本文整理汇总了Python中azure.storage.BlobService.set_blob_metadata方法的典型用法代码示例。如果您正苦于以下问题：Python BlobService.set_blob_metadata方法的具体用法？Python BlobService.set_blob_metadata怎么用？Python BlobService.set_blob_metadata使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类azure.storage.BlobService的用法示例。
在下文中一共展示了BlobService.set_blob_metadata方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: upload
# 需要导入模块: from azure.storage import BlobService [as 别名]
# 或者: from azure.storage.BlobService import set_blob_metadata [as 别名]
def upload():
file = request.files['fileInput']
print "File is" + file.filename
if file:
data = file.read()
blob_service = BlobService(account_name='squadshots', account_key='UgxaWKAKv2ZvhHrPt0IHi4EQedPpZw35r+RXkAYB2eICPrG3TjSwk2G8gUzG/PNDDTV+4CVCYWCvZSiad5xMQQ==')
blob_service.create_container('album')
blob_service.put_block_blob_from_bytes(
'album',
file.filename + "_blob",
data,
x_ms_blob_content_type='image/png'
)
if 'username' in session:
un = session['username']
else:
print "not in session"
blob_service.set_blob_metadata(container_name="album",
blob_name=file.filename + "_blob",
x_ms_meta_name_values={'metaun': un})
blob_service.get_blob_to_path('album',file.filename + "_blob",'static/output.png')
f = open('input_person.png','w+')
f.write(data)
f.close()
[X,y] = read_images(OUTPUT_DIRECTORY, (256,256))
# Convert labels to 32bit integers. This is a workaround for 64bit machines,
y = np.asarray(y, dtype=np.int32)
# Create the Eigenfaces model.
model = cv2.createEigenFaceRecognizer()
# Learn the model. Remember our function returns Python lists,
# so we use np.asarray to turn them into NumPy lists to make
# the OpenCV wrapper happy:
model.train(np.asarray(X), np.asarray(y))
# Save the model for later use
model.save("eigenModel.xml")
# Create an Eign Face recogniser
t = float(100000)
model = cv2.createEigenFaceRecognizer(threshold=t)
# Load the model
model.load("eigenModel.xml")
# Read the image we're looking for
try:
sampleImage = cv2.imread('static/output.png', cv2.IMREAD_GRAYSCALE)
if sampleImage != None:
sampleImage = cv2.resize(sampleImage, (256,256))
else:
print "sample image is null"
except IOError:
print "IO error"
# Look through the model and find the face it matches
[p_label, p_confidence] = model.predict(sampleImage)
# Print the confidence levels
print "Predicted label = %d (confidence=%.2f)" % (p_label, p_confidence)
# If the model found something, print the file path
if (p_label > -1):
count = 0
for dirname, dirnames, filenames in os.walk(OUTPUT_DIRECTORY):
for subdirname in dirnames:
subject_path = os.path.join(dirname, subdirname)
if (count == p_label):
for filename in os.listdir(subject_path):
print "subject path = " + subject_path
count = count+1
return "uploaded"