util_functions.py
def process_image(image):
    
    img_transforms = transforms.Compose([transforms.Resize(255),
                                          transforms.CenterCrop(224),
                                          transforms.ToTensor(),
                                          transforms.Normalize([0.485, 0.456, 0.406],
                                                               [0.229, 0.224, 0.225])])
    
    pil_image = Image.open(image)
    pil_image = img_transforms(pil_image).float()
    pil_image.unsqueeze_(0) 
#    im = Image.open(image)
#    im
#    im = im.resize((255,255))
#    
#    left = (255 - 224)/2
#    top = (255 - 224)/2
#    right = (255 + 224)/2
#    bottom = (255 + 224)/2
#    im = im.crop((left, top, right, bottom))
#    
#    
#    np_image = np.array(im)/255.0
#    np_image[:,:,0]= (np_image[:,:,0]-.485)/0.229
#    np_image[:,:,1]= (np_image[:,:,1]-.456)/0.224
#    np_image[:,:,2]= (np_image[:,:,2]-.406)/0.225
#    np_image=np_image.transpose(2,0,1)
    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,
        returns an Numpy array
    '''
    return pil_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):
    img_transforms = transforms.Compose([transforms.Resize(255),
                                          transforms.CenterCrop(224),
                                          transforms.ToTensor(),
                                          transforms.Normalize([0.485, 0.456, 0.406],
                                                               [0.229, 0.224, 0.225])])
    pil_image = Image.open(image)
    pil_image = img_transforms(pil_image).float()
    pil_image.unsqueeze_(0) 
 = im.resize((255,255))
left = (255 - 224)/2
top = (255 - 224)/2
right = (255 + 224)/2
bottom = (255 + 224)/2
 = im.crop((left, top, right, bottom))
np_image = np.array(im)/255.0
np_image[:,:,0]= (np_image[:,:,0]-.485)/0.229
np_image[:,:,1]= (np_image[:,:,1]-.456)/0.224
np_image[:,:,2]= (np_image[:,:,2]-.406)/0.225
np_image=np_image.transpose(2,0,1)
    return pil_image
def process_image ( image ) : img_transforms = transforms . Compose ( [ transforms . Resize ( 255 ) , transforms . CenterCrop ( 224 ) , transforms . ToTensor ( ) , transforms . Normalize ( [ 0.485 , 0.456 , 0.406 ] , [ 0.229 , 0.224 , 0.225 ] ) ] ) pil_image = Image . open ( image ) pil_image = img_transforms ( pil_image ) . float ( ) pil_image . unsqueeze_ ( 0 ) return pil_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
#    im = Image.open(image) #    im #    im = im.resize((255,255)) #     #    left = (255 - 224)/2 #    top = (255 - 224)/2 #    right = (255 + 224)/2 #    bottom = (255 + 224)/2 #    im = im.crop((left, top, right, bottom)) #     #     #    np_image = np.array(im)/255.0 #    np_image[:,:,0]= (np_image[:,:,0]-.485)/0.229 #    np_image[:,:,1]= (np_image[:,:,1]-.456)/0.224 #    np_image[:,:,2]= (np_image[:,:,2]-.406)/0.225 #    np_image=np_image.transpose(2,0,1) ''' Scales, crops, and normalizes a PIL image for a PyTorch model,
        returns an Numpy array
    '''
image