参考

Flow-code: 将flo光流文件转换为png图片
基于光流warp的temporal loss增加视频分割的连续性
Pytorch-pwc

Flow-code

1. 在 http://vision.middlebury.edu/flow/submit/ 下载flow-code.zip
2. 解压到linux中flow-code文件夹下执行以下命令：

cd imageLib
make
cd ..
make
./colortest 10 colors.png *//如果有图片colors.png出现就为成功*
有可能会报错缺失'png.h'，此时执行如下命令：

sudo apt-get install libpng-dev
此处来源：fatal error: png.h: No such file or directory

3.转化自己图片：
单张图片转化

在flow-code 文件夹下执行并建立other文件夹放入out.flo文件：

./color_flow ./other/out.flo ./out.png
得到结果。

visflow.py

import numpy as np
import os
import glob
import imageio

__all__ = ['load_flow', 'save_flow', 'vis_flow']

def load_flow(path):
    with open(path, 'rb') as f:
        magic = float(np.fromfile(f, np.float32, count = 1)[0])
        if magic == 202021.25:
            w, h = np.fromfile(f, np.int32, count = 1)[0], np.fromfile(f, np.int32, count = 1)[0]
            data = np.fromfile(f, np.float32, count = h*w*2)
            data.resize((h, w, 2))
            return data
        return None

def save_flow(path, flow):
    magic = np.array([202021.25], np.float32)
    h, w = flow.shape[:2]	# 注意对应！！！
    h, w = np.array([h], np.int32), np.array([w], np.int32)

    with open(path, 'wb') as f:
        magic.tofile(f); w.tofile(f); h.tofile(f); flow.tofile(f)

import cv2
import sys
import numpy as np
import argparse

def makeColorwheel():

	#  color encoding scheme

	#   adapted from the color circle idea described at
	#   http://members.shaw.ca/quadibloc/other/colint.htm

	RY = 15
	YG = 6
	GC = 4
	CB = 11
	BM = 13
	MR = 6

	ncols = RY + YG + GC + CB + BM + MR

	colorwheel = np.zeros([ncols, 3]) # r g b

	col = 0
	#RY
	colorwheel[0:RY, 0] = 255
	colorwheel[0:RY, 1] = np.floor(255*np.arange(0, RY, 1)/RY)
	col += RY

	#YG
	colorwheel[col:YG+col, 0]= 255 - np.floor(255*np.arange(0, YG, 1)/YG)
	colorwheel[col:YG+col, 1] = 255;
	col += YG;

	#GC
	colorwheel[col:GC+col, 1]= 255 
	colorwheel[col:GC+col, 2] = np.floor(255*np.arange(0, GC, 1)/GC)
	col += GC;

	#CB
	colorwheel[col:CB+col, 1]= 255 - np.floor(255*np.arange(0, CB, 1)/CB)
	colorwheel[col:CB+col, 2] = 255
	col += CB;

	#BM
	colorwheel[col:BM+col, 2]= 255 
	colorwheel[col:BM+col, 0] = np.floor(255*np.arange(0, BM, 1)/BM)
	col += BM;

	#MR
	colorwheel[col:MR+col, 2]= 255 - np.floor(255*np.arange(0, MR, 1)/MR)
	colorwheel[col:MR+col, 0] = 255
	return 	colorwheel

def computeColor(u, v):

	colorwheel = makeColorwheel();
	nan_u = np.isnan(u)
	nan_v = np.isnan(v)
	nan_u = np.where(nan_u)
	nan_v = np.where(nan_v) 

	u[nan_u] = 0
	u[nan_v] = 0
	v[nan_u] = 0 
	v[nan_v] = 0

	ncols = colorwheel.shape[0]
	radius = np.sqrt(u**2 + v**2)
	a = np.arctan2(-v, -u) / np.pi
	fk = (a+1) /2 * (ncols-1) # -1~1 maped to 1~ncols
	k0 = fk.astype(np.uint8)	 # 1, 2, ..., ncols
	k1 = k0+1
	k1[k1 == ncols] = 0
	f = fk - k0

	img = np.empty([k1.shape[0], k1.shape[1],3])
	ncolors = colorwheel.shape[1]
	for i in range(ncolors):
		tmp = colorwheel[:,i]
		col0 = tmp[k0]/255
		col1 = tmp[k1]/255
		col = (1-f)*col0 + f*col1
		idx = radius <= 1
		col[idx] = 1 - radius[idx]*(1-col[idx]) # increase saturation with radius    
		col[~idx] *= 0.75 # out of range
		img[:,:,2-i] = np.floor(255*col).astype(np.uint8)

	return img.astype(np.uint8)


def vis_flow(flow):
	eps = sys.float_info.epsilon
	UNKNOWN_FLOW_THRESH = 1e9
	UNKNOWN_FLOW = 1e10

	u = flow[:,:,0]
	v = flow[:,:,1]

	maxu = -999
	maxv = -999

	minu = 999
	minv = 999

	maxrad = -1
	#fix unknown flow
	greater_u = np.where(u > UNKNOWN_FLOW_THRESH)
	greater_v = np.where(v > UNKNOWN_FLOW_THRESH)
	u[greater_u] = 0
	u[greater_v] = 0
	v[greater_u] = 0 
	v[greater_v] = 0

	maxu = max([maxu, np.amax(u)])
	minu = min([minu, np.amin(u)])

	maxv = max([maxv, np.amax(v)])
	minv = min([minv, np.amin(v)])
	rad = np.sqrt(np.multiply(u,u)+np.multiply(v,v))
	maxrad = max([maxrad, np.amax(rad)])
	# print('max flow: %.4f flow range: u = %.3f .. %.3f; v = %.3f .. %.3f\n' % (maxrad, minu, maxu, minv, maxv))

	u = u/(maxrad+eps)
	v = v/(maxrad+eps)
	img = computeColor(u, v)
	return img[:,:,[2,1,0]]


for flo in glob.glob(os.path.join('image','flow','*.flo')):
	name = flo.split('.')[0]
	flow = load_flow(flo)
	img = vis_flow(flow)
	imageio.imsave(name+'.png', img)

标签：colorwheel,nan,flow,np,flo,光流,col,png,255
来源： https://www.cnblogs.com/hsiangyu-meng/p/15352441.html