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-rw-r--r--megapixels/datasets/commands/crop.py104
-rw-r--r--megapixels/datasets/commands/extract.py86
-rw-r--r--megapixels/datasets/commands/face.py117
-rw-r--r--megapixels/datasets/commands/resize.py81
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)
-
generated by cgit v1.2.3-70-g09d2 (git 2.51.0) at 2025-12-11 12:38:27 +0000