ashhadulislam

while 1==1:
    out = kinesis.get_records(shard_it, limit=1)
    shard_it = out["NextShardIterator"]
    if len(out["Records"])>0:
    	print(out["Records"][0]["Data"])
    time.sleep(0.3)

ashhadulislam

from boto import kinesis
import time

kinesis = kinesis.connect_to_region("us-east-2")
shard_id = 'shardId-000000000000' #we only have one shard!
shard_it = kinesis.get_shard_iterator("end-stream", shard_id, "LATEST")["ShardIterator"]
while 1==1:
    out = kinesis.get_records(shard_it, limit=1)
    shard_it = out["NextShardIterator"]
    # print(out)

ashhadulislam

from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time
from bs4 import BeautifulSoup
import pandas as pd
import os
from selenium.webdriver.chrome.options import Options  
from selenium.webdriver.support.select import Select
import pickle

ashhadulislam

chrome_options = Options()  
# chrome_options.add_argument("--headless")  
# above should be uncommented if you are
# running the code in a machine without GUI
chromedriver_loc = 'software/chromedriver'
browser = webdriver.Chrome(executable_path=chromedriver_loc, chrome_options=chrome_options)  

ashhadulislam

import cv2
import matplotlib.pyplot as plt
import os, shutil

img=cv2.imread("raw_yolo_keras/train/001446_jpg.rf.00a11ab7cffb0a8fec57e98b2c4663d5.jpg")
crop_img = img[ 318:373, 216:265]
fig,axs=plt.subplots(1,2)
axs[0].imshow(img)
axs[1].imshow(crop_img)

ashhadulislam

import cv2
import matplotlib.pyplot as plt
import os, shutil

#create a folder to store the images of heads with and without helmets
if os.path.isdir("raw_yolo_keras/train/yes_helmet"):
    shutil.rmtree("raw_yolo_keras/train/yes_helmet")
os.mkdir("raw_yolo_keras/train/yes_helmet")

ashhadulislam

import tensorflow as tf
import os
import cv2
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ReduceLROnPlateau
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.inception_resnet_v2 import preprocess_input, decode_predictions

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def load_data(path):
    x_train = []
    y_train = []
    yes_helmet_location=os.path.join(location,"yes_helmet")
    no_helmet_location=os.path.join(location,"no_helmet")
    yes_helmet_files=os.listdir(yes_helmet_location)
    no_helmet_files=os.listdir(no_helmet_location)
    
    for ffile in yes_helmet_files[:5000]:
        img=cv2.imread(os.path.join(yes_helmet_location,ffile))

ashhadulislam

import tensorflow as tf
import numpy as np
from tensorflow.keras import models
from tensorflow.keras import Model
from tensorflow.keras.models import load_model
import matplotlib.pyplot as plt
import os
import tensorflow
import keras
import cv2

ashhadulislam

# read the saved model
def setup_hard_hat_detector():
    return load_model("res/vgg16_model/ninety2_percent_just_vgg16.h5")
hh_model=setup_hard_hat_detector()

location="raw_yolo_keras/end_end_test"
#location where the images downloaded from the internet are placed

dim=(76,76)