Python：对象检测无法绘制边界框

Question

我已按照this链接中提供的逐步说明进行操作，以对自己的图像进行自定义对象检测。我也设法训练了模型，并导出了推理图。但是，当我运行脚本来测试模型时，它显示图像但不显示边界框。我正在使用python 3.7 and tensorflow 2.1.0

我正在尝试识别一段文字中的自定义手写字母。例如： 我可以使用不同的方式来写字母'o'和/或字母'a'，例如在这些图像中（所有图像尺寸均为128 x 128并以.jpg格式）：

我还使用LabelImg程序创建了相应的.xml文件。因此，我将这些图像视为对象，并针对诸如此类的手写文本图像进行测试：

我用于检测物体的代码：

import os
import cv2
import numpy as np
import tensorflow as tf
import sys

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

# Import utilites
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

# Name of the directory containing the object detection module we're using
MODEL_NAME = 'inference_graph'  # The path to the directory where frozen_inference_graph is stored.
IMAGE_NAME = 'HandText.jpg'  # The path to the image in which the object has to be detected.

# Grab path to current working directory
CWD_PATH = os.getcwd()

# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH, MODEL_NAME, 'frozen_inference_graph.pb')

# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH, 'training', 'labelmap.pbtxt')

# Path to image
PATH_TO_IMAGE = os.path.join(CWD_PATH, IMAGE_NAME)

# Number of classes the object detector can identify
NUM_CLASSES = 2

# Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `king`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
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)

# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.compat.v1.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='')

    sess = tf.compat.v1.Session(graph=detection_graph)

# Define input and output tensors (i.e. data) for the object detection classifier

# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')

# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')

# Load image using OpenCV and
# expand image dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
image = cv2.imread(PATH_TO_IMAGE)
image_expanded = np.expand_dims(image, axis=0)

# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
    [detection_boxes, detection_scores, detection_classes, num_detections],
    feed_dict={image_tensor: image_expanded})

# Draw the results of the detection (aka 'visualize the results')

vis_util.visualize_boxes_and_labels_on_image_array(
    image,
    np.squeeze(boxes),
    np.squeeze(classes).astype(np.int32),
    np.squeeze(scores),
    category_index,
    use_normalized_coordinates=True,
    line_thickness=8,
    min_score_thresh=0.60)

# All the results have been drawn on the image. Now display the image.
cv2.imshow('Object detector', image)

# Press any key to close the image
cv2.waitKey(0)

# Clean up
cv2.destroyAllWindows()

我不知道为什么它没有在文本周围绘制边框。图像为24位，我认为是因为它是3通道图像（每通道8位）。另一方面，可能是因为图像是黑白图像，并且没有RGB颜色吗？

任何帮助或建议将不胜感激。