OpenMV Cam H7 Object Tracking Tutorial: Complete How-To Guide

The OpenMV Cam H7 transforms complex machine vision into accessible Python programming, making object tracking achievable for makers, students, and professionals alike. This comprehensive tutorial guides you through implementing robust object tracking using color blob detection, one of the most fundamental and practical computer vision techniques.

Object tracking with the OpenMV Cam H7 opens doors to applications ranging from robotic ball following and automated surveillance to industrial quality control and interactive installations.

Understanding OpenMV Cam H7 Capabilities

Hardware Overview

The OpenMV Cam H7 features an STM32H743VI ARM Cortex M7 processor running at 480MHz with 1MB of RAM and 2MB of flash memory. The integrated OV7725 camera sensor captures images at resolutions up to 640x480 pixels, providing sufficient detail for most tracking applications.

Key Features for Object Tracking:

• Real-time image processing at 30+ FPS

• Built-in machine vision algorithms optimized for microcontrollers

• Python/MicroPython programming environment

• Extensive I/O pins for controlling external devices

• Native support for color blob detection and tracking

Machine Vision Capabilities

The OpenMV platform excels at color-based object tracking through its optimized find_blobs() function. You can detect up to 16 different colors simultaneously, with each color supporting multiple distinct blobs. The system provides position, size, centroid, and orientation data for each detected object.

Setting Up Your Development Environment

Installing OpenMV IDE

1. Download the IDE from openmv.io/pages/download

2. Install the software, accepting all default settings

3. Connect your OpenMV Cam H7 via USB cable

4. Launch OpenMV IDE and click the connect button

The IDE provides a live frame buffer view, integrated histogram display for color analysis, and real-time script execution capabilities.

Initial Configuration

When you first open OpenMV IDE, a basic "Hello World" script appears. This demonstrates the fundamental camera setup:

python

import sensor, image, time

sensor.reset()                      # Reset and initialize sensor

sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565

sensor.set_framesize(sensor.QVGA)   # Set frame size to 320x240

sensor.skip_frames(time = 2000)     # Wait for settings take effect

clock = time.clock()                # Create a clock object

while(True):

    clock.tick()                    # Update the FPS clock

    img = sensor.snapshot()         # Take a picture and return image

    print(clock.fps())              # Print the FPS

Click the green run button to execute this script and verify your camera is working properly.

Color Threshold Determination

Using the Histogram Tool

Successful object tracking begins with accurate color threshold determination. The OpenMV IDE's histogram tool provides an interactive method for finding optimal color ranges.

Steps for Color Calibration:

1. Place your target object in the camera's field of view

2. Select the object area by clicking and dragging on the frame buffer

3. Observe the histogram showing color distribution in LAB color space

4. Note the L (lightness), A (green-red), and B (blue-yellow) values

5. Create threshold tuples based on observed ranges

LAB Color Space Advantages

LAB color space provides superior color tracking compared to RGB because it separates lightness from color information, making it more robust under varying lighting conditions.

A typical color threshold format: (L_min, L_max, A_min, A_max, B_min, B_max)

Example red threshold: (30, 100, 15, 127, 15, 127)

Automatic Threshold Generation

Use the OpenMV IDE's threshold editor tool:

1. Navigate to Tools → Machine Vision → Threshold Editor

2. Select your target object in the live view

3. Adjust threshold sliders for optimal detection

4. Copy the generated threshold values to your code

Basic Color Blob Tracking Implementation

Simple Tracking Script

Here's a fundamental color tracking implementation:

python

import sensor, image, time

# Color Tracking Thresholds (L Min, L Max, A Min, A Max, B Min, B Max)

red_threshold = (30, 100, 15, 127, 15, 127)

# Initialize camera

sensor.reset()

sensor.set_pixformat(sensor.RGB565)

sensor.set_framesize(sensor.QVGA)

sensor.skip_frames(time = 2000)

sensor.set_auto_gain(False)  # Disable auto gain for color tracking

sensor.set_auto_whitebal(False)  # Disable auto white balance

clock = time.clock()

while(True):

    clock.tick()

    img = sensor.snapshot()

    

    # Find blobs matching our color threshold

    blobs = img.find_blobs([red_threshold], pixels_threshold=200, area_threshold=200)

    

    if blobs:

        # Track the largest blob

        largest_blob = max(blobs, key=lambda b: b.pixels())

        

        # Draw bounding rectangle and center cross

        img.draw_rectangle(largest_blob.rect())

        img.draw_cross(largest_blob.cx(), largest_blob.cy())

        

        # Print tracking information

        print("Object at:", largest_blob.cx(), largest_blob.cy())

        print("Size:", largest_blob.pixels())

        

    print("FPS:", clock.fps())

Understanding Blob Properties

Each detected blob provides comprehensive tracking data:

• Position: blob.x(), blob.y() - top-left corner coordinates

• Center: blob.cx(), blob.cy() - centroid coordinates

• Dimensions: blob.w(), blob.h() - width and height

• Area: blob.pixels() - total pixel count

• Rotation: blob.rotation() - object orientation in radians

Advanced Tracking Techniques

Multi-Object Tracking

Track multiple objects simultaneously using different color thresholds:

python

import sensor, image, time

# Define multiple color thresholds

red_threshold = (30, 100, 15, 127, 15, 127)

blue_threshold = (0, 30, -64, -8, -32, 32)

green_threshold = (30, 100, -64, -8, -32, 32)

thresholds = [red_threshold, blue_threshold, green_threshold]

colors = [(255, 0, 0), (0, 0, 255), (0, 255, 0)]  # RGB colors for visualization

sensor.reset()

sensor.set_pixformat(sensor.RGB565)

sensor.set_framesize(sensor.QVGA)

sensor.skip_frames(time = 2000)

sensor.set_auto_gain(False)

sensor.set_auto_whitebal(False)

clock = time.clock()

while(True):

    clock.tick()

    img = sensor.snapshot()

    

    # Track each color separately

    for i, threshold in enumerate(thresholds):

        blobs = img.find_blobs([threshold], pixels_threshold=100, area_threshold=100)

        

        for blob in blobs:

            # Draw rectangle with color-specific visualization

            img.draw_rectangle(blob.rect(), color=colors[i])

            img.draw_cross(blob.cx(), blob.cy(), color=colors[i])

            

            print(f"Color {i} at: ({blob.cx()}, {blob.cy()})")

    

    print("FPS:", clock.fps())

Object Following with Servo Control

Implement active tracking using servo motors to follow detected objects:

python

import sensor, image, time, math

from pyb import Servo

# Initialize servos (pan and tilt)

pan_servo = Servo(1)   # P7

tilt_servo = Servo(2)  # P8

# Servo position variables

pan_angle = 90

tilt_angle = 90

# Color threshold

target_threshold = (30, 100, 15, 127, 15, 127)

# Camera setup

sensor.reset()

sensor.set_pixformat(sensor.RGB565)

sensor.set_framesize(sensor.QVGA)

sensor.skip_frames(time = 2000)

sensor.set_auto_gain(False)

sensor.set_auto_whitebal(False)

clock = time.clock()

while(True):

    clock.tick()

    img = sensor.snapshot()

    

    blobs = img.find_blobs([target_threshold], pixels_threshold=200, area_threshold=200)

    

    if blobs:

        largest_blob = max(blobs, key=lambda b: b.pixels())

        

        # Calculate error from center

        error_x = largest_blob.cx() - img.width() // 2

        error_y = largest_blob.cy() - img.height() // 2

        

        # Simple proportional control

        pan_angle -= error_x * 0.1

        tilt_angle += error_y * 0.1

        

        # Limit servo angles

        pan_angle = max(0, min(180, pan_angle))

        tilt_angle = max(0, min(180, tilt_angle))

        

        # Update servo positions

        pan_servo.angle(pan_angle)

        tilt_servo.angle(tilt_angle)

        

        # Visualization

        img.draw_rectangle(largest_blob.rect())

        img.draw_cross(largest_blob.cx(), largest_blob.cy())

        

        print(f"Tracking: ({largest_blob.cx()}, {largest_blob.cy()})")

        print(f"Servo angles: Pan={pan_angle:.1f}, Tilt={tilt_angle:.1f}")

    

    print("FPS:", clock.fps())

Optimizing Tracking Performance

Camera Settings for Optimal Tracking

Disable Auto Settings:

python

sensor.set_auto_gain(False)     # Prevents gain fluctuations

sensor.set_auto_whitebal(False) # Maintains consistent colors

sensor.set_auto_exposure(False) # Optional: prevents exposure changes

Manual Fine-Tuning:

python

sensor.set_contrast(3)          # Increase contrast for better separation

sensor.set_brightness(0)        # Adjust brightness as needed

sensor.set_saturation(3)        # Enhance color saturation

Frame Processing Optimization

Region of Interest (ROI): Limit processing to specific image areas for improved performance:

python

# Process only center portion of image

roi = (img.width()//4, img.height()//4, img.width()//2, img.height()//2)

blobs = img.find_blobs([threshold], roi=roi, pixels_threshold=100)

Filtering Parameters: Adjust blob detection parameters for optimal results:

python

blobs = img.find_blobs([threshold], 

                      pixels_threshold=200,    # Minimum blob size in pixels

                      area_threshold=200,      # Minimum blob area

                      merge=True,              # Merge overlapping blobs

                      margin=10)               # Merge margin in pixels

Troubleshooting Common Issues

Poor Color Detection

Symptoms: Inconsistent blob detection, false positives Solutions:

• Recalibrate color thresholds using histogram tool

• Ensure consistent lighting conditions

• Adjust contrast and saturation settings

• Use tighter threshold ranges for specific colors

Tracking Jitter

Symptoms: Unstable tracking, rapid position changes Solutions:

• Implement smoothing filters for position data

• Increase minimum blob size requirements

• Add temporal filtering to reject momentary false detections

• Use larger area thresholds to ignore noise

Low Frame Rate

Symptoms: Sluggish tracking response, low FPS Solutions:

• Reduce image resolution using sensor.set_framesize(sensor.QQVGA)

• Limit processing to regions of interest

• Optimize blob detection parameters

• Remove unnecessary visualization code for production use

Practical Applications and Extensions

Security Camera with Motion Detection

Combine person detection with tracking for intelligent surveillance:

python

import sensor, image, time

from pyb import LED

# Load person detection model

net = None

try:

    net = tf.load("person_detection.tflite", load_to_fb=True)

except:

    print("Person detection model not found")

red_led = LED(1)

sensor.reset()

sensor.set_pixformat(sensor.GRAYSCALE)

sensor.set_framesize(sensor.QVGA)

sensor.set_windowing((240, 240))  # Crop to square

sensor.skip_frames(time = 2000)

clock = time.clock()

while(True):

    clock.tick()

    img = sensor.snapshot()

    

    if net:

        # Run person detection

        for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):

            if obj.output()[1] > 0.7:  # Person confidence threshold

                red_led.on()

                img.draw_rectangle(obj.rect())

                print("Person detected!")

            else:

                red_led.off()

    

    print("FPS:", clock.fps())

Robot Ball Following

Create a robot that autonomously follows a colored ball:

python

import sensor, image, time, math

from pyb import Pin, Timer

# Motor control pins

motor_left = Pin('P0', Pin.OUT_PP)

motor_right = Pin('P1', Pin.OUT_PP)

# Ball color threshold (adjust for your ball)

ball_threshold = (35, 100, 15, 127, 15, 127)

sensor.reset()

sensor.set_pixformat(sensor.RGB565)

sensor.set_framesize(sensor.QVGA)

sensor.skip_frames(time = 2000)

sensor.set_auto_gain(False)

sensor.set_auto_whitebal(False)

clock = time.clock()

while(True):

    clock.tick()

    img = sensor.snapshot()

    

    blobs = img.find_blobs([ball_threshold], pixels_threshold=300, area_threshold=300)

    

    if blobs:

        largest_blob = max(blobs, key=lambda b: b.pixels())

        

        # Calculate steering based on ball position

        center_x = img.width() // 2

        error = largest_blob.cx() - center_x

        

        # Simple robot control logic

        if abs(error) < 20:  # Ball centered - move forward

            motor_left.high()

            motor_right.high()

        elif error > 0:  # Ball to the right - turn right

            motor_left.high()

            motor_right.low()

        else:  # Ball to the left - turn left

            motor_left.low()

            motor_right.high()

        

        img.draw_rectangle(largest_blob.rect())

        img.draw_cross(largest_blob.cx(), largest_blob.cy())

        

    else:

        # No ball detected - stop motors

        motor_left.low()

        motor_right.low()

    

    print("FPS:", clock.fps())

Conclusion

The OpenMV Cam H7 provides an accessible entry point into computer vision and object tracking applications. Through color blob detection, you can create sophisticated tracking systems that respond to real-world objects in real-time.

The key to successful object tracking lies in proper color threshold calibration, understanding the blob detection parameters, and optimizing your code for the specific application requirements. Start with simple single-object tracking, then gradually add complexity as you become comfortable with the platform.

The combination of Python programming simplicity and powerful built-in computer vision functions makes the OpenMV Cam H7 ideal for educational projects, prototyping, and even production applications where traditional computer vision systems would be overkill.

Whether you're building a ball-following robot, creating an interactive art installation, or developing an industrial quality control system, the techniques covered in this tutorial provide the foundation for robust object tracking implementations.

Frequently Asked Questions

1. What's the maximum number of objects I can track simultaneously?

The OpenMV Cam H7 can theoretically track up to 16 different colors with multiple blobs per color. Practically, tracking 3-4 objects simultaneously provides optimal performance while maintaining real-time frame rates.

2. How do I improve tracking accuracy in varying lighting conditions?

Disable auto gain and auto white balance, use LAB color space thresholds, and consider implementing adaptive threshold adjustment based on ambient lighting. You can also use multiple threshold sets for different lighting scenarios.

3. Can I track objects other than colored blobs?

Yes, OpenMV supports template matching, face detection, feature point tracking, and TensorFlow Lite models for custom object detection. However, color blob tracking remains the fastest and most reliable method for real-time applications.

4. What frame rates can I expect for object tracking?

Typical frame rates range from 15-30 FPS depending on image resolution and processing complexity. QQVGA resolution (160x120) provides the highest frame rates, while VGA (640x480) offers more detail but lower speed.

5. How do I save tracking data or images to storage?

Add a microSD card to the OpenMV Cam H7 for expanded storage. You can save images using img.save("filename.jpg") and log tracking data to text files. The internal storage is limited to about 100KB and should only store your Python code.