主要是修改检测程序：
- 原来使用image，改为读取avi
- 原来使用visualization.plt_bboxes(img, rclasses, rscores, rbboxes)函数直接画图，修改为visualization.bboxes_draw_on_img(image_np, rclasses, rscores, rbboxes)将框改到image上
- 对visualization中做修改，使得之前代码只能类别显示为数字的，变为直接显示为英文类别文字
主代码修改为：
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 17 10:30:37 2017
ssd_ttest_300
@author: IRay
"""
import os
import math
import random
import numpy as np
import tensorflow as tf
import cv2
slim = tf.contrib.slim
#%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import sys
sys.path.append('../')
from tensorflow.models.SSD_Tensorflow_master.nets import ssd_vgg_300, ssd_common, np_methods
from tensorflow.models.SSD_Tensorflow_master.preprocessing import ssd_vgg_preprocessing
from tensorflow.models.SSD_Tensorflow_master.notebooks import visualization
from tensorflow.models.SSD_Tensorflow_master.datasets import pascalvoc_2007
# 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 = r'D:softwareanacondaenvs ensorflowLibsite-packages ensorflowmodelsSSD_Tensorflow_mastercheckpoints/ssd_300_vgg.ckpt'
# 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)
#print(ssd_net)
#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
# Test on some demo image and visualize output.
#path = r'D:softwareanacondaenvs ensorflowLibsite-packages ensorflowmodelsSSD_Tensorflow_master/demo/'
#image_names = sorted(os.listdir(path))
#
#img = mpimg.imread(path + image_names[-2])
#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)
cap = cv2.VideoCapture(r'D:
awDatamovieshighway2raw.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.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()
visualization文件代码修改为：
# 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
def num2class(n):
import tensorflow.models.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
###########################added
# =========================================================================== #
# 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
######################added
#colors = dict()
######################added
for i in range(bboxes.shape[0]):
bbox = bboxes[i]
######################added
if classes[i] not in colors:
colors[classes[i]] = (random.random(), random.random(), random.random())
######################added
#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]))
#################modified color
cv2.rectangle(img, p1[::-1], p2[::-1], colors[classes[i]], thickness)
# Draw text...
#s = '%s/%.3f' % (classes[i], scores[i])
#######################added
s = '%s/%.3f' % (num2class(classes[i]), scores[i])
########################added
p1 = (p1[0]-5, p1[1])
#################modified color
cv2.putText(img, s, p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.4, colors[classes[i]], 1)
# =========================================================================== #
# Matplotlib show...
# modifed by wangjc,2017.10.18
# =========================================================================== #
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]
#score = 0.01
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)
###################added
#class_name = ['haha','a','ss']
class_name = num2class(cls_id)
##################added
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()