predict.py
def process_image(image):
    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,
        returns an Numpy array
    '''
    im = Image.open (image) #loading image
    width, height = im.size #original size

    # smallest part: width or height should be kept not more than 256
    img_pil = Image.open(image)
    img_transforms = 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 = img_transforms(img_pil)
    
    return image
 Scales, crops, and normalizes a PIL image for a PyTorch model,
        returns an Numpy array
    
Scales, crops, and normalizes a PIL image for a PyTorch model,
returns an Numpy array
Scales crops and normalizes a PIL image for a PyTorch model returns an Numpy array
def process_image(image):
    im = Image.open (image) 
    width, height = im.size 
    img_pil = Image.open(image)
    img_transforms = 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 = img_transforms(img_pil)
    return image
def process_image ( image ) : im = Image . open ( image ) width , height = im . size img_pil = Image . open ( image ) img_transforms = 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 = img_transforms ( img_pil ) return image
Scales, crops, and normalizes a PIL image for a PyTorch model,
returns an Numpy array
Scales crops and normalizes a PIL image for a PyTorch model returns an Numpy array
''' Scales, crops, and normalizes a PIL image for a PyTorch model,
        returns an Numpy array
    ''' #loading image #original size # smallest part: width or height should be kept not more than 256
image