ref: cbd966f1ab4d44af7c3de84316a543f3d3516d15
parent: e14958ea7312295ccce00e57b386085d15f6e554
author: Angie Chiang <[email protected]>
date: Tue Mar 5 13:13:45 EST 2019
Include mv_mode_arr info in dump_tpl_stats Change-Id: Ib8e635fc7522d27ff7fdb62661597115e5bbc9b8
--- a/tools/non_greedy_mv/non_greedy_mv.py
+++ b/tools/non_greedy_mv/non_greedy_mv.py
@@ -91,7 +91,14 @@
feature_score = feature_score.reshape(mv_rows, mv_cols)
return feature_score
+def read_mv_mode_arr(fp, mv_rows, mv_cols):
+ line = fp.readline()
+ word_ls = line.split()
+ mv_mode_arr = np.array([int(v) for v in word_ls])
+ mv_mode_arr = mv_mode_arr.reshape(mv_rows, mv_cols)
+ return mv_mode_arr
+
def read_frame_dpl_stats(fp):
line = fp.readline()
word_ls = line.split()
@@ -117,9 +124,10 @@
mv_ls.append([col, row, mv_col, mv_row])
mv_ls = np.array(mv_ls)
feature_score = read_feature_score(fp, mv_rows, mv_cols)
+ mv_mode_arr = read_mv_mode_arr(fp, mv_rows, mv_cols)
img = yuv_to_rgb(read_frame(fp))
ref = yuv_to_rgb(read_frame(fp))
- return rf_idx, frame_idx, ref_frame_idx, gf_frame_offset, ref_gf_frame_offset, mv_ls, img, ref, bs, feature_score
+ return rf_idx, frame_idx, ref_frame_idx, gf_frame_offset, ref_gf_frame_offset, mv_ls, img, ref, bs, feature_score, mv_mode_arr
def read_dpl_stats_file(filename, frame_num=0):
@@ -140,7 +148,7 @@
if __name__ == '__main__':
filename = sys.argv[1]
data_ls = read_dpl_stats_file(filename, frame_num=5)
- for rf_idx, frame_idx, ref_frame_idx, gf_frame_offset, ref_gf_frame_offset, mv_ls, img, ref, bs, feature_score in data_ls:
+ for rf_idx, frame_idx, ref_frame_idx, gf_frame_offset, ref_gf_frame_offset, mv_ls, img, ref, bs, feature_score, mv_mode_arr in data_ls:
fig, axes = plt.subplots(2, 2)
axes[0][0].imshow(img)
@@ -159,12 +167,14 @@
axes[0][1].set_xlim(0, ref.shape[1])
axes[1][0].imshow(feature_score)
- feature_score_arr = feature_score.flatten()
- feature_score_max = feature_score_arr.max()
- feature_score_min = feature_score_arr.min()
- step = (feature_score_max - feature_score_min) / 20.
- feature_score_bins = np.arange(feature_score_min, feature_score_max, step)
- axes[1][1].hist(feature_score_arr, bins=feature_score_bins)
+ #feature_score_arr = feature_score.flatten()
+ #feature_score_max = feature_score_arr.max()
+ #feature_score_min = feature_score_arr.min()
+ #step = (feature_score_max - feature_score_min) / 20.
+ #feature_score_bins = np.arange(feature_score_min, feature_score_max, step)
+ #axes[1][1].hist(feature_score_arr, bins=feature_score_bins)
+ im = axes[1][1].imshow(mv_mode_arr)
+ #axes[1][1].figure.colorbar(im, ax=axes[1][1])
print rf_idx, frame_idx, ref_frame_idx, gf_frame_offset, ref_gf_frame_offset, len(mv_ls)
plt.show()
--- a/vp9/encoder/vp9_encoder.c
+++ b/vp9/encoder/vp9_encoder.c
@@ -6792,6 +6792,16 @@
}
printf("\n");
+ for (mi_row = 0; mi_row < cm->mi_rows; mi_row += mi_height) {
+ for (mi_col = 0; mi_col < cm->mi_cols; mi_col += mi_width) {
+ const int mv_mode =
+ tpl_frame
+ ->mv_mode_arr[rf_idx][mi_row * tpl_frame->stride + mi_col];
+ printf("%d ", mv_mode);
+ }
+ }
+ printf("\n");
+
dump_frame_buf(gf_picture[frame_idx].frame);
dump_frame_buf(ref_frame_buf);
}