Question about net.GetNetworkFPS()

[r2ba7]

I'm using detectnet.py for many things and doing a lot of processing using OpenCV. Anyway, does this function return the FPS for the whole script or just the network ?

[r2ba7]

` while True: # capture the next image img = input.Capture()

detections = net.Detect(img, overlay='box,labels,conf')
# Convert from Cuda to BGR numpy array
img_array = jetson.utils.cudaToNumpy(img)
#print('shape of the image', img_array.shape)
# Object Detection and Distance Estimation
#X, Y, _ = img_array.shape
sep1 = int(img_array.shape[0]*0.333)
sep2 = int(img_array.shape[0]*0.666)
Data_arr = []
#For loop start
for detection in detections:
    #xmax = round(detection.Right)
    #xmin = round(detection.Left)
    #ymin = round(detection.Top)
    #ymax = round(detection.Bottom)

    #Detected labels
    if detection.ClassID == 1:
        label = 'P'
    elif detection.ClassID == 2:
        label = 'C'
    #End detected labels
    
    #Position Handling    
    location = position_handling(detection, sep1, sep2)
    #End Position Handling
    #Handle distance for both human and car because different sizes
    #Distance handling
    distance = distance_estimation(detection, img_array.shape[0], img_array.shape[1])
    #End Distance Handling
    #For UART
    Data = "{}{}{}".format(str(distance), location, label)
    
    # convert to RGB
    # rgb_array = cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB)
    # cv2.rectangle(rgb_array, 
    #             (int(detection.Left), int(detection.Bottom)), (int(detection.Right), int(detection.Top)), 
    #             (255, 0, 0), 4)

    cv2.putText(img_array, location,
                (int(detection.Right) - 20, int(detection.Top) + 40),
                cv2.FONT_HERSHEY_SIMPLEX,
                1,
                (255, 0, 0), 2)

    # cv2.putText(rgb_array, str(label),
    #             (int(detection.Left) + 20, int(detection.Top) + 40),
    #             cv2.FONT_HERSHEY_SIMPLEX,
    #             1,
    #             (0, 255, 0), 2)
                
    cv2.putText(img_array, str(distance),
                (int(detection.Left) + 20, int(detection.Bottom) - 40),
                cv2.FONT_HERSHEY_SIMPLEX,
                1, (0, 0, 255), 2)

    Data_arr.append(Data)
#Lane Detection Part  
img_array = lane_follower.follow_lane(img_array)
if lane_follower.curr_steering_angle >= 84 and lane_follower.curr_steering_angle <= 95:
    lane_follower.dir.append('F')
elif lane_follower.curr_steering_angle >= 64 and lane_follower.curr_steering_angle <= 83:
    lane_follower.dir.append('LF')
elif lane_follower.curr_steering_angle >= 45 and lane_follower.curr_steering_angle <= 63:
    lane_follower.dir.append('L')
elif lane_follower.curr_steering_angle >= 96 and lane_follower.curr_steering_angle <= 115:
    lane_follower.dir.append('RF')
elif lane_follower.curr_steering_angle >= 116 and lane_follower.curr_steering_angle <= 135:
    lane_follower.dir.append('R')

if len(lane_follower.dir) == 20:
    direction = max(set(lane_follower.dir), key=lane_follower.dir.count)
    lane_follower.dir = []

cv2.putText(img_array, str(lane_follower.curr_steering_angle) + " " + direction, (50,50), cv2.FONT_HERSHEY_SIMPLEX , 1 , (255, 0, 0)  , 2 , cv2.LINE_AA )
#End Lane Detection  
#FPS


#UART Writing
# try:
#     # Send a simple header
#     serial_port.write("#".encode())
#     while True:
#         Data = Data.encode()
#             #print(data)
#         serial_port.write(Data)
#             #serial_port.write("Kosomk\r\n".encode())
#             # if we get a carriage return, add a line feed too
#             # \r is a carriage return; \n is a line feed
#             # This is to help the tty program on the other end 
#             # Windows is \r\n for carriage return, line feed
#             # Macintosh and Linux use \n
#         serial_port.write("# \r\n".encode())
#         break


# except KeyboardInterrupt:
#     print("Exiting Program")

# except Exception as exception_error:
#     # print("Error occurred. Exiting Program")
#     # print("Error: " + str(exception_error))
#     continue
# time.sleep(0.05)
#UART DONE


                
    #convert back to BGR
    ## rgb_array

    #Calculating Frames

    #End Calcularting
#For Loop Ends

#END FPS
# composite new image onto the original
# render the image
#new_img = cv2.cvtColor(rgb_array, cv2.COLOR_RGB2BGR)
img = jetson.utils.cudaFromNumpy(img_array)
output.Render(img)
print("{:.0f} FPS".format(net.GetNetworkFPS()))
# update the title bar
#output.SetStatus("{:s} | Network {:.0f} FPS".format(opt.network, net.GetNetworkFPS()))

#time.sleep(0.5)



# print out performance info
#net.PrintProfilerTimes()

# exit on input/output EOS
if not input.IsStreaming() or not output.IsStreaming():
#     serial_port.close()
    break`
    

I added all the previous, and to my surprise, it got me 40+ FPS. Custom trained Mobilenet V1 from your repo

[dusty-nv]

net.GetNetworkFPS() returns the FPS for just the DNN network.

videoOutput.GetFrameRate() would return the frame rate that images are rendered to the display.

[r2ba7]

net.GetNetworkFPS() returns the FPS for just the DNN network.

videoOutput.GetFrameRate() would return the frame rate that images are rendered to the display.

when I replaced print("{:.0f} FPS".format(net.GetNetworkFPS())) with print("{:.0f} FPS".format(output.GetFrameRate())) it gave me a steady 30 FPS even though the video freezes sometimes. I put it right after output.Render(img) before the end of the for loop

[abhinavrawat27]

Did you solved your issue? I think the reason you are getting steady 30FPS is because GetFrameRate() is giving you the FPS of your video not the FPS of inferencing.