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()
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):
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)
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()