What's ur reason?
thanks

pywrap_tensorflow.TF_GetCode(status))

tensorflow.python.framework.errors_impl.NotFoundError: train.csv
[[Node: ReaderReadV2 = ReaderReadV2[_device="/job:localhost/replica:0/task:0/cpu:0"](TextLineReaderV2, input_producer)]]

It through this error can you help me to solve
Subscript indices must either be real positive integers or logicals.

Error in fmArgmax (line 8)
topIndices(numImgs).ind = 0;

Error in saveImgsAll (line 7)
topIndices = fmArgmax(fmVals, imgNamesAll);

Error in fmArgmax (line 8)
topIndices(numImgs).ind = 0;

Error in saveImgsAll (line 2)
topIndices = fmArgmax(fmVals,imgNamesAll);

hi i trained the network successfully, and i have done test by using "evaluate" but i need to generate output images for test set. i am going to regenrate the images that are shown in "DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation", is there any method in your implementation to regenerate the images.
i will be appreciate for your response.

in dcan-tensorflow/tf-dcan/bbbc006_input.py
line 71: result.label = tf.concat([contour, segment], 2) and dcan-tensorflow/tf-dcan/traceBounds.m
it seems that two classes can both be 1 in same pixel in label array?

    vals = unique(nonzeros(img));
    bwImg = false(size(img));
    for j = 1:length(vals)
        cur = (img == vals(j, 1));
        [bounds, ~] = bwboundaries(cur, 'noholes');
        indices = bounds{1};
        for k = 1:size(indices, 1)
            bwImg(indices(k, 1), indices(k, 2)) = 1;
        end
    end
    bwImg = imdilate(bwImg, strel('disk', 3));
    bwImg = uint8(bwImg) * 255;
    imwrite(bwImg, [OUT_DIR_C '/' imgNames{i}]);

I found the pixel size of contour is 6 when disk equals to 3, and it will cover pixels (==3) inside nucleis and outside nucleis (==3). And if contours are thick，the prediction covers more pixels inside the nucleis. What's more, if they are overlapped, the pixels outside this nucleis may cover some pixels inside other nucleis. It's hard to discriminate them.

Do I misunderstand?

hi，i have trained the model successful，then i want to make a test by a new image and create the contour and segment image or label consistent with this test image,so i want know weather just to run the "images=bbbc006.inputs(eval_data=FLAGS.eval_data)" and "c_fuse, s_fuse = bbbc006.inference(images, train=False)"under the pre-train model or not ,
i will be appreciate for your response.

can you upload your pretained model?

I make a same network, but it hard to detect anything...So I want to make sure if it is:
One is the whole segment label, and the other one is the boundary with dilate-->disk(3)?
thanks

I want to know how to calculate the object-level dice loss