ラズパイで人物読み取りをした映像にフレームを付けたい

前提・実現したいこと

Raspberry PiとUSBカメラを用いてisaaxを使って人物映像処理をしたい。

発生している問題・エラーメッセージ

ストリーミングはできているが、人物を検知した際に出るフレームが表示されない。

該当のソースコード

以下の通りです。

Python
1＜study-picamera-examples/camera/main.py＞　
2from flask import Flask, render_template, Response
3# from processor.simple_streamer import SimpleStreamer as VideoCamera
4# from processor.pedestrian_detector import PedestrianDetector as VideoCamera
5# from processor.motion_detector import MotionDetector as VideoCamera
6# from processor.qr_detector import QRDetector as VideoCamera
7# from processor.face_detector import FaceDetector as VideoCamera
8from processor.person_detector import PersonDetector as VideoCamera
9
10import time
11import threading
12
13video_camera = VideoCamera(flip=True)
14
15app = Flask(__name__)
16
17def gen(camera):
18    while True:
19        frame = camera.get_frame()
20        yield (b'--frame\r\n'
21               b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
22
23@app.route('/')
24def index():
25    return Response(gen(video_camera),
26                    mimetype='multipart/x-mixed-replace; boundary=frame')
27
28@app.route('/save')
29def save():
30    while True:
31        video_camera.save_frame()
32
33if __name__ == '__main__':
34    app.run(host='0.0.0.0', debug=False, threaded=True)

Python
1＜study-picamera-examples/camera/processor/person_detector.py＞
2from __future__ import print_function
3from imutils.video.pivideostream import PiVideoStream
4from imutils.object_detection import non_max_suppression
5import imutils
6import time
7import numpy as np
8import cv2
9
10
11net = cv2.dnn.readNetFromCaffe('/home/pi/models/MobileNetSSD_deploy.prototxt',
12        '/home/pi/models/MobileNetSSD_deploy.caffemodel')
13
14
15class PersonDetector(object):
16    def __init__(self, flip = False):
17        # Define the codec and create VideoWriter object
18        fourcc = cv2.VideoWriter_fourcc(*'XVID')
19        self.out = cv2.VideoWriter('output.avi',fourcc, 20.0, (400,304))
20        # Video Capture
21        try:
22            self.vc = cv2.VideoCapture(0)
23        except:
24            print(self.vc)
25        self.flip = flip
26        time.sleep(2.0)
27
28    def __del__(self):
29        self.out.release()
30        self.vc.release()
31
32    def get_output_image(self, frame):
33        if self.flip:
34            flipped_frame = cv2.flip(frame, 0)
35#            print ('pass1')
36            return cv2.imencode('.jpg', flipped_frame)
37        return cv2.imencode('.jpg', frame)
38
39    def save_frame(self):
40        ret, frame = self.vc.read()
41        if self.flip:
42            flipped_frame = cv2.flip(frame, 0)
43#            print('pass2')
44            return self.out.write(flipped_frame)
45        return self.out.write(frame)
46
47    def get_frame(self):
48        ret, frame = self.vc.read()
49        ret, image = self.get_output_image(frame)
50        frame = self.process_image(frame)
51        ret, jpeg = cv2.imencode('.jpg', frame)
52#        print('pass3')
53        return image.tobytes()
54
55    def process_image(self, frame):
56        frame = imutils.resize(frame, width=600)
57        (h, w) = frame.shape[:2]
58        blob = cv2.dnn.blobFromImage(frame, 0.007843, (300, 300), 127.5)
59        net.setInput(blob)
60        detections = net.forward()
61
62        count = 0
63        for i in np.arange(0, detections.shape[2]):
64            confidence = detections[0, 0, i, 2]
65#            print('pass4')
66            if confidence < 0.2:
67                print('pass6')
68                continue
69
70            idx = int(detections[0, 0, i, 1])
71            if idx != 15:
72                print('pass7')
73                continue
74
75            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
76            (startX, startY, endX, endY) = box.astype('int')
77            label = '{}: {:.2f}%'.format('Person', confidence * 100)
78            cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 255, 0), 2)
79            y = startY - 15 if startY - 15 > 15 else startY + 15
80            cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
81            count += 1
82        
83        if count > 0:
84            print('Count: {}'.format(count))
85            print('pass5')
86                
87        return frame