.

4 files changed, 0 insertions, 388 deletions

diff --git a/megapixels/datasets/commands/crop.py b/megapixels/datasets/commands/crop.py
deleted file mode 100644
index 778be0c4..00000000
--- a/megapixels/datasets/commands/crop.py
+++ /dev/null
@@ -1,104 +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
-
-@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/datasets/commands/extract.py b/megapixels/datasets/commands/extract.py
deleted file mode 100644
index 4e77a978..00000000
--- a/megapixels/datasets/commands/extract.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""
-Crop images to prepare for training
-"""
-
-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('--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('--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('--padding', 'opt_padding', default=0,
-  help='Facial padding')
-@click.option('--ext', 'opt_ext_out', default='jpg', type=click.Choice(['jpg', 'png']),
-  help='Output image type')
-@click.pass_context
-def cli(ctx, opt_fp_in, opt_dir_media, opt_dir_out, opt_size, opt_slice, 
-  opt_padding, opt_ext_out):
-  """Extrace 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)
-  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
-
-  for group in groups:
-    
-    # get image
-    group_rows = df_rois_grouped.get_group(group)
-
-    row = group_rows.iloc[0]
-    fp_im = join(opt_dir_media, '{fn}{ext}'.format(**row))  #TODO change to ext
-    im = Image.open(fp_im)
-
-    
-    for idx, roi in group_rows.iterrows():
-      log.info('{}'.format(roi['fn']))
-      # 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
-      bbox_dim_exp = bbox_dim.expand_dim(opt_padding, dim)
-      # crop
-      x1y2 = bbox_dim_exp.pt_tl + bbox_dim_exp.pt_br
-      im_crop = im.crop(box=x1y2)
-      # save
-      idx_zpad = file_utils.zpad(idx, zeros=3)
-      fp_im_out = join(opt_dir_out, '{}_{}.{}'.format(roi['fn'], idx_zpad, opt_ext_out))
-      im_crop.save(fp_im_out)
-
diff --git a/megapixels/datasets/commands/face.py b/megapixels/datasets/commands/face.py
deleted file mode 100644
index 6b7b18b7..00000000
--- a/megapixels/datasets/commands/face.py
+++ /dev/null
@@ -1,117 +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
-
-@click.command()
-@click.option('-i', '--input', 'opt_dir_in', required=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('-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('--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.pass_context
-def cli(ctx, opt_dir_in, opt_fp_out, opt_ext, opt_size, opt_detector_type, 
-  opt_gpu, opt_conf_thresh, opt_pyramids, opt_slice, opt_display):
-  """Extrace face"""
-  
-  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 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.HAAR:
-    log.error('{} not yet implemented'.format(opt_detector_type.name))
-    return
-
-  
-  # -------------------------------------------------
-  # process here
-  
-  # get list of files to process
-  fp_ims = glob(join(opt_dir_in, '*.{}'.format(opt_ext)))
-  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)
-    bboxes = detector.detect(im, opt_size=opt_size, opt_pyramids=opt_pyramids)
-    fpp_im = Path(fp_im)
-    for bbox in bboxes:
-      roi = {
-        'fn': fpp_im.stem, 
-        'ext': fpp_im.suffix, 
-        'x': bbox.x, 
-        'y': bbox.y, 
-        'w': bbox.w, 
-        'h': bbox.h}
-      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=opt_size[0])
-      for bbox in bboxes:
-        dim = bbox.to_dim(im_md.shape[:2][::-1])
-        cv.rectangle(im_md, dim.pt_tl, 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
-  df = pd.DataFrame.from_dict(data)
-  df.to_csv(opt_fp_out)
\ No newline at end of file
diff --git a/megapixels/datasets/commands/resize.py b/megapixels/datasets/commands/resize.py
deleted file mode 100644
index 5e2d31aa..00000000
--- a/megapixels/datasets/commands/resize.py
+++ /dev/null
@@ -1,81 +0,0 @@
-"""
-Crop images to prepare for training
-"""
-
-import click
-
-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   ⭐⭐⭐⭐  ⭐⭐⭐⭐  ⭐
-"""
-
-@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='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 (square)')
-@click.option('--method', 'opt_scale_method', 
-  type=click.Choice(['LANCZOS', 'BICUBIC', 'HAMMING', 'BILINEAR', 'BOX', 'NEAREST']),
-  default='LANCZOS',
-  help='Scaling method to use')
-@click.pass_context
-def cli(ctx, opt_dir_in, opt_dir_out, opt_glob_ext, opt_size, opt_scale_method):
-  """Crop, mirror images"""
-  
-  import os
-  from os.path import join
-  from pathlib import Path
-  from glob import glob
-  from tqdm import tqdm
-  from PIL import Image, ImageOps, ImageFilter
-  from app.utils import logger_utils, file_utils, im_utils
-  
-  # -------------------------------------------------
-  # init
-
-  log = logger_utils.Logger.getLogger()
-  
-  methods = {
-    'LANCZOS': Image.LANCZOS,
-    'BICUBIC': Image.BICUBIC,
-    'HAMMING': Image.HAMMING,
-    'BILINEAR': Image.BILINEAR,
-    'BOX': Image.BOX,
-    'NEAREST': Image.NEAREST
-    }
-
-  # -------------------------------------------------
-  # process here
-
-  # get list of files to process
-  fp_ims = glob(join(opt_dir_in, '*.{}'.format(opt_glob_ext)))
-  log.info('processing {:,} files'.format(len(fp_ims)))
-  
-  # set scale method
-  scale_method = methods[opt_scale_method]
-
-  # 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)
-    im = ImageOps.fit(im, opt_size, method=scale_method, centering=(0.5, 0.5))
-    fp_out = join(opt_dir_out, Path(fp_im).name)
-    im.save(fp_out)
-