矩阵转换图片

时间：20180723-20180729

背景：基于上一篇tvnet博客里所输出的mat格式的图片转换为jpg或者其他格式的图片本文以jpg为例

import cv2
import scipy.io as scio
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt

data = scio.loadmat('result.mat')
a=data['flow']
def MatrixToImage(data):
    data = data*255
    new_im = Image.fromarray(data.astype(np.uint8))
    return new_im

new_im = MatrixToImage(a)
for i in range(len(a[0][0])):
    plt.imshow(a[:][:][i],cmap=plt.cm.gray, interpolation='nearest')
    new_im.show()
    new_im=new_im.convert('RGB')
    new_im.save(str.format("a%d.jpg"%i)) # 保存图片

也可以在未生成mat格式的图片前直接在tvnetdemo里输出图片，将前面的demo改为demo2

import os
import cv2
import numpy as np
import tensorflow as tf
import scipy.io as sio
import matplotlib.pyplot as plt
from tvnet import TVNet

from PIL import Image

flags = tf.app.flags
flags.DEFINE_integer("scale", 5, " TVNet scale [3]")
flags.DEFINE_integer("warp", 5, " TVNet warp [1]")
flags.DEFINE_integer("iteration", 50, " TVNet iteration [10]")
flags.DEFINE_string("gpu", '0', " gpu to use [0]")
FLAGS = flags.FLAGS

scale = FLAGS.scale
warp = FLAGS.warp
iteration = FLAGS.iteration
if int(FLAGS.gpu) > -1:
    os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu

print ('TVNet Params:\n scale: %d\n warp: %d\n iteration: %d\nUsing gpu: %s' \
      % (scale, warp, iteration, FLAGS.gpu))

# load image
img1 = cv2.imread('frame/img1.png')
img2 = cv2.imread('frame/img2.png')
h, w, c = img1.shape

# model construct
x1 = tf.placeholder(shape=[1, h, w, 3], dtype=tf.float32)
x2 = tf.placeholder(shape=[1, h, w, 3], dtype=tf.float32)
tvnet = TVNet()
u1, u2, rho = tvnet.tvnet_flow(x1,x2,max_scales=scale,
                     warps=warp,
                     max_iterations=iteration)

# init
sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True), allow_soft_placement=True))
sess.run(tf.global_variables_initializer())

# run model
u1_np, u2_np = sess.run([u1, u2], feed_dict={x1: img1[np.newaxis, ...], x2: img2[np.newaxis, ...]})

u1_np = np.squeeze(u1_np)
u2_np = np.squeeze(u2_np)
flow_mat = np.zeros([h, w, 2])
flow_mat[:, :, 0] = u1_np
flow_mat[:, :, 1] = u2_np

if not os.path.exists('result'):
    os.mkdir('result')
new_im = tvnet.MatrixToImage(flow_mat)
for i in range(len(flow_mat[0][0])):
    plt.imshow(flow_mat[:][:][i],cmap=plt.cm.gray, interpolation='nearest')
    # new_im.show()
    new_im=new_im.convert('RGB')
    new_im.save(str.format("result/a%d.jpg" % i))  # 保存图片

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