def ActivityHandler(activity):
global ActivityCount
global FileHandle
global Output
if Output is not None:
FileHandle = open(Output, "a")
if FileHandle is None:
sys.stdout.write(json.dumps(activity))
else:
locate( activity['id'], 1, 10)
FileHandle.write(json.dumps(activity) + "\n")
FileHandle.close()
ActivityCount += 1
def action(self, *data):
# casestep
steps = Action.readxls(filepath, 1)
A = Action()
k = 4
for step in steps:
if step[0] == data[0]:
desc = step[2]
key_word, sign, param, wish = step[3], step[4], step[5], step[
7]
if sign != "":
ele = locate.locate(sign, filepath, 3)
tag, loc = ele[0], ele[1]
if key_word.lower() == 'input':
param = data[4]
step = self.buildStep(key_word, tag, loc, param)
print desc + ':' + step
k += 1
eval(step)
elif key_word.lower() == 'gettext':
step = self.buildStep(key_word, tag, loc)
text = eval(step)
if text == wish:
A.action_sign('Screenshot', data[0], 'PASS')
else:
A.action_sign('Screenshot', data[0], 'FAIL')
else:
step = self.buildStep(key_word, tag, loc, param)
print desc + ':' + step
eval(step)
else:
step = self.buildStep(key_word, param)
print desc + ':' + step
eval(step)
def callback():
data = locate.locate(e1.get())
angle = data[0]
angle2 = data[1]
for val in data:
tree.insert('', 'end', values = data)
redraw(angle, angle2)
def dispatch(values=None):
if (values == None):
return {'error': 'parameter is missing'}
if (not (isinstance(values, dict))):
return {'error': 'parameter is not a dictionary'}
if ('error' in values):
values.pop('error')
return values
if (not ('op' in values) or values['op'] == ''):
values['error'] = 'no op is specified'
return values
#Perform designated function
if (values['op'] == 'adjust'):
result = adjust.adjust(values)
return result
elif (values['op'] == 'predict'):
result = predict.predict(values)
return result
elif (values['op'] == 'correct'):
result = correct.correct(values)
return result
elif (values['op'] == 'locate'):
result = locate.locate(values)
return result
else:
values['error'] = 'op is not a legal operation'
return values
def request(self,name,pandora,*args,**kwargs): url=locate.locate(pandora,self.model) method,addr=self.m[name] addr=addr % kwargs url='https://%s%s' % (url,addr) req=requests.Request(method,url,params=kwargs) r=req.prepare() s=requests.Session() return s.send(r)
def get_points(in_dir):
'''Get points latlngs'''
points = []
file_format = ['jpg', 'jpeg', 'png']
if os.path.isdir(in_dir):
files = os.scandir(in_dir)
for file in files:
if file.name.split('.')[-1] in file_format and file.is_file():
# Include address infomation and latlng
address = locate(file.path)
if address:
lat, lng = address.google_latlng.split(',')
points.append((float(lat), float(lng)))
return points
def only_show_address(imgpath, detail=True):
'''只显示地址信息
detail: bool, 是否显示详细的地址信息
'''
address = locate(imgpath, detail)
# 用于绘制在地图上的坐标
lat, lng = address.google_latlng.split(',')
gmap = gmplot.GoogleMapPlotter(float(lat), float(lng), zoom=15)
gmap.heatmap([float(lat)], [float(lng)], radius=15, opacity=1)
gmap.draw('imgmap.html')
logging.debug('address: {}, detail: {}'.format(address, detail))
if detail:
print(imgpath, address.formatted_address, address.sematic_description)
else:
print(imgpath, address.formatted_address)
def classify(class_, imgpath, showmap):
if class_ and os.path.isdir(imgpath) and showmap:
logging.debug('class_: {}, isdir: {}'.format(class_,
os.path.isdir(imgpath)))
base_path = imgpath
fileformats = ['jpg', 'jpeg']
# 用于绘制在地图上的坐标
lats = []
lngs = []
# 使用 os.scandir() 要比 os.listdir() 更有效率
it = os.scandir(base_path)
for entry in it:
if entry.name.split('.')[-1] in fileformats and entry.is_file():
logging.info(entry.name)
address = locate(entry.path)
# 如果没有得到GPS信息则将其分到未知地点目录
if address is None:
logging.warning('{} 未知地点'.format(entry.name))
unknown_path = os.path.join(base_path, '未知地点')
os.makedirs(unknown_path, exist_ok=True)
os.rename(entry.path, os.path.join(unknown_path,
entry.name))
continue
logging.debug(address)
lat, lng = address.google_latlng.split(',')
lats.append(float(lat))
lngs.append(float(lng))
if address.province == address.city:
# 按照国家、省份、城市、区分类，并创建目录
path = os.path.join(base_path, address.country,
address.city, address.district)
else:
path = os.path.join(base_path, address.city,
address.district)
os.makedirs(path, exist_ok=True)
os.rename(entry.path, os.path.join(path, entry.name))
logging.info('entry name: {}, address: {}, path: {}'.format(
entry.name, address, path))
# 去除重复的坐标
latlng_set = set()
for i, j in zip(lats, lngs):
latlng_set.add((i, j))
lats, lngs = zip(*latlng_set)
center_lat = (max(lats) + min(lats)) / 2
center_lng = (max(lngs) + min(lngs)) / 2
logging.debug('center_lat: {}, center_lng: {}'.format(
center_lat, center_lng))
logging.debug('lats: {}, lngs: {}'.format(lats, lngs))
gmap = gmplot.GoogleMapPlotter(center_lat, center_lng, zoom=10)
gmap.heatmap(lats, lngs, radius=15, opacity=1, dissipating=True)
gmap.draw('imgmap.html')
elif not class_ and os.path.isfile(imgpath):
logging.debug('class_: {}, isdir: {}'.format(class_,
os.path.isdir(imgpath)))
only_show_address(imgpath, detail=True)
else:
print('请检查输入的 class_ 和 imgpath 是否匹配！class_ 是 {}，而 imgpath 是 {}'.format(
class_, imgpath))
import numpy as np
import cv2
import math
from transformations import *
import os
from os import listdir
from os.path import isfile, join
from locate import locate
if __name__ == '__main__':
for filename in listdir('images'):
file = 'images/' + filename
img = cv2.imread(file)
locate(img, save_result=True)
def connect(meet_id, password, audio, video, name, fixdropdown, keytimeout,
starttimeout, jointimeout, passtimeout, cronlauncher):
"""Connect to meeting with the options above"""
#Only start zoom if not started by cronlauncher script
if not cronlauncher:
restart_zoom()
time.sleep(starttimeout)
x, y = locate.locate('btnJoin')
pyautogui.click(x, y)
time.sleep(jointimeout)
#Enter meeting id
x, y = locate.locate('txtId')
pyautogui.click(x, y)
time.sleep(keytimeout)
pyautogui.write(meet_id)
time.sleep(keytimeout)
#This is for if the V is not there, users must manually tell it
if not fixdropdown:
pyautogui.press('tab', interval=0.5)
pyautogui.press('tab', interval=0.5)
if name:
logging.info('Using custom name')
pyautogui.press('backspace', 50)
pyautogui.write(name)
else:
logging.info('Ignoring custom name')
pyautogui.press('tab', interval=0.5)
#Set audio enable
if not audio:
logging.info('Enabling audio')
pyautogui.press('enter', interval=0.5)
else:
logging.info('Disabling audio')
pyautogui.press('tab', interval=0.5)
#Set video enable
if not video:
logging.info('Enabling video')
pyautogui.press('enter', interval=0.5)
else:
logging.info('Disabling video')
#Navigate to the next screen
pyautogui.press('tab', interval=0.5)
logging.info('Submitting')
pyautogui.press('enter', interval=1)
#Password
if password:
logging.info('Using password. Waiting...')
time.sleep(passtimeout)
#Check for error message
result = locate.locate('errorid')
#If there is an error message, remove it
if result:
x, y = locate.locate('btnLeave')
pyautogui.click(x, y)
time.sleep(keytimeout)
result = locate.locate('txtPass')
#If the error was actuall fake, and password thing is there, enter the password
if result:
x, y = result
pyautogui.click(x, y)
time.sleep(keytimeout)
pyautogui.write(password)
time.sleep(keytimeout)
pyautogui.press('tab', interval=0.5)
pyautogui.press('enter', interval=1)
else:
logging.error('Fatal error occured. Invalid meeting id.')
else:
logging.info('Ignoring password')
def single_test(filepath, facePath, bottomPath):
filename = filepath.split('/')[-1]
curname = filename
RES = getROI.getROI_(filepath, facePath, 1, bottomPath)
bottom = RES[0] # 底座
ROI = RES[1] # ROI
border = RES[2] # 外轮廓掩码
faceROI = RES[3] # 脸部
faceBorderMask = RES[4] # 脸部轮廓掩码
if outPath != '' and Ui_record.save_param[0] == True:
cv2.imwrite(outPath + '/roi/' + filename, ROI)
if outPath != '' and Ui_record.save_param[1] == True:
cv2.imwrite(outPath + '/foundation/' + filename, bottom)
if outPath != '' and Ui_record.save_param[2] == True:
cv2.imwrite(outPath + '/face/' + filename, faceROI)
if outPath != '' and Ui_record.save_param[3] == True:
cv2.imwrite(outPath + '/faceMask/' + filename, faceBorderMask)
if outPath != '' and Ui_record.save_param[4] == True:
cv2.imwrite(outPath + '/borderMask/' + filename, border)
sobel = locate.getLapliacian(ROI)
canny_res = canny.canny_(ROI, faceROI, canny_params[0], canny_params[1],
canny_params[2], canny_params[3], canny_params[4],
canny_params[5]) # Canny二值
harris_res = canny.HarrisDetect(ROI, border, faceBorderMask,
harris_params[0],
harris_params[1]) # Harris二值
if outPath != '' and Ui_record.save_param[5] == True:
cv2.imwrite(outPath + '/harris/' + filename, harris_res)
if outPath != '' and Ui_record.save_param[6] == True:
cv2.imwrite(outPath + '/canny/' + filename, canny_res)
locate.th = merge_dist
res1, res2, recs, feature = locate.locate(canny_res, ROI, border,
faceBorderMask, harris_res,
merge_dist, sobel) # 融合特征
if outPath != '' and Ui_record.save_param[7] == True:
cv2.imwrite(outPath + '/res/' + filename, feature)
if resOutPath != '':
cv2.imwrite(resOutPath + '/res1_' + filename, res1)
cv2.imwrite(resOutPath + '/res2_' + filename, res2)
count = 10
for filename in os.listdir('D:/dataset0511/output/cache/scratched/'):
os.remove('D:/dataset0511/output/cache/scratched/' + filename)
for rec in recs:
block = ROI[rec.y1:rec.y2 + 1, rec.x1:rec.x2 + 1]
cv2.imwrite(
'D:/dataset0511/output/cache/scratched/' + str(count) + '.bmp',
block)
count += 1
if outPath != '' and Ui_record.save_param[8] == True:
print("filename = ", curname)
cv2.imwrite(
outPath + '/blocks/' + curname.split('.')[0] + '_' +
str(count - 10) + '.bmp', block)
# 结果子图
if len(recs) == 0:
labels = []
else:
labels = predict.predict_()
# cv2.imshow('res1', res1)
# cv2.imshow('res2', res2)
# cv2.waitKey()
return res1, res2, recs, labels
from textsaveload import *
from pic_scale import picscale
from locate import locate
import os
import cv2
import mido
# scaling picture
(loc_pic_orig, loc_pic_scaled) = locate() # define the location of the picture
infile = loc_pic_orig
outfile = loc_pic_scaled
height = input("set picture height(1~127):") # input picture height
stat_pic = picscale(loc_pic_orig, loc_pic_scaled, height)
# main
mid = mido.MidiFile()
track = mido.MidiTrack()
mid.tracks.append(track)
img = cv2.imread(loc_pic_scaled)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# initialize counters
scan_x = 1 # colomns
