ekilic / heatmap-learner-cnn-for-object-counting Goto Github PK

rawmode = RAWMODE[im.mode]
KeyError: 'RGBA'
raise OSError(f"cannot write mode {im.mode} as JPEG") from e
OSError: cannot write mode RGBA as JPEG

is this because of PILLOW library or incorrect dataset path?

This single object detection with heatmap learning. Is this the same idea with the rep https://github.com/littleaich/heatmap-regulation. (regression counting) or detection based counting.
Can you publish your source code ?

I was trying this on my jupyter notebook and got some directory issue.

def __init__(self, root, set, train = True):

    self.root = root
    self.path_devkit = os.path.join(root, 'CARPK')
    self.path_images = os.path.join(root, 'CARPK', 'Images')
    self.classes = object_categories
    self.train = train
    id_list_file = os.path.join(self.path_devkit, 'ImageSets//{0}.txt'.format(set))
    self.ids = [id_.strip() for id_ in open(id_list_file)]
    print('CARPK dataset set=%s number of classes=%03d  number of images=%d' % (
    set, len(self.classes), len(self.ids)))

have corrected with "//" in my code.

I tried to run train.py to train from scratch. The result I obtained with test.py is:

MAE: 8.969498910675382
RMSE: 17.12831237942957

When I try to download the pretrained model and run test.py, the result I obtained are:

MAE: 2.122004357298475
RMSE: 3.026390819827392

Thank you.

The model trained through open source code is on the CARPK dataset, MAE=7.17, MRES=13.88, which is much worse than the trained model "trained_model_CARPK_x8_2_12.pt" you provided. Where did the problem go wrong?
Looking forward to your reply, thank you

I was trying the testing on my local machine. is there a way to use lower resolution images? otherwise it's throwing below error:

numpy.core._exceptions._ArrayMemoryError: Unable to allocate 28.1 MiB for an array with shape (720, 1280, 4) and data type float64

Run the test.py with the trained model on the CARPK dataset gave much worse results than reported in the paper.

MAE: 16.21132897603486
RMSE: 31.96154415873697