def preprocessInputBatch()

in api/ImageSimilarity/deployment/app.py [0:0]

• 17 lines of code
• 2 McCabe index (conditional complexity)

def preprocessInputBatch(img_array):
    '''
    This method will pre-process the batch of images by running it through the ResNet50 model. The output is the 2048 vector representations for each image
    '''
    global keras_model
    global img_width
    global img_height
    global graph   
    
    #need to re-order the images to be the correct format (n, img_width, img_height, 3)
    imgs = []
    for img in img_array:
        img = np.array(img.resize((img_width,img_height)))
        imgs.append(img)
    imgs = np.array(imgs).astype(np.float)
    
    with graph.as_default():
        X = preprocess_input(imgs)
        X = keras_model.predict(X)
    return X