擅自使用他人产品做网站宣传,织梦程序如何搭建网站,微信息公众平台微网站建设,网站构建培训首先下载源代码仓库#xff0c;链接地址如下#xff1a;
maskrcnn
能够实现的效果如图所示#xff1a; 该存储库包括#xff1a;
基于FPN和ResNet101构建的Mask R-CNN的源代码。MS COCO 的训练代码MS COCO 的预训练砝码Jupyter 笔记本#xff0c;用于可视化每一步的检测…首先下载源代码仓库链接地址如下
maskrcnn
能够实现的效果如图所示 该存储库包括
基于FPN和ResNet101构建的Mask R-CNN的源代码。MS COCO 的训练代码MS COCO 的预训练砝码Jupyter 笔记本用于可视化每一步的检测管道用于多 GPU 训练的并行模型类对 MS COCO 指标 AP 的评估在自己的数据集上进行训练的示例
下载代码仓库进行解压后的目录如下 可以使用下面
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
也可以使用
python setup.py install
来安装相关的依赖包安装完成后还需要下载模型文件
下载链接地址如下
mask_rcnn_balloon.h5
测试代码如下所示
import os
import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt# 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
import mrcnn.model as modellib
from mrcnn import visualize
# Import COCO config
sys.path.append(os.path.join(ROOT_DIR, samples/coco/)) # To find local version
import coco%matplotlib inline # 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(ROOT_DIR, mask_rcnn_coco.h5)
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):utils.download_trained_weights(COCO_MODEL_PATH)# Directory of images to run detection on
IMAGE_DIR os.path.join(ROOT_DIR, images)class InferenceConfig(coco.CocoConfig):# Set batch size to 1 since well be running inference on# one image at a time. Batch size GPU_COUNT * IMAGES_PER_GPUGPU_COUNT 1IMAGES_PER_GPU 1config InferenceConfig()
config.display()# Create model object in inference mode.
model modellib.MaskRCNN(modeinference, model_dirMODEL_DIR, configconfig)# Load weights trained on MS-COCO
model.load_weights(COCO_MODEL_PATH, by_nameTrue)# 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, person, bicycle, car, motorcycle, airplane,bus, train, truck, boat, traffic light,fire hydrant, stop sign, parking meter, bench, bird,cat, dog, horse, sheep, cow, elephant, bear,zebra, giraffe, backpack, umbrella, handbag, tie,suitcase, frisbee, skis, snowboard, sports ball,kite, baseball bat, baseball glove, skateboard,surfboard, tennis racket, bottle, wine glass, cup,fork, knife, spoon, bowl, banana, apple,sandwich, orange, broccoli, carrot, hot dog, pizza,donut, cake, chair, couch, potted plant, bed,dining table, toilet, tv, laptop, mouse, remote,keyboard, cell phone, microwave, oven, toaster,sink, refrigerator, book, clock, vase, scissors,teddy bear, hair drier, toothbrush]
# 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], verbose1)# Visualize results
r results[0]
visualize.display_instances(image, r[rois], r[masks], r[class_ids], class_names, r[scores])
