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167 lines
6.6 KiB
167 lines
6.6 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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def main():
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images_dir = "face-detected-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="127.0.0.1", 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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face_detected_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+'/face_detected_image_'+str(i)+'.jpg'):
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os.remove(images_dir+'/face_detected_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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img = cv2.imread(image_name)
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# Load Haar cascade for face detection
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face_cascade = cv2.CascadeClassifier('../haarcascade_frontalface_default.xml')
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# Convert to grayscale
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# Detect faces
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faces = face_cascade.detectMultiScale(gray, 1.3, 5)
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# Draw bounding boxes around faces
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for (x,y,w,h) in faces:
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cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),2)[]
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# face = img[y:y+h, x:x+w]
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# # Apply a Gaussian blur to the face ROI
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# blurred_face = cv2.GaussianBlur(face, (23, 23), 30)
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# # Replace the face ROI with the blurred face
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# img[y:y+h, x:x+w] = blurred_face
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output_image = images_dir+'/face_detected_image_'+str(i)+'.jpg'
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# face_blurred_image = images_dir+'/face_blurred_image_'+str(i)+'.jpg'
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cv2.imwrite(output_image, img)
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# cv2.imwrite(face_blurred_image, blurred_face)
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imag = open(output_image,"rb").read()
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pickled_object = pickle.dumps(imag)
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face_detected_output = "face-detected-image"+activation_id+"-"+str(i)
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print(pickled_object)
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r.set(face_detected_output,pickled_object)
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face_detected_result.append('face_detected_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 face_detected_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({"face_detected_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({"face_detected_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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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+'/face_detected_image_'+str(i)+'.jpg'):
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os.remove(images_dir+'/face_detected_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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img = cv2.imread(image_name)
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# Load Haar cascade for face detection
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face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
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# Convert to grayscale
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# Detect faces
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faces = face_cascade.detectMultiScale(gray, 1.3, 5)
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# Draw bounding boxes around faces
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for (x,y,w,h) in faces:
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cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),2)
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output_image = images_dir+'/face_detected_image_'+str(i)+'.jpg'
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cv2.imwrite(output_image, img)
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face_detected_result.append('face_detected_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 face_detected_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({"face_detected_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({"face_detected_image_url_links":url_list,
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"activation_id": str(activation_id),
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"parts": parts,
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"pickled_object":pickled_object
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})
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if __name__ == "__main__":
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main() |