1、首先从官方下载mask_rcnn源码https://github.com/matterport/Mask_RCNN

2、当制作自己的数据集的时候，图片的大小一定要记得修改，长宽都要修改为修改为2的6次方的倍数，不然训练的时候会报错，来看源代码：

2、首先将demo.ipynb转换成demo.py，这里我顺便更改为适用于我自己数据集：

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.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

# Directory to save logs and trained model

MODEL_DIR = os.path.join(ROOT_DIR, "logs/shapes20190425T0816")

# Local path to trained weights file

COCO_MODEL_PATH = os.path.join(MODEL_DIR ,"mask_rcnn_shapes_0030.h5")

# Download COCO trained weights from Releases if needed

if not os.path.exists(COCO_MODEL_PATH):

    utils.download_trained_weights(COCO_MODEL_PATH)

    print("cuiwei***********************")

# 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 + 4  # 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 = 384

    # 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

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', 'a','b','c','e']

# 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)))

cap = cv2.VideoCapture(0)

while(1):

    # get a frame

    ret, frame = cap.read()

    # show a frame

    start =time.clock()

    results = model.detect([frame], verbose=1)

    r = results[0]

    #cv2.imshow("capture", frame)

    visualize.display_instances(frame, r['rois'], r['masks'], r['class_ids'],

                            class_names, r['scores'])

    end = time.clock()

    print(end-start)

    if cv2.waitKey(1) & 0xFF == ord('q'):

        break

cap.release()

cv2.destroyAllWindows() 

#image= cv2.imread("C:\\Users\\18301\\Desktop\\Mask_RCNN-master\\images\\9.jpg")

## Run detection

#

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

#

#print(end-start)

## Visualize results

#r = results[0]

#visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],

#                            class_names, r['scores'])

以上这段代码可以调用摄像头拍摄图片进行目标识别。

以下为训练文件：

# -*- coding: utf-8 -*-

import os

import sys

import random

import math

import re

import time

import numpy as np

import cv2

import matplotlib

import matplotlib.pyplot as plt

import tensorflow as tf

from mrcnn.config import Config

#import utils

from mrcnn import model as modellib,utils

from mrcnn import visualize

import yaml

from mrcnn.model import log

from PIL import Image

#os.environ["CUDA_VISIBLE_DEVICES"] = "0"

# Root directory of the project

ROOT_DIR = os.getcwd()

#ROOT_DIR = os.path.abspath("../")

# Directory to save logs and trained model

MODEL_DIR = os.path.join(ROOT_DIR, "logs")

iter_num=0

# 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)

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 + 4  # 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 = 384

    # 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

config = ShapesConfig()

config.display()

class DrugDataset(utils.Dataset):

    # 得到该图中有多少个实例（物体）

    def get_obj_index(self, image):

        n = np.max(image)

        return n

    # 解析labelme中得到的yaml文件，从而得到mask每一层对应的实例标签

    def from_yaml_get_class(self, image_id):

        info = self.image_info[image_id]

        with open(info['yaml_path']) as f:

            temp = yaml.load(f.read())

            labels = temp['label_names']

            del labels[0]

        return labels

    # 重新写draw_mask

    def draw_mask(self, num_obj, mask, image,image_id):

        #print("draw_mask-->",image_id)

        #print("self.image_info",self.image_info)

        info = self.image_info[image_id]

        #print("info-->",info)

        #print("info[width]----->",info['width'],"-info[height]--->",info['height'])

        for index in range(num_obj):

            for i in range(info['width']):

                for j in range(info['height']):

                    #print("image_id-->",image_id,"-i--->",i,"-j--->",j)

                    #print("info[width]----->",info['width'],"-info[height]--->",info['height'])

                    at_pixel = image.getpixel((i, j))

                    if at_pixel == index + 1:

                        mask[j, i, index] = 1

        return mask

    # 重新写load_shapes，里面包含自己的自己的类别

    # 并在self.image_info信息中添加了path、mask_path 、yaml_path

    # yaml_pathdataset_root_path = "/tongue_dateset/"

    # img_floder = dataset_root_path + "rgb"

    # mask_floder = dataset_root_path + "mask"

    # dataset_root_path = "/tongue_dateset/"

    def load_shapes(self, count, img_floder, mask_floder, imglist, dataset_root_path):

        """Generate the requested number of synthetic images.

        count: number of images to generate.

        height, width: the size of the generated images.

        """

        # Add classes

        self.add_class("shapes", 1, "a")

        self.add_class("shapes", 2, "b")

        self.add_class("shapes", 3, "c")

        self.add_class("shapes", 4, "e")

        for i in range(count):

            # 获取图片宽和高

            filestr = imglist[i].split(".")[0]

            #print(imglist[i],"-->",cv_img.shape[1],"--->",cv_img.shape[0])

            #print("id-->", i, " imglist[", i, "]-->", imglist[i],"filestr-->",filestr)

            # filestr = filestr.split("_")[1]

            mask_path = mask_floder + "/" + filestr + ".png"

            yaml_path = dataset_root_path + "labelme_json/" + filestr + "_json/info.yaml"

            print(dataset_root_path + "labelme_json/" + filestr + "_json/img.png")

            cv_img = cv2.imread(dataset_root_path + "labelme_json/" + filestr + "_json/img.png")

            self.add_image("shapes", image_id=i, path=img_floder + "/" + imglist[i],

                           width=cv_img.shape[1], height=cv_img.shape[0], mask_path=mask_path, yaml_path=yaml_path)

    # 重写load_mask

    def load_mask(self, image_id):

        """Generate instance masks for shapes of the given image ID.

        """

        global iter_num

        print("image_id",image_id)

        info = self.image_info[image_id]

        count = 1  # number of object

        img = Image.open(info['mask_path'])

        num_obj = self.get_obj_index(img)

        mask = np.zeros([info['height'], info['width'], num_obj], dtype=np.uint8)

        mask = self.draw_mask(num_obj, mask, img,image_id)

        occlusion = np.logical_not(mask[:, :, -1]).astype(np.uint8)

        for i in range(count - 2, -1, -1):

            mask[:, :, i] = mask[:, :, i] * occlusion

            occlusion = np.logical_and(occlusion, np.logical_not(mask[:, :, i]))

        labels = []

        labels = self.from_yaml_get_class(image_id)

        labels_form = []

        for i in range(len(labels)):

            if labels[i].find("a") != -1:

                labels_form.append("a")

            elif labels[i].find("b") != -1:

                labels_form.append("b")

            elif labels[i].find("c") != -1:

                labels_form.append("c")

            elif labels[i].find("e") != -1:

                labels_form.append("e")

        class_ids = np.array([self.class_names.index(s) for s in labels_form])

        return mask, class_ids.astype(np.int32)

def get_ax(rows=1, cols=1, size=8):

    """Return a Matplotlib Axes array to be used in

    all visualizations in the notebook. Provide a

    central point to control graph sizes.

    Change the default size attribute to control the size

    of rendered images

    """

    _, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows))

    return ax

#基础设置

dataset_root_path="train_data/"

img_floder = dataset_root_path + "pic"

mask_floder = dataset_root_path + "cv2_mask"

#yaml_floder = dataset_root_path

imglist = os.listdir(img_floder)

count = len(imglist)

#train与val数据集准备

dataset_train = DrugDataset()

dataset_train.load_shapes(count, img_floder, mask_floder, imglist,dataset_root_path)

dataset_train.prepare()

#print("dataset_train-->",dataset_train._image_ids)

dataset_val = DrugDataset()

dataset_val.load_shapes(7, img_floder, mask_floder, imglist,dataset_root_path)

dataset_val.prepare()

#print("dataset_val-->",dataset_val._image_ids)

# Load and display random samples

#image_ids = np.random.choice(dataset_train.image_ids, 4)

#for image_id in image_ids:

#    image = dataset_train.load_image(image_id)

#    mask, class_ids = dataset_train.load_mask(image_id)

#    visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names)

# Create model in training mode

model = modellib.MaskRCNN(mode="training", config=config,

                          model_dir=MODEL_DIR)

# Which weights to start with?

init_with = "coco"  # imagenet, coco, or last

if init_with == "imagenet":

    model.load_weights(model.get_imagenet_weights(), by_name=True)

elif init_with == "coco":

    # Load weights trained on MS COCO, but skip layers that

    # are different due to the different number of classes

    # See README for instructions to download the COCO weights

    model.load_weights(COCO_MODEL_PATH, by_name=True,

                       exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",

                                "mrcnn_bbox", "mrcnn_mask"])

elif init_with == "last":

    # Load the last model you trained and continue training

    model.load_weights(model.find_last()[1], by_name=True)

# Train the head branches

# Passing layers="heads" freezes all layers except the head

# layers. You can also pass a regular expression to select

# which layers to train by name pattern.

model.train(dataset_train, dataset_val,

            learning_rate=config.LEARNING_RATE,

            epochs=10,

            layers='heads')

# Fine tune all layers

# Passing layers="all" trains all layers. You can also

# pass a regular expression to select which layers to

# train by name pattern.

model.train(dataset_train, dataset_val,

            learning_rate=config.LEARNING_RATE / 10,

            epochs=30,

            layers="all")

以下为测试代码：

# -*- 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

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

from samples.coco import coco

# Directory to save logs and trained model

MODEL_DIR = os.path.join(ROOT_DIR, "logs/shapes20190425T0816/")

# Local path to trained weights file

COCO_MODEL_PATH = os.path.join(MODEL_DIR ,"mask_rcnn_shapes_0030.h5")

# Download COCO trained weights from Releases if needed

if not os.path.exists(COCO_MODEL_PATH):

    utils.download_trained_weights(COCO_MODEL_PATH)

    print("wancheng***********************")

# 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 + 4 # 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 = 384

    # 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()

model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)

# 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', 'a','b','c','e']

# 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("shijian",(b-a).seconds)

r = results[0]

visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],

                            class_names, r['scores'])

# 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)))

#cap = cv2.VideoCapture(0)

#

#while(1):

#    # get a frame

#    ret, frame = cap.read()

#    # show a frame

#    start =time.clock()

#    results = model.detect([frame], verbose=1)

#    r = results[0]

#    #cv2.imshow("capture", frame)

#    visualize.display_instances(frame, r['rois'], r['masks'], r['class_ids'],

#                            class_names, r['scores'])

#    end = time.clock()

#    print(end-start)

#    if cv2.waitKey(1) & 0xFF == ord('q'):

#        break

#

#cap.release()

#cv2.destroyAllWindows() 

#image= cv2.imread("C:\\Users\\18301\\Desktop\\Mask_RCNN-master\\images\\9.jpg")

## Run detection

#

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

#

#print(end-start)

## Visualize results

#r = results[0]

#visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],

#                            class_names, r['scores'])

## Root directory of the project

#ROOT_DIR = os.getcwd()

#

## Directory to save logs and trained model

#MODEL_DIR = os.path.join(ROOT_DIR, "logs/shapes20180713T1554")

#

## 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 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 = 320

#    IMAGE_MAX_DIM = 384

#

#    # 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()

#

#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)

#model_path = model.find_last()[0]

#

## Load trained weights (fill in path to trained weights here)

#assert model_path != "", "Provide path to trained weights"

#print("Loading weights from ", model_path)

#model.load_weights(model_path, by_name=True)

#

#class_names = ['BG', 'tank']

#

## 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'])

修改代码中的路径及数据集即可正常训练出自己的数据集

mask_rcnn训练自己的数据集的更多相关文章

  1. 【Tensorflow系列】使用Inception_resnet_v2训练自己的数据集并用Tensorboard监控

    [写在前面] 用Tensorflow(TF)已实现好的卷积神经网络(CNN)模型来训练自己的数据集,验证目前较成熟模型在不同数据集上的准确度,如Inception_V3, VGG16,Inceptio ...

  2. 目标检测算法SSD之训练自己的数据集

    目标检测算法SSD之训练自己的数据集 prerequesties 预备知识/前提条件 下载和配置了最新SSD代码 git clone https://github.com/weiliu89/caffe ...

  3. 可变卷积Deforable ConvNet 迁移训练自己的数据集 MXNet框架 GPU版

    [引言] 最近在用可变卷积的rfcn 模型迁移训练自己的数据集, MSRA官方使用的MXNet框架 环境搭建及配置:http://www.cnblogs.com/andre-ma/p/8867031. ...

  4. caffe训练自己的数据集

    默认caffe已经编译好了,并且编译好了pycaffe 1 数据准备 首先准备训练和测试数据集,这里准备两类数据,分别放在文件夹0和文件夹1中(之所以使用0和1命名数据类别,是因为方便标注数据类别,直 ...

  5. 使用yolo3模型训练自己的数据集

    使用yolo3模型训练自己的数据集 本项目地址:https://github.com/Cw-zero/Retrain-yolo3 一.运行环境 1. Ubuntu16.04. 2. TensorFlo ...

  6. Win10中用yolov3训练自己的数据集全过程（VS、CUDA、CUDNN、OpenCV配置，训练和测试）

    在Windows系统的Linux系统中用yolo训练自己的数据集的配置差异很大,今天总结在win10中配置yolo并进行训练和测试的全过程. 提纲: 1.下载适用于Windows的darknet 2. ...

  7. TensorFlow学习笔记——LeNet-5（训练自己的数据集）

    在之前的TensorFlow学习笔记——图像识别与卷积神经网络(链接:请点击我)中了解了一下经典的卷积神经网络模型LeNet模型.那其实之前学习了别人的代码实现了LeNet网络对MNIST数据集的训练 ...

  8. YOLO训练自己的数据集的一些心得

    YOLO训练自己的数据集 YOLO-darknet训练自己的数据 [Darknet][yolo v2]训练自己数据集的一些心得----VOC格式 YOLO模型训练可视化训练过程中的中间参数 项目开源代 ...

  9. YOLO V3训练自己的数据集

    数据的输入几乎和Faster rcnn一样,标签格式xml是一样的. 相比Faster rcnn,数据多了一步处理,通过voc_annotation.py将图片路径和bbox+class存储在txt下 ...

随机推荐

  1. 【题解】Luogu P2081 [NOI2012]迷失游乐园

    原题传送门 这是当时冬令营课上讲的题,咕咕咕到了现在 如果这题没有环套树的话,就很套路了 需要两个数组up[i]和down[i],down[i]表示从i点第一步向下走的期望距离,up[i]表示从i点第 ...

  2. 【Django模板进阶007】

    本节主要讲 Django模板中的循环,条件判断,常用的标签,过滤器的使用 列表,字典,类的实例的使用 循环:迭代显示列表,字典等中的内容 条件判断:判断是否显示该内容,比如判断是手机访问,还是电脑访问 ...

  3. CentOS下使用tcpdump网络抓包

    tcpdump是Linux下的截获分析网络数据包的工具,对优化系统性能有很大参考价值. 安装 tcpdump不是默认安装的,在CentOS下安装: yum install tcpdump 在Ubunt ...

  4. 移动web端在线观看ppt

    使用office online 移动端重定向地址,重定向地址生成方式(对ppt地址进行两次编码),代码如下.将代码中http://video.ch9.ms/build/2011/slides/TOOL ...

  5. criteo marketing api 相关

    官网登陆地址:https://marketing.criteo.com/ 官网api介绍:https://marketing.criteo.com/e/s/article?article=360001 ...

  6. Go语言库之strconv包（转载自--http://blog.csdn.net/alvine008/article/details/51283189）

    golang strconv.ParseInt 是将字符串转换为数字的函数 func ParseInt(s string, base int, bitSize int) (i int64, err e ...

  7. leecode第二百三十七题（删除链表中的节点）

    /** * Definition for singly-linked list. * struct ListNode { * int val; * ListNode *next; * ListNode ...

  8. C#操作PDF文件打印

    操作PDF文档时,打印是常见的需求之一.针对不同的打印需求,可分多种情况来进行,如设置静默打印.指定打印页码范围和打印纸张大小.双面打印.黑白打印等等.经过测试,下面将对常见的几种PDF打印需求做一些 ...

  9. android -------- 沉浸式状态栏和沉浸式导航栏（ImmersionBar）

    android 4.4以上沉浸式状态栏和沉浸式导航栏管理,包括状态栏字体颜色,适用于Activity.Fragment.DialogFragment.Dialog,并且适配刘海屏,适配软键盘弹出等问题 ...

  10. Appium TestNg Maven Android Eclipse java自动化环境搭建

    1.环境准备 1)Eclipse + maven + appium + TestNg 确保已经在Eclipse 上面安装maven TestNg的插件 2)打开Eclipse,新建一个maven项目 ...