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Steps:

1. Install python3 (follow https://docs.python-guide.org/starting/install3/osx/)
2. Install virtualenv pip3 install virtualenv
3. Make a virtual environment within current directory virtualenv -p python3 ./venv
4. Activate the virtual environment for working with it source ./venv/bin/activate
5. Deactivate it when done (Why? So you can work with other virtual environments or the global environment) deactivate

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"""
author: Timothy C. Arlen
date: 28 Feb 2018

revised by: Sadjad Asghari Esfeden
date: 10 Sep 2018

Calculate Mean Average Precision (mAP) for a set of bounding boxes corresponding to specific
image Ids. Usage:

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Add these two lines to your ~/.zshrc file:

zstyle ':completion:*' matcher-list '' 'm:{a-zA-Z}={A-Za-z}' 'r:|=*' 'l:|=* r:|=*'
autoload -Uz compinit && compinit

Tested on:

MacOS BigSur Version 11.5.1

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# TFLite quantized inference example
#
# Based on:
# https://www.tensorflow.org/lite/performance/post_training_integer_quant
# https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/Tensor.QuantizationParams

import numpy as np
import tensorflow as tf

# Location of tflite model file (float32 or int8 quantized)

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import cv2
import time

CONFIDENCE_THRESHOLD = 0.2
NMS_THRESHOLD = 0.4
COLORS = [(0, 255, 255), (255, 255, 0), (0, 255, 0), (255, 0, 0)]

class_names = []
with open("classes.txt", "r") as f:
    class_names = [cname.strip() for cname in f.readlines()]

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# -*- coding: utf-8 -*-
import cv2
import tensorflow as tf
import numpy as np

# https://www.tensorflow.org/lite/guide/hosted_models
# http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip


def detect_from_camera():

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model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',

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# echo "blacklist nouveau" | sudo tee -a /etc/modprobe.d/blacklist-nouveau.conf > /dev/null
# echo "options nouveau modeset=0" | sudo tee -a /etc/modprobe.d/blacklist-nouveau.conf > /dev/null
# sudo update-initramfs -u

sudo apt update
sudo apt -y install build-essential

sudo apt update
sudo apt -y install linux-headers-$(uname -r)

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import matplotlib
import matplotlib.pyplot as plt

import os
import random
import io
import imageio
import glob
import scipy.misc

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from object_detection.utils import config_util
from object_detection.builders import model_builder

#Where to save the SavedModel
output_directory = './try2_trained_model'
#Recover our saved model with the latest checkpoint:
pipeline_config = 'pipeline.config'
#Put the last ckpt from training in here, don't use long pathnames:
model_dir = './trained_checkpoints/ckpt-31'