zero-size array to reduction operation minimum which has no identity

[silvio-barbotto]

Hi! Thank you for your work!
I really love to use your library but I'm stuck to the beginnig, I don't want bother you but can you give me some advise?
I'm trying to cluster jpg images from a Google Drive folder, I've read the documentations and the blog but I can't make it right

Here is the snippet from Coolab

`
!pip install -U clustimage

from google.colab import drive
from clustimage import Clustimage

drive.mount('/content/drive', force_remount=True)

cl = Clustimage(method='pca')

X = cl.import_data('/content/drive/MyDrive/images/immagini/1')

Xfeat = cl.extract_feat(X)
...
`

It import the data but throws on feat extraction:
ValueError: zero-size array to reduction operation minimum which has no identity

Best regards, Silvio

[kudzishaun]

@silvio-barbotto @erdogant Did you find a solution to the problem? I am also stuck as well.

Load example dataset

pathnames = cl.import_data("/content/drive/MyDrive/Colab Notebooks/Cassava/data/test/cbsd")

Preprocessing of the first image

img = cl.imread(X[0], dim=(128,128), colorscale=1, flatten=False)

This the error I get
KeyError Traceback (most recent call last)
in <cell line: 10>()
8 # 0: cv2.IMREAD_GRAYSCALE
9 # 1: cv2.IMREAD_COLOR
---> 10 img = cl.imread(X[0], dim=(128,128), colorscale=1, flatten=False)
11
12 # # Flattened array

KeyError: 0

[erdogant]

Thanks for notifying! I created a few fixes and the examples work on my machine. Can you check whether this also works for you?
This example works on my machine.

update to the latest version:
pip install -U clustimage

[kudzishaun]

Dear @erdogant, I have tried have it and the error is still the same. I have included an example of the code I am using below and the output I get from running pathnames. The output is the same when running from Colab or on my PC.

#install
!pip install -U clustimage
//
import numpy as np
import os
import matplotlib.pyplot as plt
from clustimage import Clustimage
#Initialise
cl = Clustimage()
#Load example dataset
pathnames = cl.import_data("D:\Python\DSA\DSA_2023\Computer Vision\cassava_leaves_dsa_512\test\cbsd")

Preprocessing of the first image

img = cl.imread(pathnames[0], dim=(128,128), colorscale=1, flatten=False)

KeyError Traceback (most recent call last)
in <cell line: 10>()
8 # 0: cv2.IMREAD_GRAYSCALE
9 # 1: cv2.IMREAD_COLOR
---> 10 img = cl.imread(pathnames[0], dim=(128,128), colorscale=1, flatten=False)
11
12 # # Flattened array

KeyError: 0
clustmage.docx

[erdogant]

Why is there an attached docx?

[erdogant]

The underneath works. I can not check it without having the images. What kind of images are you using?

import numpy as np
import os
import matplotlib.pyplot as plt
from clustimage import Clustimage
#Initialise
cl = Clustimage()
#Load example dataset
# pathnames = cl.import_data("D:\Python\DSA\DSA_2023\Computer Vision\cassava_leaves_dsa_512\test\cbsd")
pathnames = cl.import_example('flowers')
img = cl.imread(pathnames[0], dim=(128,128), colorscale=1, flatten=False)

[kudzishaun]

Hi @erdogant,
The word document was so that you can see the output from running pathnames using the path to the directory with the images as shown below. The images I am using are jpg images and the path included leads to a folder with all the images inside.

#Load example dataset

pathnames = cl.import_data("D:\Python\DSA\DSA_2023\Computer Vision\cassava_leaves_dsa_512\test\cbsd")

[erdogant]

please copy paste the output from the docx here.
Use formatting for pathnames:

r”d:\python\etc

or

d://python//etc

[kudzishaun]

@erdogant I am using windows 11 and this is the output when I load

pathnames = cl.import_data("D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd")

and run

pathnames

{'img': array([[125, 161, 144, ..., 114, 101, 125],
[ 66, 92, 107, ..., 51, 131, 105],
[115, 129, 131, ..., 60, 85, 83],
...,
[ 91, 108, 129, ..., 58, 77, 98],
[ 31, 24, 31, ..., 183, 194, 197],
[124, 160, 136, ..., 98, 122, 144]], dtype=uint8),
'feat': None,
'xycoord': None,
'pathnames': array(['D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1616821573094.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1616821669327.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1616833319262.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1616833345793.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1616834021933.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1616834460941.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1616854309992.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617091703959.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617091921011.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617092466328.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617092862352.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617093193877.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617093398717.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617093492751.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617095290124.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617095301237.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617095341121.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617095407410.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617095442473.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617095444792.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617095587454.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617095670929.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617095752573.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617097100289.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617097441032.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617097495870.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617098592790.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617098799092.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617099222286.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617099373115.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617099615929.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617099943864.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617103454176.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617103455280.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617103771254.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617103812795.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617103815345.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617103843642.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617103910154.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617104212307.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617104240376.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617104407574.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617104528003.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617104564325.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617104566099.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617104585951.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617104914370.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617104923875.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617104992961.jpg',
'D://Python//DSA//DSA_2023//Computer Vision//cassava_leaves_dsa_512//test//cbsd\1617105550338.jpg'],
dtype='<U96'),
'labels': None,
'url': None,
'filenames': array(['1616821573094.jpg', '1616821669327.jpg', '1616833319262.jpg',
'1616833345793.jpg', '1616834021933.jpg', '1616834460941.jpg',
'1616854309992.jpg', '1617091703959.jpg', '1617091921011.jpg',
'1617092466328.jpg', '1617092862352.jpg', '1617093193877.jpg',
'1617093398717.jpg', '1617093492751.jpg', '1617095290124.jpg',
'1617095301237.jpg', '1617095341121.jpg', '1617095407410.jpg',
'1617095442473.jpg', '1617095444792.jpg', '1617095587454.jpg',
'1617095670929.jpg', '1617095752573.jpg', '1617097100289.jpg',
'1617097441032.jpg', '1617097495870.jpg', '1617098592790.jpg',
'1617098799092.jpg', '1617099222286.jpg', '1617099373115.jpg',
'1617099615929.jpg', '1617099943864.jpg', '1617103454176.jpg',
'1617103455280.jpg', '1617103771254.jpg', '1617103812795.jpg',
'1617103815345.jpg', '1617103843642.jpg', '1617103910154.jpg',
'1617104212307.jpg', '1617104240376.jpg', '1617104407574.jpg',
'1617104528003.jpg', '1617104564325.jpg', '1617104566099.jpg',
'1617104585951.jpg', '1617104914370.jpg', '1617104923875.jpg',
'1617104992961.jpg', '1617105550338.jpg'], dtype='<U17')}