# 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. ## đŸ“Ĩ 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. ## 🕹ī¸ 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. ## ⚙ī¸ 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.) ## 🛠ī¸ 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. 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