The function to plot similar images plot 16 images, how do we know which image is related to or similar to the which image according to the algorithm?

I mean like it should say these two are similar and the other two are similar to each other, no?

Classification.py:

from DeepImageSearch import Index, LoadData, SearchImage

folders = [] folders.append("monos_segmented") image_list = LoadData().from_folder(folders)

print (image_list)

Index(image_list).Start()

SearchImage().get_similar_images(image_path=image_list[0],number_of_images=5)

SearchImage().plot_similar_images(image_path = image_list[0])

Running...

Traceback (most recent call last): File "Classification.py", line 13, in SearchImage().plot_similar_images(image_path = image_list[0]) File "/home/mike/.local/lib/python3.8/site-packages/DeepImageSearch/DeepImageSearch.py", line 132, in plot_similar_images plt.show(fig) File "/home/mike/.local/lib/python3.8/site-packages/matplotlib/pyplot.py", line 378, in show return _backend_mod.show(*args, **kwargs) TypeError: show() takes 1 positional argument but 2 were given

Hello,

What i want to ask is, cant we make extracting features parallel? I'm using 3060 Ti and it seems a little bit slow for this GPU.

Or am i wrong?

1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3070/242451 [02:25<3:08:09, 21.20it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step                                                                                                                    | 3073/242451 [02:25<3:07:27, 21.28it/s]
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3076/242451 [02:25<3:07:21, 21.29it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3079/242451 [02:25<3:06:30, 21.39it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3082/242451 [02:26<3:07:04, 21.33it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3085/242451 [02:26<3:08:38, 21.15it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3088/242451 [02:26<3:09:21, 21.07it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 15ms/step                                                                                                                    | 3091/242451 [02:26<3:09:04, 21.10it/s]
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step                                                                                                                    | 3094/242451 [02:26<3:11:12, 20.86it/s]
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step

Best regards.

Hello,

when trying to install DeepImageSearch on a Windows machine I got this:

ERROR: Cannot install deepimagesearch==1.0, deepimagesearch==1.1, deepimagesearch==1.2, deepimagesearch==1.3 and deepimagesearch==1.4 because these package versions have conflicting dependencies.

The conflict is caused by:
    deepimagesearch 1.4 depends on tensorflow
    deepimagesearch 1.3 depends on tensorflow
    deepimagesearch 1.2 depends on tensorflow
    deepimagesearch 1.1 depends on tensorflow
    deepimagesearch 1.0 depends on tensorflow`

I tried to install it like stated here: https://stackoverflow.com/questions/69751318/i-had-trouble-installing-python-deepimagesearch-library but also same error as mentioned there by using this.

ERROR: Could not find a version that satisfies the requirement tensorflow==2.3.2 (from versions: none)
ERROR: No matching distribution found for tensorflow==2.3.

Digging into TensorFlow itself, it seems that it is not running on windows properly anymore beginning from version 2.11 - that would not matter, if the version required by your library would still be available

Using Windows 10 with Python 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] on win32

Installing https://pypi.org/project/tensorflow-intel/ and changing requirements in your library did not help either.

So, what else I can do ?

Thanks in advance for any help !