import cv2
import imutils
import numpy as np
import os
from sklearn.svm import LinearSVC
from sklearn.externals import joblib
from scipy.cluster.vq import *
from sklearn.preprocessing import StandardScaler
# Get the training classes names and store them in a list
train_path = "dataset/train/"
training_names = os.listdir(train_path)
# Get all the path to the images and save them in a list
# image_paths and the corresponding label in image_paths
image_paths = []
image_classes = []
class_id = 0
for training_name in training_names:
    dir = os.path.join(train_path, training_name)
    class_path = imutils.imlist(dir)
    image_paths += class_path
    image_classes += [class_id] * len(class_path)
    class_id += 1
# 创建SIFT特征提取器
sift = cv2.xfeatures2d.SIFT_create()
# 特征提取与描述子生成
des_list = []
for image_path in image_paths:
    im = cv2.imread(image_path)
    im = cv2.resize(im, (300, 300))
    kpts = sift.detect(im)
    kpts, des = sift.compute(im, kpts)
    des_list.append((image_path, des))
    print("image file path : ", image_path)
# 描述子向量
descriptors = des_list[0][1]
for image_path, descriptor in des_list[1:]:
    descriptors = np.vstack((descriptors, descriptor))
# 100 聚类 K-Means
k = 100
voc, variance = kmeans(descriptors, k, 1)
# 生成特征直方图
im_features = np.zeros((len(image_paths), k), "float32")
for i in range(len(image_paths)):
    words, distance = vq(des_list[i][1], voc)
    for w in words:
        im_features[i][w] += 1
# 实现动词词频与出现频率统计
nbr_occurences = np.sum((im_features > 0) * 1, axis=0)
idf = np.array(np.log((1.0 * len(image_paths) + 1) / (1.0 * nbr_occurences + 1)), 'float32')
# 尺度化
stdSlr = StandardScaler().fit(im_features)
im_features = stdSlr.transform(im_features)
# Train the Linear SVM
clf = LinearSVC()
clf.fit(im_features, np.array(image_classes))
# Save the SVM
print("training and save model...")
joblib.dump((clf, training_names, stdSlr, k, voc), "startup.pkl", compress=3)

import cv2 as cvimport numpy as npfrom imutils import pathsfrom scipy.cluster.vq import *from sklearn.externals import joblib
def predict_image(image_path, pkl):    # Load the classifier, class names, scaler, number of clusters and vocabulary    clf, classes_names, stdSlr, k, voc = joblib.load("eleme.pkl")    # Create feature extraction and keypoint detector objects    sift = cv.xfeatures2d.SIFT_create()    # List where all the descriptors are stored    des_list = []    im = cv.imread(image_path, cv.IMREAD_GRAYSCALE)    im = cv.resize(im, (300, 300))    kpts = sift.detect(im)    kpts, des = sift.compute(im, kpts)    des_list.append((image_path, des))
    descriptors = des_list[0][1]    for image_path, descriptor in des_list[0:]:        descriptors = np.vstack((descriptors, descriptor))
    test_features = np.zeros((1, k), "float32")    words, distance = vq(des_list[0][1], voc)    for w in words:        test_features[0][w] += 1
    # Perform Tf-Idf vectorization    nbr_occurences = np.sum((test_features > 0) * 1, axis=0)    idf = np.array(np.log((1.0 + 1) / (1.0 * nbr_occurences + 1)), 'float32')
    # Scale the features    test_features = stdSlr.transform(test_features)
    # Perform the predictions    predictions = [classes_names[i] for i in clf.predict(test_features)]    return predictions

from airtest.core.api import *from dingtalkchatbot.chatbot import DingtalkChatbotfrom poco.drivers.android.uiautomation import AndroidUiautomationPoco
webhook = 'https://oapi.dingtalk.com/robot/send?access_token='robot = DingtalkChatbot(webhook)
def calculate(package_name, apk_path, pkl, device_name, app_name, app_version):    sample = 'sample/screen.mp4'    test_path = "dataset/test/"    if not os.path.isdir('sample/'):        os.makedirs('sample/')    if not os.path.isdir(test_path):        os.makedirs(test_path)    try:        os.system("adb uninstall {}".format(package_name))
        os.system("adb install -r {}".format(apk_path))
        poco = AndroidUiautomationPoco()        poco.device.wake()
        time.sleep(2)
        poco(text='应用名').click()        poco(text='下一步').click()        poco(text='允许').click()        poco(text='允许').click()        poco(text='允许').click()
        os.system("adb shell am force-stop {}".format(package_name))
        subprocess.Popen("adb shell screenrecord  --time-limit 20 /sdcard/sample.mp4", shell=True,                         stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
        poco(text="应用名").click()
        time.sleep(20)
        os.system("adb pull /sdcard/sample.mp4 {}".format(sample))        os.system("adb uninstall {}".format(package_name))
        os.system("ffmpeg -i {} -r 60 {}%d.jpeg".format(sample, test_path))        image_paths = []        class_path = list(paths.list_images(test_path))        image_paths += class_path        start = []        stable = []        for image_path in image_paths:            predictions = predict_image(image_path, pkl)            if predictions[0] == '1_start':                start += [str(image_path.split('/')[2]).split('.')[0]]            elif predictions[0] == '4_stable':                stable += [str(image_path.split('/')[2]).split('.')[0]]
        start_time = int(sorted(start)[0])        stable_time = int(sorted(stable)[0])        print("耗时:%.2f 秒" % ((stable_time - start_time) / 60))        robot.send_text(            msg="启动耗时自动化测试结果:\n被测设备:{}\n被测应用:{}\n被测版本:{}\n".format(device_name, app_name,                                                                   app_version) + "启动耗时:%.2f 秒" % (                        (stable_time - start_time) / 60),            is_at_all=True)    except:        shutil.rmtree(test_path)        if os.path.exists(sample):            os.remove(sample)
if __name__ == "__main__":    calculate("package_name", "app/app-release.apk", "startup.pkl", "小米MIX3", "应用名", "10.1.1")