Classement d'une image non-MNIST après avoir appris MNIST

Question

Mon algorithme d'apprentissage automatique a déjà appris les 70000 images de la base de données MNIST. Je veux le tester sur une image non incluse dans le jeu de données MNIST. Cependant, ma fonction de prévision ne peut pas lire la représentation de tableau de mon image de test. Comment tester mon algorithme sur une image externe?

Comment tester mon algorithme sur une image externe? Pourquoi mon code échoue-t-il?

PS J'utilise python3

erreur Reçues:

Traceback (most recent call last): 
    File "hello_world2.py", line 28, in <module> 
    print(sgd_clf.predict(arr)) 
    File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py", line 336, in predict 
    scores = self.decision_function(X) 
    File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py", line 317, in decision_function 
    % (X.shape[1], n_features)) 
ValueError: X has 15 features per sample; expecting 784 

code:

# Common Imports 
import numpy as np 
from sklearn.datasets import fetch_mldata 
from sklearn.linear_model import SGDClassifier 
from PIL import Image 
from resizeimage import resizeimage 

# loading and learning MNIST data 
mnist = fetch_mldata('MNIST original') 
x, y = mnist["data"], mnist["target"] 
sgd_clf = SGDClassifier(random_state=42) 
sgd_clf.fit(x, y) 

# loading and converting to array a non-MNIST image of a "5", which is in the same folder 
img = Image.open("5.png") 
arr = np.array(img) 

# trying to predict that the image is a "5" 
img = Image.open("5.png") 
img = img.convert('L') #makes it greyscale 
img = resizeimage.resize_thumbnail(img, [28,28]) 
arr = np.array(img) 

print(sgd_clf.predict(arr)) # ERROR... why????????? How do you fix it????? 

Answer 1

S'il vous plaît, essayez ceci:

img = Image.open("5.png") 
img = img.resize((28,28)) 
img = img.convert('L') #makes it greyscale 

Answer 2

Si vous voulez lire une image, puis la redimensionner, s'il vous plaît essayer

In [1]: import PIL.Image as Image 

In [2]: img = Image.open('2.jpg', mode='r') 

In [3]: img.mode 
Out[3]: 'RGB' 

In [4]: img.size 
Out[4]: (2880, 1800) 

In [5]: img_new = img.resize([4000, 4000], Image.ANTIALIAS) 

In [6]: img_new2 = img.resize([32, 32], Image.ANTIALIAS) 

Docs sont here

C'est le 2.jpg, désolé, ce n'est pas un chiffre.

Cette image provient d'Internet, désolé, j'ai oublié la source.

Si vous rencontrez le mode est « RGBA », je vous recommande de le transférer en mode « RVB »,

newimg = Image.new('RGB', img.size) 
newimg.paste(img, mask=img.split()[3]) 
return newimg 

Answer 3

Ce n'est pas simplement une question de redimensionnement, l'image a besoin le chiffre centré et blanc noir etc. J'ai travaillé sur une fonction à ce travail. C'est la version actuelle qui utilise opencv, bien qu'elle puisse être améliorée, notamment en utilisant PIL au lieu d'opencv, mais cela devrait donner une idée des étapes requises.

def open_as_mnist(image_path): 
    """ 
    Assume this is a color or grey scale image of a digit which has not so far been preprocessed 

    Black and White 
    Resize to 20 x 20 (digit in center ideally) 
    Sharpen 
    Add white border to make it 28 x 28 
    Convert to white on black 
    """ 
    # open as greyscale 
    image = cv2.imread(image_path, 0) 

    # crop to contour with largest area 
    cropped = do_cropping(image) 

    # resizing the image to 20 x 20 
    resized20 = cv2.resize(cropped, (20, 20), interpolation=cv2.INTER_CUBIC) 

    cv2.imwrite('1_resized.jpg', resized20) 

    # gaussian filtering 
    blurred = cv2.GaussianBlur(resized20, (3, 3), 0) 

    # white digit on black background 
    ret, thresh = cv2.threshold(blurred, 127, 255, cv2.THRESH_BINARY_INV) 

    padded = to20by20(thresh) 


    resized28 = padded_image(padded, 28) 

    # normalize the image values to fit in the range [0,1] 
    norm_image = np.asarray(resized28, dtype=np.float32)/255. 

    # cv2.imshow('image', norm_image) 
    # cv2.waitKey(0) 

    # # Flatten the image to a 1-D vector and return 
    flat = norm_image.reshape(1, 28 * 28) 
    # return flat 

    # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black. 
    tva = [(255 - x) * 1.0/255.0 for x in flat] 
    return tva 



def padded_image(image, tosize): 
    """ 
    This method adds padding to the image and makes it to a tosize x tosize array, 
    without losing the aspect ratio. 
    Assumes desired image is square 

    :param image: the input image as numpy array 
    :param tosize: the final dimensions 
    """ 

    # image dimensions 
    image_height, image_width = image.shape 


    # if not already square then pad to square 
    if image_height != image_width: 

     # Add padding 
     # The aim is to make an image of different width and height to a sqaure image 
     # For that first the biggest attribute among width and height are determined. 
     max_index = np.argmax([image_height, image_width]) 


     # if height is the biggest one, then add padding to width until width becomes 
     # equal to height 
     if max_index == 0: 
      #order of padding is: top, bottom, left, right 
      left = int((image_height - image_width)/2) 
      right = image_height - image_width - left 
      padded_img = cv2.copyMakeBorder(image, 0, 0, 
              left, 
              right, 
              cv2.BORDER_CONSTANT) 

     # else if width is the biggest one, then add padding to height until height becomes 
     # equal to width 
     else: 
      top = int((image_width - image_height)/2) 
      bottom = image_width - image_height - top 
      padded_img = cv2.copyMakeBorder(image, top, bottom, 0, 0, cv2.BORDER_CONSTANT) 
    else: 
     padded_img = image 


    # now that it's a square, add any additional padding required 
    image_height, image_width = padded_img.shape 
    padding = tosize - image_height 

    # need to handle where padding is not divisiable by 2 
    left = top = int(padding/2) 
    right = bottom = padding - left 
    resized = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT) 


    return resized