DIY Timelapse Camera with M5Stack TimerCamera X

Timelapse photography compresses hours or days into seconds, revealing patterns we can’t normally see—clouds racing, plants growing, cities pulsing with life. In this tutorial, we’ll build a simple timelapse camera using the M5Stack TimerCamera X and the Camera-Tool desktop app.

To finish, we’ll use a Python script to stitch the captured images into a smooth video.

🛠️ What You’ll Need

• M5Stack TimerCamera X (ESP32 + OV3660 sensor, built-in RTC, battery)

• USB-C cable (data capable)
• Tripod or mount (optional but recommended)
• Computer (Windows for Camera-Tool)

Software:

• Camera-Tool
• Python 3.x with opencv-python

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⚙️ TimeCamX Hardware Features & Why It’s Better

The M5Stack TimerCamera X is more than just a camera module — it’s a self‑contained, low‑power imaging system designed for real‑world deployments.

Here’s what makes it stand out:

🔑 Key Hardware Features

• ESP32‑D0WDQ6 Dual‑Core MCU Provides reliable processing power for image capture, scheduling, and IoT connectivity.
• OV3660 3‑Megapixel Camera Delivers crisp still images and timelapse sequences, with wide‑angle support.
• Built‑in RTC (Real‑Time Clock) Enables precise scheduled captures, even when the device is in deep sleep.
• Deep Sleep Power Management Ultra‑low power consumption for long‑term outdoor or remote deployments.
• Lithium Battery Support + Charging Circuit Runs standalone with a LiPo battery, recharges via USB‑C.
• Compact Enclosure‑Ready Design Flat, modular form factor that fits into 3D‑printed or off‑the‑shelf cases.

⚡ Step 1: Flash or Verify Firmware

• Connect TimerCamera X via USB-C.

• Download and extract the CameraTool software from the M5Stack site

• CameraTool.exe fromCameraTool.exe the files.

🎥 Step 2: Connect with Camera-Tool

• Open Camera-Tool.

• We can use either Serial interface or Wi-Fi to connect the camera. To use the Wi-Fi. Click on the settings near to the port.

• And enter the credentials.

• Then click on Ok and click burn.

• Wait until the Burn finish.

• Then we can change the connection mode to Wi-Fi.

• Now you can able to see the feed in the tool.

• Preview the live feed and adjust focus/exposure from panel
• Set interval (e.g., 10s for clouds, 1min for plants).

• Choose resolution (e.g., 1600×1200).

• Select a save folder (e.g., captures/).

• Change the folder as where you want to save the images.

⏱️ Step 3: Capture Your Timelapse

• Mount the camera securely.
• Start the timelapse in Camera-Tool.

• Let it run for your desired duration.
• Verify that images are being saved with timestamped filenames.

🐍 Step 4: Convert Images to Video with Python

Here’s a simple Python script you can include in your blog:

import cv2
import os
import re
from datetime import datetime
import time

# 📁 Configuration
image_folder = r'C:\Users\prade\OneDrive\Desktop\TimeLapse From M5'  # updated path
video_name = 'timelapse_annotated.avi'
fps = 30
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 1
font_color = (255, 255, 255)
thickness = 2
position = (30, 50)  # Top-left corner

def parse_timestamp_from_filename(filename):
    """
    Parse timestamps like:
      2025_10_25_13_32_53_355.jpg
      2025-10-25-13-32-53-355.jpg
    Returns a (datetime, formatted_string) tuple or (None, filename) on failure.
    """
    base = os.path.splitext(filename)[0]
    pattern = re.compile(
        r'(?P<year>\d{4})[_-](?P<month>\d{1,2})[_-](?P<day>\d{1,2})[_-]'
        r'(?P<hour>\d{1,2})[_-](?P<minute>\d{1,2})[_-](?P<second>\d{1,2})'
        r'(?:[_-](?P<msec>\d{1,3}))?$'
    )
    m = pattern.search(base)
    if not m:
        return None, base
    groups = m.groupdict()
    year = int(groups['year'])
    month = int(groups['month'])
    day = int(groups['day'])
    hour = int(groups['hour'])
    minute = int(groups['minute'])
    second = int(groups['second'])
    msec = int(groups['msec']) if groups.get('msec') else 0
    try:
        dt = datetime(year, month, day, hour, minute, second, msec * 1000)
        formatted = dt.strftime('%Y-%m-%d %H:%M:%S')
        if msec:
            formatted = f"{formatted}.{msec:03d}"
        return dt, formatted
    except ValueError:
        return None, base

def print_progress(count, total, start, bar_len=40):
    """
    Simple terminal progress bar showing percentage, counts, elapsed and ETA.
    """
    elapsed = time.time() - start
    pct = count / total if total else 1.0
    filled = int(bar_len * pct)
    if filled >= bar_len:
        bar = '=' * bar_len
    else:
        bar = '=' * filled + '>' + '.' * (bar_len - filled - 1)
    eta = (elapsed / count * (total - count)) if count and count < total else 0.0
    print(f"\rProgress: [{bar}] {count}/{total} ({pct*100:5.1f}%) Elapsed: {elapsed:.1f}s ETA: {eta:.1f}s", end='', flush=True)

# 📸 Collect and sort image files
images = [img for img in os.listdir(image_folder) if img.lower().endswith(('.jpg', '.jpeg', '.png'))]
if not images:
    print("No images found in", image_folder)
    raise SystemExit(1)

# Parse timestamps and sort by datetime when possible
images_with_ts = []
for img in images:
    dt, label = parse_timestamp_from_filename(img)
    images_with_ts.append((dt, img, label))

# Sort: parsed datetimes first (by dt), then by filename for those without dt
images_with_ts.sort(key=lambda x: (x[0] is None, x[0] or datetime.min, x[1]))

sorted_images = [t[1] for t in images_with_ts]

# 🖼️ Load first image to get dimensions
first_frame = cv2.imread(os.path.join(image_folder, sorted_images[0]))
if first_frame is None:
    print("Failed to read first image:", sorted_images[0])
    raise SystemExit(1)
height, width, _ = first_frame.shape

# 🎞️ Setup video writer
fourcc = cv2.VideoWriter_fourcc(*'XVID')
video = cv2.VideoWriter(video_name, fourcc, fps, (width, height))

# 🔁 Frame processing
total_frames = len(sorted_images)
start_time = time.time()

for idx, img in enumerate(sorted_images, start=1):
    frame = cv2.imread(os.path.join(image_folder, img))
    if frame is None:
        print(f"\nWarning: failed to read {img} — skipping")
        continue

    # 🧠 Extract timestamp from filename (again) for label
    _, timestamp_label = parse_timestamp_from_filename(img)

    # 📝 Put timestamp on frame
    cv2.putText(frame, timestamp_label, position, font, font_scale, font_color, thickness, cv2.LINE_AA)
    
    # ➕ Add frame to video
    video.write(frame)

    # update progress
    print_progress(idx, total_frames, start_time)

video.release()
print()  # newline after progress bar
print("✅ Annotated video generated:", video_name)

Why this is great:

• Cross-platform: Works on Windows, macOS, Linux.
• Customizable: Change FPS, resolution, or add overlays.
• Beginner-friendly: No command-line ffmpeg needed.

🌟 Step 5: Enhance and Share

• Try different intervals for different subjects.
• Add overlays (date/time) in Python with cv2.putText.
• Share your timelapse on YouTube, Instagram, or embed in your blog.

🚀 Optional Extensions

• Outdoor enclosure: 3D-print or repurpose a weatherproof box.
• Solar power: Add a small panel for long-term deployment.
• Cloud sync: Push images to Google Drive, Dropbox, or an FTP server.
• Advanced firmware: Use Arduino/ESP-IDF for standalone capture without PC.

✅ Wrap-Up

With just the TimerCamera X, Camera-Tool, and a short Python script, you’ve built a DIY timelapse camera that’s simple enough for beginners but expandable for advanced makers.