©2016-2024 Matools All rights reserved,
粤ICP备17059708号
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 <https://blog.csdn.net/c20081052/article/details/80391627>
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……）
热门工具 换一换