Score flat blocks and always return some.
Adding score of flat blocks so that we can return some flat
blocks in the extremely noisy cases. This is a no-op for easier cases.
Change-Id: I99387a642bc84ceb468e5360d3e023735f693843
diff --git a/test/noise_model_test.cc b/test/noise_model_test.cc
index 72aa840..d9869ac 100644
--- a/test/noise_model_test.cc
+++ b/test/noise_model_test.cc
@@ -286,7 +286,7 @@
template <typename T>
class FlatBlockEstimatorTest : public ::testing::Test, public T {
public:
- virtual void SetUp() { random_.Reset(1071); }
+ virtual void SetUp() { random_.Reset(171); }
typedef std::vector<typename T::data_type_t> VecType;
VecType data_;
libaom_test::ACMRandom random_;
@@ -409,12 +409,34 @@
EXPECT_EQ(0, flat_blocks[1]);
// Next 4 blocks are flat.
- EXPECT_NE(0, flat_blocks[2]);
- EXPECT_NE(0, flat_blocks[3]);
- EXPECT_NE(0, flat_blocks[4]);
- EXPECT_NE(0, flat_blocks[5]);
+ EXPECT_EQ(255, flat_blocks[2]);
+ EXPECT_EQ(255, flat_blocks[3]);
+ EXPECT_EQ(255, flat_blocks[4]);
+ EXPECT_EQ(255, flat_blocks[5]);
- // Last 2 are not.
+ // Last 2 are not flat by threshold
+ EXPECT_EQ(0, flat_blocks[6]);
+ EXPECT_EQ(0, flat_blocks[7]);
+
+ // Add the noise from non-flat block 1 to every block.
+ for (int y = 0; y < kBlockSize; ++y) {
+ for (int x = 0; x < kBlockSize * num_blocks_w; ++x) {
+ this->data_[y * stride + x] +=
+ (this->data_[y * stride + x % kBlockSize + kBlockSize] -
+ (128 << shift));
+ }
+ }
+ // Now the scored selection will pick the one that is most likely flat (block
+ // 0)
+ EXPECT_EQ(1, aom_flat_block_finder_run(&flat_block_finder,
+ (uint8_t *)&this->data_[0], w, h,
+ stride, &flat_blocks[0]));
+ EXPECT_EQ(1, flat_blocks[0]);
+ EXPECT_EQ(0, flat_blocks[1]);
+ EXPECT_EQ(0, flat_blocks[2]);
+ EXPECT_EQ(0, flat_blocks[3]);
+ EXPECT_EQ(0, flat_blocks[4]);
+ EXPECT_EQ(0, flat_blocks[5]);
EXPECT_EQ(0, flat_blocks[6]);
EXPECT_EQ(0, flat_blocks[7]);
