如何使用tensorflow对象检测API计算检测到的对象(在边界框中)的数量

问题描述 投票:0回答:1

我使用edje electronics的Faster R-CNN教程,可以正常工作但我想改善它。我想数对象问题是.......我如何删除准确度的百分比并将其替换为计数的边界框的数量。

我不知道我必须添加哪一个并将其删除以计算边界框这是代码

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

sys.path.append("..")

from utils import label_map_util
from utils import visualization_utils as vis_util

MODEL_NAME = 'inference_graph'
VIDEO_NAME = 'animal.mov'

# 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_VIDEO = os.path.join(CWD_PATH,VIDEO_NAME)

NUM_CLASSES = 6

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)

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

image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')

video = cv2.VideoCapture(PATH_TO_VIDEO)

while(video.isOpened()):

    ret, frame = video.read()
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    frame_expanded = np.expand_dims(frame_rgb, axis=0)


    (boxes, scores, classes, num) = sess.run(
        [detection_boxes, detection_scores, detection_classes, num_detections],
        feed_dict={image_tensor: frame_expanded})

    vis_util.visualize_boxes_and_labels_on_image_array(
        frame,
        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)


    cv2.imshow('Object detector', frame)


    if cv2.waitKey(1) == ord('q'):
        break

video.release()
cv2.destroyAllWindows()
tensorflow object-detection-api
1个回答
0
投票

您必须修改utils / visualization_utils.py中的visualize_boxes_and_labels_on_image_array()函数以删除conf得分显示并显示盒子数组的长度

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