开发笔记:tensorflow利用预训练模型进行目标检测：预训练模型的使用

Traceback (most recent call last):
File "ODtest.py", line 103, in
Detection()
File "ODtest.py", line 96, in Detection
plt.figure(figsize=IMAGE_SIZE)
File "/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.py", line 533, in figure
**kwargs)
File "/usr/local/lib/python2.7/dist-packages/matplotlib/backend_bases.py", line 161, in new_figure_manager
return cls.new_figure_manager_given_figure(num, fig)
File "/usr/local/lib/python2.7/dist-packages/matplotlib/backends/_backend_tk.py", line 1046, in new_figure_manager_given_figure
window = Tk.Tk(className="matplotlib")
File "/usr/lib/python2.7/lib-tk/Tkinter.py", line 1822, in __init__
self.tk = _tkinter.create(screenName, baseName, className, interactive, wantobjects, useTk, sync, use)
_tkinter.TclError: no display name and no $DISPLAY environment variable

# do this before importing pylab or pyplot
Import matplotlib
matplotlib.use(‘Agg‘)
import matplotlib.pyplot asplt

#!usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib
matplotlib.use(‘Agg‘)
import matplotlib.pyplot
from matplotlib import pyplot as plt
import os
import tensorflow as tf
from PIL import Image
from utils import label_map_util
from utils import visualization_utils as vis_util
import datetime
# 关闭tensorflow警告
import sys
reload(sys)
sys.setdefaultencoding(‘utf8‘)
os.environ[‘TF_CPP_MIN_LOG_LEVEL‘]=‘3‘
detection_graph = tf.Graph()
# 加载模型数据-------------------------------------------------------------------------------------------------------
def loading():
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
PATH_TO_CKPT = ‘ssd_mobilenet_v1_coco_11_06_2017‘ + ‘/frozen_inference_graph.pb‘
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=‘‘)
return detection_graph
# Detection检测-------------------------------------------------------------------------------------------------------
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)
# 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‘)
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
def Detection(image_path="images/image1.jpg"):
loading()
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
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‘)
# 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‘)
# 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.将识别结果标记在图片上
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)
# output result输出
for i in range(3):
if classes[0][i] in category_index.keys():
class_name = category_index[classes[0][i]][‘name‘]
else:
class_name = ‘N/A‘
print("object：%s gailv：%s" % (class_name, scores[0][i]))

# matplotlib输出图片
# Size, in inches, of the output images.
IMAGE_SIZE = (20, 12)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)
plt.show()
# 运行
Detection()