开发笔记:Tensorflow实战目标检测

import os
import time
import argparse
import multiprocessing
import numpy as np
import tensorflow as tf
import tarfile
from matplotlib import pyplot as plt

from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
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视频目标追踪
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#1.得到模型 （这里首先下载流模型并在解压在path/to/models/research/object_detection里面）
MODEL_NAME = ‘ssd_mobilenet_v1_coco_2017_11_17‘
PATH_TO_CKPT = os.path.join(MODEL_NAME, ‘frozen_inference_graph.pb‘)
PATH_TO_LABELS = os.path.join(‘data‘, ‘mscoco_label_map.pbtxt‘)
print(‘Loading model...‘)
#load frozen of tensorflow to memeory
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, ‘rb‘) as fid: #文本操作句柄，类似python里面的open()
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name=‘‘) #将图像从od_graph_def导入当前的默认Graph
#label map to class name 如预测为5,知道它是对应飞机
NUM_CLASS = 90
print("Loading label map...")
label_map = label_map_util.load_labelmap(PATH_TO_LABELS) #得到label map proto
categories = label_map_util.convert_label_map_to_categories(label_map, NUM_CLASS) #得到类别
category_index = label_map_util.create_category_index(categories)
#2.对视频进行物体检测
def detect_objects(image_np, sess, detection_graph):
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name(‘image_tensor:0‘)
boxes = detection_graph.get_tensor_by_name(‘detection_boxes:0‘)
scores = detection_graph.get_tensor_by_name(‘detection_scores:0‘)
classes = detection_graph.get_tensor_by_name(‘detection_classes:0‘)
num_detections = detection_graph.get_tensor_by_name(‘num_detections:0‘)
#Actual detection
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections], feed_dict={image_tensor : image_np_expanded})
#Visualization of the results of a detection
vis_util.visualize_boxes_and_labels_on_image_array(image_np, np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
return image_np
from moviepy.editor import VideoFileClip
from IPython.display import html
def process_image(image):
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
image_process = detect_objects(image, sess, detection_graph)
return image_process
white_output = ‘/home/magic/111_out.mp4‘
clip1 = VideoFileClip("/home/magic/111.avi")
white_clip = clip1.fl_image(process_image) #This function expects color images!
white_clip.write_videofile(white_output, audio=False)

#等待一段时间后，得到111_out.mp4，可以去查看效果 我的测试结果如下