python - google - tensorflow github object detection

TensorFlow Object Detection API imprime los objetos encontrados en la imagen a la consola

Question

Estoy intentando devolver la lista de objetos que se han encontrado en la imagen con la API de detección de objetos de TF .

Para hacerlo, estoy usando print([category_index.get(i) for i in classes[0]]) para imprimir la lista de objetos encontrados o print(num_detections) para mostrar el número de objetos encontrados, pero en ambos casos me da una lista con 300 valores o simplemente un valor [300.] correspondiente.

¿Cómo es posible devolver solo los objetos que están en la imagen? O si hay algún error, por favor ayude a averiguar qué está mal.

Estaba usando el archivo de configuración de los modelos RCNN más rápidos y los puntos de control durante el entrenamiento. Asegúrese de que realmente detecta pocos objetos en la imagen, aquí está:

Mi código:

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

PATH_TO_CKPT = ''frozen_graph/frozen_inference_graph.pb''

PATH_TO_LABELS = ''object_detection/pascal_label_map.pbtxt''

NUM_CLASSES = 7

detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_CKPT, ''rb'') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='''')

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)


PATH_TO_TEST_IMAGES_DIR = ''object_detection/test_images/''
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, ''image{}.jpg''.format(i)) for i in range(1, 2) ]

IMAGE_SIZE = (12, 8)

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    sess.run(tf.global_variables_initializer())
    img = 1
    for image_path in TEST_IMAGE_PATHS:
      image = Image.open(image_path)
      image_np = load_image_into_numpy_array(image)
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      image_tensor = detection_graph.get_tensor_by_name(''image_tensor:0'')
      # Each box represents a part of the image where a particular object was detected.
      boxes = detection_graph.get_tensor_by_name(''detection_boxes:0'')
      scores = detection_graph.get_tensor_by_name(''detection_scores:0'')
      classes = detection_graph.get_tensor_by_name(''detection_classes:0'')
      num_detections = detection_graph.get_tensor_by_name(''num_detections:0'')

      (boxes, scores, classes, num_detections) = sess.run(
          [boxes, scores, classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})

      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=8)
      plt.figure(figsize=IMAGE_SIZE)
      plt.imsave(''RESULTS/'' + str(img) + ''.jpg'', image_np)
      img += 1

      # Return found objects
      print([category_index.get(i) for i in classes[0]])
      print(boxes.shape)
      print(num_detections)

Lo que da el siguiente resultado:

[{''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''chesterfield_blue'', ''id'': 1}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_gold'', ''id'': 5}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''chesterfield_red'', ''id'': 2}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_red'', ''id'': 7}, {''name'': ''lucky_strike_blue'', ''id'': 3}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''lucky_strike_red'', ''id'': 4}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''marlboro_mentol'', ''id'': 6}, {''name'': ''lucky_strike_red'', ''id'': 4}]
(1, 300, 4)
[ 300.]

¡Gracias de antemano por cualquier información!

UPD:

Mil gracias a todos los que ayudaron con esta pregunta. La siguiente línea de código es exactamente lo que necesitaba, me da una lista con los objetos encontrados para que pueda realizar otras operaciones en ellos.

print [category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.5]

Answer 1

De la función visualize_boxes_and_labels_on_image_array , debe establecer los argumentos max_boxes_to_draw , min_score_thresh ,

visualize_boxes_and_labels_on_image_array(image,
                                          boxes,
                                          classes,
                                          scores,
                                          category_index,
                                          instance_masks=None,
                                          keypoints=None,
                                          use_normalized_coordinates=False,
                                          max_boxes_to_draw=20,
                                          min_score_thresh=.5,
                                          agnostic_mode=False,
                                          line_thickness=4)

Answer 2

Intente establecer min_score_thresh en 0. Entonces probablemente verá 300 detecciones.

Answer 3

Me encuentro con este problema hoy. debe cambiar dos parámetros en "visualize_boxes_and_labels_on_image_array ()"

1. max_boxes_to_draw = 20 (solo dibujar 20 cajas) "

2. min_score_thresh = .5 (solo puntuación de sorteo> =. 5 casillas) "

   Cambia los dos números para tu detección.

Answer 4

Por lo que puedo ver, tienes 300 detecciones. visualize_boxes_and_labels_on_image_array muestra muy pocos de ellos porque min_score_thresh=.5 (este es el valor predeterminado) es demasiado alto para la mayoría de ellos.

Si desea agregar dicho filtrado a la salida, puede escribir:

min_score_thresh = 0.5
print([category_index.get(i) for i in classes[0] if scores[0, i] > min_score_thresh)

Puede cambiar min_score_thresh para elegir el valor de umbral que necesita. Puede ser útil imprimir los valores de puntaje con los nombres de las categorías.

Answer 5

añadiendo print(class_name) después de

else:        
 class_name = ''N/A''
      display_str = ''{}: {}%''.format(
          class_name,
          int(100*scores[i]))

en el archivo visualization_utils.py imprime el objeto detectado. Me pregunto dónde agregar el comando de impresión para imprimir las marcas de tiempo, así como el porcentaje de precisión en la salida.

Answer 6

abra visualization_utils.py y agregue -> print(class_name) después de

else:        
 class_name = ''N/A''
      display_str = ''{}: {}%''.format(
          class_name,
          int(100*scores[i])) 

esto imprimirá los objetos detectados

Answer 7

// this will load the labels and categories along with category index

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)

categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)

category_index = label_map_util.create_category_index(categories)


//to print the identified object do the following :

Imprimir categoría en lugar de índice de categoría. El índice contiene el valor numérico y la categoría contiene el nombre de los objetos. Una vez identificado con el umbral mencionado el

min_score_thresh = 0.5

print([category.get(1)] for i in classes[0] if scores[0, i] > min_score_thresh)

Esto imprimirá la categoría identificada.