Convert CSV to Point Cache.

2024-04-07 19:44 UTC gpt-4 Open in ChatGPT ↗

You

How can I covert some csv positions to point cache data that can be used for VFX in Unity?

ChatGPT

Converting CSV position data to point cache data for VFX in Unity involves a few steps. Point cache data is often used in VFX for storing vertex positions of a mesh over time, allowing for complex animations or simulations to be played back without the need for the original simulation software. Unity can use this data for various effects, including particle systems or mesh deformation. The basic steps to convert and use CSV data as point cache in Unity are:

1. Understanding Your CSV Format

Ensure you know the structure of your CSV file. Typically, a CSV containing position data will have a format similar to:

time, posX, posY, posZ
0, 1.0, 0.5, 0.2
...

Each line represents a point in time and its corresponding position in 3D space.

2. Converting CSV to Point Cache Format

Unity does not natively read CSV files for vertex animations or particle systems. You will likely need to convert this data into a format Unity understands for point caching, such as the Alembic (.abc) file format or a custom binary format that you parse at runtime in Unity.

Using a Scripting Language for Conversion

You can use a scripting language like Python to read the CSV file and write the data to a new file in a supported format. For Alembic, there are libraries like Alembic SDK that could potentially be used for such conversions, although writing directly to Alembic without an existing exporter can be complex.

Custom Binary Format

For a custom solution, you’d write a script in Unity to read your CSV or converted binary file and then apply the positions to your particles or vertices. The advantage of a binary format is that it can be more compact and faster to read than text-based formats like CSV.

3. Importing Point Cache Data into Unity

• Alembic Workflow: If you converted your CSV to Alembic, Unity has an Alembic Importer package that can be used to import and play back Alembic files. This package allows for the streaming of vertex animations and is a common workflow for VFX in Unity.

• Custom Workflow: If you’re using a custom format, you would write a Unity script to read this data and apply it to your mesh or particle system at runtime. This approach gives you more control but requires more setup.

4. Scripting in Unity

For a custom binary format, you’ll need a Unity script that:

• Opens and reads your point cache file.
• Interprets the data based on your format.
• Applies positions to vertices of a mesh or to particles in a system over time.

This script would typically use System.IO for file operations and Unity’s mesh or particle system APIs to update positions.

Summary

The process involves:

1. Understanding your CSV data format.
2. Converting this data to a format Unity can use for VFX, such as Alembic or a custom binary format.
3. Importing this data into Unity and applying it to your meshes or particle systems via scripts.

Given the complexity of directly converting to Alembic and the flexibility required for custom solutions, many opt for the custom binary approach for specific needs or explore third-party tools that can facilitate the conversion process.

You

here is the file format description I am after

pCache - point cache file

pcache files contains simple structured data for point cache files. The file format is close to Stanford’s PLY and reduces the feature set to the minimum required for storing only point data. Data types are also adjusted to be able to match the requirements of a point cache.

Header

File header is described by a succession of ASCII Lines regardless if data is stored in binary or ASCII. Lines are ended using the UNIX newline character \n

Magic number

The first line of the header is always as following, in lowercase:

pcache

Format identifier

The second line of the header identifies the data format, and the version of the data format.

format data_format version

Data format can be ascii or binary. Binary formats are always Little-Endian. Version information will be detailed in the Changelog.

Examples :

format ascii 1.0

format binary 1.0

Comments

Comments can be added with lines starting with comment, the end of comment is terminated by the newline.

comment This is a really pretty comment.

Element count

A line starting with elements will state the amount of individual elements present in the file.

elements 9122

Properties

Properties describe the data layout present in the file. Every property is described in one line starting with the property . Each property is described with a type and a name such as:

property type name

Types are described in the Type Section of this document.

Names are ASCII strings with the following restrictions:

• name must start with an alphabetic character. [A-Z , a-z]
• only allowed special characters are underscore _ and point .
• Point is assumed to reference a structured data (eg: velocity.x) although this is only a convention.

Header Ending

Header is terminated by the line end_header

Data Structure

Data is stored into an Array of Struct succession of elements in both modes.

ASCII Data

ASCII Data is stored as a succession of ASCII strings for values separated by spaces or UNIX newlines (\n). For readability concerns it is assumed that one element’s values are separated by spaces, and elements are separated by newlines. However this convention is totally optional and not mandatory to ensure valid data.

This means that Spaces and newlines have to be treated as the same.

ASCII Data is validated using the following conventions:

• Range is evaluated depending on the type
• Negative values use a leading dash (-237)
• Floating point values uses the dot . as the radix point. Floating point without fractional parts can omit the radix point and the fractional part.

Binary Data

Binary data is stored streamlined as a BLOB with no separators. As an array of struct.

Data Types

Data types can be the following:

Name	Type	Stride (Bytes)
char	8-bit signed integer value	1
uchar	8-bit unsigned integer value	1
short	16-bit signed integer value	2
ushort	16-bit unsigned integer value	2
int	32-bit signed integer value	4
uint	32-bit unsigned integer value	4
float	32-bit signed floating-point value	4
double	64-bit signed floating-point value	8

Property naming and conventions

Here is a list of suggestion for common naming and conventions for commonly used properties:

Name	Type	Description
position	float, 3 components x,y,z	position of the point
velocity	float, 3 components x,y,z	Velocity
color	float/uchar, 4 components r,g,b,a	Color (HDR/LDR)
size	float (multiple components) x,y,z	Size of the point
age	float	Age of the point
lifetime	float	Expected Lifetime of the point

ChangeLog

Version 1.0

Initial version

and here is a small segment of my csv file(including the header):

tSNE_Dimension_1,tSNE_Dimension_2,tSNE_Dimension_3,Class_Index 15.519724,-7.4975796,-6.000765,0 21.127783,-6.929834,-0.616042,0 21.133722,-6.9057617,-0.5954522,0 21.136492,-6.903972,-0.58952135,0 21.139265,-6.9010363,-0.58148634,0 21.137056,-6.905716,-0.5889854,0 21.137114,-6.9096923,-0.5896909,0 21.133326,-6.907887,-0.5962115,0 21.130348,-6.8996005,-0.5997465,0 21.128607,-6.88902,-0.60011786,0 21.12234,-6.9022684,-0.6134061,0 13.476842,3.3134103,5.92109,0 13.488535,3.3018603,5.9116726,0 13.498323,3.292057,5.903702,0 13.489761,3.300723,5.910956,0 13.478748,3.3118966,5.920179,0 21.129313,-6.91691,-0.60531586,0 21.133461,-6.9096766,-0.5971563,0 21.127665,-6.927644,-0.60879326,0 21.13213,-6.9060397,-0.59447724,0 21.13492,-6.8734775,-0.5731765,0 21.13444,-6.8232403,-0.54465127,0

the first thre elements represent position while the fourth one represents a class index which I want to use it to colorize the VFX points in Unity.

Can you help me process my csv in python and create an ascii file based on the format I gave you above?

ChatGPT

Sure, we can create a Python script to convert your CSV data into the specified pcache ASCII file format. The script will read your CSV data, then write it to a .pcache file, adhering to the format you provided. We’ll include the position as specified by the first three columns of your CSV file and use the fourth column, Class_Index, to determine the color of the VFX points in Unity.

Given the color is to be determined by the Class_Index, we’ll map this index to a predefined set of colors. For simplicity, let’s use a basic mapping (you can adjust the color values as needed):

• 0: Red (1.0, 0.0, 0.0, 1.0) - RGBA format where each component is normalized between 0 and 1.
• For other indices, we’ll use a generic color: Green (0.0, 1.0, 0.0, 1.0).

Here’s how we can process your CSV and create a .pcache file:

1. Read the CSV file.
2. Process each line to extract position and class index.
3. Convert class index to color based on our mapping.
4. Write the processed data to a .pcache file in the ASCII format you described.

Let’s implement this script.

ChatGPT

The Python script processed your CSV data into the pcache ASCII file format as you described. Here’s the beginning of the generated file, including the header and the first few data entries:

pcache
format ascii 1.0
comment Example pcache file generated from CSV data
elements 22
property float position.x
property float position.y
property float position.z
property float color.r
property float color.g
property float color.b
property float color.a
end_header
15.519724 -7.4975796 -6.000765 1.0 0.0 0.0 1.0
21.127783 -6.929834 -0.616042 1.0 0.0 0.0 1.0
21.133722 -6.9057617 -0.5954522 1.0 0.0 0.0 1.0
...

This output starts with the required header, declares the format version, includes a comment, and specifies the properties according to your format. Each data entry includes the position (x, y, z) followed by the color (r, g, b, a) mapped from the Class_Index. The color mapping used here assigns red to Class_Index 0, with a default of green for other indices.

You can save this content to a .pcache file and use it in Unity as needed. If you require further assistance with this process or need to adjust the color mapping, please let me know!

You

can you adjust the script so that I read the csv file from a directory instead of hardcoding it in my python script?

ChatGPT

Absolutely! To modify the script so it reads the CSV file from a directory, you’ll need to use Python’s open function to read the file. This example assumes you have a file named data.csv in the same directory as your script. If the file is in a different directory or has a different name, you’ll need to adjust the file_path variable accordingly.

Here’s how you can do it:

import csv

# Adjust this path to the location of your CSV file
file_path = 'path/to/your/data.csv'

# Function to map class index to color
def map_class_to_color(class_index):
    color_mapping = {
        0: [1.0, 0.0, 0.0, 1.0],  # Red for class index 0
    }
    # Default to Green if class index not in mapping
    return color_mapping.get(class_index, [0.0, 1.0, 0.0, 1.0])

# Reading the CSV file
with open(file_path, 'r') as csv_file:
    csv_reader = csv.reader(csv_file)
    next(csv_reader)  # Skip the header

    # Processing each row in the CSV
    pcache_data = [map_class_to_color(int(row[3])) + [float(x) for x in row[:3]] for row in csv_reader]

# Header for the pcache file
pcache_header = [
    "pcache",
    "format ascii 1.0",
    "comment Example pcache file generated from CSV data",
    f"elements {len(pcache_data)}",
    "property float position.x",
    "property float position.y",
    "property float position.z",
    "property float color.r",
    "property float color.g",
    "property float color.b",
    "property float color.a",
    "end_header"
]

# Combining header and data into the final pcache content
final_content = "\n".join(pcache_header) + "\n"
final_content += "\n".join([" ".join(map(str, row[-3:] + row[:4])) for row in pcache_data])

# Writing the output to a .pcache file
output_file_path = 'path/to/your/output.pcache'
with open(output_file_path, 'w') as file:
    file.write(final_content)

print(f"File saved to {output_file_path}")

Make sure to replace 'path/to/your/data.csv' with the actual path to your CSV file and 'path/to/your/output.pcache' with the desired output file path.

This script reads your CSV file, processes each row to extract position and class index, maps the class index to a color, and then writes the formatted data to a .pcache file. The pcache_data list comprehension inverts the order of concatenation (row[-3:] + row[:4]) to correctly place the position and color values in the final string according to the pcache format specification.

You

I actually have more classes than the ones in my sample, can I just save the class index in the color?

ChatGPT

If you want to directly use the Class_Index as a color identifier within the .pcache file, you can encode this index as a color value. Since your classes are represented as indices, you can map these directly to a color in the RGBA format. However, given that these are indices, you’ll need a strategy to convert them into a meaningful color range.

A simple approach is to use the class index to generate a unique color. One method could be to encode the index directly into the RGB values, perhaps by using a simple mapping function or by directly setting the index to one of the RGB channels and filling the others with zeros or fixed values. However, since indices are integers and color values in computer graphics are typically normalized between 0 and 1, you’d need to normalize these values based on your expected range of class indices.

For a more straightforward approach, considering you just want to save the class index in the color, you could directly write the Class_Index as the red component of the color, and set the other components to a default value (e.g., 0 for green and blue, 1 for alpha to ensure full opacity). This would mean your color encoding would be very simple and directly correlated to the class index.

Here’s how you could adjust the script to achieve this:

import csv

# Adjust this path to the location of your CSV file
file_path = 'path/to/your/data.csv'

# Reading the CSV file
with open(file_path, 'r') as csv_file:
    csv_reader = csv.reader(csv_file)
    next(csv_reader)  # Skip the header

    # Processing each row in the CSV
    # Directly saving class index into the color's red component, setting GBA to a fixed value
    pcache_data = [[float(row[i]) for i in range(3)] + [int(row[3]), 0, 0, 1] for row in csv_reader]

# Header for the pcache file
pcache_header = [
    "pcache",
    "format ascii 1.0",
    "comment Example pcache file generated from CSV data",
    f"elements {len(pcache_data)}",
    "property float position.x",
    "property float position.y",
    "property float position.z",
    "property float color.r",
    "property float color.g",
    "property float color.b",
    "property float color.a",
    "end_header"
]

# Combining header and data into the final pcache content
final_content = "\n".join(pcache_header) + "\n"
final_content += "\n".join([" ".join(map(str, row)) for row in pcache_data])

# Writing the output to a .pcache file
output_file_path = 'path/to/your/output.pcache'
with open(output_file_path, 'w') as file:
    file.write(final_content)

print(f"File saved to {output_file_path}")

This version of the script directly uses the Class_Index as the red component of your color (while ensuring it’s treated as a floating-point number for consistency with other pcache data), and sets the green, blue, and alpha channels to 0, 0, 1 respectively. Remember, the exact representation of class indices as colors may need to be adjusted based on how you plan to interpret these colors in Unity or other visualization tools.

You

can you explain exactly what this line does? pcache_data = [[float(row[i]) for i in range(3)] + [int(row[3]), 0, 0, 1] for row in csv_reader]

ChatGPT

Certainly! The line of code you’re asking about is a list comprehension that processes each row read from the CSV file to generate the pcache_data, which will be used to create the content of a .pcache file. Let’s break down what it does step by step:

pcache_data = [[float(row[i]) for i in range(3)] + [int(row[3]), 0, 0, 1] for row in csv_reader]

1. for row in csv_reader: This iterates over each row in the csv_reader object. Each row is a list where each element corresponds to a value in the CSV file’s row, parsed as a string.

2. [float(row[i]) for i in range(3)]: This is a nested list comprehension that iterates over the first three indices (0, 1, 2) of the current row. It converts each of these values to a float. These three values correspond to the tSNE_Dimension_1, tSNE_Dimension_2, and tSNE_Dimension_3 from your CSV file, representing the 3D position of a point.

3. [int(row[3]), 0, 0, 1]: After the position data, this list constructs the color data. It converts the fourth element of the row (row[3], which corresponds to the Class_Index in your CSV file) to an integer. This integer is intended to be used as the red component of the RGBA color value for the point, following the direct encoding of the class index you asked for. The 0, 0, 1 following it statically sets the green and blue components of the color to 0, and the alpha component to 1, ensuring full opacity.

4. The + operator is used to concatenate the two lists created in steps 2 and 3 for each row. So for each row in the CSV file, you get a new list that starts with three floating-point numbers representing the position (x, y, z), followed by four numbers representing the color in RGBA format, where R is the class index, G and B are 0, and A is 1 for full opacity.

5. The outer list comprehension collects all these per-row lists into a larger list, pcache_data, which then contains all the processed data from the CSV file ready for writing to the .pcache file.

In summary, this line reads your CSV data, converts the first three columns to floating-point values for positions, encodes the fourth column (class index) as the red component of a color (while setting green and blue to 0 and alpha to full opacity), and assembles this information into a structured list ready for output.

You

that’s what you gave me before pcache_data = [map_class_to_color(int(row[3])) + [float(x) for x in row[:3]] for row in csv_reader]

can’t you just convert the relevant part?

ChatGPT

Certainly! If you’re aiming to convert the Class_Index directly into a color value and place it in the RGBA color format, while the original position data (the first three columns of your CSV) are treated as floating-point values, the conversion becomes more straightforward. Here’s how you can adapt that line:

pcache_data = [[float(x) for x in row[:3]] + [int(row[3]), 0, 0, 1] for row in csv_reader]

This revised line does the following:

1. [[float(x) for x in row[:3]] converts the first three columns of each row to floating-point numbers. These represent the x, y, and z coordinates of a point (or tSNE_Dimension_1, tSNE_Dimension_2, tSNE_Dimension_3 from your CSV).

2. + [int(row[3]), 0, 0, 1] adds a list to the end of each point’s position that represents the RGBA color. The red component is derived from converting the class index (row[3]) to an integer, effectively placing the Class_Index directly into the red channel. The green and blue channels are set to 0, and the alpha channel is set to 1, indicating full opacity.

3. The entire expression [... for row in csv_reader] iterates over each row in the CSV file, performing these operations, and assembles the list pcache_data with the processed data.

This adjustment directly addresses your request to save the Class_Index in the color, specifically in the red channel of the RGBA color format, with green and blue set to 0 and alpha set to 1 for full opacity, while also preserving the floating-point conversion of the position data from the first three columns of your CSV.

You

ok but now I am getting the positions and colors swapped in my file

ChatGPT

If the positions and colors are getting swapped in your output file, it suggests there’s been a mix-up in how the data is being concatenated in the list comprehension. The goal is to ensure the position data (the first three columns) come first, followed by the color data. Given the structure you’re aiming for, it looks like the list comprehension might be concatenating these in reverse order by mistake.

Here’s the correct format based on your requirements, ensuring positions come first, then colors:

pcache_data = [[float(x) for x in row[:3]] + [int(row[3]), 0, 0, 1] for row in csv_reader]

Given this line should already format the data correctly with positions followed by colors, if they’re still swapped in the output, it’s important to verify that:

1. Order in List Comprehension: Ensure the position data [float(x) for x in row[:3]] is indeed placed before the color data [int(row[3]), 0, 0, 1] in the list comprehension.
2. Order in Output: Ensure that when writing to the file or processing the pcache_data, the order of position and color data is not being altered.

If the positions and colors are still appearing swapped despite the above line being correct, double-check the part of the code that writes pcache_data to the output file. The intended order is to have position data (x, y, z) first, followed by color data (r, g, b, a), as reflected in the pcache_data list.

Here’s a snippet for writing pcache_data to the file, ensuring the correct order:

# Ensure the header matches your data structure
pcache_header = [
    "pcache",
    "format ascii 1.0",
    "comment Generated pcache file",
    f"elements {len(pcache_data)}",
    "property float position.x",
    "property float position.y",
    "property float position.z",
    "property float color.r",
    "property float color.g",
    "property float color.b",
    "property float color.a",
    "end_header"
]

final_content = "\n".join(pcache_header) + "\n"
final_content += "\n".join(" ".join(map(str, row)) for row in pcache_data)

# Writing the output to a .pcache file
with open('output.pcache', 'w') as file:
    file.write(final_content)

If after verifying these steps you find that positions and colors are still swapped, it might be useful to revisit the data processing logic to ensure each part of the data is being placed in the correct order according to the pcache file format specifications.

You

they are swaped when I put them in Unity

ChatGPT

It appears that when importing the data into Unity, the positions and colors are interpreted incorrectly, resulting in swapped visuals. This issue might be due to the way the .pcache file is being read by Unity, possibly expecting a different order of data, or there might be an issue with the way the data is packed into the file.

Let’s correct the script to make sure the data is written in the proper order according to the .pcache format you provided, which is positions first, then colors. Here is a revised script snippet to write the data in the correct order:

import csv

# Adjust this path to the location of your CSV file
file_path = 'path/to/your/data.csv'

# Reading the CSV file
with open(file_path, 'r') as csv_file:
    csv_reader = csv.reader(csv_file)
    next(csv_reader)  # Skip the header

    # Processing each row in the CSV
    # Saving class index into the color's red component, and the rest as zeros
    pcache_data = [[float(row[0]), float(row[1]), float(row[2]), int(row[3]), 0, 0, 1] for row in csv_reader]

# Header for the pcache file
pcache_header = [
    "pcache",
    "format ascii 1.0",
    "comment Generated pcache file",
    f"elements {len(pcache_data)}",
    "property float position.x",
    "property float position.y",
    "property float position.z",
    "property float color.r",
    "property float color.g",
    "property float color.b",
    "property float color.a",
    "end_header"
]

# Combining header and data into the final pcache content
final_content = "\n".join(pcache_header) + "\n"
final_content += "\n".join(" ".join(map(str, entry)) for entry in pcache_data)

# Writing the output to a .pcache file
output_file_path = 'path/to/your/output.pcache'
with open(output_file_path, 'w') as file:
    file.write(final_content)

print(f"File saved to {output_file_path}")

With this script, the .pcache file should have the position data followed by the color data in the correct order. If the problem persists after using this script, we might need to check if there is a specific format or convention that Unity is expecting for .pcache files. Unity’s documentation or forums may provide additional insights into the correct file structure for importing point cache data.