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  • mask_rcnn调用训练好的模型

    mask_rcnn调用训练好的模型

    # -*- coding: utf-8 -*-
    import os
    import sys
    import random
    import math
    import numpy as np
    import skimage.io
    import matplotlib
    import matplotlib.pyplot as plt
    import cv2
    import time
    from mrcnn.config import Config
    from datetime import datetime 
    # Root directory of the project
    ROOT_DIR = os.getcwd()
    
    # Import Mask RCNN
    
    from mrcnn import utils
    import mrcnn.model as modellib
    from mrcnn import visualize
    # Import COCO config
    
    #from samples.coco import coco
    
    
    # Directory to save logs and trained model
    MODEL_DIR = os.path.join(ROOT_DIR, "logs")
    
    # Local path to trained weights file
    COCO_MODEL_PATH = os.path.join(MODEL_DIR ,"lh0050.h5")
    # Download COCO trained weights from Releases if needed
    
    
    # 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 + 18  # 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 = 320
        IMAGE_MAX_DIM = 640
    
        # 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 =100
    
        # Use a small epoch since the data is simple
        STEPS_PER_EPOCH = 100
    
        # use small validation steps since the epoch is small
        VALIDATION_STEPS = 50
    
    #import train_tongue
    #class InferenceConfig(coco.CocoConfig):
    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()
    
    
    
    # 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','worker_helmet','worker_nohelmet','rebar_working','rebar_material',
                     'steel','concrete_pouring','formwork_working','formwork_material','scaffolding',
                     'excavator','bulldozer','dump_truck','concrete_bucket','concrete_mixer',
                     'concrete_pump','tower_crane','crane','machine_other',
                     'pile','container','concrete_mixing_station','construction_base','beam','pile_cap']
    # 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)))
    
    a=datetime.now() 
    # Run detection
    results = model.detect([image], verbose=1)
    b=datetime.now() 
    # Visualize results
    print("times:",(b-a).seconds)
    r = results[0]
    visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], 
                                class_names, r['scores'])

    ##################################33

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  • 原文地址:https://www.cnblogs.com/herd/p/14696453.html
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