关于SSD的源代码详细讲解，请参考文章：https://blog.csdn.net/c20081052/article/details/80391627  代码详解

本文是实战系列的第四篇，逼自己抽空写篇博客，把之前运行的程序po出来，供需要的人参考。

下载 SSD-Tensorflow-master 解压找到里面notebooks文件夹，本文主要针对这个文件夹下提供的事例做讲解；

主要涉及的文件有ssd_notebook.ipynb和visualization.py ；

通过cmd切换到这个目录下，然后用jupyter notebook打开ssd_notebook.ipynb 运行这个文件，run 每个cell，你会得到这个源代码提供的事例检测结果。这个只是针对图片做检测。接下来将做视频流的目标检测。操作和运行结果如下。

结果如下；

 

下面要做的是对ssd_notebook.ipynb 做些更改。我们将其保存成.py文件，然后重命名个，名字叫：ssd_notebook_camera.py ； 还有原来事例中图片bbox上方没有显示目标的类别名称，接下来还要对visualization.py 做些更改，我们copy一份它，重命名叫做visualization_camera.py 吧。

*****

说明下图片中SSD_Tensorflow_master这个文件夹其实就是你下载的SSD-Tensorflow-master这个文件解压得到的，我把它copy了一份并做了重命名（看文件夹的 ‘ _ ’ 不同）放在notebook文件夹下了，因为visualization_camera.py中会引用master里的一些函数。为了图方便，就这么操作了，其实就是文件包含路径的问题。

*****

下面是更改后的两个文件：

ssd_notebook_camera.py代码如下：

# coding: utf-8


import os
import math
import random

import numpy as np
import tensorflow as tf
import cv2

slim = tf.contrib.slim


#get_ipython().magic('matplotlib inline')
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_camera    #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)


# ## SSD 300 Model
# 
# The SSD 300 network takes 300x300 image inputs. In order to feed any image, the latter is resize to this input shape (i.e.`Resize.WARP_RESIZE`). Note that even though it may change the ratio width / height, the SSD model performs well on resized images (and it is the default behaviour in the original Caffe implementation).
# 
# SSD anchors correspond to the default bounding boxes encoded in the network. The SSD net output provides offset on the coordinates and dimensions of these anchors.

# 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 = '../checkpoints/ssd_300_vgg.ckpt'   #可更改为自己的模型路径
# ckpt_filename = '../checkpoints/VGG_VOC0712_SSD_300x300_ft_iter_120000.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)


# ## Post-processing pipeline
# 
# The SSD outputs need to be post-processed to provide proper detections. Namely, we follow these common steps:
# 
# * Select boxes above a classification threshold;
# * Clip boxes to the image shape;
# * Apply the Non-Maximum-Selection algorithm: fuse together boxes whose Jaccard score > threshold;
# * If necessary, resize bounding boxes to original image 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


# # Test on some demo image and visualize output.
# path = '../demo/'
# image_names = sorted(os.listdir(path))

# img = mpimg.imread(path + image_names[-5])
# rclasses, rscores, rbboxes =  process_image(img)

# # visualization.bboxes_draw_on_img(img, rclasses, rscores, rbboxes, visualization.colors_plasma)
# visualization.plt_bboxes(img, rclasses, rscores, rbboxes)


##### following are added for camera demo####
cap = cv2.VideoCapture(r'D:\person.avi')
fps = cap.get(cv2.CAP_PROP_FPS) 
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) 
fourcc = cap.get(cv2.CAP_PROP_FOURCC) 
#fourcc = cv2.CAP_PROP_FOURCC(*'CVID') 
print('fps=%d,size=%r,fourcc=%r'%(fps,size,fourcc))
delay=30/int(fps)


while(cap.isOpened()):
      ret,frame = cap.read()
      if ret==True:  
#          image = Image.open(image_path)
#          gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
          image = frame
          # the array based representation of the image will be used later in order to prepare the
          # result image with boxes and labels on it.
          image_np = image
#          image_np = load_image_into_numpy_array(image)
          # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
          image_np_expanded = np.expand_dims(image_np, axis=0)
          # Actual detection.
          rclasses, rscores, rbboxes =  process_image(image_np)
          # Visualization of the results of a detection.
          visualization_camera.bboxes_draw_on_img(image_np, rclasses, rscores, rbboxes)
#          plt.figure(figsize=IMAGE_SIZE)
#          plt.imshow(image_np)
          cv2.imshow('frame',image_np)
          cv2.waitKey(np.uint(delay))
          print('Ongoing...')  
      else:
          break
cap.release()
cv2.destroyAllWindows()

其中 

cap = cv2.VideoCapture(r'D:\person.avi') 是你读取视频的文件目录，自行更改。

以下是visualization_camera.py内容：

# Copyright 2017 Paul Balanca. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import cv2
import random

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.cm as mpcm

#added 20180516#####
def num2class(n):
    import SSD_Tensorflow_master.datasets.pascalvoc_2007 as pas
    x=pas.pascalvoc_common.VOC_LABELS.items()
    for name,item in x:
        if n in item:
            #print(name)
            return name
#adden end #########

# =========================================================================== #
# Some colormaps.
# =========================================================================== #
def colors_subselect(colors, num_classes=21):
    dt = len(colors) // num_classes
    sub_colors = []
    for i in range(num_classes):
        color = colors[i*dt]
        if isinstance(color[0], float):
            sub_colors.append([int(c * 255) for c in color])
        else:
            sub_colors.append([c for c in color])
    return sub_colors

colors_plasma = colors_subselect(mpcm.plasma.colors, num_classes=21)
colors_tableau = [(255, 255, 255), (31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
                  (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
                  (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
                  (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
                  (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]


# =========================================================================== #
# OpenCV drawing.
# =========================================================================== #
def draw_lines(img, lines, color=[255, 0, 0], thickness=2):
    """Draw a collection of lines on an image.
    """
    for line in lines:
        for x1, y1, x2, y2 in line:
            cv2.line(img, (x1, y1), (x2, y2), color, thickness)


def draw_rectangle(img, p1, p2, color=[255, 0, 0], thickness=2):
    cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness)


def draw_bbox(img, bbox, shape, label, color=[255, 0, 0], thickness=2):
    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)
    p1 = (p1[0]+15, p1[1])
    cv2.putText(img, str(label), p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.5, color, 1)


def bboxes_draw_on_img(img, classes, scores, bboxes, colors=dict(), thickness=2):
    shape = img.shape
	####add 20180516#####
    #colors=dict()
	####add #############
    for i in range(bboxes.shape[0]):
        bbox = bboxes[i]
        if classes[i] not in colors:
            colors[classes[i]] = (random.random(), random.random(), random.random())
        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], colors[classes[i]], thickness)
        s = '%s/%.3f' % (num2class(classes[i]), scores[i])
        p1 = (p1[0]-5, p1[1])
        cv2.putText(img, s, p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.4, colors[classes[i]], 1)  


# =========================================================================== #
# Matplotlib show...
# =========================================================================== #
def plt_bboxes(img, classes, scores, bboxes, figsize=(10,10), linewidth=1.5):
    """Visualize bounding boxes. Largely inspired by SSD-MXNET!
    """
    fig = plt.figure(figsize=figsize)
    plt.imshow(img)
    height = img.shape[0]
    width = img.shape[1]
    colors = dict()
    for i in range(classes.shape[0]):
        cls_id = int(classes[i])
        if cls_id >= 0:
            score = scores[i]
            if cls_id not in colors:
                colors[cls_id] = (random.random(), random.random(), random.random())
            ymin = int(bboxes[i, 0] * height)
            xmin = int(bboxes[i, 1] * width)
            ymax = int(bboxes[i, 2] * height)
            xmax = int(bboxes[i, 3] * width)
            rect = plt.Rectangle((xmin, ymin), xmax - xmin,
                                 ymax - ymin, fill=False,
                                 edgecolor=colors[cls_id],
                                 linewidth=linewidth)
            plt.gca().add_patch(rect)
            ##class_name = str(cls_id) #commented 20180516
			#### added 20180516#####
            class_name = num2class(cls_id)
			#### added end #########
            plt.gca().text(xmin, ymin - 2,
                           '{:s} | {:.3f}'.format(class_name, score),
                           bbox=dict(facecolor=colors[cls_id], alpha=0.5),
                           fontsize=12, color='white')
    plt.show()

 

 

OK 了，运行上面那个ssd_notebook_camera.py文件，以下是视频检测结果（带目标类别名称）：视频流的检测效果没有那么好，可能是训练模型用的是它自带推荐的，可自行训练试试效果。（我这还是CPU跑的……）