alonlavian

  {
	  "public_identifier": "alon-lavian",
	  "profile_pic_url": "https://s3.us-west-000.backblazeb2.com/proxycurl/person/alon-lavian/profile?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=0004d7f56a0400b0000000001%2F20230523%2Fus-west-000%2Fs3%2Faws4_request&X-Amz-Date=20230523T111453Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=cf270c9149d667b4e1b2124e815f02e1380f03388632b834dfc9c83798e75eb6",
	  "background_cover_image_url": "https://s3.us-west-000.backblazeb2.com/proxycurl/person/alon-lavian/cover?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=0004d7f56a0400b0000000001%2F20230523%2Fus-west-000%2Fs3%2Faws4_request&X-Amz-Date=20230523T111453Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=4fd51e1f1feb5235fafad3a69fefb5c0f4a1b9008a0a036254cc0f7311f2e202",
	  "first_name": "Alon",
	  "last_name": "Lavian",
	  "full_name": "Alon Lavian",
	  "follower_count": null,
	  "occupation": "Director of Product Management at Orca Security",
	  "headline": "Director, Product Management 

alonlavian

import logging
import boto3
from argparse import ArgumentParser, HelpFormatter
from botocore.exceptions import ClientError, ProfileNotFound

# logger config
logger = logging.getLogger()
logging.basicConfig(level=logging.INFO,
                    format='%(message)s')

alonlavian

import logging
import boto3
from argparse import ArgumentParser, HelpFormatter
from botocore.exceptions import ClientError, ProfileNotFound

# from botocore.errorfactory import UnauthorizedException

# logger config
logger = logging.getLogger()
logging.basicConfig(level=logging.INFO,

alonlavian

architecting.with.gke.workloads.kubernetes.io/safe-to-evict": "true"

alonlavian

from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras import layers
from tensorflow.keras.models import Model

base_model = VGG16(weights='imagenet', #pre-trained weights
                      include_top=False,  #remove original classifier
                      input_shape=(HEIGHT, WIDTH, 3))

#Freeze all layers
for layer in base_model.layers: 

alonlavian

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(HEIGHT, WIDTH, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(1024, activation='relu'),
    tf.keras.layers.Dropout(0.2),

alonlavian

original_images_dir = os.fsencode('/content/drive/My Drive/Google Photos/2019')
cropped_images_lib  = '/content/drive/My Drive/CropMix/'
    
for path, dirs, files in os.walk(original_images_dir):
    for file in files:
      image_name = os.fsdecode(file)
      if image_name.endswith(".jpg"):
        image_path = os.path.join(path,file)
        crop_faces(image_path,image_name,cropped_images_lib)            

alonlavian

from PIL import Image

def crop_faces(image_path,image_name,cropped_images_lib):
    with open(image_path, 'rb') as image:
        faces = detect_face(image, 8)
      
    im = Image.open(image_path)
    
    for idx,face in enumerate(faces):
      vects = face.fd_bounding_poly.vertices

alonlavian

from google.cloud import vision
from google.cloud.vision import types

def detect_face(face_file, max_results=4):
  
    client = vision.ImageAnnotatorClient()
    
    content = face_file.read()
    image = types.Image(content=content)
    

alonlavian

import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"]="/content/drive/My Drive/ML/Misc/vision_service_account.json"