Is there a way to review my marks?

wdma commented 5 years ago

I am using an algorithm to extract annotation marks per the description given here https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects and would like to check to make certain that YOLO will interpret it correctly. Is there a way to use this?

The problem is that the saved *.jpg files (that will be used for training) are up-side-down from the ones used to generate the marks.

AlexeyAB commented 5 years ago

Can you explain in more detail with screenshots?

wdma commented 5 years ago

Yes. Actually, it occurs to me, I also need to know if the origin is considered to be the upper left, lower left or other.

I will training on spectrogram images with times selected from the audio file. I have attached a spectrogram image, the associated JPG and the text file. The bounding box is not shown on the saved JPG as it will be used for training, but the overlayed bounding box on my computer screen is appropriately placed. I am currently using the lower left as the origin for calculating relative distances, but this is easily changed. The problem (again) is that the saved *jpg file appears to be flipped.

nonlubricious-Russellp100

The text file notation is as follows: 0 0.4782960912711807 0.4918990076284344 0.9283737257814082 0.951664078849659

AlexeyAB commented 5 years ago

@wdma

Just un-comment these part of code and recompile Darknet with OPENCV=1 in Makefile: https://github.com/AlexeyAB/darknet/blob/f4825906916130ac1c32eb69c9fb812aacde1838/src/data.c#L854-L871

Then run training, and you will see many windows with images with labels, to continue press ESC button many times. In such a way you can check wether your label are correct.

wdma commented 5 years ago

@AlexeyAB Thank you for your response. I am not an expert, so I think I may require more instruction.

Can you tell me if Yolo_mark uses the upper left as origin? If not, what orientation is used?

I am guessing that the code that you show is from darknet/src/data.c as I cannot find a closer match in either the darknet or Yolo_mark repositories. My data.c file is different and I cannot find the code you reference to uncomment. I tried uncommenting the section load_data_study but got the following error on compile:

./src/data.c: In function ‘load_data_study’: ./src/data.c:1219:5: error: ‘data {aka struct }’ has no member named ‘indexes’ d.indexes = calloc(n, sizeof(int));

I tried replacing my data.c with your data.c (linked above) and got the following error on compile:

./src/data.c:4:23: fatal error: dark_cuda.h: No such file or directory

AlexeyAB commented 5 years ago

Can you tell me if Yolo_mark uses the upper left as origin?

What is the "upper left as origin" ?

Yolo_mark uses default cv::imread() function for loading images: https://github.com/AlexeyAB/Yolo_mark/blob/61ae338c6ee75fc4ca857777a865ccbfa747d3bd/main.cpp#L425

from OpenCV library: https://docs.opencv.org/3.4.5/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56

and would like to check to make certain that YOLO will interpret it correctly. Is there a way to use this?

Just try to download the latest version of this repository https://github.com/AlexeyAB/darknet to another one directory
Do these changes: https://github.com/AlexeyAB/Yolo_mark/issues/123#issuecomment-476897348
And run training

In such a way you can check that this repository https://github.com/AlexeyAB/darknet with OPENCV=1 in the Makefile correctly interpret your labels.

wdma commented 5 years ago

@AlexeyAB Thank you for your help! I am making progress, but not there yet.

I have followed all of the instructions found here: https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects

My image files are listed in a file named train.txt located in the x64/data directory. The file locations are listed with reference to the folder darknet/x64/data/obj

I am getting the following error:

Couldn't open file: data/train.txt

What am I doing wrong?

wdma commented 5 years ago

Okay, I have made progress.

Question: What is the optimal size for the *.jpg file? I can make it whatever size. The program states: Resizing 736 x 736

I am getting a different error now - detailed below. I am not certain if this is correct, but this is the way that I got the program to "work" (produce the error). Changes to the instructions:

Create file obj.data in the directory build\darknet\x64\data\, containing (where classes = number of objects):

classes= 2 train = x64/data/train.txt valid = x64/data/test.txt names = x64/data/obj.names backup = backup/

Create file train.txt in directory build\darknet\x64\data\, with filenames of your images, each filename in new line, with path relative to darknet.exe, for example containing:

x64/data/obj/img1.jpg x64/data/obj/img2.jpg x64/data/obj/img3.jpg

The error that I am getting now may indicate a problem in the uncommented code per https://stackoverflow.com/questions/11743569/buffer-overflow-detected/11770565:

buffer overflow detected : ======= Backtrace: ========= /lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7f9b9864f /lib/x86_64-linux-gnu/libc.so.6(__fortify_fail+0x5c)[ /lib/x86_64-linux-gnu/libc.so.6(+0x117160)[0x7f9b986e /lib/x86_64-linux-gnu/libc.so.6(+0x1166c9)[0x7f9b986e /lib/x86_64-linux-gnu/libc.so.6(_IO_default_xsputn+0x /lib/x86_64-linux-gnu/libc.so.6(_IO_vfprintf+0x139b)[ /lib/x86_64-linux-gnu/libc.so.6(vsprintf_chk+ /lib/x86_64-linux-gnu/libc.so.6(sprintf_chk+ ./darknet[0x44fafe] ./darknet[0x450c53] /lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba)[0x7f9b /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d)[0x7f9b986 ======= Memory map: ======== 00400000-004b5000 r-xp 00000000 fd:00 16122960 006b5000-006b6000 r--p 000b5000 fd:00 16122960 006b6000-006b7000 rw-p 000b6000 fd:00 16122960 006b7000-006b8000 rw-p 00000000 00:00 0 009cc000-00cf9000 rw-p 00000000 00:00 0 200000000-300200000 ---p 00000000 00:00 0 10000000000-10100000000 ---p 00000000 00:00 0 7f9906000000-7f990c000000 ---p 00000000 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/usr/lib/x86_64-linux-gnu/libswresample-ffmpeg.so.1.2.101 /usr/lib/x86_64-linux-gnu/libv4lconvert.so.0.0.0 /usr/lib/x86_64-linux-gnu/libv4lconvert.so.0.0.0 /usr/lib/x86_64-linux-gnu/libv4lconvert.so.0.0.0 /usr/lib/x86_64-linux-gnu/libv4lconvert.so.0.0.0 /lib/x86_64-linux-gnu/libusb-1.0.so.0.1.0 /lib/x86_64-linux-gnu/libusb-1.0.so.0.1.0 /lib/x86_64-linux-gnu/libusb-1.0.so.0.1.0 /lib/x86_64-linux-gnu/libusb-1.0.so.0.1.0 /usr/lib/x86_64-linux-gnu/libraw1394.so.11.1.0 /usr/lib/x86_64-linux-gnu/libraw1394.so.11.1.0 /usr/lib/x86_64-linux-gnu/libraw1394.so.11.1.0 /usr/lib/x86_64-linux-gnu/libraw1394.so.11.1.0 /usr/lib/x86_64-linux-gnu/libgstvideo-1.0.so.0.803.0 /usr/lib/x86_64-linux-gnu/libgstvideo-1.0.so.0.803.0 /usr/lib/x86_64-linux-gnu/libgstvideo-1.0.so.0.803.0 /usr/lib/x86_64-linux-gnu/libgstvideo-1.0.so.0.803.0 /usr/lib/x86_64-linux-gnu/libgsttag-1.0.so.0.803.0 /usr/lib/x86_64-linux-gnu/libgsttag-1.0.so.0.803.0 /usr/lib/x86_64-linux-gnu/libgsttag-1.0.so.0.803.0 /usr/lib/x86_64-linux-gnu/libgsttag-1.0.so.0.803.0 /usr/lib/x86_64-linux-gnu/libgstaudio-1.0.so.0.803.0 /usr/lib/x86_64-linux-gnu/libgstaudio-1.0.so.0.803.0Aborted (core dumped)

DarylWM commented 5 years ago

Hi @AlexeyAB . I'm trying to use this technique to check the labels of my custom training set. When I run: ./darknet detector train data/peptides/peptides-obj.data ./yolov3-peptides.cfg

After the model initialisation I get: buffer overflow detected : ./darknet terminated Aborted (core dumped)

Thinking I was running out of memory, I tried to reduce the width and height in the cfg to 256 but that didn't help.

For info this is the latest repo, pulled this morning.

AlexeyAB commented 5 years ago

@DarylWM Hi,

Try to change char buff[10]; to char buff[1000]; in un-commented part of code. https://github.com/AlexeyAB/darknet/blob/f4825906916130ac1c32eb69c9fb812aacde1838/src/data.c#L854-L871

DarylWM commented 5 years ago

Wonderful - thanks for your fast response.

DarylWM commented 5 years ago

I'm not seeing any bounding boxes overlaid on my images, which is a bit of a worry. Here's one of my label text files. frame-41049-tile-38-mz-791-809.txt

I have followed the instructions here and I don't get errors during training (such as can't find the label text files).

AlexeyAB commented 5 years ago

@DarylWM

Do you use negative samples for training (with empty txt-files)? Did you compile Darknet with OPENCV=1 in the Makefile? Does it show windows with your training images during training? When you see windows - press and hold ESC button. Can you show screenshot of these windows with images?

Also you can try to use the latest version of Darknet: https://github.com/AlexeyAB/darknet And use flag -show_imgs for Training to show augmented images with bounded boxes, without changing source code. (It also saves images to the jpg-files, even if compiled without OpenCV)

DarylWM commented 5 years ago

Hi @AlexeyAB .

I pulled the latest from the repo and used -show_imgs; thanks for this feature. It saved a few JPEGs with bounding boxes, and then I got:

[xcb] Unknown request in queue while dequeuing
[xcb] Most likely this is a multi-threaded client and XInitThreads has not been called
[xcb] Aborting, sorry about that.
darknet: ../../src/xcb_io.c:179: dequeue_pending_request: Assertion `!xcb_xlib_unknown_req_in_deq' failed.

before it exited. This seems relevant?

Running it a few more times to get a few different JPEGs at a time, I can see that the bounding boxes match my labels, which is a relief. There must be other reasons my network isn't learning very well on my custom training set. I'll investigate some more and ask for advice in your darknet repo's issues.

Thanks!

AlexeyAB commented 5 years ago

@DarylWM Hi,

I pulled the latest from the repo and used -show_imgs; thanks for this feature. It saved a few JPEGs with bounding boxes, and then I got:

[xcb] Unknown request in queue while dequeuing [xcb] Most likely this is a multi-threaded client and XInitThreads has not been called

Yes, it is a WindowX issue.

In the last commit I added ability to run training with both flags -show_imgs -dont_show so it will not cause this error (because it will not show windows), and it will save augmented images to the jpg-files.

AlexeyAB commented 5 years ago

@DarylWM I just added another bug fix.

DarylWM commented 5 years ago

Brilliant. Thanks for your work on this @AlexeyAB .

wdma commented 5 years ago

@DarylWM Can you tell me how you configured darknet to make this work (e.g. What are the contents of your obj.data file?)

My data are on an external harddrive, so I initially thought my problem was with links. I have tried several different configurations and am now pointing directly to the actual files (as versus using links). The closest I seem to get is the error stack listed above:

buffer overflow detected : ./darknet terminated

I am stumped.

DarylWM commented 5 years ago

Hi @wdma. I just used the -show_imgs flag to see the labelled training images as they were fed in, which helped me confirm my labels were correct.

The buffer overflow issue I had has been resolved in the latest repo. Do you have the latest?

My obj.data file was modelled on AlexeyAB's: classes=4 train=data/peptides/data-files/train-list.txt valid=data/peptides/data-files/validation-list.txt names=data/peptides/peptides-obj.names backup=backup/

I use symlinks to point to the peptides directory, so your link to another drive should work.

What problems are you seeing?

wdma commented 5 years ago

@DarylWM @AlexeyAB Thank you for your reply. I was still getting the buffer overflow error. I have rebuilt my computer from scratch (long story), downloaded the latest repo and reinstalled everything, but now I cannot find the correct code to uncomment(??) I am attaching a screenshot of the specified lines (854-871) in src/data.c - they are different.

Screenshot from 2019-04-09 14-26-46

wdma commented 5 years ago

@AlexeyAB @DarylWM Perhaps disregard(?) I figured out a way to give myself some confidence that I am doing it "right." I had been looking for a match in the first few lines. Finally, I read the printed dialog.

The -show_imgs flag saves augmented images.

Thank you so much for your help and for making this tool available!!

AlexeyAB / Yolo_mark

Is there a way to review my marks? #123