Feature Detector

This function block finds an object in a live or static image by matching features from a provided training image. It compares distinctive points between the training image and the input image, attempts to estimate the object's position and rotation, and provides a visual and numeric result.

📥 Inputs

Train Image The reference image that contains the object to detect. Provide a clear, well-lit crop of the object.

Input Image From Camera The image where the block will search for the object (live camera frame or loaded image).

These are input sockets.

📤 Outputs

Detected Image The input image annotated with detection graphics (e.g., contour and center) when the object is found.

Detect Status A boolean indicating whether the object was detected (true/false).

Center The (x, y) coordinates of the detected object's center.

These are output sockets.

🕹️ Controls

Homography Type Choose the method used for estimating the geometric transform between images.

Compute Type Select the internal computation preference (quality vs performance).

Number of features How many keypoints/features to detect in the training image.

Distance Threshold Tolerance used when matching features (affects match acceptance).

K nearest Number of nearest matches considered when comparing descriptors.

Pyramid Decrease Ratio Controls scale down steps for multi-scale detection.

Pyramid Levels Number of pyramid levels used in multi-scale processing.

Point Compare Type Internal parameter affecting how points are compared.

These are widgets you can tune to adjust detection behavior.

⚙️ How it runs

• The block extracts features from the provided Train Image using the chosen feature/computation settings.

• It then searches the Input Image From Camera for matching features and attempts to estimate the object's location and orientation.

• If a valid transformation is found, the block annotates the input image with a bounding contour and a center point, sets Detect Status to true, and outputs the detected center coordinates.

• If no valid match is found, Detect Status is false and Center is returned as a default coordinate.

The block continuously evaluates when connected to live sources, so tuning controls will update behavior in near real time.

✨ Key features

• Robust feature-based detection that handles rotation and scale changes.

• Multi-parameter tuning for quality vs performance trade-offs.

• Visual feedback via an annotated Detected Image to confirm results.

• Outputs both a boolean status and exact center position for downstream logic.

📝 Usage instructions

1. Provide a clear cropped example of the object to Train Image. Prefer images with distinct textures or corners.

2. Connect a camera frame or image to Input Image From Camera.

3. Start with moderate values: set Number of features to a few hundred and Distance Threshold to a middle value.

4. Run the system and observe the Detected Image. Adjust Homography Type, Number of features, Distance Threshold, and K nearest to improve reliability.

5. Use the boolean Detect Status to trigger actions (logging, saving, counting) or feed the Center into other blocks.

💡 Tips and Tricks

• If object and camera scales differ, use Image Resize before this block so the training image and input image are closer in scale.

• Crop the search area with Image ROI Select to speed up detection and avoid false matches from cluttered backgrounds.

• Improve robustness under noisy conditions with Blur to remove high-frequency noise, or use Contrast Optimization to enhance contrast before detection.

• Use Show Image to preview detection results interactively while you tune controls.

• For drawing and reporting results, combine with Draw Detections to overlay detections cleanly on output images.

• If template-based matching is more suitable for your object, consider trying the Find Object block as an alternative or complementary approach.

• To store examples or results for offline review, pair with Image Logger.

(hints above refer to available function blocks you can combine in a flow.)

🛠️ Troubleshooting

• No detection: increase Number of features, relax Distance Threshold, or provide a clearer training image. Try different Homography Type values.

• False positives: reduce Number of features or tighten Distance Threshold. Crop the image using Image ROI Select to eliminate confusing backgrounds.

• Slow performance: lower Number of features, reduce pyramid levels, or run a smaller search region. Preprocess with Image Resize to reduce input dimensions.

• Poor results under low contrast: apply Contrast Optimization first or capture a higher-quality training image.

If results remain inconsistent, iterate: adjust controls incrementally and use Show Image to visually confirm the changes.