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116 lines
4.2 KiB
116 lines
4.2 KiB
#!/usr/bin/env python3
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import requests
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import os
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import boto3
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import redis
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import pickle
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import json
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import cv2
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import sys
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import matplotlib.pyplot as plt
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def main():
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images_dir = "histogram-images"
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is_images_dir = os.path.isdir(images_dir)
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if(is_images_dir == False):
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os.mkdir(images_dir)
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r = redis.Redis(host="10.129.28.219", port=6379, db=2)
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activation_id = os.environ.get('__OW_ACTIVATION_ID')
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params = json.loads(sys.argv[1])
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histogram_result = []
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try:
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decode_activation_id = params["activation_id"]
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parts = params["parts"]
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for i in range(0,parts):
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if os.path.exists(images_dir+'/histogram_image_'+str(i)+'.jpg'):
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os.remove(images_dir+'/histogram_image_'+str(i)+'.jpg')
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for i in range(0,parts):
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decode_output = "decode-output-image"+decode_activation_id+"-"+str(i)
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load_image = pickle.loads(r.get(decode_output))
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image_name = 'Image'+str(i)+'.jpg'
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with open(image_name, 'wb') as f:
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f.write(load_image)
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# Load image
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img = cv2.imread(image_name, 0) # 0 for grayscale
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# Calculate histogram
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hist = cv2.calcHist([img], [0], None, [256], [0, 256])
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bins = range(256)
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output_image = images_dir+'/histogram_image_'+str(i)+'.jpg'
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# Plot histogram
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plt.hist(img.ravel(), bins, [0, 256])
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plt.title('Histogram')
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plt.xlabel('Intensity')
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plt.ylabel('Pixel Count')
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plt.savefig(output_image)
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histogram_result.append('histogram_image_'+str(i)+'.jpg')
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except Exception as e: #If not running as a part of DAG workflow and implemented as a single standalone function
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image_url_list = params["image_url_links"]
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parts = len(image_url_list)
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for i in range(0,parts):
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if os.path.exists(images_dir+'/histogram_image_'+str(i)+'.jpg'):
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os.remove(images_dir+'/histogram_image_'+str(i)+'.jpg')
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for i in range(0,parts):
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response = requests.get(image_url_list[i])
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image_name = 'Image'+str(i)+'.jpg'
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with open(image_name, "wb") as f:
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f.write(response.content)
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# Load image
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img = cv2.imread(image_name, 0) # 0 for grayscale
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# Calculate histogram
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hist = cv2.calcHist([img], [0], None, [256], [0, 256])
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bins = range(256)
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output_image = images_dir+'/histogram_image_'+str(i)+'.jpg'
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# Plot histogram
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plt.hist(img.ravel(), bins, [0, 256])
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plt.title('Histogram')
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plt.xlabel('Intensity')
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plt.ylabel('Pixel Count')
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plt.savefig(output_image)
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histogram_result.append('histogram_image_'+str(i)+'.jpg')
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aws_access_key_id = os.getenv('AWS_ACCESS_KEY_ID')
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aws_secret_access_key = os.getenv('AWS_SECRET_ACCESS_KEY')
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aws_region = os.getenv('AWS_REGION')
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s3 = boto3.client('s3', aws_access_key_id=aws_access_key_id,aws_secret_access_key=aws_secret_access_key,region_name=aws_region)
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bucket_name = 'dagit-store'
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folder_path = images_dir
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folder_name = images_dir
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for subdir, dirs, files in os.walk(folder_path):
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for file in files:
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file_path = os.path.join(subdir, file)
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s3.upload_file(file_path, bucket_name, f'{folder_name}/{file_path.split("/")[-1]}')
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s3.put_object_acl(Bucket=bucket_name, Key=f'{folder_name}/{file_path.split("/")[-1]}', ACL='public-read')
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url_list=[]
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for image in histogram_result:
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url = "https://dagit-store.s3.ap-south-1.amazonaws.com/"+images_dir+"/"+image
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url_list.append(url)
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print(json.dumps({"histogram_image_url_links":url_list,
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"activation_id": str(activation_id),
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"parts": parts
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}))
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return({"histogram_image_url_links":url_list,
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"activation_id": str(activation_id),
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"parts": parts
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})
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if __name__ == "__main__":
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main() |