def process_arguments():
parser = ArgParserWithDefaultHelp(description='Images test classifier tool')
parser.add_argument('classifier', help='Classifier')
parser.add_argument('folder', help='Folders with frames (divided into two parts: positive and negative examples')
parser.add_argument('-o', '--output', help='Incorrect results folder')
parser.add_argument('--save-correct', dest='save_correct', action='store_true', help='Save correct determined images')
parser.add_argument('-j', '--jobs', default=-1, type=int, help='Processes amount for feature extraction')
parser.add_argument('--verbose', dest='verbose', action='store_true', help='Debug output')
parser.set_defaults(save_correct=False)
parser.set_defaults(verbose=False)
return parser.parse_args()
def process_arguments():
parser = ArgParserWithDefaultHelp(description='Extract frames sample tool from several folders')
parser.add_argument('folder', help='Folder, that contains folders with frames')
parser.add_argument('output', help='Folder, to copy selected frames')
parser.add_argument('-s', '--size', dest='size', default='middle', choices=['large', 'middle', 'small', 'extrasmall'], help='Size of sample')
parser.add_argument('--save-structure', dest='saveStructure', action='store_true', help='Save folder structure in output folder')
parser.set_defaults(saveStructure=False)
return parser.parse_args()
def process_arguments():
parser = ArgParserWithDefaultHelp(description="Dataset creation tool from several folders")
parser.add_argument("folder", help="Folder, that contains folders with frames")
parser.add_argument("train", help="Train dataset")
parser.add_argument("test", help="Test dataset")
parser.add_argument("--train_files", default=None, help="File with list of images, that included in train set")
parser.add_argument("--test_files", default=None, help="File with list of images, that included in test set")
parser.add_argument(
"-p",
"--positive-fragments-folder",
dest="positive_fragments_folder",
default=None,
help="Folder to put positive fragments of frames",
)
parser.add_argument("-t", "--type", default="large", choices=["large", "small"], help="Size of train set")
parser.add_argument(
"-m", "--negmult", default=3, type=int, help="Negative multiplicator: how more negative examples than positive"
)
parser.add_argument(
"-n",
"--neighbours-for-positive",
dest="neighbours",
default=7,
type=int,
help="Generate this neighbours amount for every positive window",
)
parser.add_argument("-j", "--jobs", default=-1, type=int, help="Processes amount for feature extraction")
parser.add_argument(
"-o",
"--dataset-type",
dest="dataset_type",
default="csv",
choices=["pkl", "csv"],
help="Type of dataset output",
)
parser.add_argument("-w", "--window-size", dest="window_size", default=64, type=int, help="Window size")
parser.add_argument("-s", "--shift-size", dest="shift_size", default=32, type=int, help="Shift size")
parser.add_argument(
"-r", "--features-window-size", dest="features_window_size", default=32, type=int, help="Features window size"
)
parser.add_argument(
"-f", "--features-type", dest="features_type", default="hog", choices=["hog", "daisy"], help="Features detector"
)
parser.add_argument(
"--only-first-symbol",
dest="first_symbol_tag",
action="store_true",
help="Positive only on first symbol from tag",
)
parser.set_defaults(first_symbol_tag=False)
return parser.parse_args()
action = self.wait_for_action()
if action == 'break':
return False
elif action == 'next':
if not self.show_next():
shutil.rmtree(folder)
return True
def destroy_windows(self):
cv2.destroyAllWindows()
if __name__ == '__main__':
'''
$ find /share/strg/frames/ -mtime -10 -type d >temp_ls
remove first line from file
$ while read file;do echo ln -s "$file" ./frames_291013/; done <temp_ls <-- remove echo
'''
parser = ArgParserWithDefaultHelp(description='Tagging images tool')
parser.add_argument('folder', help='Folder, that contains folders with frames')
parser.add_argument('output', help='Output folder for tagged images')
args = parser.parse_args()
ft = FolderTagger()
for fn in os.listdir(args.folder):
if os.path.isdir(os.path.join(args.folder, fn)):
if not ft.tag_folder(os.path.join(args.folder, fn), args.output):
break
# ft.tag_folder(args.folder, args.output)
color = (0, 255, 0)
shift = 1
else:
color = (255, 255, 0)
shift = -1
cv2.rectangle(image, (loc[0]+shift, loc[1]+shift), (loc[0]+patternShape[0]+shift, loc[1]+patternShape[1]+shift), color, 1)
for r, (x1, y1, x2, y2) in result:
cv2.rectangle(image, (y1, x1), (y2, x2), colorMap[r], 1)
path, bf = os.path.split(filename)
cv2.imwrite(os.path.join(output, bf), image)
if __name__ == '__main__':
parser = ArgParserWithDefaultHelp(description='Visualising tool for pattern matcher and classifier')
parser.add_argument('folder', help='Folder, that contains folders with frames')
parser.add_argument('patterns', help='Folder with patterns')
parser.add_argument('classifier', help='Classifier')
parser.add_argument('output', help='Output folder for processed images')
parser.add_argument('-j', '--jobs', default=-1, type=int, help='Processes amount for parallel processing')
args = parser.parse_args()
classifier = load(args.classifier)
FileHelper.create_or_clear_dir(args.output)
wfc = WindowedFeatureClassifier(classifier)
pm = PatternMatcher()
pm.load_patterns_folder(args.patterns)
