> For the complete documentation index, see [llms.txt](https://docs.augelab.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.augelab.com/function-blocks/blocks-reference/detections-shapes/shape-analysis/point-polygon-test.md). # Point Polygon Test This function block checks whether a test shape (contour/points) is located inside another enclosing shape. It annotates the input image with visual markers and returns a boolean indicating whether all test points are inside the enclosing contour. ## đŸ“Ĩ Inputs `Input Image (Grayscale)` A grayscale image used as background for visualization and drawing. `Test Shape` A shape (contour or list of points) to be tested for inclusion. `Enclosing Shape` A contour that acts as the reference enclosure. ## 📤 Outputs `Output Image` An annotated image showing the test points and the enclosing contour. Points inside are marked in one color, points outside in another. `Plot Image` A boolean result: True if all test points lie inside the enclosing contour, otherwise False. ## 🕹ī¸ Controls This block has no additional interactive controls. It runs based on the provided inputs. ## 🎨 Features * Visual annotation of the reference contour and each test point on the image for easy verification. * Per-point indication (inside vs outside) using color-coded markers. * Returns a single boolean summarizing whether the test shape is fully enclosed. ## ⚙ī¸ Running mechanism * When executed, the block reads the three sockets listed under inputs. * If either shape input is empty, the block returns the input image unchanged and does not perform the test. * If both shapes are provided, the block draws the enclosing contour and evaluates each point of the test shape against the enclosure. * Each test point is drawn on the image with a color indicating inside or outside. The boolean output reflects whether all test points are inside. ## 📝 Usage instructions 1. Provide a suitable grayscale image to `Input Image (Grayscale)` for visualization. 2. Feed the contour or point list you want to check into `Test Shape`. 3. Feed the enclosing contour into `Enclosing Shape`. 4. Read the annotated image from `Output Image` and the inclusion result from `Plot Image`. ## 💡 Tips and Tricks * Use `Find Contour` to extract contours from a binary image and connect its result to `Enclosing Shape` or `Test Shape`. * Use `Approximate Contour` to simplify complex contours before testing to speed up evaluation and reduce false negatives. * Crop the region of interest first with `Image ROI` or `Image ROI Select` to focus the test on relevant areas and reduce processing. * To inspect or create the shape to test, use `Contour to Image` or draw points with `Draw Point` and then use that output as the `Test Shape`. * If the test shape should be derived from segmentation, try `Image Threshold` or `Fill Contour` beforehand to create clean contours for more reliable results. ## 🛠ī¸ Troubleshooting * If all points report outside unexpectedly, verify that the coordinate systems match (cropping or resizing upstream can shift coordinates). * If the result is inconclusive, visualize intermediate contours using `Show Image` to confirm shapes are fed correctly. * If contour extraction fails to provide usable shapes, preprocess the image with `Blur` or `Image Threshold` for better edge detection.