mask-rcnn检测物体时，用固定颜色

我自己训练了mask-rcnn权重后，就4类物体，检测的时候随机颜色很麻烦，就自己写了，一种类就用固定颜色表示。

主要是修改这个/data/Mask_RCNN/mrcnn/visualize.py

附上我的代码：

"""
Mask R-CNN
Display and Visualization Functions.
Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
"""

import os
import sys
import random
import itertools
import colorsys

import numpy as np
from skimage.measure import find_contours
import matplotlib.pyplot as plt
from matplotlib import patches,  lines
from matplotlib.patches import Polygon
import IPython.display

# Root directory of the project
ROOT_DIR = os.path.abspath("../")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils


############################################################
#  Visualization
############################################################

def display_images(images, titles=None, cols=4, cmap=None, norm=None,
                   interpolation=None):
    """Display the given set of images, optionally with titles.
    images: list or array of image tensors in HWC format.
    titles: optional. A list of titles to display with each image.
    cols: number of images per row
    cmap: Optional. Color map to use. For example, "Blues".
    norm: Optional. A Normalize instance to map values to colors.
    interpolation: Optional. Image interpolation to use for display.
    """
    titles = titles if titles is not None else [""] * len(images)
    rows = len(images) // cols + 1
    plt.figure(figsize=(14, 14 * rows // cols))
    i = 1
    for image, title in zip(images, titles):
        plt.subplot(rows, cols, i)
        plt.title(title, fontsize=9)
        plt.axis('off')
        plt.imshow(image.astype(np.uint8), cmap=cmap,
                   norm=norm, interpolation=interpolation)
        i += 1
    plt.show()


def random_colors(N, bright=True):
    """
    Generate random colors.
    To get visually distinct colors, generate them in HSV space then
    convert to RGB.
    """
    brightness = 1.0 if bright else 0.7
    hsv = [(i / N, 1, brightness) for i in range(N)]
    colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
    random.shuffle(colors)
    return colors


def apply_mask(image, mask, color, alpha=0.5):
    """Apply the given mask to the image.
    """
    for c in range(3):
        image[:, :, c] = np.where(mask == 1,
                                  image[:, :, c] *
                                  (1 - alpha) + alpha * color[c] * 255,
                                  image[:, :, c])
    return image


def display_instances(image, boxes, masks, class_ids, class_names,
                      scores=None, title="",
                      figsize=(16, 16), ax=None,
                      show_mask=True, show_bbox=False,
                      colors=None, captions=None, img_name=None, IMG_RES_DIR = None):
    """
    boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
    masks: [height, width, num_instances]
    class_ids: [num_instances]
    class_names: list of class names of the dataset
    scores: (optional) confidence scores for each box
    title: (optional) Figure title
    show_mask, show_bbox: To show masks and bounding boxes or not
    figsize: (optional) the size of the image
    colors: (optional) An array or colors to use with each object
    captions: (optional) A list of strings to use as captions for each object
    """
    # Number of instances

    N = boxes.shape[0]

    # print(N,'11111111111111111111111111111111111')

    if not N:
        print("\n*** No instances to display *** \n")
    else:
        assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]

    # If no axis is passed, create one and automatically call show()
    auto_show = False
    if not ax:
        fig, ax = plt.subplots(1, figsize=figsize)
        auto_show = True

    # Generate random colors
    colors = [(0.5, 0.0, 1.0), (0.0, 1.0, 1.0), (0.5, 1.0, 0.0), (1.0, 0.0, 0.0)]
    colors = colors or random_colors(N)
    # colors = [(0.5, 0.0, 1.0), (0.0, 1.0, 1.0), (0.5, 1.0, 0.0), (1.0, 0.0, 0.0)]
    # print(colors,'222222222222222222222222222222222222222222222222222222222222')

    # Show area outside image boundaries.
    height, width = image.shape[:2]
    ax.set_ylim(h