change to cli_proc

1 files changed, 0 insertions, 187 deletions

diff --git a/megapixels/commands/cv/face_roi.py b/megapixels/commands/cv/face_roi.py
deleted file mode 100644
index e83b0f61..00000000
--- a/megapixels/commands/cv/face_roi.py
+++ /dev/null
@@ -1,187 +0,0 @@
-"""
-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='all',
-  help='Filter to keep color or grayscale images (color = keep color')
-@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)')
-@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