gaiar

def get_batch(self,bs=5):
    bsize = bs if self.Q.qsize() > bs else self.Q.qsize()
    if self.more() or not self.stopped:
        frames = []
        for i in range(bsize):
            frames.append(self.Q.get())
        return frames
    return None

gaiar

#!/bin/bash
HOST='<IP ADDRESS>'
USER='<USER>'
PASS='<PASSWORD>'
TARGETFOLDER='/media/external/record'
SOURCEFOLDER='/tmp/sd/record'

lftp -f "
set ftp:passive-mode on
set ftp:ssl-allow yes

gaiar

import tensorflow as tf
from tensorflow.python.compiler.tensorrt import trt_convert as trt

BATCH_SIZE = 2
#GRAPH_PATH = '/home/gaiar/developer/megadetector/megadetector_v3_optimized.pb'
input_saved_model_dir = "/home/gaiar/developer/megadetector/megadetector/exported_model/saved_model"
output_saved_model_dir = "/home/gaiar/developer/megadetector/model_optimization/trt"

converter = trt.TrtGraphConverter(
    input_saved_model_dir=input_saved_model_dir,

gaiar

# import the necessary packages
from threading import Thread
import sys
import cv2
import time
from queue import Queue


class FileVideoStream:
    def __init__(self, path, transform=None, resize=None, queue_size=128):

gaiar

# Playing video from file
cap = cv2.VideoCapture('rackoon.mp4')

# Default resolutions of the frame are obtained.The default resolutions are system dependent.
# We convert the resolutions from float to integer.
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))

# Define the codec and create VideoWriter object.The output is stored in 'output.avi' file.
out = cv2.VideoWriter(FILE_OUTPUT, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),

gaiar

#!/usr/bin/env bash

wget https://github.com/Archiconda/build-tools/releases/download/0.2.2/Archiconda3-0.2.2-Linux-aarch64.sh
chmod +x Archiconda3-0.2.2-Linux-aarch64.sh
./Archiconda3-0.2.2-Linux-aarch64.sh

gaiar

Epoch 1/10
185/185 [==============================] - 266s 1s/step - loss: 0.2160 - acc: 0.9130 - val_loss: 0.7465 - val_acc: 0.6078

Epoch 00001: val_loss improved from inf to 0.74645, saving model to models/bird-vs-not-bird-weights-01.h5
Epoch 2/10
185/185 [==============================] - 258s 1s/step - loss: 0.1484 - acc: 0.9437 - val_loss: 0.6983 - val_acc: 0.6101

Epoch 00002: val_loss improved from 0.74645 to 0.69832, saving model to models/bird-vs-not-bird-weights-02.h5
Epoch 3/10
185/185 [==============================] - 258s 1s/step - loss: 0.1262 - acc: 0.9530 - val_loss: 0.6837 - val_acc: 0.6073

gaiar

#Based on helper function from https://github.com/daanraman

import os
import shutil
import numpy as np

#Make sure all folders are present before running the script
ALL_DATA_HOME_DIR = "data"
DATA_TRAIN_DIR = "dataset/train"
DATA_TEST_DIR = "dataset/valid"

gaiar

#!/usr/bin/env python
#Based on https://stackoverflow.com/questions/748675/finding-duplicate-files-and-removing-them


import sys
import os
import hashlib
import logging
logging.basicConfig(
    filename='duplicates.log',

gaiar

import glob
import os
import shutil
import filetype
import logging

logging.basicConfig(
    filename='renaming.log',
    level=logging.DEBUG,
    format='%(asctime)s.%(msecs)03d %(levelname)s %(module)s - %(funcName)s: %(message)s',