#SNS HDR SOFTWARE FULL#

Pixel-wise difference is chosen because spatial difference is more important in our case of unaltered adjacent views. In our approach, we apply histogram equalization to balance out multiple exposure levels, then a pixel-wise absolute difference. These methods are combined and tuned based on the practical constraints of the problem. pixel-wise or block-wise distance, edge or contour difference, norm-1 or norm-2 distances), and an association method. histogram equalization), a pair-wise similarity metric of choice (e.g. There exists several methods to carry out near-duplicate image detection which all involve using a pre-processing step (e.g. Since they are different in exposure, we cannot apply direct comparison methods like checksum. Each view has three images taken at different exposure level (shutter speed varied), and we need to robustly divide the 12 images into those groups of three. In this context, we have 12 fisheye images (180 deg vertical by 180 deg horziontal) of four adjacent views (left, front, right, back). Then OpenCV can be used to detect the four sets of near-duplicate three images of the same view. We use DCRAW to convert the DNG files to TIFF since OpenCV does not work directly with raw files (CR2, NEF, DNG). This is done using OpenCV (Python binding with Numpy), one of the most comprehensive Computer Vision libraries to date. The second problem of using SNS-HDR batch processing is to figure out the three images of the same view. One possible artifact with SNS-HDR on original raw files SNS-HDR works with RAW files, but it works especially well with converted raw DNG format (using DNGConverter), compared to two common formats CR2 or NEF. The first problem is on accepted formats for image input. The tricky part for using SNS-HDR as a batch tool is its limited support on file format input and the auto-grouping of images of the same view, and that is where Computer Vision comes into play. It has support for batch processing, excellent HDR quality, and adequate deghosting support (compared to PTGui). People often use alternative tools for HDR merging. While PTGui is a good choice for image stitching, its HDR quality is not the best available. The stitching process will return one equirectangular panorama.

We use PTGui, a stitching software, to carry out batch stitching of 12 fisheye images (180 degree x 180 degree) of the four different views (left, right, front, back) with three images of different exposure for each view. HDR imaging is normally achieved by merging multiple low-dynamic-range photographs. It is especially useful for panorama imaging where an evenly distributed look will greatly improve the quality. Meanwhile, HDR imaging is a common technique to produce a greater dynamic range of luminosity than standard digital imaging. Panorama images have now become a trending and must-have media type for most image-oriented websites. They provide an immersive experience for viewers to explore a ubiquitous view of the venue - whether it is outside at the pool, on the rooftop or inside a hotel room.

Panorama images at Oyster have full angle range with 180 degrees vertically and 360 degrees horizontally. In this first part of the series, we will introduce our automated pipeline for generating High Dynamic Range (HDR) panoramas. In this three-part series, we will be looking at the Computer Vision work that has been part of our panorama pipeline. One key component of our imagery database is panoramas, produced at high quality and large scale (over 150,000 to date). Here at Oyster, we are the leading website for comprehensive photographic reviews of hotels.

Generating HDR panoramas at scale by Tuan

#SNS HDR SOFTWARE FULL#

Sns hdr software