bmabir17

import 'dart:io' as Io;
import 'package:image/image.dart';
void main(){
  //Read an image from file
  //You can also use other file format like jpeg, webp,TIFF, PSD, GIF
  var imageFile = new Io.File('test.png').readAsBytesSync();
  //decodeImage will identify the format of the image and 
  // decode the image accordingly
  Image image = decodeImage(imageFile);

bmabir17

import subprocess as sp 
import time

while(True):
    sp.call('ffmpeg -f v4l2 -framerate 25 -t 4 -video_size 640x480 -i /dev/video0 ./webcam_output.mp4 -y',shell=True)
    print('Video Captured')
    time.sleep(1)

bmabir17

from hikvisionapi import Client
import cv2
import numpy as np
cam = Client('http://192.168.1.2','admin','admin123',timeout=1)
cap=cv2.VideoCapture('rtsp://admin:admin123@192.168.1.2:554/main/av_stream')
#response = cam.System.deviceInfo(method='get')

#print(response)

#motion_detection_info = cam.System.Video.inputs.channels[1].motionDetection(method='get')

bmabir17

const functions = require('firebase-functions');
const admin = require('firebase-admin');
// var serviceAccount = require("/home/bmabir/dev/tran-dao-8e837f43806d.json");
admin.initializeApp({
    credential: admin.credential.applicationDefault()
});
const db = admin.firestore();
// // Create and Deploy Your First Cloud Functions
// // https://firebase.google.com/docs/functions/write-firebase-functions
//

bmabir17

# Default Optimization is choosen
converter.optimizations = [tf.lite.Optimize.DEFAULT]

# Convert to TFLite Model
tflite_model = converter.convert()
# Save Model as tflite format
tflite_path = "deeplabv3_mnv2_custom_257.tflite"
tflite_model_size = open(tflite_path, 'wb').write(tflite_model)

bmabir17

import os
import numpy as np
import tensorflow as tf
print(tf.__version__)

bmabir17

MODEL_FILE = "frozen_inference_graph_257.pb"
# Load the TensorFlow model
converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph(
    graph_def_file = MODEL_FILE, 
    input_arrays = ['sub_2'], # For the Xception model it needs to be `sub_7`, for MobileNet it would be `sub_2`
    output_arrays = ['ResizeBilinear_2'],
    input_shapes={'sub_2':[1,257,257,3]}
)

bmabir17

git_branch () {
  git branch 2> /dev/null | sed -e '/^[^*]/d' -e 's/* \(.*\)/\1/'
  }
HOST='\[\033[02;36m\]\h'; HOST=' '$HOST
TIME='\[\033[01;31m\]\t \[\033[01;32m\]'
LOCATION=' \[\033[01;34m\]`pwd | sed "s#\(/[^/]\{1,\}/[^/]\{1,\}/[^/]\{1,\}/\).$'
BRANCH=' \[\033[00;33m\]$(git_branch)\[\033[00m\] '
PS1=$TIME$BRANCH$USER$HOST$LOCATION

bmabir17

import numpy as np
from PIL import Image
from pathlib import Path
from collections import defaultdict
import keras
# import tensorflow as tf
# from tensorflow.python import keras
# from tensorflow.python.keras import backend as Keras
from tensorflow.keras.utils import Sequence
import logging

bmabir17

import numpy as np
from PIL import Image
from pathlib import Path
from collections import defaultdict
import keras
from tensorflow.keras.utils import Sequence
import logging
import matplotlib.pyplot as plt
import cv2