EXR To PNG Conversion Issue: Exposure Mismatch In Dataset?
Hey guys! 👋 There's been a discussion in the community about a potential issue with the indoor_real/classroom dataset. It seems like there's an incorrect conversion from EXR to PNG, leading to a noticeable exposure mismatch between the two formats. Let's dive into what's happening and how we can address it.
The Problem: Mismatched Exposures
The core of the issue lies in the discrepancy between the EXR and PNG images within the dataset. As you can see in the provided images, the PNG versions appear significantly different in terms of exposure compared to their EXR counterparts. This can be a major headache for anyone relying on the dataset for tasks like training machine learning models or conducting research, as accurate and consistent data is crucial for reliable results.
This exposure difference suggests that the PNG images might not be a faithful representation of the original EXR data. It raises questions about the conversion process and whether a proper tonemapping or exposure adjustment was applied. Without the correct conversion, the luminance and color information in the PNGs could be skewed, impacting any analysis or application that uses them. It's super important to figure out the root cause to ensure the dataset's integrity and usability for the community.
The Proposed Solution: Using a Conversion Script
To address this issue, a solution has been proposed involving a specific script for converting EXR to PNG. This script, adapted from the EyefulTower project (https://github.com/facebookresearch/EyefulTower/blob/main/README.md#example-code-reading-exr-images-to-create-jpegs), aims to perform a more accurate conversion that preserves the intended exposure and dynamic range.
The script utilizes OpenCV, a popular library for computer vision tasks, along with NumPy for numerical operations. A crucial step involves enabling OpenEXR support within OpenCV by setting the environment variable OPENCV_IO_ENABLE_OPENEXR = "1". This ensures that OpenCV can correctly read the EXR image format, which stores high-dynamic-range (HDR) data.
The key to this conversion process lies in the tonemapping step. EXR images store radiance values linearly, representing the actual amount of light in the scene. However, these values often exceed the displayable range of standard image formats like PNG, which are typically encoded in sRGB color space. Tonemapping is the technique used to map these high dynamic range values into a displayable range, while preserving as much detail and visual fidelity as possible. The script applies an sRGB curve for tonemapping, which is a common approach for achieving a visually pleasing result.
Here's the Python script suggested for the conversion:
import os, cv2, numpy as np
# Enable OpenEXR support in OpenCV (https://github.com/opencv/opencv/issues/21326).
# This environment variable needs to be defined before the first EXR image is opened.
os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
# Read an EXR image using OpenCV.
img = cv2.imread("input.exr", cv2.IMREAD_UNCHANGED)
# Tonemap using sRGB curve.
linear_part = 12.92 * img
exp_part = 1.055 * (np.maximum(img, 0.0) ** (1 / 2.4)) - 0.055
img = np.where(img <= 0.0031308, linear_part, exp_part)
# Write resulting image as JPEG.
img = np.clip(255 * img, 0.0, 255.0).astype(np.uint8)
cv2.imwrite("output.png", img, params=[cv2.IMWRITE_JPEG_QUALITY, 100])
This script first reads the EXR image using cv2.imread with the cv2.IMREAD_UNCHANGED flag, ensuring that the image is read in its original format without any automatic conversions. Then, it applies the sRGB tonemapping curve using NumPy operations. The np.where function is used to apply different formulas based on the pixel value, effectively implementing the sRGB curve. Finally, the tonemapped image is clipped to the range of 0-255, converted to an 8-bit unsigned integer format (np.uint8), and saved as a PNG image using cv2.imwrite.
The Results: A More Accurate Conversion
By using the provided script, a significant improvement in the conversion quality can be observed. The resulting PNG images more closely resemble the original EXR images in terms of exposure and overall appearance. This suggests that the script effectively addresses the exposure mismatch issue, providing a more accurate representation of the scene's radiance.
The visual comparison between the original PNGs and the newly converted ones highlights the importance of proper tonemapping in EXR to PNG conversion. The script ensures that the high dynamic range information in the EXR image is effectively compressed into the limited dynamic range of the PNG format, resulting in a more visually consistent and accurate representation. This is super crucial for applications where accurate color and luminance information is essential.
The Question: Is This Behavior Intentional?
Now, the big question arises: is this exposure difference between the original PNGs and EXRs in the dataset intentional? 🤔 There are a couple of possibilities to consider.
One possibility is that the original conversion process involved dividing the highest radiance value in the EXR image by a certain factor, effectively normalizing the overall brightness. This could have been done to bring the image into a more manageable range for display or processing. However, this approach can sometimes lead to a loss of detail in darker areas of the image, which might explain the observed differences. This method of normalizing the radiance could inadvertently dim the overall image, causing the exposure mismatch we see.
Another possibility is that a constant number was divided across the entire scene to lower the overall radiance. This is another way to reduce the dynamic range, but it can also have unintended consequences. Dividing by a constant value might not accurately preserve the relative brightness differences between objects in the scene, potentially leading to an unnatural or washed-out appearance. Essentially, this method could uniformly darken the image, leading to the observed exposure discrepancies.
Understanding the original intent behind the conversion process is crucial for determining the best way to use the dataset. If the exposure difference was intentional, it might be necessary to account for it in any analysis or application. However, if it was an unintended consequence of a suboptimal conversion method, using the provided script to generate more accurate PNGs would be the preferred approach. It’s super important to clarify this to ensure the dataset is used correctly and effectively.
Conclusion: Ensuring Data Integrity
In conclusion, the identified incorrect conversion from EXR to PNG in the indoor_real/classroom dataset highlights the importance of careful data handling and conversion techniques. The proposed script offers a viable solution for generating more accurate PNG representations of the EXR images, addressing the exposure mismatch issue. However, the question of whether the original behavior was intentional remains open.
It's essential for the dataset maintainers to clarify the reasoning behind the original conversion process. This will help users understand the data better and make informed decisions about how to use it. In the meantime, using the provided script is a good way to ensure that you're working with PNG images that accurately reflect the information contained in the EXR files. Let's keep the discussion going and work together to maintain the integrity of this valuable dataset! 💪