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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 *
MODEL_DIMENSIONS = 224
def predict_image(model, img, top):
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
logger = logging.getLogger(__name__)
#cv2.imshow("test", img)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
array = np.expand_dims(img, axis=0)
array = preprocess_input(array)
predictions = model.predict(array)
classes = decode_predictions(predictions, top=top)
logger.debug("Predicted image classes: {}".format(classes[0]))
return set([(name, prob) for _, name, prob in classes[0]])
def predict_partial(tags, model, img, x, y, rot, top):
#cv2.imshow("test", img[x:(x+MODEL_DIMENSIONS), y:(y+MODEL_DIMENSIONS)])
#cv2.waitKey(0)
if rot is None:
tmp = img[x:(x+MODEL_DIMENSIONS), y:(y+MODEL_DIMENSIONS)]
else:
tmp = cv2.rotate(img[x:(x+MODEL_DIMENSIONS), y:(y+MODEL_DIMENSIONS)], rot)
tags.update(predict_image(model, tmp, top))
'''
Walk over all files for the given base directory and all subdirectories recursively.
Parameters:
args: Argument dict.
'''
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["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"]:
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 i in range(args["index"], len(files)):
file_path = files[i]
logger.info("Handling file {}, {}".format(i, file_path))
tags = tmsu_tags(args["base"], file_path)
not_empty = bool(tags)
logger.info("Existing tags: {}".format(tags))
if args["open_system"]:
open_system(file_path)
# Detect MIME-type for file
mime_type = mime.from_file(file_path)
# Handle images
if mime_type.split("/")[0] == "image":
logger.debug("File is image")
img = cv2.imread(file_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if args["predict_images"]:
logger.info("Predicting image tags ...")
tags_predict = set()
for _ in range(4):
logger.debug("Raw scan")
raw = cv2.resize(img.copy(), dsize=(MODEL_DIMENSIONS, MODEL_DIMENSIONS), interpolation=cv2.INTER_CUBIC)
tags_predict.update(predict_image(model, raw, args["predict_images_top"]))
img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
if not args["predict_images_skip_detail"]:
pool = ThreadPool(max(1, os.cpu_count() - 2), 10000)
if img.shape[0] > img.shape[1]:
detail = image_resize(img.copy(), height=(args["predict_images_detail_factor"] * MODEL_DIMENSIONS))
else:
detail = image_resize(img.copy(), width=(args["predict_images_detail_factor"] * MODEL_DIMENSIONS))
for x in range(0, detail.shape[0], int(MODEL_DIMENSIONS/2)):
for y in range(0, detail.shape[1], int(MODEL_DIMENSIONS/2)):
pool.add_task(predict_partial, tags_predict, model, detail, x, y, None, args["predict_images_top"])
pool.add_task(predict_partial, tags_predict, model, detail, x, y, cv2.ROTATE_90_CLOCKWISE, args["predict_images_top"])
pool.add_task(predict_partial, tags_predict, model, detail, x, y, cv2.ROTATE_180, args["predict_images_top"])
pool.add_task(predict_partial, tags_predict, model, detail, x, y, cv2.ROTATE_90_COUNTERCLOCKWISE, args["predict_images_top"])
pool.wait_completion()
tags_sorted = [tag[0] for tag in sorted(tags_predict, key=lambda tag: tag[1], reverse=True)]
tags_predict = set(list(dict.fromkeys(tags_sorted))[0:args["predict_images_top"]])
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
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(","))
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='.', 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('--predict-images', nargs='?', const=1, default=False, type=bool, help='Use prediction for image tagging (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('--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('-s', '--skip-prompt', nargs='?', const=1, default=False, type=bool, help='Skip prompt for file tags (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,
"predict_images": args.predict_images,
"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,
"gui_tag": args.gui_tag,
"gui_image_length": args.gui_image_length,
"open_system": args.open_system,
"skip_prompt": args.skip_prompt,
"index": args.index,
"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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