SSD-Tensorflow训练总结

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

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

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-_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-(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

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