“fcn tensorflow” Ответ

fcn tensorflow

# Visualization Utilities
 
def fuse_with_pil(images):
    '''
    Creates a blank image and pastes input images
 
    Args:
        images (list of numpy arrays) - numpy array representations of the images to paste
  
    Returns:
        PIL Image object containing the images
    '''
 
    widths = (image.shape[1] for image in images)
    heights = (image.shape[0] for image in images)
    total_width = sum(widths)
    max_height = max(heights)
 
    new_im = PIL.Image.new('RGB', (total_width, max_height))
 
    x_offset = 0
    for im in images:
        pil_image = PIL.Image.fromarray(np.uint8(im))
        new_im.paste(pil_image, (x_offset,0))
        x_offset += im.shape[1]
  
    return new_im
 
 
def give_color_to_annotation(annotation):
    '''
    Converts a 2-D annotation to a numpy array with shape (height, width, 3) where
    the third axis represents the color channel. The label values are multiplied by
    255 and placed in this axis to give color to the annotation
 
    Args:
        annotation (numpy array) - label map array
  
    Returns:
        the annotation array with an additional color channel/axis
    '''
    seg_img = np.zeros( (annotation.shape[0],annotation.shape[1], 3) ).astype('float')
  
    for c in range(12):
        segc = (annotation == c)
        seg_img[:,:,0] += segc*( colors[c][0] * 255.0)
        seg_img[:,:,1] += segc*( colors[c][1] * 255.0)
        seg_img[:,:,2] += segc*( colors[c][2] * 255.0)
  
    return seg_img
 
 
def show_predictions(image, labelmaps, titles, iou_list, dice_score_list):
    '''
    Displays the images with the ground truth and predicted label maps
 
    Args:
        image (numpy array) -- the input image
        labelmaps (list of arrays) -- contains the predicted and ground truth label maps
        titles (list of strings) -- display headings for the images to be displayed
        iou_list (list of floats) -- the IOU values for each class
        dice_score_list (list of floats) -- the Dice Score for each vlass
    '''
 
    true_img = give_color_to_annotation(labelmaps[1])
    pred_img = give_color_to_annotation(labelmaps[0])
 
    image = image + 1
    image = image * 127.5
    images = np.uint8([image, pred_img, true_img])
 
    metrics_by_id = [(idx, iou, dice_score) for idx, (iou, dice_score) in enumerate(zip(iou_list, dice_score_list)) if iou > 0.0]
    metrics_by_id.sort(key=lambda tup: tup[1], reverse=True)  # sorts in place
  
    display_string_list = ["{}: IOU: {} Dice Score: {}".format(class_names[idx], iou, dice_score) for idx, iou, dice_score in metrics_by_id]
    display_string = "\n\n".join(display_string_list) 
 
    plt.figure(figsize=(15, 4))
 
    for idx, im in enumerate(images):
        plt.subplot(1, 3, idx+1)
        if idx == 1:
            plt.xlabel(display_string)
        plt.xticks([])
        plt.yticks([])
        plt.title(titles[idx], fontsize=12)
        plt.imshow(im)
 
 
def show_annotation_and_image(image, annotation):
    '''
    Displays the image and its annotation side by side
 
    Args:
        image (numpy array) -- the input image
        annotation (numpy array) -- the label map
    '''
    new_ann = np.argmax(annotation, axis=2)
    seg_img = give_color_to_annotation(new_ann)
  
    image = image + 1
    image = image * 127.5
    image = np.uint8(image)
    images = [image, seg_img]
  
    images = [image, seg_img]
    fused_img = fuse_with_pil(images)
    plt.imshow(fused_img)
 
 
def list_show_annotation(dataset):
    '''
    Displays images and its annotations side by side
 
    Args:
        dataset (tf Dataset) - batch of images and annotations
    '''
 
    ds = dataset.unbatch()
    ds = ds.shuffle(buffer_size=100)
 
    plt.figure(figsize=(25, 15))
    plt.title("Images And Annotations")
    plt.subplots_adjust(bottom=0.1, top=0.9, hspace=0.05)
 
    # we set the number of image-annotation pairs to 9
    # feel free to make this a function parameter if you want
    for idx, (image, annotation) in enumerate(ds.take(9)):
        plt.subplot(3, 3, idx + 1)
        plt.yticks([])
        plt.xticks([])
        show_annotation_and_image(image.numpy(), annotation.numpy())