Build your own application based on Google's open source tensorflow object detection API video object recognition system (IV)

The main content of this chapter is to use mqtt、 Multithreading 、 The queue implementation model is loaded at one time , Batch image recognition and classification function

The directory structure is as follows ：

mqtt Connection and multithreaded queue management

​ ​MqttManager.py​​

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        # -*- coding:utf8 -*-
        
import paho.mqtt.client as mqtt
        
from multiprocessing import Process, Queue
        
import images_detect
        

        
MQTTHOST = "192.168.3.202"
        
MQTTPORT = 1883
        
mqttClient = mqtt.Client()
        
q = Queue() 
        

        

        
#  Connect MQTT The server 
        
def on_mqtt_connect():
        
    mqttClient.connect(MQTTHOST, MQTTPORT, 60)
        
    mqttClient.loop_start()
        

        

        
#  Message handler 
        
def on_message_come(mqttClient, userdata, msg):
        
    q.put(msg.payload.decode("utf-8"))  #  Put in queue 
        
    print(" Generate news ", msg.payload.decode("utf-8"))
        

        

        
def consumer(q, pid):
        
    print(" Start the process of consumption sequence ", pid)
        
    #  Publishing messages in multiple processes requires reinitialization mqttClient
        
    ImagesDetect = images_detect.ImagesDetect()
        
    ImagesDetect.detect(q)
        

        

        
# subscribe  News subscription 
        
def on_subscribe():
        
    mqttClient.subscribe("test", 1)  #  The theme is "test"
        
    mqttClient.on_message = on_message_come  #  Message arrival processing function 
        

        

        
# publish  News release 
        
def on_publish(topic, msg, qos):
        
    mqttClient.publish(topic, msg, qos);
        

        

        
def main():
        
    on_mqtt_connect()
        
    on_subscribe()
        
    for i in range(1, 3):
        
        c1 = Process(target=consumer, args=(q, i))
        
        c1.start()
        
    while True:
        
        pass
        

        

        
if __name__ == '__main__':
        
    main()
       
       

        
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Image recognition

images_detect.py

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        # coding: utf-8
        
import numpy as np
        
import os
        
import sys
        
import tarfile
        
import tensorflow as tf
        
from object_detection.utils import label_map_util
        
from object_detection.utils import visualization_utils as vis_util
        
import cv2
        
import decimal
        
import MyUtil
        

        
context = decimal.getcontext() 
        
context.rounding = decimal.ROUND_05UP
        

        

        
class ImagesDetect():
        

        
    def __init__(self):
        
        sys.path.append("..")
        

        
        MODEL_NAME = 'faster_rcnn_inception_v2_coco_2018_01_28'
        
        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('data', 'mscoco_label_map.pbtxt')
        

        
        NUM_CLASSES = 90
        

        
        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())
        

        
        # ## Load a (frozen) Tensorflow model into memory.
        
        self.detection_graph = tf.Graph()
        
        with self.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='')
        

        
        # ## Loading label map
        
        # Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine
        
        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)
        
        self.category_index = label_map_util.create_category_index(categories)
        

        
        self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
        
        #  Each box represents an object detected 
        
        self.boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
        
        #  Each score represents the reliability of the detected object .  
        
        self.scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
        
        self.classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
        
        self.num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
        

        
    def detect(self, q):     
        
        with self.detection_graph.as_default():
        
            config = tf.ConfigProto() 
        
            # config.gpu_options.allow_growth = True
        
            config.gpu_options.per_process_gpu_memory_fraction = 0.2 
        
            with tf.Session(graph=self.detection_graph, config=config) as sess:
        
                while True:    
        

        
                    img_src = q.get()
        

        
                    print('------------start------------' + MyUtil.get_time_stamp())
        
                    image_np = cv2.imread(img_src)
        
                    #  Expand dimensions , Expected for model : [1, None, None, 3]
        
                    image_np_expanded = np.expand_dims(image_np, axis=0)
        

        
                    #  Perform detection task .  
        
                    (boxes, scores, classes, num_detections) = sess.run(
        
                        [self.boxes, self.scores, self.classes, self.num_detections],
        
                        feed_dict={self.image_tensor: image_np_expanded})
        
                    #  Visualization of test results 
        
                    vis_util.visualize_boxes_and_labels_on_image_array(
        
                        image_np,
        
                        np.squeeze(boxes),
        
                        np.squeeze(classes).astype(np.int32),
        
                        np.squeeze(scores),
        
                        self.category_index,
        
                        use_normalized_coordinates=True,
        
                        line_thickness=8)
        
                    print('------------end------------' + MyUtil.get_time_stamp())
        
                    # cv2.imshow('object detection', cv2.resize(image_np, (800, 600)))    
        
                    if cv2.waitKey(25) & 0xFF == ord('q'):
        
                        cv2.destroyAllWindows()
        
                        break
       
       

        
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​ ​MyUtil.py​​

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        import time
        

        
def get_time_stamp():
        
    ct = time.time()
        
    local_time = time.localtime(ct)
        
    data_head = time.strftime("%Y-%m-%d %H:%M:%S", local_time)
        
    data_secs = (ct - int(ct)) * 1000
        
    time_stamp = "%s.%03d" % (data_head, data_secs)
        
    return time_stamp
       
       

        
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effect ：