Read Visual Layer exported json and parse it into a list of videos, video frames, their timestamp, and their category

import json  
import pandas as pd  
import re

## Load JSON file

with open("metadata 8.json", "r") as f:  
    data = json.load(f)

## Prepare list for DataFrame

records = \[]

## Iterate over media items

for item in data.get("media_items", \[]):  
    video_name = None  
    frame_timestamp = None  
    categories = \[]  
    file_name = item.get("file_name", "")

	for metadata in item.get("metadata_items", []):
	    if metadata["type"] == "video_info":
 	       video_name = metadata["properties"].get("video_name")
 	       frame_timestamp = metadata["properties"].get("frame_timestamp")
 	   elif metadata["type"] == "image_label":
    	    categories.append(metadata["properties"].get("category_name"))

	# Extract frame number from file name
	match = re.search(r'output_(\d+).jpg', file_name)
	frame_number = int(match.group(1)) if match else None

	# Append record with categories as a list
	records.append({
	    "video_name": video_name,
	    "file_name": file_name,
	    "frame_number": frame_number,
	    "time_in_video": frame_timestamp,
	    "categories": categories
	})

## Convert to DataFrame

df = pd.DataFrame(records)  
print(df.tail())

Output:

                                       video_name          file_name  \

51 7370082532203988000_7453155857585442070_2024-1... output_000001.jpg
52 7370082532203988000_7453516762223496470_2024-1... output_000001.jpg
53 7370082532203988000_7457153883383614742_2025-0... output_000002.jpg
54 7370082532203988000_7460204779604692246_2025-0... output_000002.jpg
55 7370082532203988000_7462069977454071062_2025-0... output_000032.jpg

frame_number  time_in_video  \

51 1 0.000000
52 1 0.000000
53 2 8.351707
54 2 8.333333
55 32 84.440002

                                       categories  

51 [blanket, person, man, mountain, peak, ridge, ...
52 [blanket, foot, hill, person, mountain, ski sl...
53 [blue, paddle, calm, clear, float, person, lag...
54 [blanket, person, man, mountain, stone, skier,...
55 [blanket, building, mountain, snow, snowy, tra...