Tensorflow Object Detection API for 1-channel grayscale image

[baimukashev]

System information

• What is the top-level directory of the model you are using:
• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
• TensorFlow installed from (source or binary):
• TensorFlow version (use command below):
• Bazel version (if compiling from source):
• CUDA/cuDNN version:
• GPU model and memory:
• Exact command to reproduce:

Describe the problem

Is there any way to use pre-trained models in Object Detection API of Tensorflow, which trained for RGB images, for single channel grayscale images(depth) ?

Source code / logs

[mttang]

You could convert a single channel grayscale image to a 3 channel RGB image to use the pre-trained RGB models. Alternatively, you could train a grayscale model yourself by using convert_to_grayscale in models/research/object_detection/protos/image_resizer.proto.

[vinay0410]

Hi @mttang,
I did that but when I try to run inferences on a single grayscale ( depth ) image using the ipynb notebook available. I get an error stating ValueError: Cannot feed value of shape (1, 480, 640) for Tensor u'image_tensor:0', which has shape '(?, ?, ?, 3)'

[cmbowyer13]

Have you figured a workout around for grey scale images @vinay0410 using the object detection api?

[richknight]

@mttang - how do you convert a single channel grayscale image to an RGB image to use with one of the well known models ?

[renandepadua]

@richknight you can use OpenCV's applyColorMap function. I do that to apply the common models on IR image. https://docs.opencv.org/2.4/modules/contrib/doc/facerec/colormaps.html

[TwistedHardware]

Hi @vinay0410,

ValueError: Cannot feed value of shape (1, 480, 640) for Tensor u'image_tensor:0', which has shape '(?, ?, ?, 3)'

The expected shape of the tensor is (BATCH_SIZE, WIDTH, HEIGHT, CHANNELS). and you are passing (BATCH_SIZE, WIDTH, HEIGHT) without channel.

When you reshape your images you need to keep them as rank-3 images. meaning (480, 640, 1) instead of (480, 640). To do that in numpy:

import numpy as np
img = np.random.random((480,640)) # <-- this is rank-2 image
img = img.reshape(x.shape[0],x.shape[1],1) # <-- this makes it a rank-3 image

This changes your rank-2 image of shape 480 x 640 to a rank-3 image of shape 480 x 640 x 1

[Arturexe]

I managed to get it to work with the help of the Stackoverflow community. Take a look: https://stackoverflow.com/questions/51872412/tensorflow-numpy-image-reshape

The model is taken from Sentdexs tutorial on Youtube: https://www.youtube.com/watch?v=srPndLNMMpk&list=PLQVvvaa0QuDcNK5GeCQnxYnSSaar2tpku&index=6

[Zrufy]

@vinay0410 how do you solve that error?I'm in stuck with this problem!

[denisb411]

Hi @vinay0410,

ValueError: Cannot feed value of shape (1, 480, 640) for Tensor u'image_tensor:0', which has shape '(?, ?, ?, 3)'

The expected shape of the tensor is (BATCH_SIZE, WIDTH, HEIGHT, CHANNELS). and you are passing (BATCH_SIZE, WIDTH, HEIGHT) without channel.

When you reshape your images you need to keep them as rank-3 images. meaning (480, 640, 1) instead of (480, 640). To do that in numpy:

import numpy as np
img = np.random.random((480,640)) # <-- this is rank-2 image
img = img.reshape(x.shape[0],x.shape[1],1) # <-- this makes it a rank-3 image

This changes your rank-2 image of shape 480 x 640 to a rank-3 image of shape 480 x 640 x 1

I can't see this working as the expected shape has 3 channels. Anyone tested it?

[hongym7]

img_path = os.path.join(image_subdirectory, data['filename'])
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()

encoded_jpg_io = io.BytesIO(encoded_jpg)
create_image = PIL.Image.open(encoded_jpg_io)

input image is grayscale image.
create_image is [ w, h, 3]