def uploadFileName():
path = '/Users/hugh/Projects/Feeling_Code/ToolProject/Upload-Python/static/wedding/'
fileNames = os.listdir(path)
print(fileNames)
for fileName in fileNames:
photoSet = PhotoSet()
photoSet.set('tags', ['婚纱'])
photoSet.set('title', '婚纱')
print(fileName + ' uploading!')
with open(path + fileName) as f:
print(path + fileName)
avatar = leancloud.File(fileName, f, 'image/jpeg')
photo = Photo()
photo.set(
'metaData', {
"format": "jpeg",
"height": 1365,
"orientation": "Top-left",
"colorModel": "ycbcr",
"width": 2048
})
photo.set('photoFile', avatar)
photo.save()
photoSet.set('coverPhoto', photo)
relation = photoSet.relation('photos')
relation.add(photo)
photoSet.save()
def addIcon(rootdir, extradir):
query = leancloud.Query(Clothes)
cnt = 0
for parent, dirnames, filenames in os.walk(rootdir):
for filename in filenames:
if filename.find('PNG') > 0 or filename.find('png') > 0:
rid = str(filename).split('.')[0].split('icon')[1]
print(rid)
query.equal_to('rid', rid)
res = query.find()
if len(res) == 1:
cloth = query.first()
try:
with open(os.path.join(parent, filename)) as f:
avatar = leancloud.File('fileFromLocalFile', f)
avatar.save()
# avatar_id = avatar.id
iconUrl = avatar.url
cloth.set('iconUrl', iconUrl)
cloth.save()
cnt += 1
except:
shutil.copy(os.path.join(parent, filename),
os.path.join(extradir, filename))
print filename, 'failed to update'
else:
shutil.copy(os.path.join(parent, filename),
os.path.join(extradir, filename))
print filename, 'failed to find'
print cnt, 'updated'
def uploadImageByPath(folderpath=str, filename=str):
tempfilepath = folderpath + '/' + filename
with open(tempfilepath, 'r+b') as f:
avatar = leancloud.File(filename, f)
avatar.save()
object_id = avatar.id
return object_id
def decode(key, value):
if isinstance(value, get_dumpable_types()):
return value
if isinstance(value, (tuple, list)):
return [decode(key, x) for x in value]
if not isinstance(value, dict):
return value
if '__type' not in value:
return dict([(k, decode(k, v)) for k, v in iteritems(value)])
_type = value['__type']
if _type == 'Pointer':
value = copy.deepcopy(value)
class_name = value['className']
pointer = leancloud.Object.create(class_name)
if 'createdAt' in value:
value.pop('__type')
value.pop('className')
pointer._update_data(value)
else:
pointer._update_data({'objectId': value['objectId']})
return pointer
if _type == 'Object':
value = copy.deepcopy(value)
class_name = value['className']
value.pop('__type')
value.pop('className')
obj = leancloud.Object.create(class_name)
obj._update_data(value)
return obj
if _type == 'Date':
return arrow.get(iso8601.parse_date(value['iso'])).to('local').datetime
if _type == 'GeoPoint':
return leancloud.GeoPoint(latitude=value['latitude'], longitude=value['longitude'])
if key == 'ACL':
if isinstance(value, leancloud.ACL):
return value
return leancloud.ACL(value)
if _type == 'Relation':
relation = leancloud.Relation(None, key)
relation.target_class_name = value['className']
return relation
if _type == 'File':
f = leancloud.File(value['name'])
meta_data = value.get('metaData')
if meta_data:
f._metadata = meta_data
f._url = value['url']
f.id = value['objectId']
return f
def post(self, request):
src = request.body.decode('utf-8')
# 检测用户上传文件是否为图片
if src[9:14] != 'image':
return JsonResponse({"msg": "fail"})
# 定位文件所包含base64编码信息的位置并进行解码转换存入内存文件
res = src.find('base64,')
result_src = src[res + len('base64,'):]
f = BytesIO()
data = base64.b64decode(result_src)
f.write(data)
img = InMemoryUploadedFile(f, None, "image", len(data), None, None)
# 将文件上传至云服务器
leancloud.init('tiM1T9UGqwf6QTyXkn9kcL9z-gzGzoHsz',
'1GzzyA6uv4l8JNGd6etu5V5o')
execute = leancloud.File('image', img)
execute.save()
result_qrcode = qrcode.make(execute.url)
buf = BytesIO()
result_qrcode.save(buf)
img_stream = buf.getvalue()
result_img = base64.b64encode(img_stream)
return JsonResponse({
"msg": "success",
"image": bytes.decode(result_img, 'utf-8')
})
def upload_file():
fn = str(int(time.time() * 1000)) + ".jpg"
io = StringIO()
io.write(request.files['file'].read())
file = leancloud.File(fn, io)
file.save()
io.close()
return '{"status": 0, "url": "%s", "id": "%s"}' % (file.url, file.id)
def uplean(upurl, filename):
# leancloud 密钥 上传文件代码
# leancloud密钥
leancloud.init("<您的AppID>", "<您的appKey>")
with open(upurl, 'rb') as f:
file = leancloud.File(filename, f)
files = file.save()
return file._url
def get_image(url, key):
print('http://app.haidais.cn' + url, key)
r = requests.get('http://app.haidais.cn' + url, stream=True)
data = StringIO('LeanCloud')
file = leancloud.File(key, r.content)
try:
file.save()
print(file.url)
return file.url
except:
pass
def post(self, request):
file_obj = request.FILES.get('file')
if file_obj:
print(file_obj)
leancloud.init('tiM1T9UGqwf6QTyXkn9kcL9z-gzGzoHsz',
'1GzzyA6uv4l8JNGd6etu5V5o')
execute = leancloud.File('File', file_obj)
execute.save()
result_qrcode = qrcode.make(execute.url)
buf = BytesIO()
result_qrcode.save(buf)
image_stream = buf.getvalue()
resule_image = base64.b64encode(image_stream)
return JsonResponse({
"msg": "success",
"image": bytes.decode(resule_image, 'utf-8')
})
return JsonResponse({"msg": 'fail'})
def updatePic(rootdir):
query = leancloud.Query(Clothes)
cnt = 0
for parent, dirnames, filenames in os.walk(rootdir):
for filename in filenames:
if filename.find('PNG') > 0 or filename.find('png') > 0:
im = Image.open(rootdir + filename)
clothname = str(filename).split('.PNG')[0]
query.equal_to('name', clothname)
res = query.find()
if len(res) == 1:
cloth = query.first()
cloth.get('pic')
old_pic = str(cloth.get('picUrl')).strip()
# noinspection PyBroadException
if old_pic is not None or old_pic is not '':
try:
desc = PicProcessing.getDesc(
os.path.join(parent, filename))
with open(os.path.join(parent, filename)) as f:
avatar = leancloud.File('fileFromLocalFile', f)
avatar.save()
# avatar_id = avatar.id
picUrl = avatar.url
cloth.set('pic', avatar)
cloth.set('picUrl', picUrl)
cloth.set('description', resFormat(desc))
cloth.save()
cnt += 1
except:
print filename, 'failed to update'
im.save(
'/users/gejiali/Documents/Game/nikkic/pic/en/'
+ filename)
else:
print filename, 'already has pic'
else:
print filename, 'failed to find'
im.save('/users/gejiali/Documents/Game/nikkic/pic/en/' +
filename)
print cnt, 'updated'
def save_file(filename, data):
# data_hash = hashlib.md5(data).hexdigest() # 一致 26771cad667b860261090a8d52f3299c wwj 缓存在user_img_cache里做
# https://leancloud.cn/docs/leanstorage_guide-python.html#从数据流构建文件
cloud_file = leancloud.File(filename, data)
cloud_file.save()
return cloud_file.url
import leancloud
import logging
logging.basicConfig(level=logging.DEBUG)
leancloud.init("3AJTBW5QyGw17hLxNN3njGHW-gzGzoHsz", "P8cKdEvRxusfuR27MuhvFOzr")
with open('JBL-GO2-QSG_Multilingual_HD.pdf') as f:
test_upload_text_file = leancloud.File('fileFromLocalFile', f)
test_upload_text_file.save()
def print_group_image_msg(msg):
file = leancloud.File(msg.file_name, msg.get_file())
file.save()
rcv_msg(msg.receiver.puid, msg.receiver.name, msg.sender.puid,
msg.sender.name, msg.file_name)
def print_friend_image_msg(msg):
file = leancloud.File(msg.file_name, msg.get_file())
file.save()
rcv_msg(msg.sender.puid, None, msg.sender.puid, msg.sender.name,
msg.file_name)
import leancloud
leancloud.init("Yf3MSyqMdVjhofKx5vflC4eF-MdYXbMMI", "n63Ne1Jj2bs17yGUPzSuYq7K")
Person = leancloud.Object.extend('Person')
with open('testfile.jpg', 'rb') as f:
file = leancloud.File('testfile.jpg', f)
file.save()
D = Person()
D.set('Name', 'Unknown')
D.set('Profile', file)
D.save()
face = cv2.imdecode(arr, -1)
# now I get the image: face
name = 'unknown'
face_locations = face_recognition.face_locations(face)
face_encodings = face_recognition.face_encodings(face, face_locations)
for face_encoding in face_encodings: # actually, only one face
# See if the face is a match for the known face(s)
match = face_recognition.compare_faces([master_face_encoding],
face_encoding)
name = "unknown"
if match[0]: #
name = "master"
g_count = g_count + 1
Todo = visitRecord()
Todo.set('name', name)
mbuffer = cv2.imencode('.jpg', face)[1].tostring()
recoged_face = leancloud.File('image.jpg', StringIO(mbuffer))
t_time = str(datetime.now()).split('.')[0]
Todo.set('visittime', t_time)
Todo.set('image', recoged_face)
Todo.set('num', g_count)
Todo.save()
cv2.imshow('face', face)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def decode(key, value):
if isinstance(value, get_dumpable_types()):
return value
if isinstance(value, (tuple, list)):
return [decode(key, x) for x in value]
if not isinstance(value, dict):
return value
if key == "ACL":
if isinstance(value, leancloud.ACL):
return value
return leancloud.ACL(value)
if "__type" not in value:
return dict([(k, decode(k, v)) for k, v in six.iteritems(value)])
_type = value["__type"]
if _type == "Pointer":
value = copy.deepcopy(value)
class_name = value["className"]
pointer = leancloud.Object.create(class_name)
if "createdAt" in value:
value.pop("__type")
value.pop("className")
pointer._update_data(value)
else:
pointer._update_data({"objectId": value["objectId"]})
return pointer
if _type == "Object":
value = copy.deepcopy(value)
class_name = value["className"]
value.pop("__type")
value.pop("className")
obj = leancloud.Object.create(class_name)
obj._update_data(value)
return obj
if _type == "Date":
return arrow.get(iso8601.parse_date(value["iso"])).to("local").datetime
if _type == "GeoPoint":
return leancloud.GeoPoint(
latitude=value["latitude"], longitude=value["longitude"]
)
if _type == "Relation":
relation = leancloud.Relation(None, key)
relation.target_class_name = value["className"]
return relation
if _type == "File":
f = leancloud.File(value["name"])
meta_data = value.get("metaData")
if meta_data:
f._metadata = meta_data
f._url = value["url"]
f.id = value["objectId"]
return f
async def update(self, fileUrl, fileName):
def Filter(data_acc, Low_cutoff, High_cutoff, F_sample, N):
[Low_point, High_point] = map(lambda F: F / F_sample * N,
[Low_cutoff, High_cutoff])
yf = np.fft.fft(data_acc)
Filtered_spectrum = [
yf[i] if i > Low_point and i <= High_point else 0.0
for i in range(N // 2 + 1)
]
return Filtered_spectrum
def integration_disp(Filtered_spectrum):
[omiga] = map(lambda x: 2 * np.pi * x, [xf])
D_lefthalf = 0.0 - Filtered_spectrum[1:N // 2] * (
1 / (omiga[1:N // 2] * omiga[1:N // 2]))
D_righthalf = np.flip(D_lefthalf, 0)
D_righthalf_conj = np.conjugate(D_righthalf)
D_index0 = np.insert(D_lefthalf, 0, Filtered_spectrum[0])
D_index0_ = np.append(D_index0, Filtered_spectrum[-1].real)
D = np.hstack((D_index0_, D_righthalf_conj))
return D
def fpy(beta, h, Ts, Te, F_sample, acc):
N = len(acc)
freq = np.linspace(0, F_sample, N)
T = 1 / freq
T[0] = N
Spec = Spectrum(dt, Ts, Te, T)
Sa = Spec.responseSpectrum(acc, dt, T, h, beta)
return Sa
#now = time.strftime('%H%M%S', time.localtime(time.time()))
date = time.strftime('%Y%m%d', time.localtime(time.time()))
fileUrl = fileUrl
fileName = fileName
#fileUrl = '/Users/mashanmu/Desktop/十九中数据转换/十九中振动监测_20200712063859.txt'
#fileName = '2020-07-02 06:38:59.txt'
with open(fileUrl, 'rb') as f:
data = f.read()
data = data[3002:]
data = data.decode()
data = re.split("\r\n|\t", data)
data.pop()
x = []
y = []
z = []
SaX = []
SaY = []
SaZ = []
lenth = int(len(data) / 35)
for j in range(0, lenth):
accX = float(data[32 + j * 35])
accY = float(data[33 + j * 35])
accZ = float(data[34 + j * 35])
x.append(accX)
y.append(accY)
z.append(accZ)
#print(x)
pga = []
pgaUD = []
pgaValue = ''
pgaUDValue = ''
pga.append(max(x) * 100)
pga.append(max(y) * 100)
pgaUD.append(max(z) * 100)
pgaValue = max(pga)
pgaUDValue = max(pgaUD)
print(pgaValue)
print(pgaUDValue)
try:
TriggeredData = leancloud.Object.extend('SHD_PGAValue')
triggeredData = TriggeredData()
triggeredData.set('project', '北京市第十九中学')
triggeredData.set('device', '震创')
triggeredData.set('pga', pgaValue)
triggeredData.set('latitude', '39.969362')
triggeredData.set('longitude', '116.305142')
triggeredData.save()
except:
pass
print('pga save failed.')
## %%
beta = 1 / 6
h = 0.05
Ts = 0.1 # Spectrum Period Starts at Ts (Sec)
Te = 10 # Spectrum Period End at Te (Sec)
F_sample = 100
dt = 1 / F_sample
N = len(x) # Length of signal
freq = np.linspace(0, F_sample, N)
xf = np.linspace(0.0, F_sample / 2, N // 2 + 1)
T = 1 / freq
T[0] = N
Spec = Spectrum(dt, Ts, Te, T)
#SaT = Spec.designSpectrum(T)
SaX = Spec.responseSpectrum(x, dt, T, h, beta)
SaY = Spec.responseSpectrum(y, dt, T, h, beta)
SaZ = Spec.responseSpectrum(z, dt, T, h, beta)
#print(SaX)
##通道：1-6 5层
try:
#for i in range(0, 35):
str = ''
lenth = int(len(data) / 35)
x = []
y = []
z = []
diftX = []
diftY = []
for j in range(0, lenth):
accX = float(data[3 + j * 35]) * 100
accY = float(data[4 + j * 35]) * 100
accZ = float(data[5 + j * 35]) * 100
dispX = float(data[1 + j * 35])
dispY = float(data[2 + j * 35])
value = ';%.6f;%.6f;%.6f;%.6f;%.6f;;%.6f;%.6f;%.6f;' % (
accX, accY, accZ, dispX, dispY, SaX[j], SaY[j], SaZ[j])
str = str + value + '\n'
x.append(accX)
y.append(accY)
z.append(accZ)
diftX.append(dispX)
diftY.append(dispY)
Filtered_spectrum_FLX = Filter(x, 0.1, 10, F_sample, N)
Filtered_spectrum_FLY = Filter(y, 0.1, 10, F_sample, N)
D_FLX = integration_disp(Filtered_spectrum_FLX)
D_FLY = integration_disp(Filtered_spectrum_FLY)
d_FLX = np.fft.ifft(D_FLX)
d_FLY = np.fft.ifft(D_FLY)
maxDiftX = max(d_FLX)
maxDiftY = max(d_FLY)
maxDispX = max(diftX)
#maxDispY = max(diftY)
print("5层东西向最大层间位移及层间位移角", maxDiftX.real, maxDiftX.real / 420)
print("5层南北向最大层间位移及层间位移角", maxDiftY.real, maxDiftY.real / 420)
print("maxDispX", maxDispX)
print("End")
pga = []
pgaUD = []
pgaValue = ''
pgaUDValue = ''
pga.append(max(x))
pga.append(max(y))
pgaUD.append(max(z))
pgaValue = '%.6f' % max(pga)
pgaUDValue = '%.6f' % max(pgaUD)
summary = 'Sampling Rate; 100'
str = str + summary
#print(str)
fileData = StringIO(str)
file = leancloud.File(fileName, fileData)
file.save()
deviceName = '教学楼L5'
TriggeredData = leancloud.Object.extend('TriggeredData')
triggeredData = TriggeredData()
triggeredData.set('project', '北京市第十九中学')
triggeredData.set('device', deviceName)
triggeredData.set('date', date)
triggeredData.set('data', file)
triggeredData.set('pga', pgaValue)
triggeredData.set('pgaUD', pgaUDValue)
triggeredData.save()
except:
pass
print('通道：1-6 save file failed.')
##通道：7-11
try:
#for i in range(0, 35):
str = ''
lenth = int(len(data) / 35)
x = []
y = []
z = []
for j in range(0, lenth):
accX = float(data[8 + j * 35]) * 100
accY = float(data[9 + j * 35]) * 100
accZ = float(data[10 + j * 35]) * 100
dispX = float(data[6 + j * 35])
dispY = float(data[7 + j * 35])
value = ';%.6f;%.6f;%.6f;%.6f;%.6f;;%.6f;%.6f;%.6f;' % (
accX, accY, accZ, dispX, dispY, SaX[j], SaY[j], SaZ[j])
str = str + value + '\n'
x.append(accX)
y.append(accY)
z.append(accZ)
Filtered_spectrum_FLX = Filter(x, 0.1, 10, F_sample, N)
Filtered_spectrum_FLY = Filter(y, 0.1, 10, F_sample, N)
D_FLX = integration_disp(Filtered_spectrum_FLX)
D_FLY = integration_disp(Filtered_spectrum_FLY)
d_FLX = np.fft.ifft(D_FLX)
d_FLY = np.fft.ifft(D_FLY)
maxDiftX = max(d_FLX)
maxDiftY = max(d_FLY)
print("4层东西向最大层间位移及层间位移角", maxDiftX.real, maxDiftX.real / 420)
print("4层南北向最大层间位移及层间位移角", maxDiftY.real, maxDiftY.real / 420)
print("End")
pga = []
pgaUD = []
pgaValue = ''
pgaUDValue = ''
pga.append(max(x))
pga.append(max(y))
pgaUD.append(max(z))
pgaValue = '%.6f' % max(pga)
pgaUDValue = '%.6f' % max(pgaUD)
#print(value)
#print(len(value))
summary = 'Sampling Rate; 100'
str = str + summary
#print(str)
fileData = StringIO(str)
file = leancloud.File(fileName, fileData)
file.save()
deviceName = '教学楼L4'
TriggeredData = leancloud.Object.extend('TriggeredData')
triggeredData = TriggeredData()
triggeredData.set('project', '北京市第十九中学')
triggeredData.set('device', deviceName)
triggeredData.set('date', date)
triggeredData.set('data', file)
triggeredData.set('pga', pgaValue)
triggeredData.set('pgaUD', pgaUDValue)
triggeredData.save()
except:
pass
print('通道：7-11 save file failed.')
##通道：12-14
try:
#for i in range(0, 35):
str = ''
lenth = int(len(data) / 35)
x = []
y = []
z = []
for j in range(0, lenth):
accX = float(data[11 + j * 35]) * 100
accY = float(data[12 + j * 35]) * 100
accZ = float(data[13 + j * 35]) * 100
#dispX = float(data[7+j*35])
#dispY = float(data[8+j*35])
value = ';%.6f;%.6f;%.6f;%.6f;%.6f;;%.6f;%.6f;%.6f;' % (
accX, accY, accZ, dispX, dispY, SaX[j], SaY[j], SaZ[j])
str = str + value + '\n'
x.append(accX)
y.append(accY)
z.append(accZ)
Filtered_spectrum_FLX = Filter(x, 0.1, 10, F_sample, N)
Filtered_spectrum_FLY = Filter(y, 0.1, 10, F_sample, N)
D_FLX = integration_disp(Filtered_spectrum_FLX)
D_FLY = integration_disp(Filtered_spectrum_FLY)
d_FLX = np.fft.ifft(D_FLX)
d_FLY = np.fft.ifft(D_FLY)
maxDiftX = max(d_FLX)
maxDiftY = max(d_FLY)
print("3层东西向最大层间位移及层间位移角", maxDiftX.real, maxDiftX.real / 420)
print("3层南北向最大层间位移及层间位移角", maxDiftY.real, maxDiftY.real / 420)
print("End")
pga = []
pgaUD = []
pgaValue = ''
pgaUDValue = ''
pga.append(max(x))
pga.append(max(y))
pgaUD.append(max(z))
pgaValue = '%.6f' % max(pga)
pgaUDValue = '%.6f' % max(pgaUD)
#print(value)
#print(len(value))
summary = 'Sampling Rate; 100'
str = str + summary
#print(str)
fileData = StringIO(str)
file = leancloud.File(fileName, fileData)
file.save()
deviceName = '教学楼L3'
TriggeredData = leancloud.Object.extend('TriggeredData')
triggeredData = TriggeredData()
triggeredData.set('project', '北京市第十九中学')
triggeredData.set('device', deviceName)
triggeredData.set('date', date)
triggeredData.set('data', file)
triggeredData.set('pga', pgaValue)
triggeredData.set('pgaUD', pgaUDValue)
triggeredData.save()
except:
pass
print('通道：12-14 save file failed.')
##通道：15-19
try:
#for i in range(0, 35):
str = ''
lenth = int(len(data) / 35)
x = []
y = []
z = []
for j in range(0, lenth):
accX = float(data[14 + j * 35]) * 100
accY = float(data[15 + j * 35]) * 100
accZ = float(data[16 + j * 35]) * 100
#dispX = float(data[7+j*35])
#dispY = float(data[8+j*35])
value = ';%.6f;%.6f;%.6f;%.6f;%.6f;;%.6f;%.6f;%.6f;' % (
accX, accY, accZ, dispX, dispY, SaX[j], SaY[j], SaZ[j])
str = str + value + '\n'
x.append(accX)
y.append(accY)
z.append(accZ)
Filtered_spectrum_FLX = Filter(x, 0.1, 10, F_sample, N)
Filtered_spectrum_FLY = Filter(y, 0.1, 10, F_sample, N)
D_FLX = integration_disp(Filtered_spectrum_FLX)
D_FLY = integration_disp(Filtered_spectrum_FLY)
d_FLX = np.fft.ifft(D_FLX)
d_FLY = np.fft.ifft(D_FLY)
maxDiftX = max(d_FLX)
maxDiftY = max(d_FLY)
print("2层东西向最大层间位移及层间位移角", maxDiftX.real, maxDiftX.real / 420)
print("2层南北向最大层间位移及层间位移角", maxDiftY.real, maxDiftY.real / 420)
print("End")
pga = []
pgaUD = []
pgaValue = ''
pgaUDValue = ''
pga.append(max(x))
pga.append(max(y))
pgaUD.append(max(z))
pgaValue = '%.6f' % max(pga)
pgaUDValue = '%.6f' % max(pgaUD)
#print(value)
#print(len(value))
summary = 'Sampling Rate; 100'
str = str + summary
#print(str)
fileData = StringIO(str)
file = leancloud.File(fileName, fileData)
file.save()
deviceName = '教学楼L2'
TriggeredData = leancloud.Object.extend('TriggeredData')
triggeredData = TriggeredData()
triggeredData.set('project', '北京市第十九中学')
triggeredData.set('device', deviceName)
triggeredData.set('date', date)
triggeredData.set('data', file)
triggeredData.set('pga', pgaValue)
triggeredData.set('pgaUD', pgaUDValue)
triggeredData.save()
except:
pass
print('通道：15-19 save file failed.')
##通道：20-22
try:
#for i in range(0, 35):
str = ''
lenth = int(len(data) / 35)
x = []
y = []
z = []
for j in range(0, lenth):
accX = float(data[19 + j * 35]) * 100
accY = float(data[20 + j * 35]) * 100
accZ = float(data[21 + j * 35]) * 100
#dispX = float(data[7+j*35])
#dispY = float(data[8+j*35])
value = ';%.6f;%.6f;%.6f;%.6f;%.6f;;%.6f;%.6f;%.6f;' % (
accX, accY, accZ, dispX, dispY, SaX[j], SaY[j], SaZ[j])
str = str + value + '\n'
x.append(accX)
y.append(accY)
z.append(accZ)
Filtered_spectrum_FLX = Filter(x, 0.1, 10, F_sample, N)
Filtered_spectrum_FLY = Filter(y, 0.1, 10, F_sample, N)
D_FLX = integration_disp(Filtered_spectrum_FLX)
D_FLY = integration_disp(Filtered_spectrum_FLY)
d_FLX = np.fft.ifft(D_FLX)
d_FLY = np.fft.ifft(D_FLY)
maxDiftX = max(d_FLX)
maxDiftY = max(d_FLY)
print("1层东西向最大层间位移及层间位移角", maxDiftX.real, maxDiftX.real / 420)
print("1层南北向最大层间位移及层间位移角", maxDiftY.real, maxDiftY.real / 420)
print("End")
pga = []
pgaUD = []
pgaValue = ''
pgaUDValue = ''
pga.append(max(x))
pga.append(max(y))
pgaUD.append(max(z))
pgaValue = '%.6f' % max(pga)
pgaUDValue = '%.6f' % max(pgaUD)
#print(value)
#print(len(value))
summary = 'Sampling Rate; 100'
str = str + summary
#print(str)
fileData = StringIO(str)
file = leancloud.File(fileName, fileData)
file.save()
deviceName = '教学楼L1'
TriggeredData = leancloud.Object.extend('TriggeredData')
triggeredData = TriggeredData()
triggeredData.set('project', '北京市第十九中学')
triggeredData.set('device', deviceName)
triggeredData.set('date', date)
triggeredData.set('data', file)
triggeredData.set('pga', pgaValue)
triggeredData.set('pgaUD', pgaUDValue)
triggeredData.save()
except:
pass
print('通道：20-22 save file failed.')
##通道：23-32
try:
#for i in range(0, 35):
str = ''
lenth = int(len(data) / 35)
x = []
y = []
z = []
for j in range(0, lenth):
accX = float(data[22 + j * 35]) * 100
accY = float(data[23 + j * 35]) * 100
accZ = float(data[24 + j * 35]) * 100
#dispX = float(data[7+j*35])
#dispY = float(data[8+j*35])
value = ';%.6f;%.6f;%.6f;%.6f;%.6f;;%.6f;%.6f;%.6f;' % (
accX, accY, accZ, dispX, dispY, SaX[j], SaY[j], SaZ[j])
str = str + value + '\n'
x.append(accX)
y.append(accY)
z.append(accZ)
Filtered_spectrum_FLX = Filter(x, 0.1, 10, F_sample, N)
Filtered_spectrum_FLY = Filter(y, 0.1, 10, F_sample, N)
D_FLX = integration_disp(Filtered_spectrum_FLX)
D_FLY = integration_disp(Filtered_spectrum_FLY)
d_FLX = np.fft.ifft(D_FLX)
d_FLY = np.fft.ifft(D_FLY)
maxDiftX = max(d_FLX)
maxDiftY = max(d_FLY)
print("B1层东西向最大层间位移及层间位移角", maxDiftX.real, maxDiftX.real / 420)
print("B1层南北向最大层间位移及层间位移角", maxDiftY.real, maxDiftY.real / 420)
print("End")
pga = []
pgaUD = []
pgaValue = ''
pgaUDValue = ''
pga.append(max(x))
pga.append(max(y))
pgaUD.append(max(z))
pgaValue = '%.6f' % max(pga)
pgaUDValue = '%.6f' % max(pgaUD)
#print(value)
#print(len(value))
summary = 'Sampling Rate; 100'
str = str + summary
#print(str)
fileData = StringIO(str)
file = leancloud.File(fileName, fileData)
file.save()
deviceName = '教学楼B1'
TriggeredData = leancloud.Object.extend('TriggeredData')
triggeredData = TriggeredData()
triggeredData.set('project', '北京市第十九中学')
triggeredData.set('device', deviceName)
triggeredData.set('date', date)
triggeredData.set('data', file)
triggeredData.set('pga', pgaValue)
triggeredData.set('pgaUD', pgaUDValue)
triggeredData.save()
except:
pass
print('通道：23-32 save file failed.')
##通道：33-35
try:
#for i in range(0, 35):
str = ''
lenth = int(len(data) / 35)
x = []
y = []
z = []
for j in range(0, lenth):
accX = float(data[32 + j * 35]) * 100
accY = float(data[33 + j * 35]) * 100
accZ = float(data[34 + j * 35]) * 100
#dispX = float(data[7+j*35])
#dispY = float(data[8+j*35])
value = ';%.6f;%.6f;%.6f;%.6f;%.6f;;%.6f;%.6f;%.6f;' % (
accX, accY, accZ, dispX, dispY, SaX[j], SaY[j], SaZ[j])
str = str + value + '\n'
x.append(accX)
y.append(accY)
z.append(accZ)
pga = []
pgaUD = []
pgaValue = ''
pgaUDValue = ''
pga.append(max(x))
pga.append(max(y))
pgaUD.append(max(z))
pgaValue = '%.6f' % max(pga)
pgaUDValue = '%.6f' % max(pgaUD)
#print(value)
#print(len(value))
summary = 'Sampling Rate; 100'
str = str + summary
#print(str)
fileData = StringIO(str)
file = leancloud.File(fileName, fileData)
file.save()
deviceName = '自由场点'
TriggeredData = leancloud.Object.extend('TriggeredData')
triggeredData = TriggeredData()
triggeredData.set('project', '北京市第十九中学')
triggeredData.set('device', deviceName)
triggeredData.set('date', date)
triggeredData.set('data', file)
triggeredData.set('pga', pgaValue)
triggeredData.set('pgaUD', pgaUDValue)
triggeredData.set('isFreeField', '1')
triggeredData.save()
except:
pass
print('通道：33-35 save file failed.')
def camare():
# 初始化摄像头并且获取一个指向原始数据的引用
camera = PiCamera()
camera.resolution = tuple(conf["resolution"])
camera.framerate = conf["fps"]
camera.vflip = conf["vertical_flip"]
camera.hflip = conf["horizontal_flip"]
rawCapture = PiRGBArray(camera, size=tuple(conf["resolution"]))
# 等待摄像头模块启动, 随后初始化平均帧, 最后
# 上传时间戳, 以及运动帧计数器
print "[INFO] warming up..."
time.sleep(conf["camera_warmup_time"])
avg = None
lastUploaded = datetime.datetime.now()
motionCounter = 0
# 从摄像头逐帧捕获数据
for f in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
# 抓取原始NumPy数组来表示图像并且初始化
# 时间戳以及occupied/unoccupied文本
frame = f.array
timestamp = datetime.datetime.now()
text = "Unoccupied"
# 调整帧尺寸，转换为灰阶图像并进行模糊
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# 如果平均帧是None，初始化它
if avg is None:
print "[INFO] starting background model..."
avg = gray.copy().astype("float")
rawCapture.truncate(0)
continue
# accumulate the weighted average between the current frame and
# previous frames, then compute the difference between the current
# frame and running average
cv2.accumulateWeighted(gray, avg, 0.5)
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
# 对变化图像进行阀值化, 膨胀阀值图像来填补
# 孔洞, 在阀值图像上找到轮廓线
thresh = cv2.threshold(frameDelta, conf["delta_thresh"], 255,
cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# 遍历轮廓线
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < conf["min_area"]:
continue
# 计算轮廓线的外框, 在当前帧上画出外框,
# 并且更新文本
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "Occupied"
# 在当前帧上标记文本和时间戳
ts = timestamp.strftime("%A %d %B %Y %I:%M:%S%p")
cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, ts, (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.35, (0, 0, 255), 1)
# check to see if the room is occupied
if text == "Occupied":
# check to see if enough time has passed between uploads
if (timestamp - lastUploaded).seconds >= conf["min_upload_seconds"]:
# increment the motion counter
motionCounter += 1
# check to see if the number of frames with consistent motion is
# high enough
if motionCounter >= conf["min_motion_frames"]:
# 检查是否需要双船
if conf["use_leancloud"]:
# write the image to temporary file
t = TempImage()
cv2.imwrite(t.path, frame)
try:
with open(t.path) as f:
avatar = leancloud.File(t.path, f)
avatar.save()
finally:
t.cleanup()
# update the last uploaded timestamp and reset the motion
# counter
lastUploaded = timestamp
motionCounter = 0
# otherwise, the room is not occupied
else:
motionCounter = 0
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
def feedback():
file = request.files
form = request.form
try:
jwtoken = form.get("jwt")
email = form.get("email")
except KeyError:
return Response("", 400)
try:
info = jwt.decode(jwtoken, JWT_KEY)
except jwt.InvalidTokenError:
return Response("Unexited User", 401)
imgs = file.getlist("imgs")
fileList = list()
for img in imgs:
if allowed_file(img.filename) and img:
s = StringIO(img.read())
fileObject = leancloud.File(img.filename, s)
width, height = get_image_size(s, img.filename)
fileObject.metadata['width'] = width
fileObject.metadata['height'] = height
fileObject.save()
fileList.append(fileObject)
else:
return Response("", 400)
# fileObject = leancloud.File(imgs.filename,StringIO(imgs.read()))
# fileObject.save()
# fileList.append(fileObject)
description = form.get("description")
u = leancloud.User.become(info["token"])
Obj = leancloud.Object.create("bugList")
Obj.set("description", description)
Obj.set("imgs", fileList)
Obj.set("upload", u)
Obj.save()
html = \
u"""<!doctype html>
<head>
<meta charset="utf-8">
</head>
<html lang="zh-hans">
<body>
<p>存在一个 Bug,由 <b><a href="mailto:{email}">{name}</a></b> 用户提出,说明如下:</p>
<p>{description}</p>
</br>
{img}
</body></html>""".format(
name=u.get("name"),
description=description,
email=email,
img="\n".join([u"<img src=\"{}\" alt=\"img\" width=\"{}\" height=\"{}\">".format(
img.url,
img.metadata['width'],
img.metadata['height']
)
for img in fileList])
)
send(html)
return Response("Upload success!", 200)
