> ## Documentation Index > Fetch the complete documentation index at: https://docs.videodb.io/llms.txt > Use this file to discover all available pages before exploring further. # Annual Video Statistics Recap > Transform video analytics into a cinematic recap video with AI-powered visualizations Open In Colab ## The Idea You've uploaded hundreds of videos this year. You've run thousands of searches. Your automation has processed countless scenes. But how do you celebrate and share those numbers in a way that actually captivates people? Traditional dashboards and spreadsheets are boring. What if you could transform your video analytics into a shareable cinematic recap video? ## What You'll Build Turn your video analytics into a shareable recap video. This system creates a cinematic journey through your video stats: * Total minutes uploaded with growth metrics * Search activity visualization * Clips generated via automation * Scenes and frames analyzed * All set to music with dynamic video grids and text overlays All powered by **VideoDB's Editor SDK** — turning data into a visual story through code. ## Setup ### Install Dependencies ```bash theme={null} pip install videodb ``` ### Connect to VideoDB ```python theme={null} import json import random import videodb from videodb import MediaType, play_stream # Connect to VideoDB api_key = "your_api_key" conn = videodb.connect(api_key=api_key) coll = conn.get_collection() ``` ## Implementation ### Step 1: Calculate Your Video Metrics ```python theme={null} # Get all videos from your collection videos = coll.get_videos() TOTAL_SECONDS = 0 for v in videos: TOTAL_SECONDS += v.length # Core metrics TOTAL_MINUTES = round(TOTAL_SECONDS / 60, 1) TOTAL_SEARCHES = 2573 CLIPS_GENERATED = 574 SCENES_ANALYZED = 248 FRAMES_ANALYZED = 3200 # Previous year comparison PREV_YEAR_MINUTES = 6400 # Time conversions TOTAL_HOURS = round(TOTAL_MINUTES / 60, 1) TOTAL_DAYS = round(TOTAL_HOURS / 24, 1) # Growth percentage MINUTES_GROWTH = round(((TOTAL_MINUTES - PREV_YEAR_MINUTES) / PREV_YEAR_MINUTES) * 100) ``` ### Step 2: Upload Background Music ```python theme={null} # Upload background music as audio audio = coll.upload("https://youtu.be/vIEi8AoP_Fw?si=AUVUt6Komqtt7rWI", media_type="audio") ``` ### Step 3: Create Timeline with 5 Sections Initialize the timeline and set up base configuration: ```python theme={null} from videodb.editor import ( Timeline, Track, Clip, VideoAsset, AudioAsset, TextAsset, Offset, Transition, Font, Filter, ) # Use subset of videos videos = videos[20:40] # Create timeline timeline = Timeline(conn) timeline.background = "#E85E00" track = Track() video_track = Track() # Add background music b_music = Clip( asset=AudioAsset(id=audio.id, start=0, volume=1), duration=28, ) track.add_clip(4, b_music) ``` ### Step 4: Build Intro Section (0-5s) Create a dynamic mosaic effect with 60 video clips: ```python theme={null} # Intro text intro_text = Clip( asset=TextAsset( text="2025 was a blur. Let's index it.", font=Font(size=60, family="Roboto Bold"), ), duration=5, transition=Transition(in_="fade"), ) track.add_clip(1, intro_text) # Create 60 video clips with varying scales and random positioning for i in range(60): scale_value = 0.5 + (i * 0.025) # Scale: 0.5 to 0.975 start_time = random.randint(0, 4) volume = 0.5 if start_time == 3: volume = 0.3 elif start_time == 4: volume = 0.3 video = random.choice(videos) video_clip = Clip( asset=VideoAsset( id=video.id, start=random.randint(10, int(video.length - 10)), volume=volume, ), duration=1, scale=scale_value, fit=None, offset=Offset(x=random.uniform(-0.5, 0.5), y=random.uniform(-0.5, 0.5)), ) video_track.add_clip(start=start_time, clip=video_clip) ``` ### Step 5: Build Upload Metrics Section (6-13s) Display total minutes uploaded with zoom-out effect: ```python theme={null} # Section 1 text section1_text = Clip( asset=TextAsset( text=f"You uploaded {TOTAL_MINUTES:,} minutes\nof video content.\n\nThat's {TOTAL_HOURS} hours.\nOr {TOTAL_DAYS} full days of footage.\n\n↑ {MINUTES_GROWTH}% from 2024", font=Font(size=48, family="default"), ), duration=7, ) track.add_clip(6, section1_text) # Zoom out effect with greyscale filter for i in range(20): scale_value = 0.5 + (i * 0.0263) # Scale: 0.5 to 1.0 start_time = 6 + (i * 0.175) video = random.choice(videos) video_start = random.randint(10, max(11, int(video.length - 10))) video_clip = Clip( asset=VideoAsset( id=video.id, start=video_start, volume=0.15, ), duration=1, scale=scale_value, fit="crop", filter=Filter.greyscale, ) video_track.add_clip(start=start_time, clip=video_clip) ``` ### Step 6: Build Search Analytics Section (13-19s) Create 3 rows of horizontal scanning video clips: ```python theme={null} # Section 2 text section2_text = Clip( asset=TextAsset( text=f"{TOTAL_SEARCHES:,} questions asked.\n\nYour videos aren't just stored—\nthey're understood.", font=Font(size=58, family="default"), ), duration=6, ) track.add_clip(13, section2_text) # Create horizontal scanning effect with 3 rows rows = [ {"y": -0.5, "num_clips": 20, "duration": 0.25, "scale": 0.35}, # Top row {"y": 0, "num_clips": 15, "duration": 0.33, "scale": 0.45}, # Middle row {"y": 0.5, "num_clips": 12, "duration": 0.42, "scale": 0.35}, # Bottom row ] clip_idx = 0 for row in rows: for i in range(row["num_clips"]): x_pos = -0.9 + (i * 0.1) video = videos[clip_idx % len(videos)] video_start = 30 + (clip_idx % 10) if video_start > video.length - 10: video_start = 15 video_clip = Clip( asset=VideoAsset( id=video.id, start=video_start, volume=0.15, ), duration=row["duration"], scale=row["scale"], fit="crop", offset=Offset(x=x_pos, y=row["y"]), ) video_track.add_clip(start=12 + (i * row["duration"]), clip=video_clip) clip_idx += 1 ``` ### Step 7: Build Automation Stats Section (19-25s) Large background clips with cycling effect: ```python theme={null} # Section 3 text section3_text = Clip( asset=TextAsset( text=f"{CLIPS_GENERATED:,} clips\ngenerated via code.\n\nAutomation at its finest.", font=Font(size=62, family="default"), ), duration=6, ) track.add_clip(19, section3_text) # Cycling background effect for i in range(3): video = videos[(i + 5) % len(videos)] video_start = 20 + (i * 15) if video_start > video.length - 10: video_start = 10 bg_clip = Clip( asset=VideoAsset( id=video.id, start=video_start, volume=0.05, ), duration=2, scale=1.2, fit="crop", filter=Filter.greyscale, opacity=0.2, z_index=0, ) video_track.add_clip(start=19 + (i * 2), clip=bg_clip) ``` ### Step 8: Build Intelligence Metrics Section (25-31s) 3x3 grid with scanning animation: ```python theme={null} # Section 4 text section4_text = Clip( asset=TextAsset( text=f"{SCENES_ANALYZED:,} scenes analyzed\n{FRAMES_ANALYZED:,} Frames Analyzed\n\nPure video intelligence.\nPowered by you.", font=Font(size=52, color="#FFFFFF", family="Arial Bold"), ), duration=5, ) track.add_clip(25, section4_text) # 3x3 grid with scanning effect grid_positions = [ (-0.4, -0.4), (0, -0.4), (0.4, -0.4), # Top row (-0.4, 0), (0, 0), (0.4, 0), # Middle row (-0.4, 0.4), (0, 0.4), (0.4, 0.4), # Bottom row ] for idx, (x_pos, y_pos) in enumerate(grid_positions): video = videos[idx % len(videos)] video_start = 50 + (idx % 10) if video_start > video.length - 10: video_start = 20 video_clip = Clip( asset=VideoAsset( id=video.id, start=video_start, volume=0.1, ), duration=0.6, scale=0.28, fit="crop", offset=Offset(x=x_pos, y=y_pos), transition=Transition(in_="fade", duration=0.15), filter=Filter.contrast if idx % 2 == 0 else Filter.boost, opacity=0.9, ) video_track.add_clip(start=24.5 + (idx * 0.08), clip=video_clip) # Add cascading columns num_columns = 2 clips_per_column = 8 for col in range(num_columns): x_pos = -0.3 + (col * 0.6) for row in range(clips_per_column): y_pos = -0.7 + (row * 0.23) clip_idx = col * clips_per_column + row video = videos[clip_idx % len(videos)] video_start = 40 + (clip_idx % 8) if video_start > video.length - 10: video_start = 25 video_clip = Clip( asset=VideoAsset( id=video.id, start=video_start, volume=0.15, ), duration=0.3, scale=0.3, fit="crop", offset=Offset(x=x_pos, y=y_pos), ) video_track.add_clip(start=25.7 + (row * 0.3) + (col * 0.1), clip=video_clip) ``` ### Step 9: Add Outro and Render ```python theme={null} # Outro text outro_text = Clip( asset=TextAsset( text="Ready for 2026? Build it.", font=Font(size=60, family="Roboto Bold") ), duration=2, ) track.add_clip(31, outro_text) # Add tracks to timeline timeline.add_track(video_track) timeline.add_track(track) # Generate stream stream_url = timeline.generate_stream() ``` ## What You Get A professional video that: * Visualizes your video metrics in real-time * Uses 100+ of your actual videos in the composition * Creates dynamic animations and transitions * Pairs everything with music * Tells the story of your year in video data Perfect for: * Marketing your platform * Celebrating milestones with users * Annual reports * Social media recap posts * Team celebrations Here's the final rendered recap video: