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()