externalize drawing, cleanup

8 files changed, 425 insertions, 325 deletions

diff --git a/megapixels/commands/cv/face_landmark.py b/megapixels/commands/cv/face_landmark.py
deleted file mode 100644
index 03ef8fc2..00000000
--- a/megapixels/commands/cv/face_landmark.py
+++ /dev/null
@@ -1,96 +0,0 @@
-"""
-
-"""
-
-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_dirs_in', required=True, multiple=True,
-  help='Input directory')
-@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('-g', '--gpu', 'opt_gpu', default=0,
-  help='GPU index')
-@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
-  help='Slice list of files')
-@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.pass_context
-def cli(ctx, opt_dirs_in, opt_fp_out, opt_ext, opt_size, opt_gpu, opt_slice, 
-  opt_recursive, opt_force):
-  """Converts face imges to 3D 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 dlib  # must keep a local reference for dlib
-  import cv2 as cv
-  import pandas as pd
-  from face_alignment import FaceAlignment, LandmarksType
-  from skimage import io
-
-  from app.utils import logger_utils, file_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
-  
-  device = 'cuda' if opt_gpu > -1 else 'cpu'
-  fa = FaceAlignment(LandmarksType._3D, flip_input=False,  device=device)
- 
-  # 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):
-    fpp_im = Path(fp_im)
-    im = io.imread(fp_im)
-    preds = fa.get_landmarks(im)
-    if preds and len(preds) > 0:
-      data[fpp_im.name]  = preds[0].tolist()
-    
-  # save date
-  file_utils.mkdirs(opt_fp_out)
-
-  file_utils.write_json(data, opt_fp_out, verbose=True)
\ No newline at end of file
diff --git a/megapixels/commands/cv/face_pose_mt.py b/megapixels/commands/cv/face_landmark_2d_5.py
index 8fef2c2c..40ec6f41 100644
--- a/megapixels/commands/cv/face_pose_mt.py
+++ b/megapixels/commands/cv/face_landmark_2d_5.py
@@ -1,5 +1,5 @@
 """
-Converts ROIs to pose: yaw, roll, pitch
+
 """
 
 import click
@@ -8,6 +8,8 @@ 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')
@@ -15,9 +17,9 @@ from app.settings import app_cfg as cfg
   help='Override enum output filename CSV')
 @click.option('-m', '--media', 'opt_dir_media', default=None,
   help='Override enum media directory')
-@click.option('--data_store', 'opt_data_store',
+@click.option('--store', 'opt_data_store',
   type=cfg.DataStoreVar,
-  default=click_utils.get_default(types.DataStore.SSD),
+  default=click_utils.get_default(types.DataStore.HDD),
   show_default=True,
   help=click_utils.show_help(types.Dataset))
 @click.option('--dataset', 'opt_dataset',
@@ -25,6 +27,10 @@ from app.settings import app_cfg as cfg
   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')
@@ -35,9 +41,9 @@ from app.settings import app_cfg as cfg
 @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"""
+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
@@ -47,33 +53,39 @@ def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset,
   
   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
-  from app.processors.face_landmarks import LandmarksDLIB
-  from app.processors.face_pose import FacePoseDLIB
+  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()
-  
-  # set data_store
+  # init filepaths
   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
+  # 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 processors
-  face_pose = FacePoseDLIB()
-  face_landmarks = LandmarksDLIB()
+  # 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)
@@ -88,51 +100,47 @@ def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset,
   df_img_groups = df_roi.groupby('record_index')
   log.debug('processing {:,} groups'.format(len(df_img_groups)))
 
-  # store poses and convert to DataFrame
-  poses = []
+  # store landmarks in list
+  results = []
 
-  # iterate 
+  # iterate groups with file/record index as key
   for record_index, df_img_group in tqdm(df_img_groups):
-    # make fp
+    
+    # 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)
-    for roi_id, df_img in df_img_group.iterrows():
+    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.shape[:2][::-1]
+      dim = im_resized.shape[:2][::-1]
       bbox = BBox.from_xywh(x, y, w, h).to_dim(dim)
-      # get pose
-      landmarks = face_landmarks.landmarks(im, bbox)
-      pose_data = face_pose.pose(landmarks, dim, project_points=opt_display)
-      pose_degrees = pose_data['degrees']  # only keep the degrees data
 
-      # use the project point data if display flag set
-      if opt_display:
-        pts_im = pose_data['points_image']
-        pts_model = pose_data['points_model']
-        pt_nose = pose_data['point_nose']
-        dst = im.copy()
-        face_pose.draw_pose(dst, pts_im, pts_model, pt_nose)
-        face_pose.draw_degrees(dst, pose_degrees)
-        # display to cv window
-        cv.imshow('', dst)
-        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
+      # get landmark points
+      points = landmark_detector.landmarks(im_resized, bbox)
+      points_norm = landmark_detector.normalize(points, dim)
+      points_flat = landmark_detector.flatten(points_norm)
 
-      # add image index and append to result CSV data
-      pose_degrees['record_index'] = record_index
-      poses.append(pose_degrees)
+      # 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)
 
-  # save date
+  # create DataFrame and save to CSV
   file_utils.mkdirs(fp_out)
-  df = pd.DataFrame.from_dict(poses)
+  df = pd.DataFrame.from_dict(results)
   df.index.name = 'index'
-  df.to_csv(fp_out)
\ No newline at end of file
+  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/cv/face_landmark_2d_68.py b/megapixels/commands/cv/face_landmark_2d_68.py
new file mode 100644
index 00000000..e24d4b60
--- /dev/null
+++ b/megapixels/commands/cv/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_flat = landmark_detector.flatten(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(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/cv/face_landmark_3d_68.py b/megapixels/commands/cv/face_landmark_3d_68.py
new file mode 100644
index 00000000..56e60cda
--- /dev/null
+++ b/megapixels/commands/cv/face_landmark_3d_68.py
@@ -0,0 +1,144 @@
+"""
+
+"""
+
+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.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('3D landmark points are normalized in a (200, 200, 200) XYZ space')
+  # 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.FaceLandmark2D_5.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_landmarks2D(im_resized, points)
+        draw_utils.draw_bbox(im_resized, bbox)
+        cv.imshow('', im_resized)
+        display_utils.handle_keyboard()
+
+      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/cv/face_pose.py b/megapixels/commands/cv/face_pose.py
index 4e35210c..70ea1f30 100644
--- a/megapixels/commands/cv/face_pose.py
+++ b/megapixels/commands/cv/face_pose.py
@@ -1,4 +1,6 @@
 """
+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
@@ -6,6 +8,13 @@ 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
@@ -19,7 +28,7 @@ from app.settings import app_cfg as cfg
   help='Override enum output filename CSV')
 @click.option('-m', '--media', 'opt_dir_media', default=None,
   help='Override enum media directory')
-@click.option('--data_store', 'opt_data_store',
+@click.option('--store', 'opt_data_store',
   type=cfg.DataStoreVar,
   default=click_utils.get_default(types.DataStore.HDD),
   show_default=True,
@@ -56,8 +65,8 @@ def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset,
   import pandas as pd
 
   from app.models.bbox import BBox
-  from app.utils import logger_utils, file_utils, im_utils
-  from app.processors.face_landmarks_2d import LandmarksDLIB
+  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
 
@@ -77,9 +86,11 @@ def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset,
 
   # init face processors
   face_pose = FacePoseDLIB()
-  face_landmarks = LandmarksDLIB()
+  face_landmarks = Dlib2D_68()
+
+  # -------------------------------------------------
+  # load data
 
-  # 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
@@ -93,59 +104,60 @@ def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset,
   log.debug('processing {:,} groups'.format(len(df_img_groups)))
 
   # store poses and convert to DataFrame
-  poses = []
+  results = []
 
+  # -------------------------------------------------
   # iterate groups with file/record index as key
   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)
+
+    # 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 = (ds_record.width, ds_record.height)
-      #dim = im.shape[:2][::-1]
+      #dim = (file_record.width, file_record.height)
+      dim = im_resized.shape[:2][::-1]
       bbox = BBox.from_xywh(x, y, w, h).to_dim(dim)
+
       # get pose
-      landmarks = face_landmarks.landmarks(im, bbox)
+      landmarks = face_landmarks.landmarks(im_resized, bbox)
       pose_data = face_pose.pose(landmarks, dim)
       #pose_degrees = pose_data['degrees']  # only keep the degrees data
       #pose_degrees['points_nose'] = pose_data
-      # use the project point data if display flag set
+      
+      # draw landmarks if optioned
       if opt_display:
-        dst = im.copy()
-        face_pose.draw_pose(dst, pose_data['point_nose'], pose_data['points'])
-        face_pose.draw_degrees(dst, pose_data)
-        # display to cv window
-        cv.imshow('', dst)
-        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
+        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][0] / dim[0]
         pose_data[f'point_{k}_y'] = v[0][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')
-      poses.append(pose_data)
+      results.append(pose_data)
 
-  # create dataframe
+  # create DataFrame and save to CSV
   file_utils.mkdirs(fp_out)
-  df = pd.DataFrame.from_dict(poses)
-  # save date
+  df = pd.DataFrame.from_dict(results)
   df.index.name = 'index'
   df.to_csv(fp_out)
+  
   # save script
-  cmd_line = ' '.join(sys.argv)
-  file_utils.write_text(cmd_line, '{}.sh'.format(fp_out))
\ No newline at end of file
+  file_utils.write_text(' '.join(sys.argv), '{}.sh'.format(fp_out))
\ No newline at end of file
diff --git a/megapixels/commands/cv/face_roi.py b/megapixels/commands/cv/face_roi.py
index c3c2ac05..6d42924e 100644
--- a/megapixels/commands/cv/face_roi.py
+++ b/megapixels/commands/cv/face_roi.py
@@ -29,7 +29,7 @@ color_filters = {'color': 1, 'gray': 2, 'all': 3}
   show_default=True,
   help=click_utils.show_help(types.Dataset))
 @click.option('--size', 'opt_size', 
-  type=(int, int), default=(300, 300),
+  type=(int, int), default=(480, 480),
   help='Output image size')
 @click.option('-d', '--detector', 'opt_detector_type',
   type=cfg.FaceDetectNetVar,
@@ -50,7 +50,7 @@ color_filters = {'color': 1, 'gray': 2, 'all': 3}
 @click.option('--color', 'opt_color_filter', 
   type=click.Choice(color_filters.keys()), default='all',
   help='Filter to keep color or grayscale images (color = keep color')
-@click.option('--largest/--all-faces', 'opt_largest', is_flag=True, default=True,
+@click.option('--keep', 'opt_largest', type=click.Choice(['largest', 'all']), default='all',
   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)')
@@ -72,7 +72,7 @@ def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_data_store, opt_dataset,
   import cv2 as cv
   import pandas as pd
 
-  from app.utils import logger_utils, file_utils, im_utils
+  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
 
@@ -113,13 +113,15 @@ def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_data_store, opt_dataset,
 
   # 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 = []
 
   for df_record in tqdm(df_records.itertuples(), total=len(df_records)):
     fp_im = data_store.face(str(df_record.subdir), str(df_record.fn), str(df_record.ext))
     im = cv.imread(fp_im)
-
+    im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
     # filter out color or grayscale iamges
     if color_filter != color_filters['all']:
       try:
@@ -130,9 +132,10 @@ def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_data_store, opt_dataset,
       except Exception as e:
         log.error('Could not check grayscale: {}'.format(fp_im))
         continue
-        
+    
     try:
-      bboxes = detector.detect(im, size=opt_size, pyramids=opt_pyramids, largest=opt_largest, zone=opt_zone)
+      bboxes = 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))
@@ -150,27 +153,22 @@ def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_data_store, opt_dataset,
     if len(bboxes) == 0:
       log.warn(f'no faces in: {fp_im}')
     
-    # debug display
+    # if display optined
     if opt_display and len(bboxes):
-      im_md = im_utils.resize(im, width=min(1200, opt_size[0]))
+      # draw each box
       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
+        bbox_dim = bbox.to_dim(im_resized.shape[:2][::-1])
+        draw_utils.draw_bbox(im_resized, bbox_dim)
 
-  # save date
+      # 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
-  cmd_line = ' '.join(sys.argv)
-  file_utils.write_text(cmd_line, '{}.sh'.format(fp_out))
\ No newline at end of file
+  file_utils.write_text(' '.join(sys.argv), '{}.sh'.format(fp_out))
\ No newline at end of file
diff --git a/megapixels/commands/cv/face_vector.py b/megapixels/commands/cv/face_vector.py
index 7c03205c..9251c053 100644
--- a/megapixels/commands/cv/face_vector.py
+++ b/megapixels/commands/cv/face_vector.py
@@ -103,15 +103,17 @@ def cli(ctx, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, opt_size,
       # get face vector
       bbox_dim = BBox.from_xywh(x, y, w, h).to_dim(dim)  # convert to int real dimensions
       # compute vec
-      # padding=opt_padding not yet implemented in 19.16 but merged in master
+      # padding=opt_padding not yet implemented in dlib===19.16 but merged in master
       vec = facerec.vec(im, bbox_dim, jitters=opt_jitters)
       vec_str = ','.join([repr(x) for x in vec])  # convert to string for CSV
       vecs.append( {'roi_index': roi_index, 'record_index': record_index, 'vec': vec_str})
 
 
-  # create dataframe
+  # create DataFrame and save to CSV
   df = pd.DataFrame.from_dict(vecs)
   df.index.name = 'index'
-  # save CSV
   file_utils.mkdirs(fp_out)
-  df.to_csv(fp_out)
\ No newline at end of file
+  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/cv/face_vector_mt.py b/megapixels/commands/cv/face_vector_mt.py
deleted file mode 100644
index 412f9806..00000000
--- a/megapixels/commands/cv/face_vector_mt.py
+++ /dev/null
@@ -1,118 +0,0 @@
-"""
-Converts ROIs to face vector
-"""
-
-import click
-
-from app.settings import types
-from app.utils import click_utils
-from app.settings import app_cfg as cfg
-
-@click.command()
-@click.option('-o', '--output', 'opt_fp_out', default=None,
-  help='Override enum output filename CSV')
-@click.option('-m', '--media', 'opt_dir_media', default=None,
-  help='Override enum media directory')
-@click.option('--data_store', 'opt_data_store',
-  type=cfg.DataStoreVar,
-  default=click_utils.get_default(types.DataStore.SSD),
-  show_default=True,
-  help=click_utils.show_help(types.Dataset))
-@click.option('--dataset', 'opt_dataset',
-  type=cfg.DatasetVar,
-  required=True,
-  show_default=True,
-  help=click_utils.show_help(types.Dataset))
-@click.option('--size', 'opt_size', 
-  type=(int, int), default=(300, 300),
-  help='Output image size')
-@click.option('-j', '--jitters', 'opt_jitters', default=cfg.DLIB_FACEREC_JITTERS,
-  help='Number of jitters')
-@click.option('-p', '--padding', 'opt_padding', default=cfg.DLIB_FACEREC_PADDING,
-  help='Percentage padding')
-@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
-  help='Slice list of files')
-@click.option('-f', '--force', 'opt_force', is_flag=True,
-  help='Force overwrite file')
-@click.option('-g', '--gpu', 'opt_gpu', default=0,
-  help='GPU index')
-@click.pass_context
-def cli(ctx, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, opt_size, 
-  opt_slice, opt_force, opt_gpu, opt_jitters, opt_padding):
-  """Converts face ROIs to vectors"""
-  
-  import sys
-  import os
-  from os.path import join
-  from pathlib import Path
-  from glob import glob
-  
-  from tqdm import tqdm
-  import numpy as np
-  import dlib  # must keep a local reference for dlib
-  import cv2 as cv
-  import pandas as pd
-
-  from app.models.bbox import BBox
-  from app.models.data_store import DataStore
-  from app.utils import logger_utils, file_utils, im_utils
-  from app.processors import face_recognition
-
-
-  # -------------------------------------------------
-  # init here
-
-  log = logger_utils.Logger.getLogger()
-  # set data_store
-  data_store = DataStore(opt_data_store, opt_dataset)
-
-  # get filepath out
-  fp_out = data_store.metadata(types.Metadata.FACE_VECTOR) if opt_fp_out is None else opt_fp_out
-  if not opt_force and Path(fp_out).exists():
-    log.error('File exists. Use "-f / --force" to overwite')
-    return
-
-  # init face processors
-  facerec = face_recognition.RecognitionDLIB()
-
-  # load data
-  fp_record = data_store.metadata(types.Metadata.FILE_RECORD)
-  df_record = pd.read_csv(fp_record).set_index('index')
-  fp_roi = data_store.metadata(types.Metadata.FACE_ROI)
-  df_roi = pd.read_csv(fp_roi).set_index('index')
-
-  if opt_slice:
-    df_roi = df_roi[opt_slice[0]:opt_slice[1]]
-  
-  # -------------------------------------------------
-  # process here
-  df_img_groups = df_roi.groupby('record_index')
-  log.debug('processing {:,} groups'.format(len(df_img_groups)))
-
-  vecs = []
-  for record_index, df_img_group in tqdm(df_img_groups):
-    # make fp
-    ds_record = df_record.iloc[record_index]
-    fp_im = data_store.face(ds_record.subdir, ds_record.fn, ds_record.ext)
-    im = cv.imread(fp_im)
-    for roi_index, df_img in df_img_group.iterrows():
-      # get bbox
-      x, y, w, h = df_img.x, df_img.y, df_img.w, df_img.h
-      imw = df_img.image_width
-      imh = df_img.image_height
-      dim = im.shape[:2][::-1]
-      # get face vector
-      dim = (imw, imh)
-      bbox_dim = BBox.from_xywh(x, y, w, h).to_dim(dim)  # convert to int real dimensions
-      # compute vec
-      # padding=opt_padding not yet implemented in 19.16 but merged in master
-      vec = facerec.vec(im, bbox_dim, jitters=opt_jitters)
-      vec_str = ','.join([repr(x) for x in vec])  # convert to string for CSV
-      vecs.append( {'roi_index': roi_index, 'record_index': record_index, 'vec': vec_str})
-
-
-  # save date
-  df = pd.DataFrame.from_dict(vecs)
-  df.index.name = 'index'
-  file_utils.mkdirs(fp_out)
-  df.to_csv(fp_out)
\ No newline at end of file