1 files changed, 195 insertions, 0 deletions
diff --git a/tools/non_greedy_mv/non_greedy_mv.py b/tools/non_greedy_mv/non_greedy_mv.py
new file mode 100644
index 000000000..a46b7e760
--- /dev/null
+++ b/tools/non_greedy_mv/non_greedy_mv.py
@@ -0,0 +1,195 @@
+##  Copyright (c) 2020 The WebM project authors. All Rights Reserved.
+##
+##  Use of this source code is governed by a BSD-style license
+##  that can be found in the LICENSE file in the root of the source
+##  tree. An additional intellectual property rights grant can be found
+##  in the file PATENTS.  All contributing project authors may
+##  be found in the AUTHORS file in the root of the source tree.
+##
+
+import sys
+import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+from matplotlib import colors as mcolors
+import numpy as np
+import math
+
+
+def draw_mv_ls(axis, mv_ls, mode=0):
+  colors = np.array([(1., 0., 0., 1.)])
+  segs = np.array([
+      np.array([[ptr[0], ptr[1]], [ptr[0] + ptr[2], ptr[1] + ptr[3]]])
+      for ptr in mv_ls
+  ])
+  line_segments = LineCollection(
+      segs, linewidths=(1.,), colors=colors, linestyle='solid')
+  axis.add_collection(line_segments)
+  if mode == 0:
+    axis.scatter(mv_ls[:, 0], mv_ls[:, 1], s=2, c='b')
+  else:
+    axis.scatter(
+        mv_ls[:, 0] + mv_ls[:, 2], mv_ls[:, 1] + mv_ls[:, 3], s=2, c='b')
+
+
+def draw_pred_block_ls(axis, mv_ls, bs, mode=0):
+  colors = np.array([(0., 0., 0., 1.)])
+  segs = []
+  for ptr in mv_ls:
+    if mode == 0:
+      x = ptr[0]
+      y = ptr[1]
+    else:
+      x = ptr[0] + ptr[2]
+      y = ptr[1] + ptr[3]
+    x_ls = [x, x + bs, x + bs, x, x]
+    y_ls = [y, y, y + bs, y + bs, y]
+
+    segs.append(np.column_stack([x_ls, y_ls]))
+  line_segments = LineCollection(
+      segs, linewidths=(.5,), colors=colors, linestyle='solid')
+  axis.add_collection(line_segments)
+
+
+def read_frame(fp, no_swap=0):
+  plane = [None, None, None]
+  for i in range(3):
+    line = fp.readline()
+    word_ls = line.split()
+    word_ls = [int(item) for item in word_ls]
+    rows = word_ls[0]
+    cols = word_ls[1]
+
+    line = fp.readline()
+    word_ls = line.split()
+    word_ls = [int(item) for item in word_ls]
+
+    plane[i] = np.array(word_ls).reshape(rows, cols)
+    if i > 0:
+      plane[i] = plane[i].repeat(2, axis=0).repeat(2, axis=1)
+  plane = np.array(plane)
+  if no_swap == 0:
+    plane = np.swapaxes(np.swapaxes(plane, 0, 1), 1, 2)
+  return plane
+
+
+def yuv_to_rgb(yuv):
+  #mat = np.array([
+  #    [1.164,   0   , 1.596  ],
+  #    [1.164, -0.391, -0.813],
+  #    [1.164, 2.018 , 0     ] ]
+  #               )
+  #c = np.array([[ -16 , -16 , -16  ],
+  #              [ 0   , -128, -128 ],
+  #              [ -128, -128,   0  ]])
+
+  mat = np.array([[1, 0, 1.4075], [1, -0.3445, -0.7169], [1, 1.7790, 0]])
+  c = np.array([[0, 0, 0], [0, -128, -128], [-128, -128, 0]])
+  mat_c = np.dot(mat, c)
+  v = np.array([mat_c[0, 0], mat_c[1, 1], mat_c[2, 2]])
+  mat = mat.transpose()
+  rgb = np.dot(yuv, mat) + v
+  rgb = rgb.astype(int)
+  rgb = rgb.clip(0, 255)
+  return rgb / 255.
+
+
+def read_feature_score(fp, mv_rows, mv_cols):
+  line = fp.readline()
+  word_ls = line.split()
+  feature_score = np.array([math.log(float(v) + 1, 2) for v in word_ls])
+  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()
+  frame_idx = int(word_ls[1])
+  mi_rows = int(word_ls[3])
+  mi_cols = int(word_ls[5])
+  bs = int(word_ls[7])
+  ref_frame_idx = int(word_ls[9])
+  rf_idx = int(word_ls[11])
+  gf_frame_offset = int(word_ls[13])
+  ref_gf_frame_offset = int(word_ls[15])
+  mi_size = bs / 8
+  mv_ls = []
+  mv_rows = int((math.ceil(mi_rows * 1. / mi_size)))
+  mv_cols = int((math.ceil(mi_cols * 1. / mi_size)))
+  for i in range(mv_rows * mv_cols):
+    line = fp.readline()
+    word_ls = line.split()
+    row = int(word_ls[0]) * 8.
+    col = int(word_ls[1]) * 8.
+    mv_row = int(word_ls[2]) / 8.
+    mv_col = int(word_ls[3]) / 8.
+    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, mv_mode_arr
+
+
+def read_dpl_stats_file(filename, frame_num=0):
+  fp = open(filename)
+  line = fp.readline()
+  width = 0
+  height = 0
+  data_ls = []
+  while (line):
+    if line[0] == '=':
+      data_ls.append(read_frame_dpl_stats(fp))
+    line = fp.readline()
+    if frame_num > 0 and len(data_ls) == frame_num:
+      break
+  return data_ls
+
+
+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, mv_mode_arr in data_ls:
+    fig, axes = plt.subplots(2, 2)
+
+    axes[0][0].imshow(img)
+    draw_mv_ls(axes[0][0], mv_ls)
+    draw_pred_block_ls(axes[0][0], mv_ls, bs, mode=0)
+    #axes[0].grid(color='k', linestyle='-')
+    axes[0][0].set_ylim(img.shape[0], 0)
+    axes[0][0].set_xlim(0, img.shape[1])
+
+    if ref is not None:
+      axes[0][1].imshow(ref)
+      draw_mv_ls(axes[0][1], mv_ls, mode=1)
+      draw_pred_block_ls(axes[0][1], mv_ls, bs, mode=1)
+      #axes[1].grid(color='k', linestyle='-')
+      axes[0][1].set_ylim(ref.shape[0], 0)
+      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)
+    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)
+
+    flatten_mv_mode = mv_mode_arr.flatten()
+    zero_mv_count = sum(flatten_mv_mode == 0);
+    new_mv_count = sum(flatten_mv_mode == 1);
+    ref_mv_count = sum(flatten_mv_mode == 2) + sum(flatten_mv_mode == 3);
+    print zero_mv_count, new_mv_count, ref_mv_count
+    plt.show()