Wolfram Language

Procesamiento de imágenes y señales

Detecte objetos giratorios

ImageDisplacements puede capturar atributos dinámicos en secuencias de videos. De un cuadro de video a otro, ImageDisplacements proporciona un campo vectorial que describe el flujo óptico que esencialmente corresponde al campo de movimiento de objetos en 2D en el video.

El análisis de campo vectorial proporciona una variedad de herramientas para analizar campos de movimiento. Este ejemplo se enfoca en el rotacional para detectar objetos en rotación.

Una secuencia de video de turbinas de viento rotando.

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In[2]:=
Click for copyable input
ListAnimate[seq]
Reproducir
Detener

Obtenga el promedio del flujo óptico de toda la secuencia de video.

In[3]:=
Click for copyable input
flow = Mean@ImageDisplacements[Append[seq, First[seq]]];
In[4]:=
Click for copyable input
Show[ First[seq], ListVectorPlot[Transpose@Reverse@flow, VectorStyle -> Yellow, VectorPoints -> Fine] ]
Out[4]=

El rotacional de un campo vectorial determina la cantidad de rotación inherente.

In[5]:=
Click for copyable input
listCurl = flow \[Function] ListConvolve[{{0.5`, 0, -0.5`}}, flow[[All, All, 2]], {{1, 2}, {1, 2}}, "Fixed"] - ListConvolve[{{-0.5`}, {0.`}, {0.5`}}, flow[[All, All, 1]], {{1, 2}, {1, 2}}, "Fixed"];
In[6]:=
Click for copyable input
curl = ImageAdjust@Image@(listCurl@flow)
Out[6]=

Una distribución de rotacional borrosa proporciona un resultado más robusto para rotaciones a gran escala. El color negro es sinónimo de rotación negativa, mientras que el color blanco denota rotación con orientación positiva.

In[7]:=
Click for copyable input
blurredCurl = ImageAdjust[GaussianFilter[curl, 8 {3, 1}]]
Out[7]=

Coloree el mapa de rotación y superpóngalo sobre la imagen original.

In[8]:=
Click for copyable input
ImageCompose[seq[[1]], SetAlphaChannel[ Colorize[blurredCurl, ColorFunction -> ColorData[{"TemperatureMap", "Reverse"}]], ColorNegate[blurredCurl]]]
Out[8]=

Ejemplos relacionados

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