ref: 7d9288f5f86e1b0a03ae5a555dc034e7055845ca
dir: /tools/non_greedy_mv/non_greedy_mv.py/
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()