Tensorflow训练结果测试

 代码参考（https://blog.csdn.net/disiwei1012/article/details/79928679）

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

# import coco
from mrcnn import utils
from mrcnn import model as modellib
from mrcnn import visualize
from mrcnn.config import Config

#%matplotlib inline

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

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

# Local path to trained weights file
COCO_MODEL_PATH = "mask_rcnn_shapes_0001.h5"


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


class ShapesConfig(Config):
　　"""Configuration for training on the toy shapes dataset.
　　Derives from the base Config class and overrides values specific
　　to the toy shapes dataset.
　　"""
　　# Give the configuration a recognizable name
　　NAME = "shapes"

　　# Train on 1 GPU and 8 images per GPU. We can put multiple images on each
　　# GPU because the images are small. Batch size is 8 (GPUs * images/GPU).
　　GPU_COUNT = 1
　　IMAGES_PER_GPU = 1

　　# Number of classes (including background)
　　NUM_CLASSES = 1 + 1 # background + 3 shapes

　　# Use small images for faster training. Set the limits of the small side
　　# the large side, and that determines the image shape.
　　IMAGE_MIN_DIM = 1024
　　IMAGE_MAX_DIM = 1280

　　# Use smaller anchors because our image and objects are small
　　RPN_ANCHOR_SCALES = (8 * 6, 16 * 6, 32 * 6, 64 * 6, 128 * 6) # anchor side in pixels

　　# Reduce training ROIs per image because the images are small and have
　　# few objects. Aim to allow ROI sampling to pick 33% positive ROIs.
　　TRAIN_ROIS_PER_IMAGE = 32

　　# Use a small epoch since the data is simple
　　STEPS_PER_EPOCH = 100

　　# use small validation steps since the epoch is small
　　VALIDATION_STEPS = 5

class InferenceConfig(ShapesConfig):
　　# 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

config = InferenceConfig()
config.display()

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

　　# Load weights trained on MS-COCO
　　model.load_weights(COCO_MODEL_PATH, by_name=True)

　　# COCO Class names
　　# Index of the class in the list is its ID. For example, to get ID of
　　# the teddy bear class, use: class_names.index('teddy bear')
　　class_names = ['BG', 'mono']

# 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)))

# 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'])
print('OK')