How to fix "“TypeError: img should be PIL Image. Got ”?
I am a beginner and I am learning to code an image classifier. My goal is to create a predict function. 

Any suggestion to fix it? 

In this project, I want to use the predict function to recognize different flower species. So I could check their labels later. 

Attempt to fix: I have already used the unsqueeze_(0) method and changing from numpy to torch method . I usually get the same error message shown below:


  TypeError: img should be PIL


Code:


    # Imports here
    import pandas as pd
    import numpy as np

    import torch
    from torch import nn
    from torchvision import datasets, transforms, models
    import torchvision.models as models
    import torch.nn.functional as F
    import torchvision.transforms.functional as F
    from torch import optim
    import json

    from collections import OrderedDict
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    %matplotlib inline
    from PIL import Image

    def process_image(image):
     #Scales, crops, and normalizes a PIL image for a PyTorch model,
            #returns an Numpy array
        # Process a PIL image for use in a PyTorch model
        process = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                                 std=[0.229, 0.224, 0.225])
        ])
        image = process(image)
        return image

    # Predict 
    #Predict the class (or classes) of an image using a trained deep learning model.
    def predict(image, model, topk=5):

        img = process_image(image)
        img = img.unsqueeze(0)

        output = model.forward(img)
        probs, labels = torch.topk(output, topk)        
        probs = probs.exp()

        # Reverse the dict
        idx_to_class = {val: key for key, val in model.class_to_idx.items()}
        # Get the correct indices
        top_classes = [idx_to_class[each] for each in classes]

        return labels, probs

    #Passing 
    probs, classes = predict(image, model)
    print(probs)
    print(classes)


Error:

TypeError                                 Traceback (most recent call last)
&lt;ipython-input-92-b49fdcab5791&gt; in &lt;module&gt;()
----&gt; 1 probs, classes = predict(image, model)
     2 print(probs)
     3 print(classes)

&lt;ipython-input-91-05809355bfe0&gt; in predict(image, model, topk)
     2     ‘’' Predict the class (or classes) of an image using a trained deep learning model.
     3     ‘’'
----&gt; 4     img = process_image(image)
     5     img = img.unsqueeze(0)
     6

&lt;ipython-input-20-02663a696e34&gt; in process_image(image)
    11                              std=[0.229, 0.224, 0.225])
    12     ])
---&gt; 13     image = process(image)
    14     return image

/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/transforms/transforms.py in __call__(self, img)
    47     def __call__(self, img):
    48         for t in self.transforms:
---&gt; 49             img = t(img)
    50         return img
    51

/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/transforms/transforms.py in __call__(self, img)
   173             PIL Image: Rescaled image.
   174         “”"
--&gt; 175         return F.resize(img, self.size, self.interpolation)
   176
   177     def __repr__(self):

/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/transforms/functional.py in resize(img, size, interpolation)
   187     “”"
   188     if not _is_pil_image(img):
--&gt; 189         raise TypeError(‘img should be PIL Image. Got {}’.format(type(img)))
   190     if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):
   191         raise TypeError(‘Got inappropriate size arg: {}’.format(size))

TypeError: img should be PIL Image. Got &lt;class ‘str’&gt;


All I want is to get these similar result. Thank you!

    predict(image,model)
    print(probs)
    print(classes)


    tensor([[ 0.5607,  0.3446,  0.0552,  0.0227,  0.0054]], device='cuda:0')   
    tensor([[  8,   1,  31,  24,   7]], device='cuda:0')


You are getting the above error because of the below line in predict function:

img = process_image(image)


The input to the process_image function should be Image.open(image), not image which is basically the path to an image(string) and hence the error message TypeError: img should be PIL Image. Got &lt;class ‘str’&gt;.

So, change img = process_image(image) to img = process_image(Image.open(image))

Modified predict function:

def predict(image, model, topk=5):
    ''' 
      Predict the class (or classes) of an image using a trained deep learning model.
      Here, image is the path to an image file, but input to process_image should be                                                         
      Image.open(image)
    '''
    img = process_image(Image.open(image))
    img = img.unsqueeze(0)

    output = model.forward(img)
    probs, labels = torch.topk(output, topk)        
    probs = probs.exp()

    # Reverse the dict
    idx_to_class = {val: key for key, val in model.class_to_idx.items()}
    # Get the correct indices
    top_classes = [idx_to_class[each] for each in classes]

    return labels, probs

