标签：colorwheel nan flow np flo 光流 col png 255
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
- 在 http://vision.middlebury.edu/flow/submit/ 下载flow-code.zip
- 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
本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享； 2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关； 3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关； 4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除； 5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。