Optical Flow Estimation, How might you calculate the velocity/speed of an object from a ".flo" output

refer to code 

..

import cv2

import numpy as np

# Load the .flo file using OpenCV

flow = cv2.readOpticalFlow('path/to/flow_file.flo')

# Convert displacement vectors to velocity vectors

time_interval = 1.0 # Time interval between frames in seconds

velocity = flow / time_interval

# Load the corresponding frames of the video

frame1 = cv2.imread('path/to/frame1.jpg')

frame2 = cv2.imread('path/to/frame2.jpg')

# Identify object pixels using object detection or segmentation

object_mask = np.zeros(frame1.shape[:2], dtype=np.uint8)

object_mask[...] = 255 # Example: assume the entire frame is the object

# Compute the magnitude of the velocity vector at each object pixel

object_velocity = np.sqrt(np.square(velocity[..., 0]) + np.square(velocity[..., 1])) * object_mask

# Calculate the average speed of the object

object_speed = np.mean(object_velocity[object_mask != 0])

print('Object speed:', object_speed, 'pixels per second')

..

In this example, we first load the .flo file using the cv2.readOpticalFlow() function from OpenCV. We then convert the displacement vectors in the .flo file to velocity vectors by dividing them by the time interval between the two frames. We assume a time interval of 1 second in this example.

Next, we load the corresponding frames of the video and identify the object pixels using a mask. In this example, we assume that the entire frame is the object for simplicity.

We then compute the magnitude of the velocity vector at each object pixel using the np.sqrt() and np.square() functions from NumPy. We apply the object mask to exclude pixels that do not belong to the object.

Finally, we calculate the average speed of the object by averaging the magnitudes of the velocity vectors over all the pixels corresponding to the object using the np.mean() function.

Note that the units of the speed will be in pixels per second, which can be converted to other units (e.g., meters per second) depending on the scale of the video frames. Also, this is just a simple example and you may need to modify it depending on the specific requirements of your application.

Thank you.