add

Author: huangtao36

AwmHb44_ouw.png

@@ -1 +1,31 @@
-# tvsum
+# tvsum
+
+```
+from load_tvsum_utils import *  
+%pylab inline  
+  
+video_dic = load_tvsum(data_root_path='./', scale=True)  
+
+for key in video_dic:  
+    video_name = key
+    fps = video_dic[key]['fps']
+    frames = video_dic[key]['frames']
+    user_score = video_dic[key]['user_score']
+    avg_score = video_dic[key]['avg_score']
+    
+    summary = get_summary(avg_score, sum_rate=0.15)
+    
+    len_ = range(0, len(summary))
+    plt.plot(len_, summary, 'r', len_, avg_score, 'b')
+    plt.title(video_name+'.mp4')
+    plt.xlabel("Frames")
+    plt.ylabel("Scores")
+    plt.savefig("./%s.png"%video)
+    plt.show()
+    break
+
+```
+
+![样图](./AwmHb44_ouw.png)
+
+

README.md

@@ -0,0 +1,139 @@
+# _*_coding:utf-8 _*_
+# Author  : Tao
+"""
+用于加载tvsum anno数据
+"""
+
+import os
+import cv2
+import csv
+import numpy as np
+
+
+def load_tvsum(data_root_path, scale=False, ):
+    """
+    :param data_root_path: tvsum data path
+    :param scale: scale to 0.0 ~ 1.0 or not
+    :return: dic
+    """
+    anno_file = os.path.join(data_root_path, 'data/ydata-tvsum50-anno.tsv')
+    video_path = os.path.join(data_root_path, 'video')
+
+    video_name, fps, nframes, user_score, avg_score = [], [], [], [], []
+
+    with open(anno_file) as fd:
+        rd = csv.reader(fd, delimiter="\t", quotechar='"')
+        for us in rd:  # user summary
+            if us[0] not in video_name:
+                video_name.append(us[0])
+                vidx = video_name.index(us[0])
+                capture = cv2.VideoCapture(
+                    os.path.join(video_path, us[0] + '.mp4'))
+
+                # get fps
+                fps.append(int(capture.get(cv2.CAP_PROP_FPS)))
+                # get frames
+                nframes.append(int(capture.get(cv2.CAP_PROP_FRAME_COUNT)))
+                user_score.append([])
+            user_score[vidx].append(np.asarray(us[2].split(',')).astype(float))
+
+        for vidx in range(len(video_name)):  # video key
+            avg_score.append(np.asarray(user_score[vidx]).mean(axis=0))
+            user_score[vidx] = np.asarray(user_score[vidx])
+
+    # scale to 0.0 ~ 1.0
+    if scale:
+        for i in range(len(avg_score)):
+            max_score = max(avg_score[i])
+            min_score = min(avg_score[i])
+            avg_s = (avg_score[i] - min_score) / (max_score - min_score)
+            avg_score[i] = avg_s
+
+    dic = {}
+    for i, video in enumerate(video_name):
+        dic[video] = {'fps': fps[i],
+                      'frames': nframes[i],
+                      'user_score': user_score[i],
+                      'avg_score': avg_score[i]
+                      }
+    return dic
+
+
+def knapsack_dp(values, weights, n_items, capacity, return_all=False):
+    check_inputs(values, weights, n_items, capacity)
+
+    table = np.zeros((n_items+1, capacity+1), dtype=np.float32)
+    keep = np.zeros((n_items+1, capacity+1), dtype=np.float32)
+
+    for i in range(1, n_items+1):
+        for w in range(0, capacity+1):
+            wi = weights[i-1]    # weight of current item
+            vi = values[i-1]     # value of current item
+            if (wi <= w) and (vi + table[i-1, w-wi] > table[i-1, w]):
+                table[i, w] = vi + table[i-1, w-wi]
+                keep[i, w] = 1
+            else:
+                table[i, w] = table[i-1, w]
+
+    picks = []
+    K = capacity
+
+    for i in range(n_items, 0, -1):
+        if keep[i, K] == 1:
+            picks.append(i)
+            K -= weights[i-1]
+
+    picks.sort()
+    picks = [x-1 for x in picks]     # change to 0-index
+
+    if return_all:
+        max_val = table[n_items, capacity]
+        return picks, max_val
+    return picks
+
+
+def check_inputs(values, weights, n_items, capacity):
+    # check variable type
+    assert(isinstance(values, list))
+    assert(isinstance(weights, list))
+    assert(isinstance(n_items, int))
+    assert(isinstance(capacity, int))
+    # check value type
+    assert(all(isinstance(val, int) or isinstance(val, float) for val in
+               values))
+    assert(all(isinstance(val, int) for val in weights))
+    # check validity of value
+    assert(all(val >= 0 for val in weights))
+    assert(n_items > 0)
+    assert(capacity > 0)
+
+
+def get_summary(score, sum_rate):
+    """
+    :param score: score list
+    :param sum_rate: summary rate
+    :return: summary mask, one hot
+    """
+    clip_scores = [x * 1000 for x in score]  # up scale
+    clip_scores = [int(round(x)) for x in clip_scores]
+
+    n = len(clip_scores)  # 总帧数
+    W = int(n * sum_rate)  # summary帧总数
+    val = clip_scores  #
+    wt = [1 for x in range(n)]  # 全1
+
+    sum_ = knapsack_dp(val, wt, n, W)
+
+    summary = np.zeros((1), dtype=np.float32)  # this element should be deleted
+    for seg_idx in range(n):
+        nf = wt[seg_idx]
+        if seg_idx in sum_:
+            tmp = np.ones((nf), dtype=np.float32)
+        else:
+            tmp = np.zeros((nf), dtype=np.float32)
+        summary = np.concatenate((summary, tmp))
+    summary = list(summary)
+
+    del summary[0]
+
+    return summary