SSD-Tensorflow训练总结

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 osimport random xmlfilepath=r'/home/whsyxt/Downloads/SSD-Tensorflow/VOC2007/Annotations'saveBasePath=r"/home/whsyxt/Downloads/SSD-Tensorflow"trainval_percent=0.8train_percent=0.7total_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

这样就可以进行训练了，运行的命令为：

DATASET_DIR=tfrecordsTRAIN_DIR=logs/CHECKPOINT_PATH=./checkpoints/ssd_300_vgg.ckptpython3 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

我主要是跑视频，我把我跑视频的预测代码和运行命令也提供给大家参考一下：

命令：

 python3 video_demo.py 

代码：

#coding=utf-8import osimport mathimport randomimport numpy as npimport tensorflow as tfimport cv2slim = tf.contrib.slimimport matplotlib.pyplot as pltimport matplotlib.image as mpimgimport syssys.path.append('../')from nets import ssd_vgg_300, ssd_common, np_methodsfrom preprocessing import ssd_vgg_preprocessingfrom 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 Nonessd_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, rbboxesdef 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=0while 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'):        breakcap.release()out.release()cv2.destroyAllWindows()

参考文献

[1] SSD-Tensorflow. https://github.com/balancap/SSD-Tensorflow