/megapixels/commands/processor/face_landmark_2d_68.py
@@ -0,0 +1,150 @@
+"""
+
+"""
+
+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('-d', '--detector', 'opt_detector_type',
+  type=cfg.FaceLandmark2D_68Var,
+  default=click_utils.get_default(types.FaceLandmark2D_68.DLIB),
+  help=click_utils.show_help(types.FaceLandmark2D_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):
+  """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_landmarks
+  from app.models.data_store import DataStore
+  from app.models.bbox import BBox
+
+  # -------------------------------------------------------------------------
+  # 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_LANDMARK_2D_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.FaceLandmark2D_68.DLIB:
+    # use dlib 68 point detector
+    landmark_detector = face_landmarks.Dlib2D_68()
+  elif opt_detector_type == types.FaceLandmark2D_68.FACE_ALIGNMENT:
+    # use dlib 5 point detector
+    landmark_detector = face_landmarks.FaceAlignment2D_68()
+  else:
+    log.error('{} not yet implemented'.format(opt_detector_type.name))
+    return
+
+  log.info(f'Using landmark detector: {opt_detector_type.name}')  
+
+  # -------------------------------------------------------------------------
+  # load filepath data
+  fp_record = data_store.metadata(types.Metadata.FILE_RECORD)
+  df_record = pd.read_csv(fp_record).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
+      x, y, w, h = df_img.x, df_img.y, df_img.w, df_img.h  # normalized values
+      #dim = (file_record.width, file_record.height)  # original w,h
+      bbox = BBox.from_xywh(x, y, w, h).to_dim(dim)
+      points = landmark_detector.landmarks(im_resized, bbox)
+      points_norm = landmark_detector.normalize(points, dim)
+      points_str = landmark_detector.to_str(points_norm)
+
+      # display if optioned
+      if opt_display:
+        dst = im_resized.copy()
+        draw_utils.draw_landmarks2D(dst, points)
+        draw_utils.draw_bbox(dst, bbox)
+        cv.imshow('', dst)
+        display_utils.handle_keyboard()
+
+      # add to results for CSV
+      results.append({'vec': points_str, 'roi_index':roi_index})
+
+
+  # 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))
\ No newline at end of file
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))
\ No newline at end of file
diff --git a/megapixels/commands/processor/face_pose.py b/megapixels/commands/processor/face_pose.py
new file mode 100644
index 00000000..cb7ec56c
--- /dev/null
+++ b/megapixels/commands/processor/face_pose.py
@@ -0,0 +1,164 @@
+"""
+NB: This only works with the DLIB 68-point landmarks.
+
+Converts ROIs to pose: yaw, roll, pitch
+pitch: looking down or up in yes gesture
+roll: tilting head towards shoulder
+yaw: twisting head left to right in no gesture
+
+"""
+
+"""
+TODO
+- check compatibility with MTCNN 68 point detector
+- improve accuracy by using MTCNN 5-point
+- refer to https://github.com/jerryhouuu/Face-Yaw-Roll-Pitch-from-Pose-Estimation-using-OpenCV/
+"""
+
+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=(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_size, 
+  opt_slice, opt_force, opt_display):
+  """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, display_utils, draw_utils
+  from app.processors.face_landmarks import Dlib2D_68
+  from app.processors.face_pose import FacePoseDLIB
+  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_POSE) 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
+  face_pose = FacePoseDLIB()
+  face_landmarks = Dlib2D_68()
+
+  # -------------------------------------------------
+  # load 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 poses and convert to DataFrame
+  results = []
+
+  # -------------------------------------------------
+  # iterate groups with file/record index as key
+  for record_index, df_img_group in tqdm(df_img_groups):
+
+    # access the file_record
+    file_record = df_record.iloc[record_index]  # pands.DataSeries
+    
+    # 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])
+
+    # 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 = (file_record.width, file_record.height)
+      dim = im_resized.shape[:2][::-1]
+      bbox_norm = BBox.from_xywh(x, y, w, h)
+      bbox_dim = bbox_norm.to_dim(dim)
+
+      # get pose
+      landmarks = face_landmarks.landmarks(im_resized, bbox_norm)
+      pose_data = face_pose.pose(landmarks, dim)
+      #pose_degrees = pose_data['degrees']  # only keep the degrees data
+      #pose_degrees['points_nose'] = pose_data
+      
+      # draw landmarks if optioned
+      if opt_display:
+        draw_utils.draw_pose(im_resized, pose_data['point_nose'], pose_data['points'])
+        draw_utils.draw_degrees(im_resized, pose_data)
+        cv.imshow('', im_resized)
+        display_utils.handle_keyboard()
+
+      # add image index and append to result CSV data
+      pose_data['roi_index'] = roi_index
+      for k, v in pose_data['points'].items():
+        pose_data[f'point_{k}_x'] = v[0] / dim[0]
+        pose_data[f'point_{k}_y'] = v[1] / dim[1]
+
+      # rearrange data structure for DataFrame
+      pose_data.pop('points')
+      pose_data['point_nose_x'] = pose_data['point_nose'][0] / dim[0]
+      pose_data['point_nose_y'] = pose_data['point_nose'][1] / dim[1]
+      pose_data.pop('point_nose')
+      results.append(pose_data)
+
+  # 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))
\ No newline at end of file
diff --git a/megapixels/commands/processor/face_roi.py b/megapixels/commands/processor/face_roi.py
new file mode 100644
index 00000000..fc933049
--- /dev/null
+++ b/megapixels/commands/processor/face_roi.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/processor/face_vector.py b/megapixels/commands/processor/face_vector.py
new file mode 100644
index 00000000..cb155d08
--- /dev/null
+++ b/megapixels/commands/processor/face_vector.py
@@ -0,0 +1,133 @@
+"""
+Converts ROIs to face vector
+NB: the VGG Face2 extractor should be used with MTCNN ROIs (not square)
+  the DLIB face extractor should be used with DLIB ROIs (square)
+see https://github.com/ox-vgg/vgg_face2 for TAR@FAR
+"""
+
+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('--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='Output image size')
+@click.option('-e', '--extractor', 'opt_extractor',
+  default=click_utils.get_default(types.FaceExtractor.VGG),
+  type=cfg.FaceExtractorVar,
+  help='Type of extractor framework/network to use')
+@click.option('-j', '--jitters', 'opt_jitters', default=cfg.DLIB_FACEREC_JITTERS,
+  help='Number of jitters (only for dlib')
+@click.option('-p', '--padding', 'opt_padding', default=cfg.FACEREC_PADDING,
+  help='Percentage ROI 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_extractor, 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_extractor
+
+
+  # -------------------------------------------------
+  # 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
+  if opt_extractor == types.FaceExtractor.DLIB:
+    log.debug('set dlib')
+    extractor = face_extractor.ExtractorDLIB(gpu=opt_gpu, jitters=opt_jitters)
+  elif opt_extractor == types.FaceExtractor.VGG:
+    extractor = face_extractor.ExtractorVGG()
+
+  # load 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')
+  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 images
+
+  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)
+    im = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
+    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 = (ds_record.width, ds_record.height)
+      # get face vector
+      bbox = BBox.from_xywh(x, y, w, h)  # norm
+      # compute vec
+      vec = extractor.extract(im, bbox)  # use normalized BBox
+      vec_str = extractor.to_str(vec)
+      vec_obj = {'vec':vec_str, 'roi_index': roi_index, 'record_index':record_index}
+      vecs.append(vec_obj)
+
+  # -------------------------------------------------
+  # save data
+
+  # create DataFrame and save to CSV
+  df = pd.DataFrame.from_dict(vecs)
+  df.index.name = 'index'
+  file_utils.mkdirs(fp_out)
+  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/processor/mirror.py b/megapixels/commands/processor/mirror.py
new file mode 100644
index 00000000..9ca1cac7
--- /dev/null
+++ b/megapixels/commands/processor/mirror.py
@@ -0,0 +1,57 @@
+"""
+Crop images to prepare for training
+"""
+
+import click
+import cv2 as cv
+from PIL import Image, ImageOps, ImageFilter
+
+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_dir_in', required=True,
+  help='Input directory')
+@click.option('-o', '--output', 'opt_dir_out', required=True,
+  help='Output directory')
+@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
+  help='Slice the input list')
+@click.pass_context
+def cli(ctx, opt_dir_in, opt_dir_out,  opt_slice):
+  """Mirror augment image directory"""
+  
+  import os
+  from os.path import join
+  from pathlib import Path
+  from glob import glob
+  from tqdm import tqdm
+
+  from app.utils import logger_utils, file_utils, im_utils
+  
+  # -------------------------------------------------
+  # init
+
+  log = logger_utils.Logger.getLogger()
+  
+  # -------------------------------------------------
+  # process here
+
+  # get list of files to process
+  fp_ims = glob(join(opt_dir_in, '*.jpg'))
+  fp_ims += glob(join(opt_dir_in, '*.png'))
+  
+  if opt_slice:
+    fp_ims = fp_ims[opt_slice[0]:opt_slice[1]]
+  log.info('processing {:,} files'.format(len(fp_ims)))
+  
+  # ensure output dir exists
+  file_utils.mkdirs(opt_dir_out)
+
+  # resize and save images
+  for fp_im in tqdm(fp_ims):
+    im = Image.open(fp_im)
+    fpp_im = Path(fp_im)
+    fp_out = join(opt_dir_out, '{}_mirror{}'.format(fpp_im.stem, fpp_im.suffix))
+    im.save(fp_out)
\ No newline at end of file
diff --git a/megapixels/commands/processor/resize.py b/megapixels/commands/processor/resize.py
new file mode 100644
index 00000000..7409ee6f
--- /dev/null
+++ b/megapixels/commands/processor/resize.py
@@ -0,0 +1,150 @@
+"""
+Crop images to prepare for training
+"""
+
+import click
+import cv2 as cv
+from PIL import Image, ImageOps, ImageFilter
+
+from app.settings import types
+from app.utils import click_utils
+from app.settings import app_cfg as cfg
+
+"""
+Filter    Q-Down Q-Up   Speed
+NEAREST              ⭐⭐⭐⭐⭐
+BOX       ⭐          ⭐⭐⭐⭐
+BILINEAR  ⭐      ⭐   ⭐⭐⭐
+HAMMING   ⭐⭐         ⭐⭐⭐
+BICUBIC   ⭐⭐⭐   ⭐⭐⭐  ⭐⭐
+LANCZOS   ⭐⭐⭐⭐  ⭐⭐⭐⭐  ⭐
+"""
+methods = {
+  'lanczos': Image.LANCZOS,
+  'bicubic': Image.BICUBIC,
+  'hamming': Image.HAMMING,
+  'bileaner': Image.BILINEAR,
+  'box': Image.BOX,
+  'nearest': Image.NEAREST
+  }
+centerings = {
+  'tl': (0.0, 0.0),
+  'tc': (0.5, 0.0),
+  'tr': (0.0, 0.0),
+  'lc': (0.0, 0.5),
+  'cc': (0.5, 0.5),
+  'rc': (1.0, 0.5),
+  'bl': (0.0, 1.0),
+  'bc': (1.0, 0.5),
+  'br': (1.0, 1.0)
+}
+
+@click.command()
+@click.option('-i', '--input', 'opt_dir_in', required=True,
+  help='Input directory')
+@click.option('-o', '--output', 'opt_dir_out', required=True,
+  help='Output directory')
+@click.option('-e', '--ext', 'opt_glob_ext',
+  default='png', type=click.Choice(['jpg', 'png']),
+  help='File glob ext')
+@click.option('--size', 'opt_size',  
+  type=(int, int), default=(256, 256),
+  help='Max output size')
+@click.option('--method', 'opt_scale_method', 
+  type=click.Choice(methods.keys()),
+  default='lanczos',
+  help='Scaling method to use')
+@click.option('--equalize', 'opt_equalize', is_flag=True,
+  help='Equalize historgram')
+@click.option('--sharpen', 'opt_sharpen', is_flag=True,
+  help='Unsharp mask')
+@click.option('--center', 'opt_center', default='cc', type=click.Choice(centerings.keys()),
+  help='Crop focal point')
+@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
+  help='Slice the input list')
+@click.option('-t', '--threads', 'opt_threads', default=8,
+  help='Number of threads')
+@click.pass_context
+def cli(ctx, opt_dir_in, opt_dir_out, opt_glob_ext, opt_size, opt_scale_method,
+ opt_equalize, opt_sharpen, opt_center, opt_slice, opt_threads):
+  """Crop, mirror images"""
+  
+  import os
+  from os.path import join
+  from pathlib import Path
+  from glob import glob
+  from tqdm import tqdm
+  from multiprocessing.dummy import Pool as ThreadPool
+  from functools import partial
+
+  from app.utils import logger_utils, file_utils, im_utils
+  
+  # -------------------------------------------------
+  # init
+
+  log = logger_utils.Logger.getLogger()
+
+
+  # -------------------------------------------------
+  # process here
+
+  def pool_resize(fp_im, opt_size, scale_method):
+    # Threaded image resize function
+    try:
+      pbar.update(1)
+      try:
+        im = Image.open(fp_im).convert('RGB')
+        im.verify()
+      except Exception as e:
+        log.warn('Could not open: {}'.format(fp_im))
+        log.error(e)
+        return False
+
+      #im = ImageOps.fit(im, opt_size, method=scale_method, centering=centering)
+
+      if opt_equalize:
+        im_np = im_utils.pil2np(im)
+        im_np_eq = eq_hist_yuv(im_np)
+        im_np = cv.addWeighted(im_np_eq, 0.35, im_np, 0.65, 0)
+        im = im_utils.np2pil(im_np)
+
+      if opt_sharpen:
+        im = im.filter(ImageFilter.UnsharpMask)
+        
+      fp_out = join(opt_dir_out, Path(fp_im).name)
+      im.save(fp_out)
+      return True
+    except:
+      return False
+
+  #centering = centerings[opt_center]
+  #scale_method = methods[opt_scale_method]
+
+  # get list of files to process
+  fp_ims = glob(join(opt_dir_in, '*.{}'.format(opt_glob_ext)))
+  if opt_slice:
+    fp_ims = fp_ims[opt_slice[0]:opt_slice[1]]
+  log.info('processing {:,} files'.format(len(fp_ims)))
+  
+  
+  # ensure output dir exists
+  file_utils.mkdirs(opt_dir_out)
+
+  # setup multithreading
+  pbar = tqdm(total=len(fp_ims))
+  #pool_resize = partial(pool_resize, opt_size=opt_size, scale_method=scale_method, centering=centering)
+  pool_resize = partial(pool_resize, opt_size=opt_size)
+  #result_list = pool.map(prod_x, data_list)
+  pool = ThreadPool(opt_threads) 
+  with tqdm(total=len(fp_ims)) as pbar:
+    results = pool.map(pool_resize, fp_ims)
+  pbar.close()
+
+  log.info('Resized: {} / {} images'.format(results.count(True), len(fp_ims)))
+
+
+
+def eq_hist_yuv(im):
+  im_yuv = cv.cvtColor(im, cv.COLOR_BGR2YUV)
+  im_yuv[:,:,0] = cv.equalizeHist(im_yuv[:,:,0])
+  return cv.cvtColor(im_yuv, cv.COLOR_YUV2BGR)
diff --git a/megapixels/commands/processor/resize_dataset.py b/megapixels/commands/processor/resize_dataset.py
new file mode 100644
index 00000000..3a6ec15f
--- /dev/null
+++ b/megapixels/commands/processor/resize_dataset.py
@@ -0,0 +1,149 @@
+"""
+Crop images to prepare for training
+"""
+
+import click
+import cv2 as cv
+from PIL import Image, ImageOps, ImageFilter
+
+from app.settings import types
+from app.utils import click_utils
+from app.settings import app_cfg as cfg
+
+cv_resize_algos = {
+  'area': cv.INTER_AREA,
+  'lanco': cv.INTER_LANCZOS4,
+  'linear': cv.INTER_LINEAR,
+  'linear_exact': cv.INTER_LINEAR_EXACT,
+  'nearest': cv.INTER_NEAREST
+}
+"""
+Filter    Q-Down Q-Up   Speed
+NEAREST              ⭐⭐⭐⭐⭐
+BOX       ⭐          ⭐⭐⭐⭐
+BILINEAR  ⭐      ⭐   ⭐⭐⭐
+HAMMING   ⭐⭐         ⭐⭐⭐
+BICUBIC   ⭐⭐⭐   ⭐⭐⭐  ⭐⭐
+LANCZOS   ⭐⭐⭐⭐  ⭐⭐⭐⭐  ⭐
+"""
+pil_resize_algos = {
+  'antialias': Image.ANTIALIAS,
+  'lanczos': Image.LANCZOS,
+  'bicubic': Image.BICUBIC,
+  'hamming': Image.HAMMING,
+  'bileaner': Image.BILINEAR,
+  'box': Image.BOX,
+  'nearest': Image.NEAREST
+  }
+
+@click.command()
+@click.option('--dataset', 'opt_dataset',
+  type=cfg.DatasetVar,
+  required=True,
+  show_default=True,
+  help=click_utils.show_help(types.Dataset))
+@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('-o', '--output', 'opt_dir_out', required=True,
+  help='Output directory')
+@click.option('-e', '--ext', 'opt_glob_ext',
+  default='png', type=click.Choice(['jpg', 'png']),
+  help='File glob ext')
+@click.option('--size', 'opt_size',  
+  type=(int, int), default=(256, 256),
+  help='Output image size max (w,h)')
+@click.option('--interp', 'opt_interp_algo', 
+  type=click.Choice(pil_resize_algos.keys()),
+  default='bicubic',
+  help='Interpolation resizing algorithms')
+@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
+  help='Slice the input list')
+@click.option('-t', '--threads', 'opt_threads', default=8,
+  help='Number of threads')
+@click.option('--recursive/--no-recursive', 'opt_recursive', is_flag=True, default=False,
+  help='Use glob recursion (slower)')
+@click.pass_context
+def cli(ctx, opt_dataset, opt_data_store, opt_dir_out, opt_glob_ext, opt_size, opt_interp_algo,
+  opt_slice, opt_threads, opt_recursive):
+  """Resize dataset images"""
+  
+  import os
+  from os.path import join
+  from pathlib import Path
+  from glob import glob
+  from tqdm import tqdm
+  from multiprocessing.dummy import Pool as ThreadPool
+  from functools import partial
+  import pandas as pd
+  import numpy as np
+
+  from app.utils import logger_utils, file_utils, im_utils
+  from app.models.data_store import DataStore
+
+  # -------------------------------------------------
+  # init
+
+  log = logger_utils.Logger.getLogger()
+
+
+  # -------------------------------------------------
+  # process here
+
+  def pool_resize(fp_in, dir_in, dir_out, im_size, interp_algo):
+    # Threaded image resize function
+    pbar.update(1)
+    try:
+      im = Image.open(fp_in).convert('RGB')
+      im.verify()  # throws error if image is corrupt
+      im.thumbnail(im_size, interp_algo)
+      fp_out = fp_in.replace(dir_in, dir_out)
+      file_utils.mkdirs(fp_out)
+      im.save(fp_out, quality=100)
+    except Exception as e:
+      log.warn(f'Could not open: {fp_in}, Error: {e}')
+      return False
+    return True
+
+
+  data_store = DataStore(opt_data_store, opt_dataset)
+  fp_records = data_store.metadata(types.Metadata.FILE_RECORD)
+  df_records = pd.read_csv(fp_records, dtype=cfg.FILE_RECORD_DTYPES).set_index('index')
+  dir_in = data_store.media_images_original()
+
+  # get list of files to process
+  #fp_ims = file_utils.glob_multi(opt_dir_in, ['jpg', 'png'], recursive=opt_recursive)
+  fp_ims = []
+  for ds_record in df_records.itertuples():
+    fp_im = data_store.face(ds_record.subdir, ds_record.fn, ds_record.ext)
+    fp_ims.append(fp_im)
+
+  if opt_slice:
+    fp_ims = fp_ims[opt_slice[0]:opt_slice[1]]
+  if not fp_ims:
+    log.error('No images. Try with "--recursive"')
+    return
+  log.info(f'processing {len(fp_ims):,} images')
+  
+  # algorithm to use for resizing
+  interp_algo = pil_resize_algos[opt_interp_algo]
+  log.info(f'using {interp_algo} for interpoloation')
+
+  # ensure output dir exists
+  file_utils.mkdirs(opt_dir_out)
+
+  # setup multithreading
+  pbar = tqdm(total=len(fp_ims))
+  # fixed arguments for pool function
+  map_pool_resize = partial(pool_resize, dir_in=dir_in, dir_out=opt_dir_out, im_size=opt_size, interp_algo=interp_algo)
+  #result_list = pool.map(prod_x, data_list)  # simple
+  pool = ThreadPool(opt_threads)
+  # start multithreading
+  with tqdm(total=len(fp_ims)) as pbar:
+    results = pool.map(map_pool_resize, fp_ims)
+  # end multithreading
+  pbar.close()
+
+  log.info(f'Resized: {results.count(True)} / {len(fp_ims)} images')
\ No newline at end of file
diff --git a/megapixels/commands/processor/videos_to_frames.py b/megapixels/commands/processor/videos_to_frames.py
new file mode 100644
index 00000000..0b56c46a
--- /dev/null
+++ b/megapixels/commands/processor/videos_to_frames.py
@@ -0,0 +1,73 @@
+from glob import glob
+import os
+from os.path import join
+from pathlib import Path
+
+import click
+
+from app.settings import types
+from app.utils import click_utils
+from app.settings import app_cfg as cfg
+from app.utils import logger_utils
+
+import dlib
+import pandas as pd
+from PIL import Image, ImageOps, ImageFilter
+from app.utils import file_utils, im_utils
+
+
+log = logger_utils.Logger.getLogger()
+
+@click.command()
+@click.option('-i', '--input', 'opt_fp_in', required=True,
+  help='Input directory')
+@click.option('-o', '--output', 'opt_fp_out', required=True,
+  help='Output directory')
+@click.option('--size', 'opt_size', default=(320, 240),
+  help='Inference size for face detection' )
+@click.option('--interval', 'opt_frame_interval', default=20,
+  help='Number of frames before saving next face')
+@click.pass_context
+def cli(ctx, opt_fp_in, opt_fp_out, opt_size, opt_frame_interval):
+  """Converts videos to frames with faces"""
+
+  # -------------------------------------------------
+  # process  
+  
+  from tqdm import tqdm
+  import cv2 as cv
+  from tqdm import tqdm
+  from app.processors import face_detector
+  
+  detector = face_detector.DetectorDLIBCNN()
+
+  # get file list
+  fp_videos = glob(join(opt_fp_in, '*.mp4'))
+  fp_videos += glob(join(opt_fp_in, '*.webm'))
+  fp_videos += glob(join(opt_fp_in, '*.mkv'))
+  
+  frame_interval_count = 0
+  frame_count = 0
+
+  file_utils.mkdirs(opt_fp_out)
+
+  for fp_video in tqdm(fp_videos):
+
+    video = cv.VideoCapture(fp_video)
+    
+    while video.isOpened():
+      res, frame = video.read()
+      if not res:
+        break
+
+      frame_count += 1  # for naming
+      frame_interval_count += 1  # for interval
+      
+      bboxes = detector.detect(frame, opt_size=opt_size, opt_pyramids=0)
+      if len(bboxes) > 0 and frame_interval_count >= opt_frame_interval:
+        # save frame
+        fname = file_utils.zpad(frame_count)
+        fp_frame = join(opt_fp_out, '{}_{}.jpg'.format(Path(fp_video).stem, fname))
+        cv.imwrite(fp_frame, frame)
+        frame_interval_count = 0
+