Запуск модели Yolo на самом сервере фляги. Как?

Question

Я работаю над приложением, в котором, как только я запускаю флеш-сервер, моя веб-камера должна запуститься (что она делает!) И показать модель yolo на самой веб-странице (ту, которая у меня работает независимо, которую я не надеваю)не хочу)Я не уверен, как мне справиться с этим.Возможно, взглянув на код, вы сможете помочь.

main.py

from flask import Flask, render_template, Response
from camera import VideoCamera
import tablib
import os

app = Flask(__name__)
dataset = tablib.Dataset()
with open(os.path.join(os.path.dirname(__file__), 'object.csv')) as f:
    dataset.csv = f.read()

@app.route('/')
def index():
    data = dataset.html
    return render_template('index.html', data=data)

def gen(camera):
    while True:
        count = 0
        frame = camera.get_frame()
        yield (b'--frame+str("%d"%count)\r\n'
               b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')

@app.route('/video_feed')
def video_feed():
    return Response(gen(VideoCamera()),
                    mimetype='multipart/x-mixed-replace; boundary=frame')

@app.route('/table')
def display_table():
    #do something to create a pandas datatable
    df = pd.DataFrame(data=[[person], [Timestamps]])
    df_html = df.to_html() #using pandas to autogenerate html
    return render_template('index.html', 'table_html=df_html')

if __name__ == '__main__':
    app.run(host='0.0.0.0', debug=True)

camera.py

import datetime
import cv2
import time
import numpy as np
from keras import backend as K
from keras.models import load_model
from yad2k.models.keras_yolo import yolo_head, yolo_eval
from yad2k.yolo_utils import read_classes, read_anchors, preprocess_webcam_image, draw_boxes, generate_colors
import pandas as pd

class VideoCamera(object):
    def __init__(self):
        # Using OpenCV to capture from device 0. If you have trouble capturing
        # from a webcam, comment the line below out and use a video file
        # instead.
        self.video = cv2.VideoCapture(0)
        self.class_names = read_classes("model_data/coco_classes.txt")
        anchors = read_anchors("model_data/yolo_anchors.txt")
        image_shape = (480., 640.)
        self.yolo_model = load_model("model_data/yolo.h5")
        #print(self.yolo_model.summary(), "fndkjcndkn")
        yolo_outputs = yolo_head(self.yolo_model.output, anchors, len(self.class_names))
        self.scores, self.boxes, self.classes = yolo_eval(yolo_outputs, image_shape)


    def predict(self,sess,frame):
        # Preprocess your image
        image, image_data = preprocess_webcam_image(frame, model_image_size=(608, 608))

        out_scores, out_boxes, out_classes = sess.run([self.scores, self.boxes, self.classes], feed_dict={self.yolo_model.input: image_data,
        K.learning_phase(): 0})
        # Print predictions info
        #print('Found {} boxes'.format(len(out_boxes))) #here it prints object names!
        # Generate colors for drawing bounding boxes.
        colors = generate_colors(self.class_names)
        # Draw bounding boxes on the image file
        draw_boxes(image, out_scores, out_boxes, out_classes, self.class_names, colors)

        return np.array(image), out_boxes


    def __del__(self):
        self.video.release()
        cv2.destroyAllWindows()

    def get_frame(self):
        sess = K.get_session()
        while True:
            # Capture frame-by-frame
            grabbed, frame = self.video.read()
            if not grabbed:
                break

            #Run detection

            start = time.time()
            output_image,image = self.predict(sess,frame)
            #df = pd.DataFrame({'Object': [image], 'Timestamp': [datetime.datetime.now().strftime("%A, %d. %B %Y %I:%M%p")]}, index=[0])
            #df.to_csv('objects3.csv', sep=';', header = 2, index=False, encoding='utf-8', mode = 'a', columns=['Object', 'Timestamp'])
            #df.to_csv('object2.csv', header= 2, mode = 'a')
            end = time.time()
            cv2.imshow('', output_image)
            if(cv2.waitKey(1) & 0xFF == ord('q')):
                return 0

            #dt = datetime.datetime.now().strftime("%A, %d. %B %Y %I:%M%p")
            #print (dt)
            #print("Inference time: {:.2f}s".format(end - start))

        success, image = self.video.read()
        # We are using Motion JPEG, but OpenCV defaults to capture raw images,
        # so we must encode it into JPEG in order to correctly display the
        # video stream.
        ret, jpeg = cv2.imencode('.jpg', image)
        # Display the resulting frame
        cv2.imshow('', output_image)
        #

        # When everything done, release the capture
        stream.release()
        cv2.destroyAllWindows()

index.html

<html>
  <head>
    <title>Video Streaming Demonstration</title>
     <link rel=stylesheet type="text/css" href="{{ url_for('static', filename='css/style.css')}}"/> 
  </head>
  <body>
    <h1>Keep Looking...</h1>
    <img id="bg" src="{{ url_for('video_feed') }}">
      {{ table_html | safe }}
      <table border="1" class="dataframe">  
    <thead>    
    <tr style="text-align: left;">      

    <th>Objects</th>      
    <th>Timestamps</th>    
    </tr>  
    </thead>
    <tbody>    
    <tr>          
    <td>{{ output_image }} </td>     
    <td>{{ dt }}</td>   
    </tr>
          </tbody>
      </table>

      <!-- new code -->
      <div class="table">
      {% block body %}
      {{ data|safe }}
      {% endblock %}
      </div>
  </body>
</html>

Answer 1

Этот пример может работать на вас.Он не потоковое видео с камеры, а из файла.Вы можете написать этот файл в Yolo из одного скрипта и прочитать его из Flask.Вам просто нужно удалить код камеры OpenCV.

test.py

from flask import Flask, render_template, Response 
import cv2
import socket 
import io 
app = Flask(__name__) 
vc = cv2.VideoCapture(0) 
@app.route('/') 
def index(): 
   """Video streaming .""" 
   return render_template('index.html') 
def gen(): 
   """Video streaming generator function.""" 
   while True: 
       rval, frame = vc.read() 
       cv2.imwrite('pic.jpg', frame) 
       yield (b'--frame\r\n' 
              b'Content-Type: image/jpeg\r\n\r\n' + open('pic.jpg', 'rb').read() + b'\r\n') 
@app.route('/video_feed') 
def video_feed(): 
   """Video streaming route. Put this in the src attribute of an img tag.""" 
   return Response(gen(), 
                   mimetype='multipart/x-mixed-replace; boundary=frame') 
if __name__ == '__main__': 
    app.run(debug=True, threaded=True)

/ templates / index.html

<html> 
 <head> 
   <title>Video Streaming </title> 
 </head> 
 <body> 
   <h1> Live Video Streaming </h1> 
   <img src="{{ url_for('video_feed') }}"> 
 </body> 
</html>