![]() The main thing that will give you trouble in this game are the riddles provided to you by Mr. Along with the memories, each area comes with its own set of collectibles, ranging from cats, to butterflies, to clogs, none of which are too terribly difficult to find. Each individual will come with a variety of memories that Morris must discover before they'll appear. The only problem is that that new Custodian must already be dead so he and Sparky will need to hunt out the proper candidates and try to convince them to take on the task. Joined by his lifetime companion, Sparky, a sharp-witted and belly-rub loving pupperz, Morris will need to find a new Custodian to calm the island before the volcano it sits on destroys everything. P.S.In I Am Dead, you play as Morris, a not-so-long deceased museum curator who is put on a journey to save the island he once inhabited. Also, if you need to perform those operations repeatedly, avoid allocating and deallocating memory in a loop: do not perform conversion "in-place", but rather use two separate arrays (one for 8-bit image, one for floating-point). OpenCV is able to use multithreading (verify that all CPU cores are utilized) and vectorization (you may check that, perhaps by looking at the actual code that performs conversion). Conversion to float is probably not the bottleneck. Does it perform any unnecessary conversions into some intermediate representation or just reads the blob of bytes? If the former is true, try to find another library or even parse the TIFF image manually (if it is not compressed, parsing TIFF is not a big deal). Try to find out, what library OpenCV uses to handle TIFF. What could perhaps matter is the TIFF image processing library. Allocate the required amount of memory and use the native API of your OS to read the file: you'll get the maximum. The straightforward answer to your question "What is the fastest way to read from HDD with c++?" is probably "C++ has (almost) no impact on speed of data transfer from HDD to RAM". Does your benchmark measure what you really intend to measure (i.e., does MatLab really convert images to float)? Are images read correctly without errors? Can file system caching affect the result? If yes, try to use larger datasize. First: try to verify that your benchmark is really correct. Time for reading 250 different images was measured by function clock() in c++ and tic-toc in matlab (and was divided over 250 to find time per 1 image). In c++ I used release 圆4 mode with full optimization. Is it possible to achieve peak bandwidth with c++, as I did with Matlab? After reading it into array of uchar, I convert my data into array of floats: image = imread(b, 0) Īfter testing this implementation, I got worse result: 8.8ms per image. Unfortunately, I cannot read image directly into array of floats with OpenCV. This corresponds to ~300 Mb/s and is close to peak bandwidth of my HDD.īut, as long as I work with CUDA, I need to use C++. With matlab ( imread) the time for reading 1 image is 5.8ms. I wrote test programs, which read about 250 images and measure a time: My Images are 8-bit grayscale 1200x1600 (size 1.92 mb). What is the fastest way to read from HDD with c++? ![]() In order to do that, first I need to read Image from HDD to CPU RAM memory(into 1d array of floats) and then use cudamemcpy to copy it into GPU RAM. The processing time is about 24ms, so the speed of reading is important for me. ![]() They must be in GPU RAM memory as 1D arrays (0-based, row-major storage). Input for CUDA kernel is two grayscale 8-bit images (. I am working with Image-Processing on GPU(CUDA).
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