Optical Flow Maps Using OpenCV in Python

#OpticalFlow
#OpenCV
#Python
#FeatureExtraction
#VideoProcessing

In this video, the workspace includes windows 10 and Anaconda with Spyder as Python editor.

OpenCV Documentation for Optical Flow:

Input video source:

[juliabraga]

Excellent video! It helped me a lot :3

[bransen]

i know the pixel width in meters. now i have a sequence of point-symmetrical images. i want to measure a speed field. #skyimager

[alefpontessilva]

import os
import cv2
import numpy as np
import skvideo

import skvideo.io

print ("hi")

# caminho para o vídeo de entrada
path2video =
outputPath =

# read the video to create frames-generator
videogen = skvideo.io.vread(path2video)

print(f'shape of video {path2video} = {videogen.shape}')

# reading first frame as first and previous frames
# New image (array) filled with zeros in same dimensions as input frame (3 chanels)
frame_ind = 0
hsv =

# conver input frame from RGB to gray
prevF = cv2.cvtColor(videogen[frame_ind], cv2.COLOR_RGB2GRAY)

print("Reading frames...")

# range com a largura de videogen
for index in range(len(videogen)):
# printando a informação de qual frame está sendo processado por iteração


# conver input frame from RGB to gray
nextF = cv2.cvtColor(videogen[index], cv2.COLOR_RGB2GRAY)

# maintain similar size of next anf previous frames; shape[1] é a width, e shape[2] é a high
dim = (prevF.shape[1], prevF.shape[0])
nextF = cv2.resize(nextF, dim)

#computing optical flow map by Farneback method. Returns 2D vector of size input frame
# cv.calcOpticalFlowFarneback(prev, next, flow, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags )
flow = cv2.calcOpticalFlowFarneback(prevF, # prev
nextF, # next
None, # flow
0.5, # pyr_sacel
3, # levels
15, # winsize
3, # iterations
5, # poly_n
1.2, # poly_sigma
0) # flags

# Computando a magnitude e o ângulo dos vetores 2D
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])

# Setting hue image de acordo com o a direção do fluxo óptico
hsv[..., 0] = (((ang * 180) / (np.pi)) / 2)

# setting hue image intensities de acordo com a magnitude do fluxo óptico normalizado
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)

# Convertendo hue image from HSV to BGR format
bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

# fluxo óptico é salvo com o nome do frame
dest = outputPath + str(index) + ".png"
cv2.imwrite(dest, bgr)

# Fluxo óptico se torna o frame anterior e loop se resume no novo nextF
prevF = nextF