Myah  Conn

Myah Conn

1591688820

Headless Installation of Raspberry Pi Using NOOBS with SSH

Nearly every tutorial I see on the internet assumes the reader has an external monitor. Yeah, the installation will be a piece of cake in case you have an external monitor. But the price of an external monitor is more or less the same as Raspberry Pi (raspi). In many cases, the project we want to build with raspi doesn’t need an external monitor, and in many cases, I just use SSH to control my raspi. It such a waste to buy a monitor just for the installation.
The installation and control over raspi without a monitor is called headless. There is an installation software called New Out Of the Box Software (NOOBS) provide officially by raspi itself. NOOBS provides several OS to install it in our raspi but the official OS of raspi is Raspbian. This story will guide you on how to install Raspbian using NOOBS and make it ready to remote it with SSH. In this case, I am using a WI-FI network to remote into the raspi.

#raspberry-pi #iot #linux #internet-of-things #programming

What is GEEK

Buddha Community

Headless Installation of Raspberry Pi Using NOOBS with SSH
Chloe  Butler

Chloe Butler

1667425440

Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

  1. Design the PCB using your favorite CAD or drawing software.
  2. Print the top and bottom copper and top silk screen layers to a PDF file.
  3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
  4. Use a Gerber viewer to double-check the output against the original PCB design.
  5. Make adjustments as needed.
  6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.


pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl 

TensorFlow Lite Object Detection using Raspberry Pi and Pi Camera

I have not created the Object Detection model, I have just merely cloned Google’s Tensor Flow Lite model and followed their Raspberry Pi Tutorial which they talked about in the Readme! You don’t need to use this article if you understand everything from the Readme. I merely talk about what I did!

Prerequisites:

  • I have used a Raspberry Pi 3 Model B and PI Camera Board (3D printed a case for camera board). **I had this connected before starting and did not include this in the 90 minutes **(plenty of YouTube videos showing how to do this depending on what Pi model you have. I used a video like this a while ago!)

  • I have used my Apple Macbook which is Linux at heart and so is the Raspberry Pi. By using Apple you don’t need to install any applications to interact with the Raspberry Pi, but on Windows you do (I will explain where to go in the article if you use windows)

#raspberry-pi #object-detection #raspberry-pi-camera #tensorflow-lite #tensorflow #tensorflow lite object detection using raspberry pi and pi camera

Tools and Images to Build a Raspberry Pi n8n server

n8n-pi

Tools and Images to Build a Raspberry Pi n8n server

Introduction

The purpose of this project is to create a Raspberry Pi image preconfigured with n8n so that it runs out of the box.

What is n8n?

n8n is a no-code/low code environment used to connect and automate different systems and services. It is programmed using a series of connected nodes that receive, transform, and then transmit date from and to other nodes. Each node represents a service or system allowing these different entities to interact. All of this is done using a WebUI.

Why n8n-pi?

Whevever a new technology is released, two common barriers often prevent potential users from trying out the technology:

  1. System costs
  2. Installation & configuration challenges

The n8n-pi project eliminates these two roadblocks by preconfiguring a working system that runs on easily available, low cost hardware. For as little as $40 and a few minutes, they can have a full n8n system up and running.

Thanks!

This project would not be possible if it was not for the help of the following:

Documentation

All documentation for this project can be found at http://n8n-pi.tephlon.xyz.

Download Details:

Author: TephlonDude

GitHub: https://github.com/TephlonDude/n8n-pi

#pi #raspberry pi #raspberry #raspberry-pi

Myah  Conn

Myah Conn

1591688820

Headless Installation of Raspberry Pi Using NOOBS with SSH

Nearly every tutorial I see on the internet assumes the reader has an external monitor. Yeah, the installation will be a piece of cake in case you have an external monitor. But the price of an external monitor is more or less the same as Raspberry Pi (raspi). In many cases, the project we want to build with raspi doesn’t need an external monitor, and in many cases, I just use SSH to control my raspi. It such a waste to buy a monitor just for the installation.
The installation and control over raspi without a monitor is called headless. There is an installation software called New Out Of the Box Software (NOOBS) provide officially by raspi itself. NOOBS provides several OS to install it in our raspi but the official OS of raspi is Raspbian. This story will guide you on how to install Raspbian using NOOBS and make it ready to remote it with SSH. In this case, I am using a WI-FI network to remote into the raspi.

#raspberry-pi #iot #linux #internet-of-things #programming

Philian Mateo

Philian Mateo

1583288477

How to Install TensorFlow and Recognize images using Raspberry Pi

Introduction

This article demonstrates how to install TensorFlow and recognize images using Raspberry Pi.

Prerequisite

  • Raspberry Pi
  • TensorFlow
  • Putty or VNC View

**About TensorFlow **

  • TensorFlow is a free and open-source software library for dataflow
  • It is a symbolic math library.
  • TensorFlow is a computational framework for building machine learning models.

TensorFlow has two components

  • a graph protocol buffer
  • a runtime that executes the (distributed) graph

Types of an image

  • BINARY IMAGE
  • BLACK AND WHITE IMAG
  • 8-bit COLOR FORMAT
  • 16-bit COLOR FORMAT

A 16-bit color format is divided into three different colors which are Red, Green, and Blue (RGB).

Step 1

Let’s go to install the raspbian stretch operating system and also get the latest version of Python, so open the Linux terminal to update Python.

This is image title

sudo apt-get update    
python --version    
python3 --version    

Installing TensorFlow needs some library file, so Libatlas library is required by the TensorFlow package installation. Once the library file is installed, then install the TensorFlow package.

Before installing TensorFlow, install the Atlas library.

sudo apt install libatlas-base-dev  

Once that is finished install TensorFlow via pip3

pip3 install --user tensorflow  

Nowwe’ve  successfully installed TensorFlowTensorFlow version-1-9-0.

Step 2

Once we install the TensorFlow we’re ready to test the basic TensorFlow script provided by the TensorFlow site and first, you can test the Hello World program. Now, I create a  new Python file like tftest.py,

sudo nano tftest.py  

Next, you have to import the TensorFlow library.

import tensorflow as tf  
hello = tf.constant('Hello, TensorFlow')  
sess = tf.Session()  
print (sess.run(hello))

Just run the code. You can see the hello TensorFlow program is successfully printed.

Run the code from the terminal:

python3 tftest.py   

This is image title

Step 3

Clone the TensorFlow classification script.

git clone https://github.com/tensorflow/models.git  

This is a panda image for reference:

This is image title

Once the script is running the panda image will be recognized.

cd models/tutorials/image/imagenet  
python3 classify_image.py 

Source Code

from __future__ import absolute_import  
from __future__ import division  
from __future__ import print_function  
  
import argparse  
import os.path  
import re  
import sys  
import tarfile  
  
import numpy as np  
from six.moves import urllib  
import tensorflow as tf  
  
FLAGS = None  
  
  
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'  
  
class NodeLookup(object):  
  
  def __init__(self,  
               label_lookup_path=None,  
               uid_lookup_path=None):  
    if not label_lookup_path:  
      label_lookup_path = os.path.join(  
          FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')  
    if not uid_lookup_path:  
      uid_lookup_path = os.path.join(  
          FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')  
    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)  
  
  def load(self, label_lookup_path, uid_lookup_path):  
    if not tf.gfile.Exists(uid_lookup_path):  
      tf.logging.fatal('File does not exist %s', uid_lookup_path)  
    if not tf.gfile.Exists(label_lookup_path):  
      tf.logging.fatal('File does not exist %s', label_lookup_path)  
  
    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()  
    uid_to_human = {}  
    p = re.compile(r'[n\d]*[ \S,]*')  
    for line in proto_as_ascii_lines:  
      parsed_items = p.findall(line)  
      uid = parsed_items[0]  
      human_string = parsed_items[2]  
      uid_to_human[uid] = human_string  
    node_id_to_uid = {}  
    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()  
    for line in proto_as_ascii:  
      if line.startswith('  target_class:'):  
        target_class = int(line.split(': ')[1])  
      if line.startswith('  target_class_string:'):  
        target_class_string = line.split(': ')[1]  
        node_id_to_uid[target_class] = target_class_string[1:-2]  
    node_id_to_name = {}  
    for key, val in node_id_to_uid.items():  
      if val not in uid_to_human:  
        tf.logging.fatal('Failed to locate: %s', val)  
      name = uid_to_human[val]  
      node_id_to_name[key] = name  
    return node_id_to_name 
  def id_to_string(self, node_id):  
    if node_id not in self.node_lookup:  
      return ''  
    return self.node_lookup[node_id]  
def create_graph():  
  with tf.gfile.FastGFile(os.path.join(  
      FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:  
    graph_def = tf.GraphDef()  
    graph_def.ParseFromString(f.read())  
    _ = tf.import_graph_def(graph_def, name='')  
def run_inference_on_image(image):   
  if not tf.gfile.Exists(image):  
    tf.logging.fatal('File does not exist %s', image)  
  image_data = tf.gfile.FastGFile(image, 'rb').read() 
  
  create_graph() 
  with tf.Session() as sess:  
    
    softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')  
    predictions = sess.run(softmax_tensor,  
                           {'DecodeJpeg/contents:0': image_data})  
    predictions = np.squeeze(predictions)  
 .  node_lookup = NodeLookup()  
    top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]  
    for node_id in top_k:  
      human_string = node_lookup.id_to_string(node_id)  
      score = predictions[node_id]  
      print('%s (score = %.5f)' % (human_string, score))  
  
def maybe_download_and_extract():  
  dest_directory = FLAGS.model_dir  
  if not os.path.exists(dest_directory):  
    os.makedirs(dest_directory)  
  filename = DATA_URL.split('/')[-1]  
  filepath = os.path.join(dest_directory, filename)  
  if not os.path.exists(filepath):  
    def _progress(count, block_size, total_size):  
      sys.stdout.write('\r>> Downloading %s %.1f%%' % (  
          filename, float(count * block_size) / float(total_size) * 100.0))  
      sys.stdout.flush()  
    filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)  
    print()  
    statinfo = os.stat(filepath)  
    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')  
  tarfile.open(filepath, 'r:gz').extractall(dest_directory)  
  
def main(_):  
  maybe_download_and_extract()  
  image = (FLAGS.image_file if FLAGS.image_file else  
           os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))  
  run_inference_on_image(image)
  
if __name__ == '__main__':  
  parser = argparse.ArgumentParser()  
  parser.add_argument(  
      '--model_dir',  
      type=str,  
      default='/tmp/imagenet',  
      help="""\ 
    
  )  
  parser.add_argument(  
      '--image_file',  
      type=str,  
      default='',  
      help='Absolute path to image file.'  
  )  
  parser.add_argument(  
      '--num_top_predictions',  
      type=int,  
      default=5,  
      help='Display this many predictions.'  
  )  
  FLAGS, unparsed = parser.parse_known_args()  
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)  

OUTPUT

This is image title

The same method is used to classify the external images like the following terminal command.

python3 classify_image.py --image_file=/PATH/  

This is the very beginning of the TensorFlow Raspberry pi, just install the TensorFlow and Classify the image.

Summary

In this article, you learned how to install TensorFlow and do image recognition using TensorFlow and Raspberry Pi.

Thank you for reading!

#tensorflow #python #raspberry pi #raspberry-pi