YOLO detection

1. Experimental purpose

Drive the car's YOLO detection

2. Experimental path source code

Enter the car's system, end the car program, enter "ip (ip is the car's ip): 8888" in the browser, enter the password "yahboom" Then log in Enter the path Rider-pi_class/5.AI Visual Recognition Course/10. YOLO detection and run yolo.ipynb. Or enter the command in the terminal to directly start the python script

cd /home/pi/Rider-pi_class/5.AI Visual Recognition Course/10. YOLO detection
python3 yolo.py

3. Experimental phenomenon

After running the source code, you can see that the car can detect objects

4. Main source code analysis

​x
 def yoloFast(self,target="camera"):
        ret=''
        self.open_camera()
        if self.yolo==None:
            self.yolo = yoloXgo('/home/pi/model/Model.onnx',
            ['person','bicycle','car','motorbike','aeroplane','bus','train','truck','boat','traffic light','fire hydrant','stop sign','parking meter','bench','bird','cat','dog','horse','sheep','cow','elephant','bear','zebra','giraffe','backpack','umbrella','handbag','tie','suitcase','frisbee','skis','snowboard','sports ball','kite','baseball bat','baseball glove','skateboard','surfboard','tennis racket','bottle','wine glass','cup','fork','knife','spoon','bowl','banana','apple','sandwich','orange','broccoli','carrot','hot dog','pizza','donut','cake','chair','sofa','pottedplant','bed','diningtable','toilet','tvmonitor','laptop','mouse','remote','keyboard','cell phone','microwave','oven','toaster','sink','refrigerator','book','clock','vase','scissors','teddy bear','hair drier','toothbrush'],
            [352,352],0.66)
        if target=="camera":
            self.open_camera()
            success,image = self.cap.read()
        else:
            image=np.array(Image.open(target))
        datas = self.yolo.run(image)
        b,g,r = cv2.split(image)
        image = cv2.merge((r,g,b))
        image = cv2.flip(image,1)
        if datas:
            for data in datas:
                XGOEDU.rectangle(self,image,data['xywh'],"#33cc00",2)
                xy= (data['xywh'][0], data['xywh'][1])
                XGOEDU.text(self,image,data['classes'],xy,1,"#ff0000",2)
                value_yolo = data['classes']
                ret=(value_yolo,xy)
        imgok = Image.fromarray(image)
​
        #Turn the color back
        r,g,b = cv2.split(image)
        image1 = cv2.merge((b,g,r))
        cv2.imshow('frame', image1)#Displayed on the terminal
        
        self.display.ShowImage(imgok)
        if ret=='':
            return None
        else:
            return ret
my_edu = my_yolo()
#Cycle through camera recognition, press c to exit
while True:
    result=my_edu.yoloFast()  #Default parameters, camera recognition is used by default
    print(result)
    if cv2.waitKey(1) & 0xFF == ord('q'): break

Through the source code, the car calls the edu API library and detects objects by turning on the camera.