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authoradamhrv <adam@ahprojects.com>2019-01-18 11:00:18 +0100
committeradamhrv <adam@ahprojects.com>2019-01-18 11:00:18 +0100
commite06af50389f849be0bfe4fa97d39f4519ef2c711 (patch)
tree49755b51e1b8b1f8031e5483333570a8e9951272 /megapixels/commands/processor/resize_dataset.py
parent03ad11fb2a3dcd425d50167b15d72d4e0ef536a2 (diff)
change to cli_proc
Diffstat (limited to 'megapixels/commands/processor/resize_dataset.py')
-rw-r--r--megapixels/commands/processor/resize_dataset.py149
1 files changed, 149 insertions, 0 deletions
diff --git a/megapixels/commands/processor/resize_dataset.py b/megapixels/commands/processor/resize_dataset.py
new file mode 100644
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+++ b/megapixels/commands/processor/resize_dataset.py
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+"""
+Crop images to prepare for training
+"""
+
+import click
+import cv2 as cv
+from PIL import Image, ImageOps, ImageFilter
+
+from app.settings import types
+from app.utils import click_utils
+from app.settings import app_cfg as cfg
+
+cv_resize_algos = {
+ 'area': cv.INTER_AREA,
+ 'lanco': cv.INTER_LANCZOS4,
+ 'linear': cv.INTER_LINEAR,
+ 'linear_exact': cv.INTER_LINEAR_EXACT,
+ 'nearest': cv.INTER_NEAREST
+}
+"""
+Filter Q-Down Q-Up Speed
+NEAREST ⭐⭐⭐⭐⭐
+BOX ⭐ ⭐⭐⭐⭐
+BILINEAR ⭐ ⭐ ⭐⭐⭐
+HAMMING ⭐⭐ ⭐⭐⭐
+BICUBIC ⭐⭐⭐ ⭐⭐⭐ ⭐⭐
+LANCZOS ⭐⭐⭐⭐ ⭐⭐⭐⭐ ⭐
+"""
+pil_resize_algos = {
+ 'antialias': Image.ANTIALIAS,
+ 'lanczos': Image.LANCZOS,
+ 'bicubic': Image.BICUBIC,
+ 'hamming': Image.HAMMING,
+ 'bileaner': Image.BILINEAR,
+ 'box': Image.BOX,
+ 'nearest': Image.NEAREST
+ }
+
+@click.command()
+@click.option('--dataset', 'opt_dataset',
+ type=cfg.DatasetVar,
+ required=True,
+ show_default=True,
+ help=click_utils.show_help(types.Dataset))
+@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('-o', '--output', 'opt_dir_out', required=True,
+ help='Output directory')
+@click.option('-e', '--ext', 'opt_glob_ext',
+ default='png', type=click.Choice(['jpg', 'png']),
+ help='File glob ext')
+@click.option('--size', 'opt_size',
+ type=(int, int), default=(256, 256),
+ help='Output image size max (w,h)')
+@click.option('--interp', 'opt_interp_algo',
+ type=click.Choice(pil_resize_algos.keys()),
+ default='bicubic',
+ help='Interpolation resizing algorithms')
+@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
+ help='Slice the input list')
+@click.option('-t', '--threads', 'opt_threads', default=8,
+ help='Number of threads')
+@click.option('--recursive/--no-recursive', 'opt_recursive', is_flag=True, default=False,
+ help='Use glob recursion (slower)')
+@click.pass_context
+def cli(ctx, opt_dataset, opt_data_store, opt_dir_out, opt_glob_ext, opt_size, opt_interp_algo,
+ opt_slice, opt_threads, opt_recursive):
+ """Resize dataset images"""
+
+ import os
+ from os.path import join
+ from pathlib import Path
+ from glob import glob
+ from tqdm import tqdm
+ from multiprocessing.dummy import Pool as ThreadPool
+ from functools import partial
+ import pandas as pd
+ import numpy as np
+
+ from app.utils import logger_utils, file_utils, im_utils
+ from app.models.data_store import DataStore
+
+ # -------------------------------------------------
+ # init
+
+ log = logger_utils.Logger.getLogger()
+
+
+ # -------------------------------------------------
+ # process here
+
+ def pool_resize(fp_in, dir_in, dir_out, im_size, interp_algo):
+ # Threaded image resize function
+ pbar.update(1)
+ try:
+ im = Image.open(fp_in).convert('RGB')
+ im.verify() # throws error if image is corrupt
+ im.thumbnail(im_size, interp_algo)
+ fp_out = fp_in.replace(dir_in, dir_out)
+ file_utils.mkdirs(fp_out)
+ im.save(fp_out, quality=100)
+ except Exception as e:
+ log.warn(f'Could not open: {fp_in}, Error: {e}')
+ return False
+ return True
+
+
+ data_store = DataStore(opt_data_store, opt_dataset)
+ fp_records = data_store.metadata(types.Metadata.FILE_RECORD)
+ df_records = pd.read_csv(fp_records, dtype=cfg.FILE_RECORD_DTYPES).set_index('index')
+ dir_in = data_store.media_images_original()
+
+ # get list of files to process
+ #fp_ims = file_utils.glob_multi(opt_dir_in, ['jpg', 'png'], recursive=opt_recursive)
+ fp_ims = []
+ for ds_record in df_records.itertuples():
+ fp_im = data_store.face(ds_record.subdir, ds_record.fn, ds_record.ext)
+ fp_ims.append(fp_im)
+
+ if opt_slice:
+ fp_ims = fp_ims[opt_slice[0]:opt_slice[1]]
+ if not fp_ims:
+ log.error('No images. Try with "--recursive"')
+ return
+ log.info(f'processing {len(fp_ims):,} images')
+
+ # algorithm to use for resizing
+ interp_algo = pil_resize_algos[opt_interp_algo]
+ log.info(f'using {interp_algo} for interpoloation')
+
+ # ensure output dir exists
+ file_utils.mkdirs(opt_dir_out)
+
+ # setup multithreading
+ pbar = tqdm(total=len(fp_ims))
+ # fixed arguments for pool function
+ map_pool_resize = partial(pool_resize, dir_in=dir_in, dir_out=opt_dir_out, im_size=opt_size, interp_algo=interp_algo)
+ #result_list = pool.map(prod_x, data_list) # simple
+ pool = ThreadPool(opt_threads)
+ # start multithreading
+ with tqdm(total=len(fp_ims)) as pbar:
+ results = pool.map(map_pool_resize, fp_ims)
+ # end multithreading
+ pbar.close()
+
+ log.info(f'Resized: {results.count(True)} / {len(fp_ims)} images') \ No newline at end of file