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data/video_highlight_detection/qvhighlights/get_coco_format.py

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+import json
+import argparse
+import os
+from copy import deepcopy
+import pdb
+import numpy as np
+import random 
+from pathlib import Path
+from collections import Counter
+
+# read json files
+def read_json(path):
+    with open(path, "r") as fin:
+        datas = json.load(fin)
+        annos = datas["annotations"]
+    return annos
+
+
+def read_jsonl(path):
+    anno = []
+    with open(path, "r") as fin:
+        datas = fin.readlines()
+        for data in datas:
+            anno.append(json.loads(data.strip()))
+    return anno
+
+
+
+def write_json(data, path):
+    with open(path, "w") as fout:
+        json.dump(data, fout)
+    return
+
+
+def read_txt(path):
+    data = []
+    with open(path, "r") as fin:
+        lines = fin.readlines()
+        for i, line in enumerate(lines):
+            # e.g. AO8RW 0.0 6.9##a person is putting a book on a shelf.
+            line = line.strip("\n")
+            cap = line.split("##")[-1]
+            if len(cap) < 2:
+                continue
+            terms = line.split("##")[0].split(" ")
+            vid = terms[0] + ".mp4"
+            start_time = float(terms[1])
+            end_time = float(terms[2])
+            data.append({"image_id": vid, "caption": cap, "timestamp": [start_time, end_time], "id": i})
+    return data
+
+
+def filter_sent(sent):
+    sent = sent.strip(" ")
+    if len(sent) < 2:
+        return False
+    sent = sent.replace("#", "")
+    return sent
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset', default='qvhighlights') # anet
+    parser.add_argument('--anno_path', default='annotations_raw/')
+    parser.add_argument('--video_path', default='videos/') # ActivityNet_asr_denseCap/anet_6fps_224
+    parser.add_argument('--outpath', default='./')
+    args = parser.parse_args()
+    '''output data example:
+    {
+        "annotations": [ 
+        {   
+            "image_id": "3MSZA.mp4", 
+            "caption": "person turn a light on.",
+            "timestamp": [24.3, 30.4],
+        }],
+    }
+    '''
+    miss_videos = []
+    num_clips = []
+    for split in ["train", "val"]: # "val", "test"
+        if args.dataset == "charades":
+            filename = f"charades_sta_{split}.txt"
+            annos = read_txt(os.path.join(args.anno_path, filename))
+            data = {}
+            data["annotations"] = annos
+        elif args.dataset == "qvhighlights":
+            filename = f"highlight_{split}_release.jsonl"
+            annos = read_jsonl(os.path.join(args.anno_path, filename))
+            new_data = []
+            for jterm in annos:
+                new_term = {}
+                new_term["image_id"] = "v_" + jterm["vid"] + ".mp4"
+                # check the existance of the video
+                if not os.path.exists(os.path.join(args.video_path, split, new_term["image_id"])):
+                    miss_videos.append(new_term["image_id"])
+                    continue
+                new_term["id"] = jterm["qid"]
+                new_term["caption"] = jterm["query"]
+                new_term["timestamp"] = jterm["relevant_windows"]
+                new_term["duration"] = jterm["duration"]
+                new_term["relevant_clip_ids"] = jterm["relevant_clip_ids"]
+                new_term["saliency_scores"] = jterm["saliency_scores"]
+                new_data.append(new_term)
+                num_clips.append(int(jterm["duration"]/2))
+            data = {}
+            data["annotations"] = new_data
+        else:
+            print("Do not support this dataset!")
+            exit(0)
+            
+        print(f"==> {args.dataset} dataset  \t# examples num: {len(new_data)} \t# miss videos num: {len(miss_videos)}\t# raw data num: {len(annos)}")
+        out_name = "{}.caption_coco_format.json".format(split)
+        Path(args.outpath).mkdir(parents=True, exist_ok=True)
+        write_json(data, os.path.join(args.outpath, out_name))
+        
+        if len(num_clips) >= 1:
+            count = Counter(num_clips)
+            # sort count dict with the clip num
+            print(count)
+            print(max(list(count.keys())))
+