Automation, sensing and robotics • Re: Raspberry pi 4 object detection

Code:

#Import the Open-CV extra functionalitiesimport cv2import os# find userusers  = []users.append(os.getlogin())#This is to pull the information about what each object is calledclassNames = []classFile = "/home/" + users[0] + "/Desktop/Object_Detection_Files/coco.names"with open(classFile,"rt") as f:    classNames = f.read().rstrip("\n").split("\n")#This is to pull the information about what each object should look likeconfigPath = "/home/"+ users[0] + "/Desktop/Object_Detection_Files/ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt"weightsPath = "/home/"+ users[0] + "/Desktop/Object_Detection_Files/frozen_inference_graph.pb"#This is some set up values to get good resultsnet = cv2.dnn_DetectionModel(weightsPath,configPath)net.setInputSize(320,320)net.setInputScale(1.0/ 127.5)net.setInputMean((127.5, 127.5, 127.5))net.setInputSwapRB(True)#This is to set up what the drawn box size/colour is and the font/size/colour of the name tag and confidence label   def getObjects(img, thres, nms, draw=True, objects=[]):    classIds, confs, bbox = net.detect(img,confThreshold=thres,nmsThreshold=nms)#Below has been commented out, if you want to print each sighting of an object to the console you can uncomment below     #print(classIds,bbox)    if len(objects) == 0: objects = classNames    objectInfo =[]    if len(classIds) != 0:        for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox):            className = classNames[classId - 1]            if className in objects:                 objectInfo.append([box,className])                if (draw):                    cv2.rectangle(img,box,color=(0,255,0),thickness=2)                    cv2.putText(img,classNames[classId-1].upper(),(box[0]+10,box[1]+30),                    cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)                    cv2.putText(img,str(round(confidence*100,2)),(box[0]+200,box[1]+30),                    cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)        return img,objectInfo# find USB cameracam1 = -1x = 0while cam1 == -1 and x < 42:    txt = "v4l2-ctl -d " + str(x) + " --list-ctrls > cam_ctrls.txt"    os.system(txt)    ctrls = []    with open("cam_ctrls.txt", "r") as file:        line = file.readline()        while line:            ctrls.append(line)            line = file.readline()    if 'User Controls\n' in ctrls and ('Camera Controls\n' in ctrls):        cam1 = x    else:        x +=1if cam1 == -1:    print(" No USB camera found !!")    #Below determines the size of the live feed window that will be displayed on the Raspberry Pi OSif __name__ == "__main__":    # start USB camera    vid1 = cv2.VideoCapture(cam1)    vid1.set(cv2.CAP_PROP_FRAME_WIDTH, 640)    vid1.set(cv2.CAP_PROP_FRAME_HEIGHT,480)       #Below is the never ending loop that determines what will happen when an object is identified.        while True:        # GET AN IMAGE from USB camera        ret, img = vid1.read()        #Below provides a huge amount of controll. the 0.45 number is the threshold number, the 0.2 number is the nms number)        result, objectInfo = getObjects(img,0.45,0.2)        #print(objectInfo)        cv2.imshow("Output",img)                    k = cv2.waitKey(200)        if k == 27:    # Esc key to stop            # EXIT            cv2.destroyAllWindows()            break