AWS Lambda invoke example code

 Refer to code

..

import boto3

import botocore

from botocore.exceptions import ClientError

from botocore.config import Config

import json

#params

ACCESS_KEY = 'your_key'

SECRET_KEY = 'your_sec_key'

REGION = 'your_region' #ex) 'eu-west-1'

LAMBDA_ARN = 'your lambda arn' #ex)arn:aws:lambda:eu-west-1:1211:function:functionname'

your_input_value = 'your_input_value'

#define labmda client

lambda_client = boto3.client('lambda',

aws_access_key_id=ACCESS_KEY,

aws_secret_access_key=SECRET_KEY,

region_name = REGION,

config = Config(read_timeout=600))

#invoke

futs = lambda_client.invoke(

FunctionName = LAMBDA_ARN,

InvocationType = "RequestResponse", #event

Payload = json.dumps({'your_input_key': your_input_value})

)

#parsing result

res = futs['Payload'].read().decode()

res_dict = json.loads(res)

#print result as dict

print(res_dict, type(res_dict))

..

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python list string to list

 

.

import ast

x = '[ "A","B","C" , " D"]'

x = ast.literal_eval(x)

>>> x

['A', 'B', 'C', ' D']

.

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TypeError: ‘tuple’ object does not support item assignment

 

Here is simple solution

..

x = (1,2,3)

y = list(x) #convert tuple -> list

y[0] = 10 #modify element value

x = tuple(y) #revert list -> tuple

print(x)

..

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save labelme json data

Save json and image for labelme

..

def make_labelme_json(list_ltrb, img, label, img_full_fn):

h, w, _ = img.shape

shape_dict =[]

for v in list_ltrb:

x1, y1, x2, y2 = v

adict={"label": label, "points":[[x1,y1],[x2,y2]], "group_id": None, "shape_type":"rectangle", "flags":{}}

shape_dict.append(adict)

image_string = cv2.imencode('.jpg', img)[1]

image_string = base64.b64encode(image_string).decode()

#make string image dict

wdict={"version":"4.5.6", "flags": {}, "shapes":shape_dict, "imagePath": img_full_fn, "imageData":image_string}

return wdict

def save_json_img(wdict, labelme_path, fn_only, img):

#save json

str_type = json.dumps(wdict) #dic to json

prefix_fn = "{}{}".format(labelme_path, fn_only)

f= open("{}.json".format(prefix_fn),"w+")

f.write(str_type)

f.close

#save image

cv2.imwrite("{}.jpg".format(prefix_fn), img)

wdict = make_labelme_json(list_ltrb, img,'lp', './'+fn )

save_json_img(wdict, labelme_path, fn_only, img)

..

python dict keys to list

 refer to example code

..

dictionary = {"a": 1, "b": 2, "c": 3}

key_iterable = dictionary.keys()

key_list = list(key_iterable)

print(key_list)

..

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get pdf file list in aws s3 bucket

Get all pdf file list in s3 bucket.

Change extension file name properly.

..

#s3 client

import boto3

from botocore.exceptions import ClientError

s3_client = boto3.client('s3')

ACCESS_KEY = 'key'

SECRET_KEY = 'secret_key'

s3_client = boto3.client(

's3',

aws_access_key_id=ACCESS_KEY,

aws_secret_access_key=SECRET_KEY,

region_name = 'eu-west-1'

)

#get pdf list

paginator = s3_client.get_paginator('list_objects_v2')

pages = paginator.paginate(Bucket='bucket_name')

cnt = 0

pdf_list = []

for page in pages:

for obj in page['Contents']:

#get file name

fn = obj['Key']

#check extension name

if fn[-3:] == 'pdf':

pdf_list.append(fn)

#print count, filename

print(f'cnt:{cnt}, fn:{fn}')

#increase count

cnt += 1

..

other code for search bucket + subfolder

..

operation_parameters = {'Bucket': 'bucket_name','Prefix': 'sub_folder_name'}

pages = paginator.paginate(**operation_parameters)

..

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flatten, unflatten Pytorch, torch, tensor reshape

 flatten -> unflatten

..

#batch, ch, row, col

input_size=torch.empty(10, 1, 256, 256)

print('input shape: ',input_size.shape)

#flatten batch, ch*row*col

reshape = torch.flatten(input_size, start_dim=1)

# reshape = input_size.reshape(-1, 1*256*256)

print('reshape or flatten: ',reshape.shape)

#unflatten batch, ch, row, col

reshape = reshape.reshape(-1,1,256,256)

print('unflatten: ',reshape.shape)

..

input shape:  torch.Size([10, 1, 256, 256])
reshape or flatten:  torch.Size([10, 65536])
unflatten:  torch.Size([10, 1, 256, 256])

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print update, rewrite python in console

 

..

print(f'iter:{i}/{total}',sep='',end="\r",flush=True)

..

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jupyter notebook, update 1 line printed string, python

 

refer to code.

...

from IPython.display import display

cnt=0

dh = display('Test2',display_id=True)

while True:

cnt += 1

dh.update(f'cnt:{cnt}') #print cnt and update

if cnt>100:

break

...

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check image is single color or not, python , PIL image

 refer to example code

..

#get image data

image = Image.open(xxx)

image = image.convert('RGB')

#skip if image has one color

colors = image.getcolors(image.size[0]*image.size[1])

imgH = image.size[1]

imgW = image.size[0]

#print(f'img object w:{imgW}, h:{imgH}')

if len(colors) == 1:

continue

..

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