still sorting CSV vectors indexes

1 files changed, 0 insertions, 156 deletions

diff --git a/megapixels/commands/cv/files_to_rois.py b/megapixels/commands/cv/files_to_rois.py
deleted file mode 100644
index 1aaf991c..00000000
--- a/megapixels/commands/cv/files_to_rois.py
+++ /dev/null
@@ -1,156 +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', required=True,
-  help='Input CSV (eg image_files.csv)')
-@click.option('-m', '--media', 'opt_dir_media', required=True,
-  help='Input media directory')
-@click.option('-o', '--output', 'opt_fp_out', required=True,
-  help='Output CSV')
-@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('-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('--largest', 'opt_largest', is_flag=True, 
-  help='Only keep largest face')
-@click.pass_context
-def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_size, opt_detector_type, 
-  opt_gpu, opt_conf_thresh, opt_pyramids, opt_slice, opt_display, opt_force, opt_color_filter,
-  opt_largest):
-  """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
-  df_files = pd.read_csv(opt_fp_in).set_index('index')
-
-  if opt_slice:
-    df_files = df_files[opt_slice[0]:opt_slice[1]]
-  log.debug('processing {:,} files'.format(len(df_files)))
-
-
-  data = []
-
-  for df_file in tqdm(df_files.itertuples(), total=len(df_files)):
-    fp_im = join(opt_dir_media, str(df_file.subdir), f'{df_file.fn}.{df_file.ext}')
-    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, size=opt_size, pyramids=opt_pyramids, largest=opt_largest)
-    except Exception as e:
-      log.error('could not detect: {}'.format(fp_im))
-      log.error('{}'.format(e))
-      continue
-
-    for bbox in bboxes:
-      roi = {
-        'image_index': int(df_file.Index),
-        'x': bbox.x, 
-        'y': bbox.y, 
-        'w': bbox.w, 
-        'h': bbox.h,
-        'image_width': im.shape[1],
-        'image_height': im.shape[0]}
-      data.append(roi)
-    
-    # debug display
-    if opt_display and len(bboxes):
-      bbox_dim = bbox.to_dim(im.shape[:2][::-1])  # w,h
-      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.index.name = 'index'
-  df.to_csv(opt_fp_out)
\ No newline at end of file