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How to use is_base64 method in localstack

Best Python code snippet using localstack_python

create_source_request_body.py

Source:create_source_request_body.py

...103        :type topic: str104        """105        self._topic = topic106    @property107    def is_base64(self):108        """Gets the is_base64 of this CreateSourceRequestBody.109        æ¯å¦payloadä½¿ç¨base64ï¼0-æ¯ 1-å¦110        :return: The is_base64 of this CreateSourceRequestBody.111        :rtype: int112        """113        return self._is_base64114    @is_base64.setter115    def is_base64(self, is_base64):116        """Sets the is_base64 of this CreateSourceRequestBody.117        æ¯å¦payloadä½¿ç¨base64ï¼0-æ¯ 1-å¦118        :param is_base64: The is_base64 of this CreateSourceRequestBody.119        :type is_base64: int120        """121        self._is_base64 = is_base64122    @property123    def contain_device_info(self):124        """Gets the contain_device_info of this CreateSourceRequestBody.125        æ¯å¦åå«è®¾å¤ä¿¡æ¯æ¯å¦åå«è®¾å¤ä¿¡æ¯ï¼0-æ¯ 1-å¦126        :return: The contain_device_info of this CreateSourceRequestBody.127        :rtype: int128        """129        return self._contain_device_info...

image_utils.py

Source:image_utils.py

1#!/usr/bin/python2import sys3import cv24import numpy5import base646import utils7import logger8import config9log = logger.get_logger(__name__)10conf = config.get_config()11# variables12detect_objects_debug = False13current_index = 114# return a cv2 image object from a binary image15def import_image(data,is_base64=False):16	if is_base64: data = base64.b64decode(data)17	image = numpy.asarray(bytearray(data), dtype="uint8")18	image = cv2.imdecode(image, cv2.IMREAD_COLOR)19	return image20# return a binary image from a cv2 object21def export_image(image,is_base64=False):22	r, buffer = cv2.imencode(".png",image)23	data = buffer.tostring()24	if is_base64: data = base64.b64encode(data)25	return data26# normalize an image27def normalize(image,hist=True,blur=False):28	if image is None: return image29	normalized = image30	normalized = cv2.cvtColor(normalized, cv2.COLOR_BGR2GRAY)31	if hist: normalized = cv2.equalizeHist(normalized)32	if blur: normalied = cv2.GaussianBlur(normalized, (21, 21), 0)33	return normalized34# detect movement between two images35def detect_movement(sensor,images,is_base64=False):36	max = 037	index = None38	for i in range(len(images)-1):39		# normalize the images40		i1 = normalize(import_image(images[i],is_base64=is_base64),hist=False)41		i2 = normalize(import_image(images[i+1],is_base64=is_base64),hist=False)42		if i1 is None or i2 is None: continue43		# calculate height and width44		i1_height, i1_width = i1.shape[:2]45		i2_height, i2_width = i2.shape[:2]46		if i1_height != i2_height or i1_width != i2_width: 47			# if they have difference sizes, the image is invalid, ignore it48			continue49		# calculate the difference50		delta = cv2.absdiff(i1,i2)51		# calculate the threshold52		delta = cv2.threshold(delta, 25, 255, cv2.THRESH_BINARY)[1]53		# return the percentage of change54		percentage = cv2.countNonZero(delta)*100/(i1_height*i1_width)55		if percentage > max: 56			max = percentage57			index = i58	if index is None: return None59	image = import_image(images[index],is_base64=is_base64)60	return [max,image]61# detect objects in a given image62def detect_objects(sensor,image,is_base64=False):63	# read the image64	image = import_image(image,is_base64=is_base64)65	if image is None: return None66	# normalize the image67	normalized = normalize(image)68	# for each detection feature69	for feature in sensor["object_detection"]:70		# load the cascade file71		filename = conf["constants"]["base_dir"]+"/"+feature["filename"]72		if not utils.file_exists(filename):73			log.error("Unable to load the detection object XML at "+filename)74			return None75		cascade = cv2.CascadeClassifier(filename)76		# perform the detection77		objects = cascade.detectMultiScale(78			normalized,79			scaleFactor=feature["scale_factor"],80			minNeighbors=feature["min_neighbors"],81			minSize=(feature["min_size"],feature["min_size"]),82			maxSize=(feature["max_size"],feature["max_size"]),83			flags = cv2.cv.CV_HAAR_SCALE_IMAGE84		)85		# nothing found, go to the next object86		if len(objects) == 0: continue87		# return the number of objects detected88		else:89			# found draw a rectangle around each object90			for (x, y, w, h) in objects:91				cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)92			# prepare the alert text93			text = str(len(objects))+" "+utils.lang(feature["display_name"])94			# prepare the image95			image = export_image(image,is_base64=is_base64)96			return [text,image]97	# nothing found, return None98	return None99# save an image into a temporary folder100def save_tmp_image(prefix,image):101	global current_index102	if not conf["constants"]["image_detection_save_on_disk"]: return103	# keep a maximum number of images104	if current_index > conf["constants"]["image_detection_max_saved_images"]: current_index = 1105	filename = conf["constants"]["tmp_dir"]+"/"+prefix+"_"+str(current_index)+".png"106	cv2.imwrite(filename,image)107	current_index = current_index +1...

digest.py

Source:digest.py

1#! /usr/bin/env python32# -*- coding: utf-8 -*-3"""4æ¶æ¯æè¦ç®æ³ç¸å³ä»£ç 5"""6import base647import hashlib8import hmac9def gen_md5_32_hexdigest(s):10    """11    # è·ååç¬¦ä¸²çMD5å¼12    :param s:13    :return:14    """15    m = hashlib.md5()16    m.update(s.encode('utf-8'))17    return m.hexdigest()18def gen_sha1_hexdigest(s):19    return hashlib.sha1(s.encode('utf-8')).hexdigest()20def gen_sha512_hexdigest(s):21    return hashlib.sha512(s.encode('utf-8')).hexdigest()22def _sign_shax_digest(algorithm, secret: str, s: str, is_base64=False):23    """24    ç¾ååç¬¦ä¸²25    :param algorithm: ç¾åç®æ³26    :param secret: å¯ç 27    :param s: è¾å¥åç¬¦ä¸²28    :param is_base64: æ¯å¦è¾åºBase6429    :return:30    """31    hashed = hmac.new(secret.encode('utf-8'), s.encode('utf-8'), algorithm)32    if is_base64:33        return base64.b64encode(hashed.digest()).decode().rstrip('\n')34    else:35        return hashed.hexdigest()36def sign_sha1_digest(secret, s, is_base64=False):37    return _sign_shax_digest(hashlib.sha1, secret, s, is_base64)38def sign_sha224_digest(secret, s, is_base64=False):39    return _sign_shax_digest(hashlib.sha224, secret, s, is_base64)40def sign_sha256_digest(secret, s, is_base64=False):41    return _sign_shax_digest(hashlib.sha256, secret, s, is_base64)42def sign_sha384_digest(secret, s, is_base64=False):43    return _sign_shax_digest(hashlib.sha384, secret, s, is_base64)44def sign_sha512_digest(secret, s, is_base64=False):...

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