TensorFlow目标检测(object_detection）api使用

请根据 models/blob/master/research/object_detection/g3doc/ 目录下的 installation.md 配置好你的环境

环境搭建可参考：基于win10，GPU的Tensorflow Object Detection API部署及USB摄像头目标检测

1. 测试opencv调用usb，c++和python两个版本

import cv2
cv2.namedWindow('testcamera', cv2.WINDOW_NORMAL)

capture = cv2.VideoCapture(0)
print (capture.isOpened())
num = 0

while 1:
  ret, img = capture.read()
  cv2.imshow('testcamera', img)
  key = cv2.waitKey(1)
  num += 1
  if key==1048603:#<ESC>
    break

capture.release()
cv2.destroyAllWindows()

#include <opencv2/core/core.hpp>    
#include <opencv2/highgui/highgui.hpp>    
using namespace cv;  
      
int main(int argc, char** argv) {
    cvNamedWindow("视频");

    CvCapture* capture = cvCreateCameraCapture(-1);
    IplImage* frame;

    while(1) {
        frame = cvQueryFrame(capture);
        if(!frame) break;
        cvShowImage("视频", frame);

        char c = cvWaitKey(50);
        if(c==27) break;
    }

    cvReleaseCapture(&capture);
    cvDestroyWindow("视频");
    return 0;
}

python:读取视频，处理后，实时计算帧数fps

2. GPU的Tensorflow Object Detection API部署及USB摄像头目标检测

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import cv2
import time  

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

from utils import label_map_util
from utils import visualization_utils as vis_util

# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
#MODEL_NAME = 'faster_rcnn_resnet101_coco_11_06_2017'
#MODEL_NAME = 'ssd_inception_v2_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('/home/dsp/ranjiewen/tensorflow_models/models/research/object_detection/data', 'mscoco_label_map.pbtxt')

#extract the ssd_mobilenet
start = time.clock()
NUM_CLASSES = 90
opener = urllib.request.URLopener()
#opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
  file_name = os.path.basename(file.name)
  if 'frozen_inference_graph.pb' in file_name:
    tar_file.extract(file, os.getcwd())
end= time.clock()
print ('load the model',(end-start))

detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)

categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

cap = cv2.VideoCapture(0)
print (cap.isOpened())
with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
      writer = tf.summary.FileWriter("logs/", sess.graph)  
      sess.run(tf.global_variables_initializer())  
      
      while(1):
        
        print("-------")
        ret, frame = cap.read()
        start = time.clock()
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
        image_np=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.
        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image_np, axis=0)
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        # Each box represents a part of the image where a particular object was detected.
        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        # Each score represent how level of confidence for each of the objects.
        # Score is shown on the result image, together with the class label.
        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=6)
        end = time.clock()  
        print ('frame fps:',1.0/(end - start))
        #print 'frame:',time.time() - start
        cv2.imshow("capture", image_np)
        cv2.waitKey(1)
cap.release()
cv2.destroyAllWindows()

- 速度感觉还可以。。。

来源：https://www.cnblogs.com/ranjiewen/p/8998235.html

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