sjain07

icons

sjain07

import picamera, json, requests, os, random
from time import sleep
from PIL import Image, ImageDraw

#capture an image
camera = picamera.PiCamera()
camera.capture('image1.jpg')
print('caputred image')

#make a prediction on the image

sjain07

import picamera, os
from PIL import Image, ImageDraw
camera = picamera.PiCamera()
camera.capture('image1.jpg')
os.system("xdg-open image1.jpg")

sjain07

import requests, os, sys

model_id = os.environ.get('NANONETS_MODEL_ID')
api_key = os.environ.get('NANONETS_API_KEY')
image_path = sys.argv[1]

url = 'https://app.nanonets.com/api/v2/ObjectDetection/Model/' + model_id + '/LabelFile/'

data = {'file': open(image_path, 'rb'),    'modelId': ('', model_id)}

sjain07

image = readImage()
NoOfCells = 7
NoOfClasses = 4
threshold = 0.7
step = height(image)/NoOfCells
prediction_class_array = new_array(size(NoOfCells,NoOfCells,NoOfClasses))
predictions_bounding_box_array = new_array(size(NoOfCells,NoOfCells,NoOfCells,NoOfCells))
final_predictions = []
for (i<0; i<NoOfCells; i=i+1):
	for (j<0; j<NoOfCells;j=j+1):

sjain07

#this is an Image of size 140x140. We will assume it to be black and white (ie only one channel, it would have been 140x140x3 for rgb)
image = readImage()

#We will break the Image into 7 coloumns and 7 rows and process each of the 49 different parts independently
NoOfCells = 7

#we will try and predict if an image is a dog, cat, cow or wolf. Therfore the number of classes is 4
NoOfClasses = 4
threshold = 0.7

sjain07

FROM tensorflow/tensorflow:1.5.0-gpu
RUN apt-get update && apt-get install -y --no-install-recommends \
    python-pip \
    protobuf-compiler \
    python-pil \
    python-lxml \
    python-numpy \
    python-scipy \
    python-dev \
    python-pip \

sjain07

#!/bin/bash
set -e
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
      build-essential \
      cmake \
      git \
      libgoogle-glog-dev \
      libprotobuf-dev \
      protobuf-compiler \

sjain07

events {}

http {
  upstream pgdb {
    postgres_server urlshortnertestdb.cccccccccccc.us-west-2.rds.amazonaws.com dbname=redirect  user=root password=password;
  }

  server {
    listen 80;
    server_name localhost;

sjain07

with tf.Session(graph=graph) as session:
  init.run()
  average_loss = 0
  for step in range(10001):
    batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)
    feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}
    _, loss_val, normalized_embeddings_np = session.run([optimizer, loss, normalized_embeddings], feed_dict=feed_dict)
    average_loss += loss_val
  final_embeddings = normalized_embeddings.eval()