Tensorflow 如何为检测到的每个对象的每个边界框指定唯一Id

我正在Windows系统上使用Tensorflow对象检测API，为此我构建了一个自定义对象检测分类器。它可以通过网络摄像头很好地检测对象，但我正在尝试找出如何通过网络摄像头检测对象，并为每个检测到的对象使用唯一的对象ID

例如，如果网络摄像头检测到两个相似的对象（例如两张相似的椅子），那么它会在每张椅子上绘制一个边界框。我想用一个唯一的ID跟踪这两张椅子，然后在我随后提取视频帧时，获取这两张椅子的质心

目前我正在使用此代码：

# Import packages
import requests
import os

os.chdir('C:\\tensorflow1\\models\\research\\object_detection')

from firebase import firebase
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'

# 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')

# Number of classes the object detector can identify
NUM_CLASSES = 1

# 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.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.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')

# Initialize webcam feed
video = cv2.VideoCapture(1)
img_counter = 0

while True:

    # Acquire frame and expand frame 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
    ret, frame = video.read()
    frame_expanded = np.expand_dims(frame, 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: frame_expanded})

    # Draw the results of the detection (aka 'visulaize the results')
    vis_util.visualize_boxes_and_labels_on_image_array(
        frame,
        np.squeeze(boxes),
        np.squeeze(classes).astype(np.int32),
        np.squeeze(scores),
        np.squeeze(num),
        category_index,
        max_boxes_to_draw=3,
        use_normalized_coordinates=True,
        line_thickness=8,
        min_score_thresh=0.90)


    # All the results have been drawn on the frame, so it's time to display it.
    frame = cv2.circle(frame, (350, 350), 1, (0, 0, 255), 5)
    #frame = cv2.circle(frame, (337, 139), 1, (0, 0, 255), 5)
    cv2.imshow('Object detector', frame)


    if not ret:
        break
    k = cv2.waitKey(1)

    if k % 256 == 27:
        # ESC pressed
        print("Escape hit, closing...")
        break
    elif k % 256 == ord('s'):
        # SPACE pressed
        img_name = "opencv_frame_{}.jpg".format(img_counter)

        print("{} written!".format(img_name))
        img_counter += 1

        # Code to find the centroid of the bounding box on the object detected
        height = frame.shape[0]
        width = frame.shape[1]
        print(height)
        print(width)

        min_score_thresh = 0.90
        true_boxes = boxes[0][scores[0] > min_score_thresh]
        for i in range(true_boxes.shape[0]):
            ymin = int(true_boxes[i][0] * height)
            xmin = int(true_boxes[i][1] * width)
            ymax = int(true_boxes[i][2] * height)
            xmax = int(true_boxes[i][3] * width)

        #print(ymin,xmin,ymax,xmax)
        y = int((ymin + ymax) / 2)
        x = int((xmin + xmax) / 2)

        print(x, y)

        frame = cv2.circle(frame, (xmin, ymin), 1, (0, 0, 255), 3)
        frame = cv2.circle(frame, (xmax, ymin), 1, (0, 0, 255), 3)
        frame = cv2.circle(frame, (xmin, ymax), 1, (0, 0, 255), 3)
        frame = cv2.circle(frame, (xmax, ymax), 1, (0, 0, 255), 3)
        frame = cv2.circle(frame, (x, y), 1, (0, 255, 255), 5)
        cv2.imshow(img_name, frame)


# Clean up
video.release()
cv2.destroyAllWindows()

但这只给出了置信度最高的椅子的质心，而不是检测到的所有椅子

如何修改我的代码以保持每个对象的唯一ID跟踪，然后在提取帧时获取每个椅子的质心？理想情况下，答案应该是可缩放的，以便检测到的3个对象提供3个唯一ID和3个质心

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最后：visualize_box_和_labels_on_image_array函数中的track_ids参数是否有助于跟踪对象及其边界框？如果是这样的话，应该如何使用呢？

我想你要找的是一种目标跟踪算法。尝试使用（简单的在线实时跟踪）或其他算法。您只需将检测结果（边界框坐标）传递给跟踪器，跟踪器就会返回边界框以及每个跟踪对象的唯一ID。

我想您需要的是对象跟踪算法。尝试使用（简单的在线实时跟踪）或其他算法。您只需将检测（边界框坐标）传递给跟踪器，跟踪器将返回边界框以及每个跟踪对象的唯一ID