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#!/usr/bin/env python3
import os
import requests
import boto3
import redis
import pickle
import json
import cv2
import sys
# Image thresholding is a simple, yet effective,
# way of partitioning an image into a foreground and background. T
# his image analysis technique is a
# type of image segmentation that isolates objects by converting grayscale images into binary images.
def main():
images_dir = "thresholded-images"
is_images_dir = os.path.isdir(images_dir)
if(is_images_dir == False):
os.mkdir(images_dir)
r = redis.Redis(host="10.129.28.219", port=6379, db=2)
activation_id = os.environ.get('__OW_ACTIVATION_ID')
params = json.loads(sys.argv[1])
thresholded_result = []
try:
decode_activation_id = params["activation_id"]
parts = params["parts"]
for i in range(0,parts):
if os.path.exists(images_dir+'/thresholded_image_'+str(i)+'.jpg'):
os.remove(images_dir+'/thresholded_image_'+str(i)+'.jpg')
for i in range(0,parts):
decode_output = "decode-output-image"+decode_activation_id+"-"+str(i)
load_image = pickle.loads(r.get(decode_output))
image_name = 'Image'+str(i)+'.jpg'
with open(image_name, 'wb') as f:
f.write(load_image)
img = cv2.imread(image_name)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
output_image = images_dir+'/thresholded_image_'+str(i)+'.jpg'
cv2.imwrite(output_image, thresh)
thresholded_result.append('thresholded_image_'+str(i)+'.jpg')
except Exception as e: #If not running as a part of DAG workflow and implemented as a single standalone function
image_url_list = params["image_url_links"]
parts = len(image_url_list)
for i in range(0,parts):
if os.path.exists(images_dir+'/thresholded_image_'+str(i)+'.jpg'):
os.remove(images_dir+'/thresholded_image_'+str(i)+'.jpg')
for i in range(0,parts):
response = requests.get(image_url_list[i])
image_name = 'Image'+str(i)+'.jpg'
with open(image_name, "wb") as f:
f.write(response.content)
img = cv2.imread(image_name)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
output_image = images_dir+'/thresholded_image_'+str(i)+'.jpg'
cv2.imwrite(output_image, thresh)
thresholded_result.append('thresholded_image_'+str(i)+'.jpg')
aws_access_key_id = os.getenv('AWS_ACCESS_KEY_ID')
aws_secret_access_key = os.getenv('AWS_SECRET_ACCESS_KEY')
aws_region = os.getenv('AWS_REGION')
s3 = boto3.client('s3', aws_access_key_id=aws_access_key_id,aws_secret_access_key=aws_secret_access_key,region_name=aws_region)
bucket_name = 'dagit-store'
folder_path = images_dir
folder_name = images_dir
for subdir, dirs, files in os.walk(folder_path):
for file in files:
file_path = os.path.join(subdir, file)
s3.upload_file(file_path, bucket_name, f'{folder_name}/{file_path.split("/")[-1]}')
s3.put_object_acl(Bucket=bucket_name, Key=f'{folder_name}/{file_path.split("/")[-1]}', ACL='public-read')
url_list=[]
for image in thresholded_result:
url = "https://dagit-store.s3.ap-south-1.amazonaws.com/"+images_dir+"/"+image
url_list.append(url)
print(json.dumps({"thresholded_image_url_links":url_list,
"activation_id": str(activation_id),
"parts": parts
}))
return({"thresholded_image_url_links":url_list,
"activation_id": str(activation_id),
"parts": parts
})
if __name__ == "__main__":
main()