Detect multiple objects

General Discussion
#1

I’m sorry, I made mistake.

I trained a neural network and it works fine in the code below.
I’m trying to modify, to detect multiple objects.
I appreciate any help.
Thank you very much.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import time

import numpy as np
from PIL import Image
import tensorflow as tf # TF2


def load_labels(filename):
  with open(filename, 'r') as f:
    return [line.strip() for line in f.readlines()]


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '-i',
      '--image',
      default='/tmp/grace_hopper.bmp',
      help='image to be classified')
  parser.add_argument(
      '-m',
      '--model_file',
      default='/tmp/mobilenet_v1_1.0_224_quant.tflite',
      help='.tflite model to be executed')
  parser.add_argument(
      '-l',
      '--label_file',
      default='/tmp/labels.txt',
      help='name of file containing labels')
  parser.add_argument(
      '--input_mean',
      default=127.5, type=float,
      help='input_mean')
  parser.add_argument(
      '--input_std',
      default=127.5, type=float,
      help='input standard deviation')
  parser.add_argument(
      '--num_threads', default=None, type=int, help='number of threads')
  args = parser.parse_args()

  interpreter = tf.lite.Interpreter(
      model_path=args.model_file, num_threads=args.num_threads)
  interpreter.allocate_tensors()

  input_details = interpreter.get_input_details()
  output_details = interpreter.get_output_details()

  # check the type of the input tensor
  floating_model = input_details[0]['dtype'] == np.float32

  # NxHxWxC, H:1, W:2
  height = input_details[0]['shape'][1]
  width = input_details[0]['shape'][2]
  img = Image.open(args.image).resize((width, height))

  # add N dim
  input_data = np.expand_dims(img, axis=0)

  if floating_model:
    input_data = (np.float32(input_data) - args.input_mean) / args.input_std

  interpreter.set_tensor(input_details[0]['index'], input_data)

  start_time = time.time()
  interpreter.invoke()
  stop_time = time.time()

  output_data = interpreter.get_tensor(output_details[0]['index'])
  results = np.squeeze(output_data)

  top_k = results.argsort()[-5:][::-1]
  labels = load_labels(args.label_file)
  for i in top_k:
    if floating_model:
      print('{:08.6f}: {}'.format(float(results[i]), labels[i]))
    else:
      print('{:08.6f}: {}'.format(float(results[i] / 255.0), labels[i]))

  print('time: {:.3f}ms'.format((stop_time - start_time) * 1000))
    indent preformatted text by 4 spaces
[/quote]


from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import time

import numpy as np
from PIL import Image
import tensorflow as tf # TF2


def load_labels(filename):
  with open(filename, 'r') as f:
    return [line.strip() for line in f.readlines()]


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '-i',
      '--image',
      default='/tmp/grace_hopper.bmp',
      help='image to be classified')
  parser.add_argument(
      '-m',
      '--model_file',
      default='/tmp/mobilenet_v1_1.0_224_quant.tflite',
      help='.tflite model to be executed')
  parser.add_argument(
      '-l',
      '--label_file',
      default='/tmp/labels.txt',
      help='name of file containing labels')
  parser.add_argument(
      '--input_mean',
      default=127.5, type=float,
      help='input_mean')
  parser.add_argument(
      '--input_std',
      default=127.5, type=float,
      help='input standard deviation')
  parser.add_argument(
      '--num_threads', default=None, type=int, help='number of threads')
  args = parser.parse_args()

  interpreter = tf.lite.Interpreter(
      model_path=args.model_file, num_threads=args.num_threads)
  interpreter.allocate_tensors()

  input_details = interpreter.get_input_details()
  output_details = interpreter.get_output_details()

  # check the type of the input tensor
  floating_model = input_details[0]['dtype'] == np.float32

  # NxHxWxC, H:1, W:2
  height = input_details[0]['shape'][1]
  width = input_details[0]['shape'][2]
  img = Image.open(args.image).resize((width, height))

  # add N dim
  input_data = np.expand_dims(img, axis=0)

  if floating_model:
    input_data = (np.float32(input_data) - args.input_mean) / args.input_std

  interpreter.set_tensor(input_details[0]['index'], input_data)

  start_time = time.time()
  interpreter.invoke()
  stop_time = time.time()

  output_data = interpreter.get_tensor(output_details[0]['index'])
  results = np.squeeze(output_data)

  top_k = results.argsort()[-5:][::-1]
  labels = load_labels(args.label_file)
  for i in top_k:
    if floating_model:
      print('{:08.6f}: {}'.format(float(results[i]), labels[i]))
    else:
      print('{:08.6f}: {}'.format(float(results[i] / 255.0), labels[i]))

  print('time: {:.3f}ms'.format((stop_time - start_time) * 1000))```
#3

It’s hard to get what the problem is here if you only paste in your code. So please add some explanation of why you created this discussion. Also, I’d recommend using codeblocks as it makes the code a lot more readable and also please try to keep the correct indentation of the code as Python code is hard to read otherwise.

1 Like
#5

Hello!
Thank you for your help.
I reviewed the post.
I apologize for the mistakes.

#7

Take a look at the following scripts and repositories:

I hope this helps you out if you have any further questions feel free to ask.

#8

Thank you very much.
I will take a look.