ValueError: Found array with dim 3. Estimator expected <= 2
Question: I'am trying to generate my own training data for recognition problem. I have
two folder `s0` and `s1` and the folder containing is `data`. `images`,
`lables` are the two list in which the `labels` contains the names of the
folder.

    
    
    |—- data
    |    |—- s0
    |    |    |—- 1.pgm
    |    |    |—- 2.pgm
    |    |    |—- 3.pgm
    |    |    |—- 4.pgm
    |    |    |—- ...
    |    |—- s1
    |    |    |—- 1.pgm
    |    |    |—- 2.pgm
    |    |    |—- 3.pgm
    |    |    |—- 4.pgm
    |    |    |—- ...
    

Below is the code, it's showing me an error on line `classifier.fit(images,
lables)`

    
    
     Traceback (most recent call last):
    File "mint.py", line 34, in <module>
     classifier.fit(images, lables)
     File "/usr/local/lib/python2.7/dist-packages/sklearn/svm/base.py",  line 150, in fit
      X = check_array(X, accept_sparse='csr', dtype=np.float64, order='C')
     File "/usr/local/lib/python2.7/dist-   packages/sklearn/utils/validation.py", line 396, in check_array
      % (array.ndim, estimator_name))
    

ValueError: Found array with dim 3. Estimator expected <= 2. here

    
    
    import os,sys
    import cv2
    import numpy as np
    from sklearn.svm import SVC
    fn_dir ='/home/aquib/Desktop/Natural/data'
    
    # Create a list of images and a list of corresponding names
    (images, lables, names, id) = ([], [], {}, 0)
    for (subdirs, dirs, files) in os.walk(fn_dir):
        for subdir in dirs:
          names[id] = subdir
          mypath = os.path.join(fn_dir, subdir)
          for item in os.listdir(mypath):
            if '.png' in item:  
            label=id
            image = cv2.imread(os.path.join(mypath, item),0)
            r_image = np.resize(image,(30,30))
            if image is not None:
                images.append(r_image)
                lables.append(int(label))
          id += 1  
    #Create a Numpy array from the two lists above
    (images, lables) = [np.array(lis) for lis in [images, lables]]
    classifier = SVC(verbose=0, kernel='poly', degree=3)
    classifier.fit(images, lables)
    

I really don't understand how to correct it in 2 dimension. I am trying the
below codes but the error is same: ` images = np.array(images) im_sq =
np.squeeze(images).shape images = images.reshape(images.shape[:2])`


Answer: There is syntax error on `images.append(cv2.imread((path, 0))` last line in
your code. The parenthesis are not closed properly. So it should be like this
`images.append(cv2.imread((path, 0)))` . Also it is always a good thing to
post the traceback for the error so that it will be easy for anyone to answer.

