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ccnn-tensorflow's Issues

A question about training datasets

Hi, Do you use flythings3D as dataset to evaluate your confidence measures CCNN? When I use disparity map from dispnet/mc-cnn with flythings3D, my output seems not right, the front scenes are black. Can you give me some suggestions or could you tell me where to change the code? Thx.

input and filter must have the same depth: 3 vs 1

Dear sir, thanks for your works.
When I test your code with my disparity, I got the error:

Caused by op u'CCNN/conv1/conv/Conv2D', defined at:
  File "./model/main.py", line 47, in <module>
    tf.app.run()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "./model/main.py", line 37, in main
    model_name=args.model_name
  File "/media/jennifer/Papers/3_Codes_now/CCNN-Tensorflow/model/model.py", line 22, in __init__
    self.build_CCNN()
  File "/media/jennifer/Papers/3_Codes_now/CCNN-Tensorflow/model/model.py", line 45, in build_CCNN
    self.conv1 = ops.conv2d(self.disp, [kernel_size, kernel_size, 1, filters], 1, True, padding='VALID')
  File "/media/jennifer/Papers/3_Codes_now/CCNN-Tensorflow/model/ops.py", line 9, in conv2d
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding=padding)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 399, in conv2d
    data_format=data_format, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
    self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): input and filter must have the same depth: 3 vs 1
	 [[Node: CCNN/conv1/conv/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/gpu:0"](_arg_div_0_0/_71, CCNN/conv1/weights/read)]]

My disparity map is one channel and uint8. I think it is the same with yours.
Hope for your help. Thanks.

The relationship between disparity map and confidence map.

Dear sir, thanks for your wonderful work. I test your algorithm with middlebury 2014dataset. Whether lighter color indicate higher confidence in confidence map? Whether warmer color and closer point in disparity map means lower confidence and dark color in confidence map?
Thanks for your help.

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