tensorflow利用预训练模型进行目标检测（二）：预训练模型的使用
一、运行样例
官网链接：https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb 但是一直有问题，没有运行起来，所以先使用一个别人写好的代码
上一个在ubuntu下可用的代码链接：https://gitee.com/bubbleit/JianDanWuTiShiBie 使用python2运行，python3可能会有问题
该代码由https://gitee.com/talengu/JianDanWuTiShiBie/tree/master而来，经过我部分的调整与修改，代码包含在ODtest.py文件中，/ssd_mobilenet_v1_coco_11_06_2017中存储的是预训练模型
原始代码如下
import numpy as np
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
os.environ['TF_CPP_MIN_LOG_LEVEL']='' 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("物体：%s 概率：%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()
git clone到本地后执行有几个错误
问题1
报错信息： UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 1: ordinal not in range(128)
solution：参考：https://www.cnblogs.com/QuLory/p/3615584.html
主要错误是上面最后一行的Unicode解码问题，网上搜索说是读取文件时使用的编码默认时ascii而不是utf8，导致的错误；
在代码中加上如下几句即可。
import sys
reload(sys)
sys.setdefaultencoding('utf8')
问题2
报错信息：_tkinter.TclError: no display name and no $DISPLAY environment variable 详情：
Traceback (most recent call last):
File "ODtest.py", line 103, in <module>
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
solution：参考：https://blog.csdn.net/qq_22194315/article/details/77984423
纯代码解决方案
这也是大部分人在网上诸如stackoverflow的问答平台得到的解决方案，在引入pyplot、pylab之前，要先更改matplotlib的后端模式为”Agg”。直接贴代码吧！
# do this before importing pylab or pyplot
Import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot asplt
修改之后代码为：
#!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']='' 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()
运行结果：
如无意外，加上时间统计函数，调用已下载好的预训练模型即可
二、使用预训练模型
使用ssd_mobilenet_v1_coco_11_06_2017
对上边的代码进行修改，注意修改PATH_TO_LABELS的路径，models中包括mscoco_label_map.pbtxt文件，在/models/research/object_detection/data目录下，另外注意from object_detection.utils import label_map_util中，在utils前边要加上object_detection
修改后的代码路径为：/models/models/mcode/ODtest.py 代码如下
#!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 object_detection.utils import label_map_util
from object_detection.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']='' 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 = '/home/yanjieliu/models/models/research/object_detection/pretrained_models/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('/home/yanjieliu/models/models/research/object_detection/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="/home/yanjieliu/dataset/img_test/p1.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()
可正确运行
若想使用该链接中的脚本http://www.baiguangnan.com/2018/02/12/objectdetection2/，则需要进行一些修改，否则会有错误，待做
传参并将时间记录到txt文档的版本，将ODtest.py进行部分修改，修改后的内容在1to10test.py中
#!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 object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util import datetime
# 关闭tensorflow警告
import time
import argparse
import sys
reload(sys)
sys.setdefaultencoding('utf8') os.environ['TF_CPP_MIN_LOG_LEVEL']='' detection_graph = tf.Graph() # 加载模型数据-------------------------------------------------------------------------------------------------------
def loading(model_name): with detection_graph.as_default():
od_graph_def = tf.GraphDef()
PATH_TO_CKPT = '/home/yanjieliu/models/models/research/object_detection/pretrained_models/'+model_name + '/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('/home/yanjieliu/models/models/research/object_detection/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(args):
image_path=args.image_path
loading(args.model_name)
#start = time.time()
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() def parse_args():
'''parse args'''
parser = argparse.ArgumentParser()
parser.add_argument('--image_path', default='/home/yanjieliu/dataset/img_test/p4.jpg')
parser.add_argument('--model_name',
default='mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28')
return parser.parse_args() # 运行
start = time.time()
Detection(parse_args())
end = time.time()
print('time:\n')
print str(end-start) with open('./outputs/1to10test_outputs.txt', 'a') as f:
f.write('\n')
f.write(str(end-start))
使用 python 1to10test_new.py --image_path /home/yanjieliu/dataset/img_test/p5.jpg --model_name mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28 便可调用运行
或者写一个shell脚本，将此命令执行10遍，然后求平均运行时间即可
例如
#!/bin/bash for i in $(seq 1 10)
do
python 1to10test_new.py --image_path /home/yanjieliu/dataset/img_test/p4.jpg --model_name mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28
done
检测单张图片的模板
#!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 object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util import datetime
# 关闭tensorflow警告
import time
import MySQLdb
import argparse
import sys
reload(sys)
sys.setdefaultencoding('utf8') os.environ['TF_CPP_MIN_LOG_LEVEL']='' detection_graph = tf.Graph() # 加载模型数据-------------------------------------------------------------------------------------------------------
def loading(model_name): with detection_graph.as_default():
od_graph_def = tf.GraphDef()
PATH_TO_CKPT = '/home/yanjieliu/models/models/research/object_detection/pretrained_models/'+model_name + '/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('/home/yanjieliu/models/models/research/object_detection/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(args):
image_path=args.image_path
image_name=args.image_name
loading(args.model_name)
#start = time.time()
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# for image_path in TEST_IMAGE_PATHS:
image = Image.open('%s%s'%(image_path, image_name)) # 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
list = []
for i in range(5):
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]))
#print(boxes)
if(float(scores[0][i])>0.5):
list.append(class_name.encode('utf-8')) # 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() def parse_args():
'''parse args'''
parser = argparse.ArgumentParser()
parser.add_argument('--image_path', default='/home/yanjieliu/rdshare/dataset/dog/')
parser.add_argument('--image_name', default='dog768_576.jpg')
parser.add_argument('--model_name',
default='faster_rcnn_resnet50_coco_2018_01_28')
return parser.parse_args() if __name__ == '__main__':
# 运行
args=parse_args()
start = time.time()
Detection(args)
end = time.time()
print('time:\n')
print str(end-start)
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