aashrafh / mozart Goto Github PK

Hi I was hopping you could help locate the training data set referred to in src/train.py on line 13

dataset_path = 'train_data/data'
target_img_size = (100, 100)
sample_count = 50

I came here from this tutorial but was unable find any links. This is an outstanding project and I would love to see how I can tweak the model myself. Thanks.

Hi, I ended up having to use the google drive API to get a complete download of the dataset because my browser kept crashing. I figured other people might be having similar issues so I uploaded a pre-zipped archive of the original dataset to google drive, which takes a couple seconds to download. I also uploaded the dataset to kaggle although I had to replace the # character with the word sharp for it to be compatible with kaggle.

Links:

• google drive
• kaggle

p.s
I'm working on a fork called wieniawski which I plan to turn into a mobile app to help users read sheet music. I made a proof of concept at a hackathon a couple years ago using this project as well. Thanks, for putting this out there.

apologies if this is a stupid question as I am just starting with computer music notation, but is the txt output of your OMR compatible with lilypond?
Thanks

Hi, when I run conda env create -f requirements.yml I got this:

Collecting package metadata (repodata.json): / Terminated

Here's my conda version:

$ conda -V
conda 23.5.0

Could anyone please give me any advice?

Hello, good to see this repo! May I ask do you have a plan to incorperate a tool to generate audio file from text representation?

Hello @aashrafh ! thank you for your repo.
I'm successfully done on install step3. "conda activate mozart"
but when I get through step4, It gives me an error 'Symbol not Found'
and also I wonder that do I need to run main.py file on Mozart-main/src/ directory? I'm not sure about this step.
my environment is macOS, BIg Sur.

this is the error I got.
Traceback (most recent call last): File "main.py", line 2, in <module> from pre_processing import * File "/Users/parkhyunwoo/opt/anaconda3/envs/mozart/Mozart-main/src/pre_processing.py", line 4, in <module> import cv2 ImportError: dlopen(/Users/parkhyunwoo/opt/anaconda3/envs/mozart/lib/python3.7/site-packages/cv2.cpython-37m-darwin.so, 2): Symbol not found: _mp_get_memory_functions Referenced from: /Users/parkhyunwoo/opt/anaconda3/envs/mozart/lib/libgnutls.30.dylib Expected in: /Users/parkhyunwoo/opt/anaconda3/envs/mozart/lib/libhogweed.4.dylib in /Users/parkhyunwoo/opt/anaconda3/envs/mozart/lib/libgnutls.30.dylib

from fit import match, remove_repeated_matches, predict

fit.py has no methods match, remove_repeated_matches declared

I got the following error:

conda env create -f requirements.yml
Collecting package metadata (repodata.json): done
Solving environment: failed

I tried on two different MacBook Pro, and got the same errors.

It will be great to provide some hints how to solve this problem.

Thanks!

In the spirit of #13 ("could you release a binary") I suggest releasing a version in a Docker container to reduce the pain of installation.

When I am trying to use this after following all the steps, I get the error below

Traceback (most recent call last): File "main.py", line 1, in <module> from commonfunctions import * ModuleNotFoundError: No module named 'commonfunctions'

Is it possible for you to provide a binary release of this project? Because I'm dumb as hell when it comes to these types of installs.

Hi, I am trying to run Mozart with one of my own PNG files and am not receiving any output. Does this PNG disagree with Mozart's specifications?

I get the following error while running the main script as

python src/main.py testcases output

Traceback (most recent call last):
  File "src/main.py", line 255, in <module>
    main(args.inputfolder, args.outputfolder)
  File "src/main.py", line 243, in main
    imgs_with_staff, imgs_spacing, imgs_rows)
  File "src/main.py", line 120, in recognize
    labels = predict(saved_img)
  File "/home/napulen/Mozart/src/fit.py", line 11, in predict
    train('NN', 'hog', 'nn_trained_model_hog')
  File "/home/napulen/Mozart/src/train.py", line 118, in train
    model, accuracy = run_experiment(model_name, feature_name, dir_names)
  File "/home/napulen/Mozart/src/train.py", line 104, in run_experiment
    features, labels, test_size=0.2, random_state=random_seed)
  File "/home/napulen/miniconda3/envs/mozart2/lib/python3.7/site-packages/sklearn/model_selection/_split.py", line 2131, in train_test_split
    default_test_size=0.25)
  File "/home/napulen/miniconda3/envs/mozart2/lib/python3.7/site-packages/sklearn/model_selection/_split.py", line 1814, in _validate_shuffle_split
    train_size)
ValueError: With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.

Any idea what's going on?

Very nice work, but I have a question in my mind. How should we evaluate the generated txt outputs, that is, with which method can we benchmark the OMR system?

Hi , was just wondering if there is an associated colab notebook available to quickly try out examples ?

Thank you very much for this wonderful repo.
I am doing research works on OMR, and tried the command:
python3 main.py <input directory path> <output directory path>
it works fine. But when I executed this:
python3 train.py
got error with:
ValueError: With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.
So I found the dataset directory missed in the project:
dataset_path = 'train_data/data'
Can you commit it again?

Thanks!