感想
今天我测试了一下我自己训练的模型，和YOLOv2做了一下对比，检测的都是对的，YOLOv2版本的准确率不高，但是SSD有很多没有检测出来，召回率不怎么高。
注意，ssd的环境是python3，在python2上跑会有问题。tensorflow-gpu, opencv的安装参考我的博客： SSD环境安装
1 制作数据集
最麻烦的是制作voc数据集，我这里用了公司的数据集生成器产生了很多张图片，总量大概有25000张左右。按照voc格式，把图片放在
JPEGImages目录下，xml格式的文件放在Annotations目录下，然后利用程序生成train.txt, test.txt, trainval.txt, val.txt四个文件就够了。生成这些txt的代码如下：
import os
import random
xmlfilepath=r'/home/whsyxt/Downloads/SSD-Tensorflow/VOC2007/Annotations'
saveBasePath=r"/home/whsyxt/Downloads/SSD-Tensorflow"
trainval_percent=0.8
train_percent=0.7
total_xml = os.listdir(xmlfilepath)
num=len(total_xml)
list=range(num)
tv=int(num*trainval_percent)
tr=int(tv*train_percent)
trainval= random.sample(list,tv)
train=random.sample(trainval,tr)
print("train and val size",tv)
print("traub suze",tr)
ftrainval = open(os.path.join(saveBasePath,'VOC2007/ImageSets/Main/trainval.txt'), 'w')
ftest = open(os.path.join(saveBasePath,'VOC2007/ImageSets/Main/test.txt'), 'w')
ftrain = open(os.path.join(saveBasePath,'VOC2007/ImageSets/Main/train.txt'), 'w')
fval = open(os.path.join(saveBasePath,'VOC2007/ImageSets/Main/val.txt'), 'w')
for i in list:
name=total_xml[i][:-4]+'\n'
if i in trainval:
ftrainval.write(name)
if i in train:
ftrain.write(name)
else:
fval.write(name)
else:
ftest.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest .close()
读者可以按照自己的方式去改。
2 voc转tfrecords
voc格式的数据集制作好以后，我们需要把数据集转换成tfrecords，这样程序才能跑，首先，我们需要修改一下源码，datasets\pascalvoc_common.py，操作也非常简单，你把你的类别填上就行了，其他的都不用管，看我的示例，我把原来的16类弄成了3类：
"""
VOC_LABELS = {
'none': (0, 'Background'),
'aeroplane': (1, 'Vehicle'),
'bicycle': (2, 'Vehicle'),
'bird': (3, 'Animal'),
'boat': (4, 'Vehicle'),
'bottle': (5, 'Indoor'),
'bus': (6, 'Vehicle'),
'car': (7, 'Vehicle'),
'cat': (8, 'Animal'),
'chair': (9, 'Indoor'),
'cow': (10, 'Animal'),
'diningtable': (11, 'Indoor'),
'dog': (12, 'Animal'),
'horse': (13, 'Animal'),
'motorbike': (14, 'Vehicle'),
'Person': (15, 'Person'),
'pottedplant': (16, 'Indoor'),
'sheep': (17, 'Animal'),
'sofa': (18, 'Indoor'),
'train': (19, 'Vehicle'),
'tvmonitor': (20, 'Indoor'),
}
"""
VOC_LABELS = {
'none': (0, 'Background'),
'person': (1, 'Person'),
'car': (2, 'Car'),
}
这样就行了。
接着跳转到SSD-tensorflow目录下，进行tfrecords操作，我的运行命令如下：
DATASET_DIR=VOC2007/
OUTPUT_DIR=tfrecords/
python3 tf_convert_data.py \
--dataset_name=pascalvoc \
--dataset_dir=${DATASET_DIR} \
--output_name=voc_2007_train \
--output_dir=${OUTPUT_DIR}
3 训练
DATASET_DIR=tfrecords
TRAIN_DIR=logs/
CHECKPOINT_PATH=./checkpoints/ssd_300_vgg.ckpt
python3 train_ssd_network.py \
--train_dir=${TRAIN_DIR} \
--dataset_dir=${DATASET_DIR} \
--dataset_name=pascalvoc_2007 \
--dataset_split_name=train \
--model_name=ssd_300_vgg \
--checkpoint_path=${CHECKPOINT_PATH} \
--save_summaries_secs=60 \
--save_interval_secs=600 \
--weight_decay=0.0005 \
--optimizer=adam \
--learning_rate=0.001 \
--batch_size=16
4 预测
命令：
python3 video_demo.py
代码：
#coding=utf-8
import os
import math
import random
import numpy as np
import tensorflow as tf
import cv2
slim = tf.contrib.slim
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import sys
sys.path.append('../')
from nets import ssd_vgg_300, ssd_common, np_methods
from preprocessing import ssd_vgg_preprocessing
from notebooks import visualization
# TensorFlow session: grow memory when needed. TF, DO NOT USE ALL MY GPU MEMORY!!!
gpu_options = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(log_device_placement=False, gpu_options=gpu_options)
isess = tf.InteractiveSession(config=config)
# Input placeholder.
net_shape = (300, 300)
data_format = 'NHWC'
img_input = tf.placeholder(tf.uint8, shape=(None, None, 3))
# Evaluation pre-processing: resize to SSD net shape.
image_pre, labels_pre, bboxes_pre, bbox_img = ssd_vgg_preprocessing.preprocess_for_eval(
img_input, None, None, net_shape, data_format, resize=ssd_vgg_preprocessing.Resize.WARP_RESIZE)
image_4d = tf.expand_dims(image_pre, 0)
# Define the SSD model.
reuse = True if 'ssd_net' in locals() else None
ssd_net = ssd_vgg_300.SSDNet()
with slim.arg_scope(ssd_net.arg_scope(data_format=data_format)):
predictions, localisations, _, _ = ssd_net.net(image_4d, is_training=False, reuse=reuse)
# Restore SSD model.
ckpt_filename = 'finetune_log/model.ckpt-41278' //修改为你的模型路径
#ckpt_filename = 'checkpoints/ssd_300_vgg.ckpt'
isess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(isess, ckpt_filename)
# SSD default anchor boxes.
ssd_anchors = ssd_net.anchors(net_shape)
# Main image processing routine.
def process_image(img, select_threshold=0.5, nms_threshold=.45, net_shape=(300, 300)):
# Run SSD network.
rimg, rpredictions, rlocalisations, rbbox_img = isess.run([image_4d, predictions, localisations, bbox_img],
feed_dict={img_input: img})
# Get classes and bboxes from the net outputs.
rclasses, rscores, rbboxes = np_methods.ssd_bboxes_select(
rpredictions, rlocalisations, ssd_anchors,
select_threshold=select_threshold, img_shape=net_shape, num_classes=21, decode=True)
rbboxes = np_methods.bboxes_clip(rbbox_img, rbboxes)
rclasses, rscores, rbboxes = np_methods.bboxes_sort(rclasses, rscores, rbboxes, top_k=400)
rclasses, rscores, rbboxes = np_methods.bboxes_nms(rclasses, rscores, rbboxes, nms_threshold=nms_threshold)
# Resize bboxes to original image shape. Note: useless for Resize.WARP!
rbboxes = np_methods.bboxes_resize(rbbox_img, rbboxes)
return rclasses, rscores, rbboxes
def bboxes_draw_on_img(img, classes, scores, bboxes, color=[255, 0, 0], thickness=2):
shape = img.shape
for i in range(bboxes.shape[0]):
bbox = bboxes[i]
#color = colors[classes[i]]
# Draw bounding box...
p1 = (int(bbox[0] * shape[0]), int(bbox[1] * shape[1]))
p2 = (int(bbox[2] * shape[0]), int(bbox[3] * shape[1]))
cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness)
# Draw text...
s = '%s/%.3f' % (classes[i], scores[i])
p1 = (p1[0]-5, p1[1])
cv2.putText(img, s, p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.4, color, 1)
cap = cv2.VideoCapture("DJI_0008.MOV") //修改为你的路径
#cap = cv2.VideoCapture(0)
# Define the codec and create VideoWriter object
#fourcc = cv2.cv.FOURCC(*'XVID')
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output1.avi', fourcc, 20, (1280, 720))
num=0
while cap.isOpened():
# get a frame
rval, frame = cap.read()
# save a frame
if rval==True:
# frame = cv2.flip(frame,0)
rclasses, rscores, rbboxes=process_image(frame)
bboxes_draw_on_img(frame,rclasses,rscores,rbboxes)
print(rclasses)
out.write(frame)
num=num+1
print(num)
else:
break
# show a frame
cv2.imshow("capture", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
out.release()
cv2.destroyAllWindows()
参考文献
[1] SSD-Tensorflow. https://github.com/balancap/SSD-Tensorflow
