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(3) Put the 'YOLO_(version).ckpt' in the 'weight' folder of downloaded code
3.Usage
(1) direct usage with default settings (display on console, show output image, no output file writing)
python YOLO_(small or tiny)_tf.py -fromfile (input image filename)
(2) direct usage with custom settings
python YOLO_(small or tiny)_tf.py argvs
where argvs are
-fromfile (input image filename) : input image file
-disp_console (0 or 1) : whether display results on terminal or not
-imshow (0 or 1) : whether display result image or not
-tofile_img (output image filename) : output image file
-tofile_txt (output txt filename) : output text file (contains class, x, y, w, h, probability)
(3) import on other scripts
import YOLO_(small or tiny)_tf
yolo = YOLO_(small or tiny)_tf.YOLO_TF()
yolo.disp_console = (True or False, default = True)
yolo.imshow = (True or False, default = True)
yolo.tofile_img = (output image filename)
yolo.tofile_txt = (output txt filename)
yolo.filewrite_img = (True or False, default = False)
yolo.filewrite_txt = (True of False, default = False)
yolo.detect_from_file(filename)
yolo.detect_from_cvmat(cvmat)
4.Requirements
Tensorflow
Opencv2
5.Copyright
According to the LICENSE file of the original code,
Me and original author hold no liability for any damages
Do not use this on commercial!
6.Changelog
2016/02/15 : First upload!
2016/02/16 : Added YOLO_tiny, Fixed bug that ignores one of the boxes in grid when both boxes detected valid objects