Counting elapsed time of prediction in real time OpenCV Python

Question

I am trying to get the elapsed time of a neural network prediction in TensorFlow running real-time inference in OpenCV w/ Python. Whenever a classification is true, I display a text label in the window and I pretty much want to measure how many seconds it is true for.

My thinking was to get the start time at the beginning of the stream and then getting the start time of the prediction and subtracting those two to get the elapsed time.

This is getting me different values than expected though. What is the best way to go about this?

Here is my code:

import time
import cv2

cap_device = args.device
cap_width = args.width
cap_height = args.height

cap = cv2.VideoCapture(cap_device, cv2.CAP_DSHOW)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, cap_width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, cap_height)



start_time = time.time() #get start time
class_time = 0 #initialize class time
with mp_pose.Pose(model_complexity = 2) as pose_tracker: #load model
  while cap.isOpened(): #while camera is running 
    ret, frame = cap.read() #read frames in
    ...
    ...
    ...

    with open("labels.txt") as file: #gets labels to show pose class
      lines = file.readlines()
      lines = [line.rstrip() for line in lines]
      
      
          #classification confidence
    for i in range(0, len(prediction[0])): 
          if prediction[0][0] > 0.50 and prediction[0][0]<=1.00:
              draw_info_text(image, lines[0],cap_width,cap_height) 
              class_time = time.time()
          elif prediction[0][1] > 0.50 and prediction[0][1]<=1.00: =
              draw_info_text(image,lines[1],cap_width,cap_height)
        
          elif prediction[0][2] > 0.50 and prediction[0][2]<= 1.00: 
              draw_info_text(image,lines[2],cap_width,cap_height)
            
          elif prediction[0][3] > 0.50 and prediction[0][3] <= 1.00: 
              draw_info_text(image,lines[3],cap_width,cap_height)
                

    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    #show video
    cv2.imshow('webcam', image)
    if cv2.waitKey(1) == ord('q'): 
      break

total_prediction_time = start_time - class_time # calculate the total time pred was true 
print(total_prediction_time)

cap.release()
cv2.destroyAllWindows()

Answer 1

I think you could make use of python decorators, here is an example:

# Timer decorator


def timer(fn):    
    from time import perf_counter
    def inner(*args, **kwargs):
        start_time = perf_counter()
        to_execute = fn(*args, **kwargs)
        end_time = perf_counter()
        execution_time = end_time - start_time
        print('{0} took {1:.8f}s to execute'.format(fn.__name__, execution_time))
        return to_execute

    return inner

@timer
def function_1():
    for i in range(100000):
        pass

@timer
def function_2():
    for i in range(10000000):
        pass


@timer
def function_3():
    for i in range(10000):
        pass

@timer
def function_4():
    for i in range(100000000):
        pass

function_1()
function_2()
function_3()
function_4()