Android 8.0 notification 不显示的bug

8.0之后的android 需要创建 channel 否则 notification 不显示。

final String CHANNEL_ID = "channel_id_1";
final String CHANNEL_NAME = "channel_name_1";

NotificationManager mNotificationManager = (NotificationManager)
getSystemService(Context.NOTIFICATION_SERVICE);

if (android.os.Build.VERSION.SDK_INT >= android.os.Build.VERSION_CODES.O) {
//只在Android O之上需要渠道
NotificationChannel notificationChannel = new NotificationChannel(CHANNEL_ID,
CHANNEL_NAME, NotificationManager.IMPORTANCE_HIGH);
//如果这里用IMPORTANCE_NOENE就需要在系统的设置里面开启渠道，
//通知才能正常弹出
mNotificationManager.createNotificationChannel(notificationChannel);
}
NotificationCompat.Builder builder= new NotificationCompat.Builder(this,CHANNEL_ID);

builder.setSmallIcon(R.mipmap.ic_launcher)
.setContentTitle("通知标题")
.setContentText("通知内容")
.setAutoCancel(true);

mNotificationManager.notify(notificationId, builder.build());

Keras 实现的性别年龄检测 （已并入颜值服务）

github  https://github.com/yu4u/age-gender-estimation

先试用下训练好的权重文件

将 demo.py 的80行改动了一下，用以识别图片

if 1==1:
    #for img in yield_images():
        img = cv2.imread("test.jpg")

113行 改动用以保存结果

        cv2.imwrite("test1.jpg",img)
        #cv2.imshow("result", img)
        #key = cv2.waitKey(30)
        #while true:
        #    if key == 27:
        #        break

效果还可以，等有空训练下再改成一个service。原权重的训练集基本都是老外。

另一个Tensorflow的实现：Age Gender Estimate TF 一个tensorflow 识别年龄的demo

整合进了颜值server

代码比较乱，凑活先用着吧，

全部代码请移步 Github  https://github.com/endpang/xindong/blob/master/facerank/server.py

权重文件超过了 github100m的限制。大家去源地址下载吧。

https://github.com/yu4u/age-gender-estimation/releases/download/v0.5/weights.18-4.06.hdf5

__author__ = 'pangzhiwei'

import cv2
import dlib
import numpy as np
import math
import itertools
from sklearn.externals import joblib
from sklearn import decomposition
import bottle
from bottle import request
import urllib.request
import json
from wide_resnet import WideResNet


def facialRatio(points):
	x1 = points[0]
	y1 = points[1]
	x2 = points[2]
	y2 = points[3]
	x3 = points[4]
	y3 = points[5]
	x4 = points[6]
	y4 = points[7]
	dist1 = math.sqrt((x1-x2)**2 + (y1-y2)**2)
	dist2 = math.sqrt((x3-x4)**2 + (y3-y4)**2)
	ratio = dist1/dist2
	return ratio


def generateFeatures(pointIndices1, pointIndices2, pointIndices3, pointIndices4, allLandmarkCoordinates):
	size = allLandmarkCoordinates.shape
	if len(size) > 1:
		allFeatures = np.zeros((size[0], len(pointIndices1)))
		for x in range(0, size[0]):
			landmarkCoordinates = allLandmarkCoordinates[x, :]
			ratios = []
			for i in range(0, len(pointIndices1)):
				x1 = landmarkCoordinates[2*(pointIndices1[i]-1)]
				y1 = landmarkCoordinates[2*pointIndices1[i] - 1]
				x2 = landmarkCoordinates[2*(pointIndices2[i]-1)]
				y2 = landmarkCoordinates[2*pointIndices2[i] - 1]
				x3 = landmarkCoordinates[2*(pointIndices3[i]-1)]
				y3 = landmarkCoordinates[2*pointIndices3[i] - 1]
				x4 = landmarkCoordinates[2*(pointIndices4[i]-1)]
				y4 = landmarkCoordinates[2*pointIndices4[i] - 1]
				points = [x1, y1, x2, y2, x3, y3, x4, y4]
				ratios.append(facialRatio(points))
			allFeatures[x, :] = np.asarray(ratios)
	else:
		allFeatures = np.zeros((1, len(pointIndices1)))
		landmarkCoordinates = allLandmarkCoordinates
		ratios = []
		for i in range(0, len(pointIndices1)):
			x1 = landmarkCoordinates[2*(pointIndices1[i]-1)]
			y1 = landmarkCoordinates[2*pointIndices1[i] - 1]
			x2 = landmarkCoordinates[2*(pointIndices2[i]-1)]
			y2 = landmarkCoordinates[2*pointIndices2[i] - 1]
			x3 = landmarkCoordinates[2*(pointIndices3[i]-1)]
			y3 = landmarkCoordinates[2*pointIndices3[i] - 1]
			x4 = landmarkCoordinates[2*(pointIndices4[i]-1)]
			y4 = landmarkCoordinates[2*pointIndices4[i] - 1]
			points = [x1, y1, x2, y2, x3, y3, x4, y4]
			ratios.append(facialRatio(points))
		allFeatures[0, :] = np.asarray(ratios)
	return allFeatures


def generateAllFeatures(allLandmarkCoordinates):
	a = [18, 22, 23, 27, 37, 40, 43, 46, 28, 32, 34, 36, 5, 9, 13, 49, 55, 52, 58]
	combinations = itertools.combinations(a, 4)
	i = 0
	pointIndices1 = []
	pointIndices2 = []
	pointIndices3 = []
	pointIndices4 = []
	for combination in combinations:
		pointIndices1.append(combination[0])
		pointIndices2.append(combination[1])
		pointIndices3.append(combination[2])
		pointIndices4.append(combination[3])
		i = i+1
		pointIndices1.append(combination[0])
		pointIndices2.append(combination[2])
		pointIndices3.append(combination[1])
		pointIndices4.append(combination[3])
		i = i+1
		pointIndices1.append(combination[0])
		pointIndices2.append(combination[3])
		pointIndices3.append(combination[1])
		pointIndices4.append(combination[2])
		i = i+1
	return generateFeatures(pointIndices1, pointIndices2, pointIndices3, pointIndices4, allLandmarkCoordinates)


def fetch_face_pic(face,predictor):
    rects = detector(face, 1)
    img_h, img_w, _ = np.shape(face)
    faces = np.empty((len(rects), img_size, img_size, 3))
    detected = rects
    img = face
    lables = []
    if len(detected) > 0:
        for i, d in enumerate(detected):
            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()
            xw1 = max(int(x1 - 0.4 * w), 0)
            yw1 = max(int(y1 - 0.4 * h), 0)
            xw2 = min(int(x2 + 0.4 * w), img_w - 1)
            yw2 = min(int(y2 + 0.4 * h), img_h - 1)
            cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
            # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)
            faces[i, :, :, :] = cv2.resize(img[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))

        # predict ages and genders of the detected faces
        results = model.predict(faces)
        predicted_genders = results[0]
        ages = np.arange(0, 101).reshape(101, 1)
        predicted_ages = results[1].dot(ages).flatten()

        # draw results
        for i, d in enumerate(detected):
            label = [predicted_ages[i],
                                    "F" if predicted_genders[i][0] > 0.5 else "M"]
            #print(label)
            lables.append(label)
            #draw_label(img, (d.left(), d.top()), label)
    arrs = []
    face_arr = []
    for faces in range(len(rects)):
        # 使用predictor进行人脸关键点识别
        #print(rects[faces])
        landmarks = np.matrix([[p.x, p.y] for p in predictor(face, rects[faces]).parts()])
        #face_img = face.copy()
        # 使用enumerate函数遍历序列中的元素以及它们的下标
        arr = []

        for idx, point in enumerate(landmarks):
            arr = np.append(arr,point[0,0])
            arr = np.append(arr,point[0,1])
            #strs += str(point[0, 0]) + ','  + str(point[0, 1]) + ','
            #pos = (point[0, 0], point[0, 1])
            #print(point)
            #f.write(str(point[0, 0]))
            #f.write(',')
            #f.write(str(point[0, 1]))
            #f.write(',')
            #f.write('\n')
        if len(arrs) == 0:
            arrs = [arr]
        else:
            arrs = np.concatenate((arrs,[arr]),axis=0)
        f = rects[faces]
        [x1,x2,y1,y2]=[f.left(),f.right(),f.top(),f.bottom()]
        a = [[x1,x2,y1,y2]]
        if len(face_arr) == 0:
            face_arr = a
        else:
            face_arr = np.concatenate((face_arr,a) ,axis=0)
    return arrs,face_arr,lables

def predict(my_features):
    predictions = []
    for i in range(len(my_features)):
        feature = my_features[i, :]
        feature_transfer = pca.transform(feature.reshape(1, -1))
        predictions.append(pre_model.predict(feature_transfer).tolist())
        print(i)
    '''
    if len(my_features.shape) > 1:
        for i in range(len(my_features)):
            feature = my_features[i, :]
            feature_transfer = pca.transform(feature.reshape(1, -1))
            predictions.append(pre_model.predict(feature_transfer))
        print('照片中的人颜值得分依次为(满分为5分)：')
        k = 1
        for pre in predictions:
            print('第%d个人：' % k, end='')
            print(str(pre)+'分')
            k += 1
    else:
        feature = my_features
        feature_transfer = pca.transform(feature.reshape(1, -1))
        predictions.append(pre_model.predict(feature_transfer))
        print('照片中的人颜值得分为(满分为5分)：')
        k = 1
        for pre in predictions:
            print(str(pre)+'分')
            k += 1
    '''
    return predictions

PREDICTOR_PATH = './model/shape_predictor_68_face_landmarks.dat'
detector = dlib.get_frontal_face_detector()
# 使用官方提供的模型构建特征提取器
predictor = dlib.shape_predictor(PREDICTOR_PATH)
pre_model = joblib.load('./model/face_rating.pkl')
features = np.loadtxt('./data/features_ALL.txt', delimiter=',')
pca = decomposition.PCA(n_components=20)
pca.fit(features)

weight_file = "./model/weights.18-4.06.hdf5"
img_size = 64
model = WideResNet(img_size, depth=16, k=8)()
model.load_weights(weight_file)

@bottle.route('/find', method='GET')
def do_find():
    w = request.query.get("url")
    #print(w)
    resp = urllib.request.urlopen(w)
    image = np.asarray(bytearray(resp.read()),dtype="uint8")
    image = cv2.imdecode(image,cv2.IMREAD_COLOR)

    arrs,faces,lables = fetch_face_pic(image,predictor)
    print("arrs:",arrs)
    if len(arrs) < 1:
        return ""
    if len(arrs) == 1:
        my_features = generateAllFeatures(arrs[0])
    else:
        my_features = generateAllFeatures(arrs)
    if len(my_features.shape) > 1:
        predictions = predict(my_features,)
        print(faces)
        print(predictions)
        # print(type(predictions))
        print(type(faces))
        a2 = np.array([1,2])
        if type(faces) == type(a2):
            print("is")
            faces = faces.tolist()
        result =[
            faces,predictions,image.shape,lables
        ]
    #print(image)
    print(faces)
    return json.dumps(result)


bottle.run(host='0.0.0.0', port=8888)

效果

测试图片

虚拟货币（比特币，以太坊）价值预测

缘由：好友开发了一个程序化交易虚拟货币的助手软件。卖的火热（想购买的朋友请加微信： endpang ）。出于对土豪的敬意，抽时间做一个时序预测的东西。

财富自由之路开始。。。。

首先得获得实时的价格数据，

火币网的接口：https://github.com/huobiapi/API_Docs/wiki/REST_introduction

websocket 获得实时数据并将价格写入文本文件

from websocket import create_connection
import gzip
import time
import json




if __name__ == '__main__':
    while(1):
        try:
            ws = create_connection("wss://api.huobipro.com/ws")
            break
        except:
            print('connect ws error,retry...')
            time.sleep(5)

    # 订阅 KLine 数据
    tradeStr="""{"sub": "market.ethusdt.kline.1min","id": "id10"}"""

    # 请求 KLine 数据
    # tradeStr="""{"req": "market.ethusdt.kline.1min","id": "id10", "from": 1513391453, "to": 1513392453}"""

    #订阅 Market Depth 数据
    # tradeStr="""{"sub": "market.ethusdt.depth.step5", "id": "id10"}"""

    #请求 Market Depth 数据
    # tradeStr="""{"req": "market.ethusdt.depth.step5", "id": "id10"}"""

    #订阅 Trade Detail 数据
    # tradeStr="""{"sub": "market.ethusdt.trade.detail", "id": "id10"}"""

    #请求 Trade Detail 数据
    # tradeStr="""{"req": "market.ethusdt.trade.detail", "id": "id10"}"""

    #请求 Market Detail 数据
    # tradeStr="""{"req": "market.ethusdt.detail", "id": "id12"}"""

    ws.send(tradeStr)
    old = {"vol": 0,"count":0,"close":0}
    i = 0
    with open('test_csv.csv',"w") as csv:
        #csv.write("lines\n")
        while(1):

            compressData=ws.recv()
            result=gzip.decompress(compressData).decode('utf-8')
            if result[:7] == '{"ping"':

                ts=result[8:21]
                pong='{"pong":'+ts+'}'
                ws.send(pong)
                ws.send(tradeStr)
            elif result[:5] == '{"ch"':
                arr = json.loads(result)
                #print("arr",arr)

                if arr['tick']['count'] < old["count"]:
                    i = i+1
                    csv.write(format(old['close'])+"\n")
                    print('count',old)
                else:
                    delta = arr['tick']['vol'] - old['vol']
                    print("delta:",delta)
                    if delta > 0:
                        deltap = arr['tick']['close'] -  old["close"]

                        print("deltap",deltap)
                old = arr['tick']

基于tensorflow  lstm 的预测模型。

import lstm
import time
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt

def plot_results(predicted_data, true_data):
    fig = plt.figure(facecolor='white')
    ax = fig.add_subplot(111)
    ax.plot(true_data, label='True Data')
    plt.plot(predicted_data, label='Prediction')
    plt.legend()
    plt.show()

def plot_results_multiple(predicted_data, true_data, prediction_len):
    fig = plt.figure(facecolor='white')
    ax = fig.add_subplot(111)
    ax.plot(true_data, label='True Data')
    #Pad the list of predictions to shift it in the graph to it's correct start
    for i, data in enumerate(predicted_data):
        padding = [None for p in range(i * prediction_len)]
        plt.plot(padding + data, label='Prediction')
        plt.legend()
    plt.show()

#Main Run Thread
if __name__=='__main__':
	global_start_time = time.time()
	epochs  = 1
	seq_len = 50

	print('> Loading data... ')

	X_train, y_train, X_test, y_test = lstm.load_data('test_csv.csv', seq_len, True)

	print('> Data Loaded. Compiling...')

	model = lstm.build_model([1, 50, 100, 1])

	model.fit(
	    X_train,
	    y_train,
	    batch_size=512,
	    nb_epoch=epochs,
	    validation_split=0.05)

	predictions = lstm.predict_sequences_multiple(model, X_test, seq_len, 50)
	#predicted = lstm.predict_sequence_full(model, X_test, seq_len)
	#predicted = lstm.predict_point_by_point(model, X_test)        

	print('Training duration (s) : ', time.time() - global_start_time)
	plot_results_multiple(predictions, y_test, 50)

市场有风险，交易需谨慎。不准莫怪我，预测准的，写出来也不会开放代码的。。。 : P

Face Rank 基于dlib的颜值计算服务

带年龄的版本 Keras 实现的性别年龄检测

本文不再更新，请移步上面链接的文章。

__author__ = 'pangzhiwei'

import cv2
import dlib
import numpy as np
import math
import itertools
from sklearn.externals import joblib
from sklearn import decomposition
import bottle
from bottle import request
import urllib.request
import json


def facialRatio(points):
	x1 = points[0]
	y1 = points[1]
	x2 = points[2]
	y2 = points[3]
	x3 = points[4]
	y3 = points[5]
	x4 = points[6]
	y4 = points[7]
	dist1 = math.sqrt((x1-x2)**2 + (y1-y2)**2)
	dist2 = math.sqrt((x3-x4)**2 + (y3-y4)**2)
	ratio = dist1/dist2
	return ratio


def generateFeatures(pointIndices1, pointIndices2, pointIndices3, pointIndices4, allLandmarkCoordinates):
	size = allLandmarkCoordinates.shape
	if len(size) > 1:
		allFeatures = np.zeros((size[0], len(pointIndices1)))
		for x in range(0, size[0]):
			landmarkCoordinates = allLandmarkCoordinates[x, :]
			ratios = []
			for i in range(0, len(pointIndices1)):
				x1 = landmarkCoordinates[2*(pointIndices1[i]-1)]
				y1 = landmarkCoordinates[2*pointIndices1[i] - 1]
				x2 = landmarkCoordinates[2*(pointIndices2[i]-1)]
				y2 = landmarkCoordinates[2*pointIndices2[i] - 1]
				x3 = landmarkCoordinates[2*(pointIndices3[i]-1)]
				y3 = landmarkCoordinates[2*pointIndices3[i] - 1]
				x4 = landmarkCoordinates[2*(pointIndices4[i]-1)]
				y4 = landmarkCoordinates[2*pointIndices4[i] - 1]
				points = [x1, y1, x2, y2, x3, y3, x4, y4]
				ratios.append(facialRatio(points))
			allFeatures[x, :] = np.asarray(ratios)
	else:
		allFeatures = np.zeros((1, len(pointIndices1)))
		landmarkCoordinates = allLandmarkCoordinates
		ratios = []
		for i in range(0, len(pointIndices1)):
			x1 = landmarkCoordinates[2*(pointIndices1[i]-1)]
			y1 = landmarkCoordinates[2*pointIndices1[i] - 1]
			x2 = landmarkCoordinates[2*(pointIndices2[i]-1)]
			y2 = landmarkCoordinates[2*pointIndices2[i] - 1]
			x3 = landmarkCoordinates[2*(pointIndices3[i]-1)]
			y3 = landmarkCoordinates[2*pointIndices3[i] - 1]
			x4 = landmarkCoordinates[2*(pointIndices4[i]-1)]
			y4 = landmarkCoordinates[2*pointIndices4[i] - 1]
			points = [x1, y1, x2, y2, x3, y3, x4, y4]
			ratios.append(facialRatio(points))
		allFeatures[0, :] = np.asarray(ratios)
	return allFeatures


def generateAllFeatures(allLandmarkCoordinates):
	a = [18, 22, 23, 27, 37, 40, 43, 46, 28, 32, 34, 36, 5, 9, 13, 49, 55, 52, 58]
	combinations = itertools.combinations(a, 4)
	i = 0
	pointIndices1 = []
	pointIndices2 = []
	pointIndices3 = []
	pointIndices4 = []
	for combination in combinations:
		pointIndices1.append(combination[0])
		pointIndices2.append(combination[1])
		pointIndices3.append(combination[2])
		pointIndices4.append(combination[3])
		i = i+1
		pointIndices1.append(combination[0])
		pointIndices2.append(combination[2])
		pointIndices3.append(combination[1])
		pointIndices4.append(combination[3])
		i = i+1
		pointIndices1.append(combination[0])
		pointIndices2.append(combination[3])
		pointIndices3.append(combination[1])
		pointIndices4.append(combination[2])
		i = i+1
	return generateFeatures(pointIndices1, pointIndices2, pointIndices3, pointIndices4, allLandmarkCoordinates)


def fetch_face_pic(face,predictor):
    rects = detector(face, 1)
    #str = ""
    #strs = ""
    arrs = []
    face_arr = []
    for faces in range(len(rects)):
        # 使用predictor进行人脸关键点识别
        #print(rects[faces])
        landmarks = np.matrix([[p.x, p.y] for p in predictor(face, rects[faces]).parts()])
        #face_img = face.copy()
        # 使用enumerate函数遍历序列中的元素以及它们的下标
        arr = []

        for idx, point in enumerate(landmarks):
            arr = np.append(arr,point[0,0])
            arr = np.append(arr,point[0,1])
            #strs += str(point[0, 0]) + ','  + str(point[0, 1]) + ','
            #pos = (point[0, 0], point[0, 1])
            #print(point)
            #f.write(str(point[0, 0]))
            #f.write(',')
            #f.write(str(point[0, 1]))
            #f.write(',')
            #f.write('\n')
        if len(arrs) == 0:
            arrs = [arr]
        else:
            arrs = np.concatenate((arrs,[arr]),axis=0)
        f = rects[faces]
        [x1,x2,y1,y2]=[f.left(),f.right(),f.top(),f.bottom()]
        a = [[x1,x2,y1,y2]]
        if len(face_arr) == 0:
            face_arr = a
        else:
            face_arr = np.concatenate((face_arr,a) ,axis=0)
    return arrs,face_arr

def predict(my_features):
    predictions = []
    for i in range(len(my_features)):
        feature = my_features[i, :]
        feature_transfer = pca.transform(feature.reshape(1, -1))
        predictions.append(pre_model.predict(feature_transfer).tolist())
        print(i)
    '''
    if len(my_features.shape) > 1:
        for i in range(len(my_features)):
            feature = my_features[i, :]
            feature_transfer = pca.transform(feature.reshape(1, -1))
            predictions.append(pre_model.predict(feature_transfer))
        print('照片中的人颜值得分依次为(满分为5分)：')
        k = 1
        for pre in predictions:
            print('第%d个人：' % k, end='')
            print(str(pre)+'分')
            k += 1
    else:
        feature = my_features
        feature_transfer = pca.transform(feature.reshape(1, -1))
        predictions.append(pre_model.predict(feature_transfer))
        print('照片中的人颜值得分为(满分为5分)：')
        k = 1
        for pre in predictions:
            print(str(pre)+'分')
            k += 1
    '''
    return predictions

PREDICTOR_PATH = './model/shape_predictor_68_face_landmarks.dat'
detector = dlib.get_frontal_face_detector()
# 使用官方提供的模型构建特征提取器
predictor = dlib.shape_predictor(PREDICTOR_PATH)
pre_model = joblib.load('./model/face_rating.pkl')
features = np.loadtxt('./data/features_ALL.txt', delimiter=',')
pca = decomposition.PCA(n_components=20)
pca.fit(features)


@bottle.route('/find', method='GET')
def do_find():
    w = request.query.get("url")
    #print(w)
    resp = urllib.request.urlopen(w)
    image = np.asarray(bytearray(resp.read()),dtype="uint8")
    image = cv2.imdecode(image,cv2.IMREAD_COLOR)
    arrs,faces = fetch_face_pic(image,predictor)
    print(arrs)
    my_features = generateAllFeatures(arrs)
    if len(my_features.shape) > 1:
        predictions = predict(my_features,)
        print(faces)
        print(predictions)
        # print(type(predictions))
        result =[
            faces.tolist(),predictions
        ]
    #print(image)
    print(faces)
    return json.dumps(result)


bottle.run(host='0.0.0.0', port=8888)

最新代码及model 文件见 https://github.com/endpang/xindong

opencv 人脸识别并返回置信度的服务

import cv2
import numpy as np
import os
import bottle
def fetch_face_pic(img,face_cascade):
        # 将图像灰度化
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # 人脸检测
        faces,rl,wl = face_cascade.detectMultiScale3(gray,scaleFactor=1.1,
                minNeighbors=3,
                minSize=(30, 30),
                flags = cv2.CASCADE_SCALE_IMAGE,
                outputRejectLevels = True
                )
        #输出分数最高的
        ol = 0

        for i,(x,y,w,h) in enumerate(faces):

            if wl[i][0] > ol:
                crop = img[y:y+h, x:x+w] # 使用切片操作直接提取感兴趣的区域
                print(wl[i][0],ol,x,y,w,h)
                ol = wl[i][0]
        return crop


face_cascade = cv2.CascadeClassifier('/root/girl/opencv-master/data/haarcascades/haarcascade_frontalface_default.xml')

@bottle.route('/find/<w>', method='GET')
def do_find(w):
    jaffe_pic = '/web/maps.cc/public/girl/img/' + w
    img = cv2.imread(jaffe_pic)
    crop = fetch_face_pic(img,face_cascade)
    if crop is not None:
        cv2.imwrite("/web/maps.cc/public/girl/thumb/"+w,crop)
        return w
    return ""

bottle.run(host='0.0.0.0', port=8080)

Android okhttp

联网权限

app/manifests/AndroidManiifest.xml 文件  <application 上 添加

<uses-permission android:name="android.permission.INTERNET"/>

build.gradle(Module:app) 文件 dependencies 内加一行

 compile 'com.squareup.okhttp3:okhttp:3.10.0'

Activity 文件。

package com.example.zhiweipang.xindong;

import android.os.Bundle;
import android.support.design.widget.FloatingActionButton;
import android.support.design.widget.Snackbar;
import android.support.v7.app.AppCompatActivity;
import android.support.v7.widget.Toolbar;
import android.util.Log;
import android.view.View;
import android.view.Menu;
import android.view.MenuItem;
import okhttp3.OkHttpClient;
import okhttp3.Request;
import okhttp3.Response;
import java.io.IOException;

public class MainActivity extends AppCompatActivity {
    OkHttpClient client = new OkHttpClient();

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);
        Toolbar toolbar = (Toolbar) findViewById(R.id.toolbar);
        setSupportActionBar(toolbar);

        FloatingActionButton fab = (FloatingActionButton) findViewById(R.id.fab);
        fab.setOnClickListener(new View.OnClickListener() {
            @Override
            public void onClick(View view) {
                Snackbar.make(view, "Replace with your own action", Snackbar.LENGTH_LONG)
                        .setAction("Action", null).show();
                getRequest();
            }
        });


    }
    private void getRequest() {

        final Request request=new Request.Builder()
                .get()
                .tag(this)
                .url("http://www.baidu.com")
                .build();

        new Thread(new Runnable() {
            @Override
            public void run() {
                Response response = null;
                try {
                    response = client.newCall(request).execute();
                    if (response.isSuccessful()) {
                        Log.i("WY","打印GET响应的数据：" + response.body().string());
                    } else {
                        throw new IOException("Unexpected code " + response);
                    }
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }).start();

    }
    @Override
    public boolean onCreateOptionsMenu(Menu menu) {
        // Inflate the menu; this adds items to the action bar if it is present.
        getMenuInflater().inflate(R.menu.menu_main, menu);
        return true;
    }

    @Override
    public boolean onOptionsItemSelected(MenuItem item) {
        // Handle action bar item clicks here. The action bar will
        // automatically handle clicks on the Home/Up button, so long
        // as you specify a parent activity in AndroidManifest.xml.
        int id = item.getItemId();

        //noinspection SimplifiableIfStatement
        if (id == R.id.action_settings) {
            return true;
        }

        return super.onOptionsItemSelected(item);
    }
}

GITHUB:https://github.com/endpang/xindong

pip3.x 报错处理

Traceback (most recent call last):
 File "/usr/local/bin/pip3.5", line 7, in <module>
 from pip import main
 File "/usr/local/lib/python3.5/dist-packages/pip/__init__.py", line 26, in <module>
 from pip.utils import get_installed_distributions, get_prog
 File "/usr/local/lib/python3.5/dist-packages/pip/utils/__init__.py", line 27, in <module>
 from pip._vendor import pkg_resources
 File "/usr/local/lib/python3.5/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 3018, in <module>
 @_call_aside
 File "/usr/local/lib/python3.5/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 3004, in _call_aside
 f(*args, **kwargs)
 File "/usr/local/lib/python3.5/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 3046, in _initialize_master_working_set
 dist.activate(replace=False)
 File "/usr/local/lib/python3.5/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 2578, in activate
 declare_namespace(pkg)
 File "/usr/local/lib/python3.5/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 2152, in declare_namespace
 _handle_ns(packageName, path_item)
 File "/usr/local/lib/python3.5/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 2092, in _handle_ns
 _rebuild_mod_path(path, packageName, module)
 File "/usr/local/lib/python3.5/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 2121, in _rebuild_mod_path
 orig_path.sort(key=position_in_sys_path)
AttributeError: '_NamespacePath' object has no attribute 'sort'

sudo vim /usr/local/lib/python3.5/dist-packages/pip/_vendor/pkg_resources/__init__.py

2121行

#orig_path.sort(key=position_in_sys_path)
#module.__path__[:] = [_normalize_cached(p) for p in orig_path]
orig_path_t = list(orig_path)
orig_path_t.sort(key=position_in_sys_path)
module.__path__[:] = [_normalize_cached(p) for p in orig_path_t]