Initial vendor packages

Signed-off-by: Valentin Popov <valentin@popov.link>

1 files changed, 129 insertions, 0 deletions

diff --git a/vendor/exr/examples/8_read_raw_blocks.rs b/vendor/exr/examples/8_read_raw_blocks.rs
new file mode 100644
index 0000000..4864c98
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+++ b/vendor/exr/examples/8_read_raw_blocks.rs
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+
+extern crate rand;
+extern crate half;
+
+use std::io::{BufReader};
+use std::fs::File;
+use exr::block::reader::ChunksReader;
+
+// exr imports
+extern crate exr;
+
+
+/// Collects the average pixel value for each channel.
+/// Does not load the whole image into memory at once: only processes the image block by block.
+/// On my machine, this program analyzes a 3GB file while only allocating 1.1MB.
+fn main() {
+    use exr::prelude::*;
+
+    let file = BufReader::new(
+        File::open("3GB.exr")
+            .expect("run example `7_write_raw_blocks` to generate this image file")
+    );
+
+
+    // -- the following structs will hold the collected data from the image --
+
+    /// Collect averages for each layer in the image
+    #[derive(Debug)]
+    struct Layer {
+        #[allow(unused)] // note: is used in Debug impl
+        layer_name: Option<Text>,
+
+        data_window: IntegerBounds,
+
+        /// Collect one average float per channel in the layer
+        channels: Vec<Channel>,
+    }
+
+    /// A single channel in the layer, holds a single average value
+    #[derive(Debug)]
+    struct Channel {
+        #[allow(unused)] // note: is used in Debug impl
+        channel_name: Text,
+
+        sample_type: SampleType, // f32, u32, or f16
+        average: f32,
+    }
+
+    let start_time = ::std::time::Instant::now();
+
+
+    // -- read the file, summing up the average pixel values --
+
+    // start reading the file, extracting the meta data of the image
+    let reader = exr::block::read(file, true).unwrap();
+
+    // print progress only if it advances more than 1%
+    let mut current_progress_percentage = 0;
+
+    // create the empty data structure that will collect the analyzed results,
+    // based on the extracted meta data of the file
+    let mut averages = reader.headers().iter()
+        // create a layer for each header in the file
+        .map(|header| Layer {
+            layer_name: header.own_attributes.layer_name.clone(),
+            data_window: header.data_window(),
+
+            // create a averaging channel for each channel in the file
+            channels: header.channels.list.iter()
+                .map(|channel| Channel {
+                    channel_name: channel.name.clone(),
+                    sample_type: channel.sample_type,
+                    average: 0.0
+                })
+                .collect()
+        })
+        .collect::<Vec<_>>();
+
+    // create a reader that loads only relevant chunks from the file, and also prints something on progress
+    let reader = reader
+
+        // do not worry about multi-resolution levels or deep data
+        .filter_chunks(true, |meta_data, tile, block| {
+            let header = &meta_data.headers[block.layer];
+            !header.deep && tile.is_largest_resolution_level()
+        }).unwrap()
+
+        .on_progress(|progress|{
+            let new_progress = (progress * 100.0) as usize;
+            if new_progress != current_progress_percentage {
+                current_progress_percentage = new_progress;
+                println!("progress: {}%", current_progress_percentage)
+            }
+        });
+
+    // read all pixel blocks from the image, decompressing in parallel
+    reader.decompress_parallel(true, |meta_data, block|{
+        let header = &meta_data.headers[block.index.layer];
+
+        // collect all pixel values from the pixel block
+        for line in block.lines(&header.channels) {
+            let layer = &mut averages[line.location.layer];
+            let channel = &mut layer.channels[line.location.channel];
+            let channel_sample_count = layer.data_window.size.area() as f32;
+
+            // now sum the average based on the values in this line section of pixels
+            match channel.sample_type {
+                SampleType::F16 => for value in line.read_samples::<f16>() {
+                    channel.average += value?.to_f32() / channel_sample_count;
+                },
+
+                SampleType::F32 => for value in line.read_samples::<f32>() {
+                    channel.average += value? / channel_sample_count;
+                },
+
+                SampleType::U32 => for value in line.read_samples::<u32>() {
+                    channel.average += (value? as f32) / channel_sample_count;
+                },
+            }
+        }
+
+        Ok(())
+    }).unwrap();
+
+    println!("average values: {:#?}", averages);
+
+    // warning: highly unscientific benchmarks ahead!
+    println!("\nprocessed file in {:?}s", start_time.elapsed().as_secs_f32());
+}
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