MobileNetv2-SSDLite安装和使用
MobileNetv2-SSDLite是MobileNet-SSD的升级版，其主要针对移动端对速度要求高的场合。
使用方法
git clone https://github.com/chuanqi305/MobileNetv2-SSDLite
cd ssdlite
wget http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz
tar -zvxf ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz
python dump_tensorflow_weights.py
python load_caffe_weights.py如果最后一步报out of memeroy的错，把deploy.prototxt中engine: CAFFE前面的#号全部去掉。
接下来给编译之前编译的ssd（编译方法见ssd-windows，其为跨平台的方案）添加ReLU6层支持，不然会报错:
Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: ReLU6
首先找到src\caffe\proto\caffe.proto,搜索message LayerParameter,并在optional ReLUParameter relu_param = 123;之后添加optional ReLU6Parameter relu6_param = 208;
然后再搜索message ReLUParameter,在这个ReLUParameter实现结构之后添加
// Message that stores parameters used by ReLU6Layer
message ReLU6Parameter {
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 2 [default = DEFAULT];
}
支持proto头文件修改完毕，接下来添加所需的头文件和实现文件。
1.在blob/ssd/include/caffe/layers文件夹下新建relu6_layer.hpp,将
https://github.com/chuanqi305/ssd/raw/ssd/include/caffe/layers/relu6_layer.hpp里面的内容复制进去。
2.在blob/ssd/src/caffe/layers文件夹下新建relu6_layer.cpp，将
https://raw.githubusercontent.com/chuanqi305/ssd/ssd/src/caffe/layers/relu6_layer.cpp里面的复制进去。
3.在blob/ssd/src/caffe/layers文件夹下新建relu6_layer.cu，将
https://raw.githubusercontent.com/chuanqi305/ssd/ssd/src/caffe/layers/relu6_layer.cu里面的复制进去。
大功告成，重新编译ssd。
这是如果你兴致勃勃的去跑demo_caffe.py会失望透顶，自带的coco模型需要的显存太大，我的1080Ti 11Gxian显存都不gouy够用。
这时需要我们将coco模型转换为voc模型，还好作者提供了脚本，直接运行
python coco2voc.py即可。转换完成后再运行
python demo_caffe_voc.py
我自己还写了个tensorflow版本的demo，也一块发上来吧:
import tensorflow as tf
import cv2
import numpy as np
def graph_create(graphpath):
with tf.gfile.FastGFile(graphpath, 'rb') as graphfile:
graphdef = tf.GraphDef()
graphdef.ParseFromString(graphfile.read())
return tf.import_graph_def(graphdef, name='',return_elements=[
'image_tensor:0','detection_boxes:0', 'detection_scores:0', 'detection_classes:0'])
image_tensor, box, score, cls = graph_create("ssdlite/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb")
image_file = "images/004545.jpg"
with tf.Session() as sess:
image = cv2.imread(image_file)
image_data = np.expand_dims(image, axis=0).astype(np.uint8)
b, s, c = sess.run([box, score, cls], {image_tensor: image_data})
boxes = b[0]
conf = s[0]
clses = c[0]
#writer = tf.summary.FileWriter('debug', sess.graph)
for i in range(8):
bx = boxes[i]
print(boxes[i])
print(conf[i])
print(clses[i])
if conf[i] < 0.5:
continue
h = image.shape[0]
w = image.shape[1]
p1 = (int(w * bx[1]), int(h * bx[0]))
p2 = (int(w * bx[3]) ,int(h * bx[2]))
cv2.rectangle(image, p1, p2, (0,255,0))
cv2.imshow("mobilenet-ssd", image)
cv2.waitKey(0)