[GSoC] Implementation of the Global Patch Collider and demo for PCAFlow (#752)

* Minor fixes

* Start adding correspondence finding

* Added finding of correspondences using GPC

* New evaluation tool for GPC

* Changed default parameters

* Display ground truth in the evaluation tool

* Added training tool for MPI Sintel dataset

* Added the training tool for Middlebury dataset

* Added some OpenCL optimization

* Added explanatory notes

* Minor improvements: time measurements + little ocl optimization

* Added demos

* Fixed warnings

* Make parameter struct assignable

* Fix warning

* Proper command line argument usage

* Prettified training tool, added parameters

* Fixed VS warning

* Fixed VS warning

* Using of compressed forest.yml.gz files by default to save space

* Added OpenCL flag to the evaluation tool

* Updated documentation

* Major speed and memory improvements:
1) Added new (optional) type of patch descriptors which are much faster. Retraining with option --descriptor-type=1 is required.
2) Got rid of hash table for descriptors, less memory usage.

* Fixed various floating point errors related to precision.
SIMD for dot product, forest traversing is a little bit faster now.

* Tolerant floating point comparison

* Triplets

* Added comment

* Choosing negative sample among nearest neighbors

* Fix warning

* Usage of parallel_for_() in critical places. Performance improvments.

* Simulated annealing heuristic

* Moved OpenCL kernel to separate file

* Moved implementation to source file

* Added basic accuracy tests for GPC and PCAFlow

* Fixing warnings

* Test accuracy constraints were too strict

* Test accuracy constraints were too strict

* Make tests more lightweight

modules/optflow/samples/gpc_train_sintel.py

@@ -0,0 +1,60 @@
+import argparse
+import glob
+import os
+import subprocess
+
+FRAME_DIST = 2
+
+assert (FRAME_DIST >= 1)
+
+
+def execute(cmd):
+    popen = subprocess.Popen(cmd,
+                             stdout=subprocess.PIPE,
+                             stderr=subprocess.PIPE)
+    for stdout_line in iter(popen.stdout.readline, ''):
+        print(stdout_line.rstrip())
+    for stderr_line in iter(popen.stderr.readline, ''):
+        print(stderr_line.rstrip())
+    popen.stdout.close()
+    popen.stderr.close()
+    return_code = popen.wait()
+    if return_code != 0:
+        raise subprocess.CalledProcessError(return_code, cmd)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description='Train Global Patch Collider using MPI Sintel dataset')
+    parser.add_argument(
+        '--bin_path',
+        help='Path to the training executable (example_optflow_gpc_train)',
+        required=True)
+    parser.add_argument('--dataset_path',
+                        help='Path to the directory with frames',
+                        required=True)
+    parser.add_argument('--gt_path',
+                        help='Path to the directory with ground truth flow',
+                        required=True)
+    parser.add_argument('--descriptor_type',
+                        help='Descriptor type',
+                        type=int,
+                        default=0)
+    args = parser.parse_args()
+    seq = glob.glob(os.path.join(args.dataset_path, '*'))
+    seq.sort()
+    input_files = []
+    for s in seq:
+        seq_name = os.path.basename(s)
+        frames = glob.glob(os.path.join(s, 'frame*.png'))
+        frames.sort()
+        for i in range(0, len(frames) - 1, FRAME_DIST):
+            gt_flow = os.path.join(args.gt_path, seq_name,
+                                   os.path.basename(frames[i])[0:-4] + '.flo')
+            assert (os.path.isfile(gt_flow))
+            input_files += [frames[i], frames[i + 1], gt_flow]
+    execute([args.bin_path, '--descriptor-type=%d' % args.descriptor_type] + input_files)
+
+
+if __name__ == '__main__':
+    main()