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import numpy as np
import argparse
import os, sys
from gui import GuiMain, GuiImage, GuiTag
import cv2
import logging
import magic
from tmsu import *
from util import *
from predictor import *
from PIL import Image
import datetime
'''
Walk over all files for the given base directory and all subdirectories recursively.
Parameters:
args: Argument dict.
'''
def walk(tmsu, args):
logger = logging.getLogger(__name__)
logger.info("Walking files ...")
mime = magic.Magic(mime=True)
files = [os.path.abspath(os.path.join(dp, f)) for dp, dn, filenames in os.walk(args["file_dir"]) for f in filenames]
logger.debug("Files: {}".format(files))
logger.info("Number of files found: {}".format(len(files)))
if args["index"] >= len(files):
logger.error("Invalid start index. index = {}, number of files = {}".format(args["index"], len(files)))
return
if args["predict_images"] or args["predict_videos"]:
backend = {
"torch": Predictor.BackendTorch,
"tensorflow": Predictor.BackendTensorflow,
"keras": Predictor.BackendTensorflow
}.get(args["predict_images_backend"])
if backend == Predictor.BackendTorch:
predictor = Predictor(Predictor.BackendTorch(top=args["predict_images_top"]))
elif backend == Predictor.BackendTensorflow:
predictor = Predictor(Predictor.BackendTensorflow(top=args["predict_images_top"], detail=(not args["predict_images_skip_detail"]), detail_factor=args["predict_images_detail_factor"]))
for i in range(args["index"], len(files)):
file_path = files[i]
logger.info("Handling file {}, {}".format(i, file_path))
tags = tmsu.tags(file_path)
not_empty = bool(tags)
logger.info("Existing tags: {}".format(tags))
if ".tmsu" in file_path:
logger.info("Database meta file, skipping.")
continue
logger.info("Renaming file {}".format(file_path))
file_path = files[i] = {
"none": lambda x: x,
"sha1": rename_sha1,
"sha256": rename_sha256,
"cdate": rename_cdate,
"mdate": rename_mdate
}.get(args["rename"])(file_path)
logger.info("New file name: {}".format(file_path))
if (not_empty and args["skip_tagged"]):
logger.info("Already tagged, skipping.")
continue
if args["open_system"]:
open_system(file_path)
if args["tag_metadata"]:
# Base name and extension
base = os.path.splitext(os.path.basename(file_path))
if base[1]:
tags.update({base[0], base[1]})
else:
tags.update({base[0]})
# File creation and modification time
time_c = datetime.datetime.fromtimestamp(os.path.getctime(file_path))
time_m = datetime.datetime.fromtimestamp(os.path.getmtime(file_path))
tags.update({time_c.strftime("%Y-%m-%d"),
time_c.strftime("%Y"),
time_c.strftime("%B"),
time_c.strftime("%A"),
time_c.strftime("%Hh")})
if time_c != time_m:
tags.update({time_m.strftime("%Y-%m-%d"),
time_m.strftime("%Y"),
time_m.strftime("%B"),
time_m.strftime("%A"),
time_m.strftime("%Hh")})
# Detect MIME-type for file
mime_type = mime.from_file(file_path).split("/")
tags.update(mime_type)
# Handle images
if mime_type[0] == "image":
logger.debug("File is image")
if args["predict_images"] or args["gui_tag"]:
img = cv2.imread(file_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if args["predict_images"]:
logger.info("Predicting image tags ...")
tags_predict = predictor.predict(img)
logger.info("Predicted tags: {}".format(tags_predict))
tags.update(tags_predict)
if args["gui_tag"]:
while(True): # For GUI inputs (rotate, ...)
logger.debug("Showing image GUI ...")
img_show = image_resize(img, width=args["gui_image_length"]) if img.shape[1] > img.shape[0] else image_resize(img, height=args["gui_image_length"])
#img_show = cv2.cvtColor(img_show, cv2.COLOR_BGR2RGB)
ret = GuiImage(i, file_path, img_show, tags).loop()
tags = set(ret[1]).difference({''})
if ret[0] == GuiImage.RETURN_ROTATE_90_CLOCKWISE:
img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
elif ret[0] == GuiImage.RETURN_ROTATE_90_COUNTERCLOCKWISE:
img = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE)
elif ret[0] == GuiImage.RETURN_NEXT:
break
elif ret[0] == GuiImage.RETURN_ABORT:
return
elif mime_type[0] == "video":
logger.debug("File is video")
if args["predict_videos"] or args["gui_tag"]:
cap = cv2.VideoCapture(file_path)
n_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)
step = n_frames / args["predict_videos_key_frames"]
print(step)
preview = None
for frame in np.arange(0, n_frames, step):
cap.set(cv2.CAP_PROP_POS_FRAMES, max(-1, round(frame - 1)))
_, f = cap.read()
f = cv2.cvtColor(f, cv2.COLOR_BGR2RGB)
if frame == 0:
preview = f
if args["predict_videos"]:
logger.info("Predictig video frame {} of {}".format(frame, n_frames))
tags_predict = predictor.predict(f)
logger.info("Predicted tags: {}".format(tags_predict))
tags.update(tags_predict)
else:
break
if args["gui_tag"]:
while(True): # For GUI inputs (rotate, ...)
logger.debug("Showing image GUI ...")
img_show = image_resize(preview, width=args["gui_image_length"]) if preview.shape[1] > preview.shape[0] else image_resize(preview, height=args["gui_image_length"])
#img_show = cv2.cvtColor(img_show, cv2.COLOR_BGR2RGB)
ret = GuiImage(i, file_path, img_show, tags).loop()
tags = set(ret[1]).difference({''})
if ret[0] == GuiImage.RETURN_ROTATE_90_CLOCKWISE:
preview = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
elif ret[0] == GuiImage.RETURN_ROTATE_90_COUNTERCLOCKWISE:
preview = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE)
elif ret[0] == GuiImage.RETURN_NEXT:
break
elif ret[0] == GuiImage.RETURN_ABORT:
return
else:
if args["gui_tag"]:
while(True):
logger.debug("Showing generic tagging GUI ...")
ret = GuiTag(i, file_path, tags).loop()
tags = set(ret[1]).difference({''})
if ret[0] == GuiTag.RETURN_NEXT:
break
elif ret[0] == GuiTag.RETURN_ABORT:
return
if ((not args["gui_tag"]) and (not args["skip_prompt"])):
tags = set(input_with_prefill("\nTags for file {}:\n".format(file_path), ','.join(tags)).split(","))
tags = { tag.lower().replace(" ", "_") for tag in tags }
logger.info("Tagging {}".format(tags))
tmsu.tag(file_path, tags, untag=not_empty)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Tag multiple files using TMSU.')
parser.add_argument('-b', '--base', nargs='?', default='.', type=dir_path, help='Base directory with database (default: %(default)s)')
parser.add_argument('-f', '--file-dir', nargs='?', default='.', type=dir_path, help='File directory for walking (default: %(default)s)')
parser.add_argument('-g', '--gui', nargs='?', const=1, default=False, type=bool, help='Show main GUI (default: %(default)s)')
parser.add_argument('--tmsu-command', nargs='?', const=1, default="tmsu", type=str, help='TMSU command override (default: %(default)s)')
parser.add_argument('-r', '--rename', nargs='?', const=1, choices=["none", "sha1", "sha256", "cdate", "mdate"], default="none", type=str.lower, help='Rename files based on given scheme (default: %(default)s)')
parser.add_argument('--tag-metadata', nargs='?', const=1, default=True, type=bool, help='Use metadata as default tags (default: %(default)s)')
parser.add_argument('--predict-images', nargs='?', const=1, default=False, type=bool, help='Use prediction for image tagging (default: %(default)s)')
parser.add_argument('--predict-images-backend', nargs='?', const=1, choices=["torch", "tensorflow", "keras"], default="torch", type=str.lower, help='Determines which backend should be used for keyword prediction (default: %(default)s)')
parser.add_argument('--predict-images-top', nargs='?', const=1, default=10, type=int, help='Defines how many top prediction keywords should be used (default: %(default)s)')
parser.add_argument('--predict-images-detail-factor', nargs='?', const=1, default=2, type=int, help='Width factor for detail scan, multiplied by 224 for ResNet50 (default: %(default)s)')
parser.add_argument('--predict-images-skip-detail', nargs='?', const=1, default=False, type=bool, help='Skip detail scan in image prediction (default: %(default)s)')
parser.add_argument('--predict-videos', nargs='?', const=1, default=False, type=bool, help='Use prediction for video tagging (default: %(default)s)')
parser.add_argument('--predict-videos-key-frames', nargs='?', const=1, default=5, type=int, help='Defines how many key frames are used to predict videos (default: %(default)s)')
parser.add_argument('--gui-tag', nargs='?', const=1, default=False, type=bool, help='Show GUI for tagging (default: %(default)s)')
parser.add_argument('--gui-image-length', nargs='?', const=1, default=800, type=int, help='Length of longest side for preview (default: %(default)s)')
parser.add_argument('--open-system', nargs='?', const=1, default=False, type=bool, help='Open all files with system default (default: %(default)s)')
parser.add_argument('--skip-prompt', nargs='?', const=1, default=False, type=bool, help='Skip prompt for file tags (default: %(default)s)')
parser.add_argument('--skip-tagged', nargs='?', const=1, default=False, type=bool, help='Skip already tagged files (default: %(default)s)')
parser.add_argument('-i', '--index', nargs='?', const=1, default=0, type=int, help='Start tagging at the given file index (default: %(default)s)')
parser.add_argument('-v', '--verbose', action="count", default=0, help="Verbosity level")
args = parser.parse_args()
if args.verbose == 0:
log_level = logging.WARNING
elif args.verbose == 1:
log_level = logging.INFO
elif args.verbose >= 2:
log_level = logging.DEBUG
logging.basicConfig(stream=sys.stdout, level=log_level)
logger = logging.getLogger(__name__)
args = {
"base": args.base,
"file_dir": args.file_dir,
"gui": args.gui,
"tmsu_command": args.tmsu_command,
"rename": args.rename,
"tag_metadata": args.tag_metadata,
"predict_images": args.predict_images,
"predict_images_backend": args.predict_images_backend,
"predict_images_top": args.predict_images_top,
"predict_images_detail_factor": args.predict_images_detail_factor,
"predict_images_skip_detail": args.predict_images_skip_detail,
"predict_videos": args.predict_videos,
"predict_videos_key_frames": args.predict_videos_key_frames,
"gui_tag": args.gui_tag,
"gui_image_length": args.gui_image_length,
"open_system": args.open_system,
"skip_prompt": args.skip_prompt,
"skip_tagged": args.skip_tagged,
"index": args.index,
"verbosity": args.verbose
}
logger.debug("args = {}".format(args))
if args["gui"]:
logger.debug("Starting main GUI ...")
args = GuiMain(args).loop()
tmsu = TMSU(args["base"], args["tmsu_command"])
if tmsu.status:
walk(tmsu, args)
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