tensorflow - 使用 Tensorflow 对象检测 API 的实时计数器

Question

我目前正在使用 tensorflow API 进行实时对象检测。我已经弄清楚了，但现在我想添加对象计数器。所以，我会有实时物体检测+计数器。

对象检测的源代码取自 tensorflow ipynb 教程，我添加了 OpenCV 进行实时检测。我已经将实时检测源代码与计数器源代码合并，最初用于从这个人的 repo中进行车辆计数。

所以，我当前的输出：没有错误也没有输出。但是我的网络摄像头灯闪烁，表明它正在被使用，因此 opencv 部分正在工作。谁能看一下代码并帮助我找出问题所在？这将是一个非常大的帮助。先感谢您。

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

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

import cv2
cap = cv2.VideoCapture(0)

# initialize .csv
with open('traffic_measurement.csv', 'w') as f:
writer = csv.writer(f)
csv_line = \
'Person Movement Direction'
writer.writerows([csv_line.split(',')])

# Variables to count persons
total_passed_person = 0 

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


# ## Object detection imports
# Here are the imports from the object detection module.

from utils import label_map_util
from utils import visualization_utils as vis_util


# # Model preparation 
# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

# ## Download Model
opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
file_name = os.path.basename(file.name)
if 'frozen_inference_graph.pb' in file_name:
tar_file.extract(file, os.getcwd())


# ## Load a (frozen) 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='')


# ## Loading label map
# Label maps map indices to category names, so that when our convolution network predicts `5`, we 
know 
that this corresponds to `airplane`.  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)

# ## Helper code
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)

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)

# Detection
def object_detection_function():
total_passed_person = 0
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:


# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(input_frame, 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')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
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')

# for all the frames that are extracted from input video
while cap.isOpened():
(ret,frame) = cap.read()

if not ret:
print ('end of the video file...')
break

input_frame = frame

# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
(counter, csv_line) = \
vis_util.visualize_boxes_and_labels_on_image_array(
cap.get(1),
input_frame,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)

total_passed_person = total_passed_person + counter

# insert information text to video frame
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(
input_frame,
'Detected Persons: ' + str(total_passed_person),
(10, 35),
font,
0.8,
(0, 0xFF, 0xFF),
2,
cv2.FONT_HERSHEY_SIMPLEX,
)

# when the vehicle passed over line and counted, make the color of ROI line green
if counter == 1:
cv2.line(input_frame, (0, 200), (640, 200), (0, 0xFF, 0), 5)
else:
cv2.line(input_frame, (0, 200), (640, 200), (0, 0, 0xFF), 5)

# insert information text to video frame
cv2.rectangle(input_frame, (10, 275), (230, 337), (180, 132, 109), -1)
cv2.putText(
input_frame,
'ROI Line',
(545, 190),
font,
0.6,
(0, 0, 0xFF),
2,
cv2.LINE_AA,
)

cv2.putText(
input_frame,
'-Movement Direction: ' + direction,
(14, 302),
font,
0.4,
(0xFF, 0xFF, 0xFF),
1,
cv2.FONT_HERSHEY_COMPLEX_SMALL,
)

if csv_line != 'not_available':
with open('traffic_measurement.csv', 'a') as f:
writer = csv.writer(f)
(direction) = \
csv_line.split(',')
writer.writerows([csv_line.split(',')])

cv2.imshow('object detection',cv2.resize(input_frame, (800,600)))
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break

标签： tensorflowobjectcounter