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• Matplotlib use Ffmpeg to save plot to be mp4 not include full step

  21 décembre 2020, par 昌翰余

  I use ffmpeg to store the dynamic graph drawn on matplotlib, but the output file is only 2 seconds
but It should have been 30 seconds.
I set a graph to run three curves, a total of 30 seconds of data,
the graph that ran on the py file is normal,
but the output is only the first two seconds of the output.
May I ask if I missed something

  


  Below is my code

  


  import matplotlib.pyplot as plt
from matplotlib import animation
from numpy import random 
import pandas as pd
from matplotlib.animation import FFMpegWriter

FFwriter=animation.FFMpegWriter(fps=30, extra_args=['-vcodec', 'libx264'])
data = pd.read_csv('apple1.csv', delimiter = ',', dtype = None)
data = data.values
AccX1=[]
AccY1=[]
AccZ1=[]
AccX2=[]
AccY2=[]
AccZ2=[]

time = []

for i in range(600):
        AccX1.append(data[i][8])
        AccY1.append(data[i][9])
        AccZ1.append(data[i][10])
        AccX2.append(data[i][24])
        AccY2.append(data[i][25])
        AccZ2.append(data[i][26])
        
        time.append(data[i][0])
        
fig = plt.figure()
ax1 = plt.axes(xlim=(0,3000), ylim=(6,-6))
line, = ax1.plot([], [], lw=2)
plt.xlabel('ACC')
plt.ylabel('Time')

plotlays, plotcols = [3], ["r","g","b"]
lines = []
for index in range(3):
    lobj = ax1.plot([],[],lw=2,color=plotcols[index])[0]
    lines.append(lobj)


def init():
    for line in lines:
        line.set_data([],[])
    return lines

x1,y1 = [],[]
x2,y2 = [],[]
x3,y3 = [],[]



i=0

def animate(frame):
    global i
    
    i+=1
    x = i
    y = AccX1[i]

    x1.append(x)
    y1.append(y)

    x = i
    y = AccY1[i]
    x2.append(x)
    y2.append(y)

    x = i
    y = AccZ1[i]
    x3.append(x)
    y3.append(y)
    

    xlist = [x1, x2,x3]
    ylist = [y1, y2,y3]


    for lnum,line in enumerate(lines):
        line.set_data(xlist[lnum], ylist[lnum]) 


    return lines


anim = animation.FuncAnimation(fig, animate,
                    init_func=init, blit=True,interval=10)
anim.save('test.mp4',writer=FFwriter)
plt.show()


  


  The dynamic picture ran out using plt.show is correct.
And I don't think I have set the length of storage. Did I add something ?

  


• Using FFMPEG, how do we add subtitles in the black bar area or under the video ?

  26 septembre 2020, par DunceDancer

  I followed these steps :

  


  


  1. Added the black bars

     


     -vf "scale=1920:1080:force_original_aspect_ratio=decrease,pad=1920:1080 :(ow-iw)/2 :(oh-ih)/2,setsar=1" Source :How to add black borders to video

     


  


  2. Added the subtitles ("burned" it into the video)

     


     ffmpeg -i "imput.mp4" -lavfi "subtitles=subtitles.srt:force_style='Alignment=0,OutlineColour=&H100000000,BorderStyle=3,Outline=1,Shadow=0,Fontsize=18,MarginL=5,MarginV=25'" -crf 1 -c:a copy "output.mp4" Source : ffmpeg subtitles alignment and position

     


  


  


  Now I am stuck as to how to place the subtitles under the video or in the black screen.

  


  Edit : Screenshot added to clarify

  


  Screenshot of the Problem

  


• dnn_backend_native_layer_mathunary : add floor support

  6 août 2020, par Mingyu Yin

  dnn_backend_native_layer_mathunary : add floor support
  It can be tested with the model generated with below python script :
  import tensorflow as tf
  
  import os
  
  import numpy as np
  
  import imageio
  
  from tensorflow.python.framework import graph_util
  
  name = 'floor'
  pb_file_path = os.getcwd()
  
  if not os.path.exists(pb_file_path+'/{}_savemodel/'.format(name)) :
  
      os.mkdir(pb_file_path+'/{}_savemodel/'.format(name))
  with tf.Session(graph=tf.Graph()) as sess :
  
      in_img = imageio.imread('detection.jpg')
  
      in_img = in_img.astype(np.float32)
  
      in_data = in_img[np.newaxis, :]
  
      input_x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
  
      y_ = tf.math.floor(input_x*255)/255
  
      y = tf.identity(y_, name='dnn_out')
  
      sess.run(tf.global_variables_initializer())
  
      constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])
      with tf.gfile.FastGFile(pb_file_path+'/{}_savemodel/model.pb'.format(name), mode='wb') as f :
  
          f.write(constant_graph.SerializeToString())
      print("model.pb generated, please in ffmpeg path use\n \n \
  
      python tools/python/convert.py {}_savemodel/model.pb —outdir={}_savemodel/ \n \nto generate model.model\n".format(name,name))
      output = sess.run(y, feed_dict= input_x : in_data)
  
      imageio.imsave("out.jpg", np.squeeze(output))
      print("To verify, please ffmpeg path use\n \n \
  
      ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 {}_savemodel/tensorflow_out.md5\n  \
  
      or\n \
  
      ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow {}_savemodel/out_tensorflow.jpg\n \nto generate output result of tensorflow model\n".format(name, name, name, name))
      print("To verify, please ffmpeg path use\n \n \
  
      ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native -f framemd5 {}_savemodel/native_out.md5\n  \
  
      or \n \
  
      ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native {}_savemodel/out_native.jpg\n \nto generate output result of native model\n".format(name, name, name, name))
  Signed-off-by : Mingyu Yin <mingyu.yin@intel.com>
  

  • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.c
  • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.h
  • [DH] tests/dnn/dnn-layer-mathunary-test.c
  • [DH] tools/python/convert_from_tensorflow.py
  • [DH] tools/python/convert_header.py