产品看到竞品可以标记物体的功能,秉承一贯的他有我也要有,他没有我更要有的作风,丢过来一网站,说这个功能很简单,一定可以实现
这时候万能的谷歌发挥了作用,在茫茫的数据大海中发现了Tensorflow机器学习框架,也就是目前非常火爆的的深度学习(人工智能),既然方案已有,就差一个程序员了
百科介绍:TensorFlow是谷歌基于DistBelief进行研发的第二代人工智能学习系统,可被用于语音识别或图像识别等多项机器学习和深度学习领域。
翻译成大白话:是一个深度学习和神经网络的框架,底层C++,通过Python进行控制,当然,也是支持Go、Java等语言
文件目录格式如下
└── tensorflow
├── Dockerfile
├── README.md
├── data
│ ├── models
│ ├── pbtxt
│ └── tf_models
├── object_detection_api.py
├── server.py
├── sh
│ ├── download_data.sh
│ └── ods.sh
├── static
├── templates
└── upload
pip3 install -r requirements.txt
sh sh/download_data.sh
echo 'export PYTHONPATH=$PYTHONPATH:
pwd
/data/tf_models/models/research'>> ~/.bashrc && source ~/.bashrc
python3 server.py
没有报错,说明你已成功搭建环境,使用过程是不是非常简单,下面介绍代码调用逻辑过程
我从谷歌提供几种模型选出来对比
为了测试方便,笔者选用轻量级(ssd_mobilenet)作为本次识别物体模型
引入Python库
import numpy as np
import os
import tensorflow as tf
import json
import time
from PIL import Image
# 兼容Python2.7版本
try:
import urllib.request as ulib
except Exception as e:
import urllib as ulib
import re
from object_detection.utils import label_map_util
载入模型
MODEL_NAME = 'data/models/ssd_mobilenet_v2_coco_2018_03_29'
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('data/pbtxt','mscoco_label_map.pbtxt') # CWH: Add object_detection path
# data/pbtxt下mscoco_label_map.pbtxt最大item.id
NUM_CLASSES = 90
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
# 加载模型
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='')
载入标签映射,内置函数返回整数会映射到pbtxt字符标签
mscoco_label_map.pbtxt格式如下
item {
name: "/m/01g317"
id: 1
display_name: "person"
}
item {
name: "/m/0199g"
id: 2
display_name: "bicycle"
}
# 加载标签
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)
with detection_graph.as_default():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(graph=detection_graph,config=config) as sess:
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# 物体坐标
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# 检测到物体的准确度
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')
def get_objects(file_name, threshold=0.5):
image = Image.open(file_name)
# 判断文件是否是jpeg格式
if not image.format=='JPEG':
result['status'] = 0
result['msg'] = file_name+ ' is ' + image.format + ' ods system allow jpeg or jpg'
return result
image_np = load_image_into_numpy_array(image)
# 扩展维度
image_np_expanded = np.expand_dims(image_np, axis=0)
output = []
# 获取运算结果
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# 去掉纬度为1的数组
classes = np.squeeze(classes).astype(np.int32)
scores = np.squeeze(scores)
boxes = np.squeeze(boxes)
for c in range(0, len(classes)):
if scores[c] >= threshold:
item = Object()
item.class_name = category_index[classes[c]]['name'] # 物体名称
item.score = float(scores[c]) # 准确率
# 物体坐标轴百分比
item.y1 = float(boxes[c][0])
item.x1 = float(boxes[c][1])
item.y2 = float(boxes[c][2])
item.x2 = float(boxes[c][3])
output.append(item)
# 返回JSON格式
outputJson = json.dumps([ob.__dict__ for ob in output])
return outputJson
server.py下的逻辑
def image():
startTime = time.time()
if request.method=='POST':
image_file = request.files['file']
base_path = os.path.abspath(os.path.dirname(__file__))
upload_path = os.path.join(base_path,'static/upload/')
# 保存上传图片文件
file_name = upload_path + image_file.filename
image_file.save(file_name)
# 准确率过滤值
threshold = request.form.get('threshold',0.5)
# 调用Api服务
objects = object_detection_api.get_objects(file_name, threshold)
# 模板显示
return render_template('index.html',json_data = objects,img=image_file.filename)
curl http://localhost:5000 | python -m json.tool
[
{
"y2": 0.9886252284049988,
"class_name": "bed",
"x2": 0.4297400414943695,
"score": 0.9562674164772034,
"y1": 0.5202791094779968,
"x1": 0
},
{
"y2": 0.9805927872657776,
"class_name": "couch",
"x2": 0.4395904541015625,
"score": 0.6422878503799438,
"y1": 0.5051193833351135,
"x1": 0.00021047890186309814
}
]
在浏览器访问网址体验
大家肯定很好奇,怎么训练自己需要检测的物体,可以期待下一篇文章
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