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

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

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

# 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__)

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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

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

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

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

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')