【安信可小安派BW21-CBV-Kit】物品识别控制舵机

用BW21-CBV-Kit开发板使用摄像头可以监测80种不同类型的物体，如人、自行车、汽车、笔记本电脑等。

ObjectClassList.h：

1. ObjectDetectionItem itemList[80] = {
   
2.     {0,  "person",         1},
   
3.     {1,  "bicycle",        1},
   
4.     {2,  "car",            1},
   
5.     {3,  "motorbike",      1},
   
6.     {4,  "aeroplane",      1},
   
7.     {5,  "bus",            1},
   
8.     {6,  "train",          1},
   
9.     {7,  "truck",          1},
   
10.     {8,  "boat",           1},
    
11.     {9,  "traffic light",  1},
    
12.     {10, "fire hydrant",   1},
    
13.     {11, "stop sign",      1},
    
14.     {12, "parking meter",  1},
    
15.     {13, "bench",          1},
    
16.     {14, "bird",           1},
    
17.     {15, "cat",            1},
    
18.     {16, "dog",            1},
    
19.     {17, "horse",          1},
    
20.     {18, "sheep",          1},

复制代码

在“文件”->“示例”->“AmebaNN”->“ObjectDetectionLoop”中打开一个对象检测示例。

1. /*
   
2. 
   
3. Example guide:
   
4. https://www.amebaiot.com/en/amebapro2-arduino-neuralnework-object-detection/
   
5. 
   
6. NN Model Selection
   
7. Select Neural Network(NN) task and models using .modelSelect(nntask, objdetmodel, facedetmodel, facerecogmodel).
   
8. Replace with NA_MODEL if they are not necessary for your selected NN Task.
   
9. 
   
10. NN task
    
11. =======
    
12. OBJECT_DETECTION/ FACE_DETECTION/ FACE_RECOGNITION
    
13. 
    
14. Models
    
15. =======
    
16. YOLOv3 model         DEFAULT_YOLOV3TINY   / CUSTOMIZED_YOLOV3TINY
    
17. YOLOv4 model         DEFAULT_YOLOV4TINY   / CUSTOMIZED_YOLOV4TINY
    
18. YOLOv7 model         DEFAULT_YOLOV7TINY   / CUSTOMIZED_YOLOV7TINY
    
19. SCRFD model          DEFAULT_SCRFD        / CUSTOMIZED_SCRFD
    
20. MobileFaceNet model  DEFAULT_MOBILEFACENET/ CUSTOMIZED_MOBILEFACENET
    
21. No model             NA_MODEL
    
22. */
    
23. 
    
24. #include "WiFi.h"
    
25. #include "StreamIO.h"
    
26. #include "VideoStream.h"
    
27. #include "RTSP.h"
    
28. #include "NNObjectDetection.h"
    
29. #include "VideoStreamOverlay.h"
    
30. #include "ObjectClassList.h"
    
31. #include <AmebaServo.h>
    
32. 
    
33. #define CHANNEL   0
    
34. #define CHANNELNN 3
    
35. 
    
36. // Lower resolution for NN processing
    
37. #define NNWIDTH  576
    
38. #define NNHEIGHT 320
    
39. 
    
40. 
    
41. AmebaServo myservo;
    
42. 
    
43. VideoSetting config(VIDEO_FHD, 30, VIDEO_H264, 0);
    
44. VideoSetting configNN(NNWIDTH, NNHEIGHT, 10, VIDEO_RGB, 0);
    
45. NNObjectDetection ObjDet;
    
46. RTSP rtsp;
    
47. StreamIO videoStreamer(1, 1);
    
48. StreamIO videoStreamerNN(1, 1);
    
49. 
    
50. char ssid[] = "ssid";    // your network SSID (name)
    
51. char pass[] = "pass";        // your network password
    
52. int status = WL_IDLE_STATUS;
    
53. int timeout=0;
    
54. 
    
55. IPAddress ip;
    
56. int rtsp_portnum;
    
57. 
    
58. void setup()
    
59. {
    
60.     Serial.begin(115200);
    
61.     myservo.attach(8);
    
62. 
    
63.     // attempt to connect to Wifi network:
    
64.     while (status != WL_CONNECTED) {
    
65.         Serial.print("Attempting to connect to WPA SSID: ");
    
66.         Serial.println(ssid);
    
67.         status = WiFi.begin(ssid, pass);
    
68. 
    
69.         // wait 2 seconds for connection:
    
70.         delay(2000);
    
71.     }
    
72.     ip = WiFi.localIP();
    
73. 
    
74.     // Configure camera video channels with video format information
    
75.     // Adjust the bitrate based on your WiFi network quality
    
76.     config.setBitrate(2 * 1024 * 1024);    // Recommend to use 2Mbps for RTSP streaming to prevent network congestion
    
77.     Camera.configVideoChannel(CHANNEL, config);
    
78.     Camera.configVideoChannel(CHANNELNN, configNN);
    
79.     Camera.videoInit();
    
80. 
    
81.     // Configure RTSP with corresponding video format information
    
82.     rtsp.configVideo(config);
    
83.     rtsp.begin();
    
84.     rtsp_portnum = rtsp.getPort();
    
85. 
    
86.     // Configure object detection with corresponding video format information
    
87.     // Select Neural Network(NN) task and models
    
88.     ObjDet.configVideo(configNN);
    
89.     ObjDet.setResultCallback(ODPostProcess);
    
90.     ObjDet.modelSelect(OBJECT_DETECTION, DEFAULT_YOLOV4TINY, NA_MODEL, NA_MODEL);
    
91.     ObjDet.begin();
    
92. 
    
93.     // Configure StreamIO object to stream data from video channel to RTSP
    
94.     videoStreamer.registerInput(Camera.getStream(CHANNEL));
    
95.     videoStreamer.registerOutput(rtsp);
    
96.     if (videoStreamer.begin() != 0) {
    
97.         Serial.println("StreamIO link start failed");
    
98.     }
    
99. 
    
100.     // Start data stream from video channel
     
101.     Camera.channelBegin(CHANNEL);
     
102. 
     
103.     // Configure StreamIO object to stream data from RGB video channel to object detection
     
104.     videoStreamerNN.registerInput(Camera.getStream(CHANNELNN));
     
105.     videoStreamerNN.setStackSize();
     
106.     videoStreamerNN.setTaskPriority();
     
107.     videoStreamerNN.registerOutput(ObjDet);
     
108.     if (videoStreamerNN.begin() != 0) {
     
109.         Serial.println("StreamIO link start failed");
     
110.     }
     
111. 
     
112.     // Start video channel for NN
     
113.     Camera.channelBegin(CHANNELNN);
     
114. 
     
115.     // Start OSD drawing on RTSP video channel
     
116.     OSD.configVideo(CHANNEL, config);
     
117.     OSD.begin();
     
118. }
     
119. 
     
120. void loop()
     
121. {
     
122.     // Do nothing
     
123.     if(timeout>0){
     
124.         timeout--;
     
125.         if(timeout==0) myservo.write(0);
     
126.     }
     
127.     delay(1000);
     
128. }
     
129. 
     
130. // User callback function for post processing of object detection results
     
131. void ODPostProcess(std::vector<ObjectDetectionResult> results)
     
132. {
     
133.     uint16_t im_h = config.height();
     
134.     uint16_t im_w = config.width();
     
135. 
     
136.     Serial.print("Network URL for RTSP Streaming: ");
     
137.     Serial.print("rtsp://");
     
138.     Serial.print(ip);
     
139.     Serial.print(":");
     
140.     Serial.println(rtsp_portnum);
     
141.     Serial.println(" ");
     
142. 
     
143.     printf("Total number of objects detected = %d\r\n", ObjDet.getResultCount());
     
144.     OSD.createBitmap(CHANNEL);
     
145. 
     
146.     if (ObjDet.getResultCount() > 0) {
     
147.         for (int i = 0; i < ObjDet.getResultCount(); i++) {
     
148.             int obj_type = results[i].type();
     
149.             if (itemList[obj_type].filter) {    // check if item should be ignored
     
150. 
     
151.                 ObjectDetectionResult item = results[i];
     
152.                 // Result coordinates are floats ranging from 0.00 to 1.00
     
153.                 // Multiply with RTSP resolution to get coordinates in pixels
     
154.                 int xmin = (int)(item.xMin() * im_w);
     
155.                 int xmax = (int)(item.xMax() * im_w);
     
156.                 int ymin = (int)(item.yMin() * im_h);
     
157.                 int ymax = (int)(item.yMax() * im_h);
     
158. 
     
159.                 // Draw boundary box
     
160.                 printf("Item %d %s:\t%d %d %d %d\n\r", i, itemList[obj_type].objectName, xmin, xmax, ymin, ymax);
     
161.                 OSD.drawRect(CHANNEL, xmin, ymin, xmax, ymax, 3, OSD_COLOR_WHITE);
     
162. 
     
163.                 // Print identification text
     
164.                 char text_str[20];
     
165.                 snprintf(text_str, sizeof(text_str), "%s %d", itemList[obj_type].objectName, item.score());
     
166.                 OSD.drawText(CHANNEL, xmin, ymin - OSD.getTextHeight(CHANNEL), text_str, OSD_COLOR_CYAN);
     
167. 
     
168.                 if(itemList[obj_type].index==7){
     
169.                     myservo.write(180);
     
170.                     timeout=3;
     
171.                 }
     
172.             }
     
173.         }
     
174.     }
     
175.     OSD.update(CHANNEL);
     
176. }
     

复制代码

#include <AmebaServo.h>
AmebaServo myservo;

myservo.attach(8);   //舵机控制io



在监测到index=7的物品（truck)时，转动舵机180度。
if(itemList[obj_type].index==7){
    myservo.write(180);
    timeout=3;
}
loop中3秒钟后转动舵机为0度
void loop()
{
    // Do nothing
    if(timeout>0){
        timeout--;
        if(timeout==0) myservo.write(0);
    }
    delay(1000);
}



这个玩具车的底盘被识别为truck。

运行效果：摄像头对准玩具车，在玩具车被识别为truck后，其上舵机转动180度，3秒钟后，舵机恢复到0度。
如果用于识别其他物品，舵机不会有反映。

添加一个效果视频