#!/usr/bin/env python3 import requests import os import boto3 import redis import pickle import json import cv2 import sys import matplotlib.pyplot as plt def main(): images_dir = "histogram-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]) histogram_result = [] try: decode_activation_id = params["activation_id"] parts = params["parts"] for i in range(0,parts): if os.path.exists(images_dir+'/histogram_image_'+str(i)+'.jpg'): os.remove(images_dir+'/histogram_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) # Load image img = cv2.imread(image_name, 0) # 0 for grayscale # Calculate histogram hist = cv2.calcHist([img], [0], None, [256], [0, 256]) bins = range(256) output_image = images_dir+'/histogram_image_'+str(i)+'.jpg' # Plot histogram plt.hist(img.ravel(), bins, [0, 256]) plt.title('Histogram') plt.xlabel('Intensity') plt.ylabel('Pixel Count') plt.savefig(output_image) histogram_result.append('histogram_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+'/histogram_image_'+str(i)+'.jpg'): os.remove(images_dir+'/histogram_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) # Load image img = cv2.imread(image_name, 0) # 0 for grayscale # Calculate histogram hist = cv2.calcHist([img], [0], None, [256], [0, 256]) bins = range(256) output_image = images_dir+'/histogram_image_'+str(i)+'.jpg' # Plot histogram plt.hist(img.ravel(), bins, [0, 256]) plt.title('Histogram') plt.xlabel('Intensity') plt.ylabel('Pixel Count') plt.savefig(output_image) histogram_result.append('histogram_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 histogram_result: url = "https://dagit-store.s3.ap-south-1.amazonaws.com/"+images_dir+"/"+image url_list.append(url) print(json.dumps({"histogram_image_url_links":url_list, "activation_id": str(activation_id), "parts": parts })) return({"histogram_image_url_links":url_list, "activation_id": str(activation_id), "parts": parts }) if __name__ == "__main__": main()