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:[email protected]: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

import torch
from torch import nn
import torch.nn.functional as F
import abc
import pytorch_ssim
import torchvision.models as models
from torch.autograd import Variable


class AbstractAutoEncoder(nn.Module):

bmabir17

import tensorflow as tf
import numpy as np
import mrcnn.model as modellib # https://github.com/matterport/Mask_RCNN/
from mrcnn.config import Config
import keras.backend as keras

PATH_TO_SAVE_FROZEN_PB ="./"
FROZEN_NAME ="saved_model.pb"

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

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

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

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

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