python 3.x - 如何裁剪来自RCNN的分割对象？

我正在尝试裁剪由MASK RCNN输出的分段对象 ，唯一的问题是，当我进行裁剪时，我得到的片段具有蒙版颜色而不是其原始颜色。

这是带有分段的输出图像：

这是一个分段（此图像中有17个分段）：

如您所见，我们具有带遮罩颜色而不是原始颜色的线段。

这是我正在使用的代码：

  from mrcnn.config import Config
  from mrcnn import model as modellib
  from mrcnn import visualize
  import numpy as np
  import colorsys
  import argparse
  import imutils
  import random
  import cv2
  import os


  import matplotlib.image as mpimg
  import cv2

  import matplotlib.pyplot as plt
  import numpy as np

  # construct the argument parse and parse the arguments
  ap = argparse.ArgumentParser()
  ap.add_argument("-w", "--weights", required=True,
      help="path to Mask R-CNN model weights pre-trained on COCO")
  ap.add_argument("-l", "--labels", required=True,
      help="path to class labels file")
  ap.add_argument("-c", "--confidence", type=float, default=0.5,
      help="minimum probability to filter weak detections")
  ap.add_argument("-i", "--image", required=True,
      help="path to input image to apply Mask R-CNN to")

  args = vars(ap.parse_args())

  # load the class label names from disk, one label per line
  CLASS_NAMES = open(args["labels"]).read().strip().split("\n")

  # generate random (but visually distinct) colors for each class label
  # (thanks to Matterport Mask R-CNN for the method!)
  hsv = [(i / len(CLASS_NAMES), 1, 1.0) for i in range(len(CLASS_NAMES))]
  COLORS = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
  random.seed(42)
  random.shuffle(COLORS)

  class SimpleConfig(Config):
      # give the configuration a recognizable name
      NAME = "fashion"

      # set the number of GPUs to use along with the number of images
      # per GPU
      GPU_COUNT = 1
      IMAGES_PER_GPU = 1

       NUM_CLASSES = 1 + 3

      # Skip detections with < 90% confidence
      DETECTION_MIN_CONFIDENCE = args["confidence"]




  # initialize the inference configuration
  config = SimpleConfig()

  # initialize the Mask R-CNN model for inference and then load the
  # weights
  print("[INFO] loading Mask R-CNN model...")
  model = modellib.MaskRCNN(mode="inference", config=config,
      model_dir=os.getcwd())
  model.load_weights(args["weights"], by_name=True)

  # load the input image, convert it from BGR to RGB channel
  # ordering, and resize the image
  # default value 512 form the width
  image = cv2.imread(args["image"])
  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
  image = imutils.resize(image, width=1150)

  # perform a forward pass of the network to obtain the results
  print("[INFO] making predictions with Mask R-CNN...")
  r = model.detect([image], verbose=1)[0]


  image = visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], 
                            ['BG', 'top', 'boots' , 'bag'], r['scores'],
                            title="")

  # get and then save the segmented objects
  i = 0
  mask = r["masks"]
  for i in range(mask.shape[2]):
      image = cv2.imread(args["image"])
      image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
      image = imutils.resize(image, width=1150)

  for j in range(image.shape[2]):

      image[:,:,j] = image[:,:,j] * mask[:,:,i]


  filename = "Output/segment_%d.jpg"%i
  cv2.imwrite(filename,image)
  i+=1

非常感谢您提供有关如何解决此问题的帮助，谢谢。

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