mask-rcnnは自分のデータセットを訓練した後のテストdemo

import os
import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt

import coco
import utils
import model as modellib
import visualize

class_names = ['BG', 'your class1', 'your class2', 'your class3']

class InferenceConfig(coco.CocoConfig):
    # Set batch size to 1 since we'll be running inference on
    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1

    NUM_CLASSES = 1 + 3  # background + 3 class

config = InferenceConfig()
config.display()

# Root directory of the project
ROOT_DIR = os.getcwd()

# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")

# Load a random image from the images folder
file_names = next(os.walk(IMAGE_DIR))[2]
image = skimage.io.imread(os.path.join(IMAGE_DIR, random.choice(file_names)))

# Create model object in inference mode.
model = modellib.MaskRCNN(mode="inference", config=config , model_dir=MODEL_DIR)

# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_shapes_0002.h5")
# Load weights trained on MS-COCO
model.load_weights(COCO_MODEL_PATH, by_name=True)


# Run detection
results = model.detect([image], verbose=1)

# Visualize results
r = results[0]
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores'])