gauravgola96

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: podname
  namespace: namespace
  annotations:
    metric-config.external.processed-events-per-second.prometheus/prometheus-server: http://prometheus.istio-system.svc
    metric-config.object.istio-requests-total.prometheus/per-replica: "true"
    metric-config.object.istio-requests-total.prometheus/query: |
      sum(

gauravgola96

class YoloHelper:
    def preprocess(self, image):
        image = img_to_array(image, )
        data = cv2.resize(image, (416, 416))
        # im_arr = img_to_array(data, )
        im_arr = data/255.0
        im_arr = np.expand_dims(im_arr, axis=0)
        return im_arr

gauravgola96

import sys
import numpy as np
import pickle
import os

# from darkflow.utils.box import BoundBox
from scipy.special import expit
from math import exp

gauravgola96

model_config_list: {

   config:{
    name: "yolov2",
    base_path: "/home/gaurav/export",
    model_platform: "tensorflow"
  }
}

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import os
import tensorflow as tf
from tensorflow.python.saved_model import builder as saved_model_builder
from darkflow.net.build import TFNet

options = {"model": "yolov2-tiny.cfg", "load": "yolov2-tiny.weights", "threshold": 0.1}

tfnet = TFNet(options)

export_path = "./export/1/"

gauravgola96

apiVersion: apps/v1
kind: Deployment
metadata:
  name: cluster-autoscaler
  labels:
    app: cluster-autoscaler
spec:
  replicas: 1
  selector:
    matchLabels:

gauravgola96

import cv2
import numpy as np
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
cap = cv2.VideoCapture(0)
scaling_factor = 0.5
while True:
    ret, frame = cap.read()
    #frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor,interpolation=cv2.INTER_AREA)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    face_rects = face_cascade.detectMultiScale(gray,1.3, 5)

gauravgola96

from keras.preprocessing import image
from keras.applications.vgg19 import VGG19
from keras.applications.vgg19 import preprocess_input
import numpy as np
from keras.layers import merge, Input


# from imagenet_utils import decode_predictions

model = VGG19(include_top=False,weights="imagenet")

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gauravgola96

import keras
import numpy as np
import os
import time
from vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
from imagenet_utils import decode_predictions
from keras.layers import Dense, Activation, Flatten
from keras.layers import merge, Input