| ffmpeg -i bg_3.mp4 -vb 30M -filter:v "crop=320:320:200:600, fps=3" frames/office_2_%04d.jpg |
| from sqlite3 import adapt | |
| import numpy as np | |
| from PIL import Image | |
| import glob | |
| import cv2 | |
| import numpy as np | |
| import tflite_runtime.interpreter as tflite |
For object detection models with very few classes (<5) and no background images, most models will struggle with false positives. The model is only learning what the thing is that it needs to detect. This is only half the problem.
The other half is that the model needs to learn what not to detect. Heres where background images come in.
The easiest way to get some background images is to take a video with a smart phone and chop it into individual frames.
| import org.jetbrains.kotlinx.multik.api.toNDArray | |
| import org.jetbrains.kotlinx.multik.ndarray.data.get | |
| import org.jetbrains.kotlinx.multik.ndarray.data.slice | |
| import org.jetbrains.kotlinx.multik.ndarray.operations.last | |
| import org.jetbrains.kotlinx.multik.ndarray.operations.minus | |
| import org.jetbrains.kotlinx.multik.ndarray.operations.plus | |
| import org.jetbrains.kotlinx.multik.ndarray.operations.toList | |
| // Filter out all items | |
| val startList = listOf(Pair(1,.2),Pair(2,.4),Pair(3,.6),Pair(4,.3)) |
| private fun argsort(needsSorting : Array<Int>){ | |
| val theRange = IntRange(0,needsSorting.size-1) | |
| val idxs = theRange.toList().sortedBy { needsSorting[it] } | |
| } |
| #from importlib.metadata import files | |
| import xml.etree.ElementTree as ET | |
| import glob | |
| # Sort through a dataset's xml files and print out file names that contain labels which are invalid | |
| valid_labels = ["frodo","baggins"] | |
| # Loop through all xml files | |
| files = glob.glob('/home/isaac/Desktop/work_area/working_on/*.xml') |
| import random | |
| from re import S | |
| import xml.etree.ElementTree as ET | |
| from glob import glob | |
| import cv2 | |
| input_dir ='/home/isaac/Desktop/work_area/working_on/' | |
| files = glob(input_dir+'*.png') | |
| # For each file with a .png extension |
Configuring a tensorflow environment to work with gpu's is really annoying. Thankfully, conda & pipmake it a lot less annoying.
Here are the core dependencies for getting this to work.
| import xml.etree.ElementTree as ET | |
| from glob import glob | |
| labels = [ | |
| "labels", | |
| "to", | |
| "be", | |
| "removed"] | |
| files = glob('directory/with/xml/files/*.xml') | |
| for file in files: |
| from re import T | |
| import os | |
| import subprocess | |
| from glob import glob | |
| import xml.etree.ElementTree as ET | |
| labels = [ | |
| "some", | |
| "labels", |