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import numpy as np
import argparse
import os, sys
from gui import GuiMain, GuiImage
import cv2
import logging
import magic
from subprocess import Popen, PIPE
import re
def dir_path(string):
if os.path.isdir(string):
return string
else:
raise NotADirectoryError(string)
def tmsu_init(base):
logger = logging.getLogger(__name__)
if not os.path.exists(os.path.join(base, ".tmsu")):
logger.info("TMSU database does not exist, creating ...")
proc = Popen(["tmsu", "init"], cwd=base)
proc.wait()
logger.debug("TMSU returncode: {}".format(proc.returncode))
if proc.returncode != 0:
logger.error("Could not initialize TMSU database.")
return False
return True
def tmsu_tags(base, file):
logger = logging.getLogger(__name__)
logger.debug("Getting existing tags for file {}".format(file))
tags = set()
proc = Popen(["tmsu", "tags", file], cwd=base, stdout=PIPE, stderr=PIPE)
proc.wait()
logger.debug("TMSU returncode: {}".format(proc.returncode))
if proc.returncode == 0:
tags.update(re.split("\s", proc.stdout.read().decode())[1:-1])
else:
logger.error("Could not get tags for file {}".format(file))
return tags
def tmsu_tag(base, file, tags, untag=True):
logger = logging.getLogger(__name__)
if untag:
logger.debug("Untagging file")
proc = Popen(["tmsu", "untag", "--all", file], cwd=base, stdout=PIPE, stderr=PIPE)
proc.wait()
if proc.returncode != 0:
logger.error("Could not untag file {}".format(file))
if tags:
logger.debug("Writing tags {}".format(tags))
proc = Popen(["tmsu", "tag", file] + list(tags), cwd=base, stdout=PIPE, stderr=PIPE)
proc.wait()
if proc.returncode != 0:
logger.error("Could not write tags to file {}".format(file))
else:
logger.info("Tags are empty, ignoring")
def walk(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["base"]) for f in filenames]
logger.debug("Files: {}".format(files))
logger.info("Number of files found: {}".format(len(files)))
if args["predict_images"]:
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Model
model = ResNet50(weights="imagenet")
for file_path in files:
logger.info("Handling file {}".format(file_path))
tags = tmsu_tags(args["base"], file_path)
not_empty = bool(tags)
logger.info("Existing tags: {}".format(tags))
mime_type = mime.from_file(file_path)
if mime_type.split("/")[0] == "image":
logger.debug("File is image")
img = cv2.imread(file_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, dsize=(800, 800), interpolation=cv2.INTER_CUBIC)
while(True):
if args["predict_images"]:
logger.info("Predicting image tags ...")
array = cv2.resize(img, dsize=(224, 224), interpolation=cv2.INTER_CUBIC)
array = np.expand_dims(array, axis=0)
array = preprocess_input(array)
predictions = model.predict(array)
classes = decode_predictions(predictions, top=10)
logger.debug("Predicted image classes: {}".format(classes[0]))
tags.update([name for _, name, _ in classes[0]])
logger.info("Predicted tags: {}".format(tags))
if args["gui_images"]:
logger.debug("Showing image GUI ...")
ret = GuiImage(img, 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
continue
break
logger.info("Tagging {}".format(tags))
tmsu_tag(args["base"], 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='./test', type=dir_path, help='Base 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('--predict-images', nargs='?', const=1, default=False, type=bool, help='Use prediction for image tagging (default: %(default)s)')
parser.add_argument('--gui-images', nargs='?', const=1, default=False, type=bool, help='Show GUI for image tagging (default: %(default)s)')
parser.add_argument('--gui-audio', nargs='?', const=1, default=False, type=bool, help='Show GUI for audio tagging (default: %(default)s)')
parser.add_argument('--gui-video', nargs='?', const=1, default=False, type=bool, help='Show GUI for video tagging (default: %(default)s)')
parser.add_argument('--open-all', nargs='?', const=1, default=False, type=bool, help='Open all files with system default (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,
"gui": args.gui,
"predict_images": args.predict_images,
"gui_images": args.gui_images,
"gui_audio": args.gui_audio,
"gui_video": args.gui_video,
"open_all": args.open_all,
"verbosity": args.verbose
}
logger.debug("args = {}".format(args))
if args["gui"]:
logger.debug("Starting main GUI ...")
args = GuiMain(args).loop()
if tmsu_init(args["base"]):
walk(args)
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