Updated download links

Author: vbalnt

download.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-base_url="http://www.iis.ee.ic.ac.uk/~vbalnt/hpatches"
+base_url="http://icvl.ee.ic.ac.uk/vbalnt/hpatches"
 hpatches_url="$base_url/hpatches-release.tar.gz"
 descrs_url="$base_url/descriptors"
 descrs_list="$base_url/descriptors/descrs.txt"
@@ -46,9 +46,4 @@ elif [ $1 = "descr" ]; then
 	    rm "./data/descriptors/$2.tar.gz"
 	fi
     fi
-else
-    echo "Usage: "
-    echo "sh download.sh hpatches  || downloads the patches dataset" 
-    echo "sh download.sh descr X   || downloads computed result files for descr X"
-    exit 1    
 fi

python/hpatches_vis.py

@@ -1,21 +1,46 @@
 from utils.hpatch import *
 import cv2 
 
+
+# all types of patches 
+tps = ['ref','e1','e3','e5','h1','h3','h5',\
+       't1','t3','t5']
+
+def vis_patches(seq,tp,ids):
+    """Visualises a set of types and indices for a sequence"""
+    h = len(tp)*65
+    vis = np.empty((h, 0))
+    # add the first column with the patch type names
+    vis_tmp = np.empty((0,55))
+    for t in tp:
+        tp_patch = 255*np.ones((65,55))
+        cv2.putText(tp_patch,t,(5,25),cv2.FONT_HERSHEY_DUPLEX , 1,0,1)                       
+        vis_tmp = np.vstack((vis_tmp,tp_patch))
+    vis = np.hstack((vis,vis_tmp))
+    # add the actual patches
+    for idx in ids:
+        vis_tmp = np.empty((0,65))
+        for t in tp:
+            vis_tmp = np.vstack((vis_tmp,get_patch(seq,t,idx)))
+        vis = np.hstack((vis,vis_tmp))
+    return vis
+
+
 # select a subset of types of patches to visualise
 # tp = ['ref','e5','h5','t5']
 #or visualise all - tps holds all possible types
 tp = tps
 
 # list of patch indices to visualise
-ids = range(10)
+ids = range(1,55)
 
 # load a sample sequence
-seq = hpatch_sequence('../data/hpatches-release/v_dogman/')
+seq = hpatch_sequence('../data/hpatches-release/v_calder/')
 vis = vis_patches(seq,tp,ids)
 
 # show 
-cv2.imshow("HPatches example", vis/255)
-cv2.waitKey(0)
+# cv2.imshow("HPatches example", vis/255)
+# cv2.waitKey(0)
 
 # or save
-# cv2.imwrite("patches.png", vis)
+cv2.imwrite("patches.png", vis)

python/readme.md

@@ -1,5 +1,5 @@
 ![logo](./utils/imgs/hpatch.png "logo") 
-## Python benchmark
+## Python implementation of the HPatches benchmark protocols
 
 This repository contains the `python` code for evaluating feature
 descriptors on the `HPatches` dataset. For more information on the
@@ -17,25 +17,10 @@ sudo apt-get install libopencv-dev python-opencv
 For other `Linux` distributions or `macOS` please see the
 [guide](utils/docs/prerequisites.md).
 
-### Dataset description
-
-After download, the folder `../data/hpatches-release` contains all the
-patches from the 116 sequences. The sequence folders are named with
-the following convention
-
-* `i_X`: patches extracted from image sequences with illumination changes
-* `v_X`: patches extracted from image sequences with viewpoint changes
-
-For each image sequence, we provide a set of reference patches
-`ref.png`. For the remaining 5 images in the sequence, we provide
-three patch sets `eK.png` and `hK.png` and `tK.png`, containing the
-corresponding patches from `ref.png` as found in the `K-th` image with
-increasing amounts of geometric noise (`e`<`h`<`t`).
-
-![patches](./utils/imgs/patches.png "patches") 
-
-Please see the [patch extraction method details](utils/docs/extraction.md) for more
-information about the extraction process. 
+### Downloading the HPatches dataset
+The rest of this document assumes you have already downloaded the
+HPatches dataset. For information on how to get it, please check
+[the guide](../readme.md).  
 
 ### Loading/visualising the dataset
 An example of how to load a sequence and visualise the patches can be

readme.md

@@ -14,15 +14,14 @@ dataset, one in `python` and one in `matlab`.
 | ------------- |:-------------:|
 |  [details](python/readme.md) | [details](matlab/README.md) |
 
-
-### Benchmark Tasks
+### Benchmark tasks
 
 Details about the benchmarking tasks can he found
 [here](docs/tasks.md).  
 For a more in-depth description, please see the CVPR
 2017 paper [[1]](#refs).
 
-### Benchmark Data
+### Getting the dataset
 
 The data required for the benchmarks are saved in the `./data` folder,
 and are shared between the two implementations.
@@ -47,6 +46,29 @@ sh download.sh descr sift  # downloads the pre-computed descriptors for sift
 
 The `HPatches` dataset is saved on `./data/hpatches-release` and the pre-computed descriptor files are saved on `./data/descriptors`.
 
+
+### Dataset description
+
+After download, the folder `../data/hpatches-release` contains all the
+patches from the 116 sequences. The sequence folders are named with
+the following convention
+
+* `i_X`: patches extracted from image sequences with illumination changes
+* `v_X`: patches extracted from image sequences with viewpoint changes
+
+For each image sequence, we provide a set of reference patches
+`ref.png`. For the remaining 5 images in the sequence, we provide
+three patch sets `eK.png` and `hK.png` and `tK.png`, containing the
+corresponding patches from `ref.png` as found in the `K-th` image with
+increasing amounts of geometric noise (`e`<`h`<`t`).
+
+![patches](./python/utils/imgs/patches.png "patches") 
+
+Please see the [patch extraction method details](./python/utils/docs/extraction.md) for more
+information about the extraction process. 
+
+
+
 ### References
 <a name="refs"></a>