基于ARCore、ViroCore/OpenGL及OpenCV的箱体识别与尺寸测算：第二步实现咨询

Hey there! Great news—step 2 is totally feasible, and it’s a core part of combining OpenCV’s computer vision with ARCore’s spatial understanding. Let’s break down exactly how to implement it, tailored to your AR use case:

First: Confirm Feasibility #

Since you’re working with a rigid, box-shaped object (like a tissue box) in an AR environment, OpenCV’s contour detection and geometric analysis are perfect for isolating the box and grabbing its coordinates. The key is to adapt static-image CV techniques to real-time video streams and pair them with ARCore’s spatial data to avoid false positives.

Step-by-Step Implementation #

1. Preprocess Your Sobel Edge Output #

The raw Sobel edge map will have noise and fragmented edges—clean it up first:

• Convert the Sobel grayscale output to a binary image using cv::threshold (or cv::adaptiveThreshold for better performance in variable lighting). This filters out weak, irrelevant edges.
• Use a morphological closing operation (dilate followed by erode, via cv::morphologyEx with MORPH_CLOSE) to fill small gaps in the box’s edge contours, making the shape more cohesive.

2. Extract Candidate Contours #

Now find potential box shapes in the cleaned edge image:

• Run cv::findContours on the binary image to get all contours in the frame.
• Filter contours by size: Use cv::contourArea to discard tiny noise contours and overly large background shapes (adjust the area threshold based on your box’s expected size in the frame).
• Approximate contours to polygons with cv::approxPolyDP. Since a box is a quadrilateral, keep only contours that simplify to 4 vertices (this weeds out curved or irregular shapes).

3. Validate the Box Contour #

Not all 4-sided contours are your box—add checks to reduce false positives:

• Check for right angles: Calculate the angle between adjacent edges of the quadrilateral (using vector dot products). A box will have angles close to 90 degrees (allow a small tolerance, like ±15 degrees).
• Pair with ARCore plane data: ARCore detects flat surfaces (like tables where your box sits). Verify that the contour lies within the bounds of an ARCore-detected plane—this eliminates floating false positives that aren’t on a real-world surface.

4. Extract Coordinates (Pixel & World Space) #

Once you’ve confirmed the box contour, grab the coordinates you need:

• Pixel coordinates: The 4 vertices of the validated quadrilateral are your box’s pixel coordinates (stored as cv::Point objects).
• AR World coordinates (critical for ARCore size calculation): Use ARCore’s Frame.transformCoordinates2dTo3d method to map the pixel coordinates to 3D world space. Project the points onto the ARCore plane where the box is located—this gives you precise 3D positions of the box’s corners.

5. Add Real-Time Tracking (Optional but Recommended) #

For smooth, stable performance in video:

• Use an OpenCV tracker like CSRT or KCF (cv::TrackerCSRT::create()) to follow the box’s contour across frames. This avoids re-running full contour detection every frame, which saves processing power.
• Sync with ARCore’s camera pose updates: When ARCore’s camera moves, adjust your OpenCV detection region to focus only on the area where the box was last seen—this speeds up detection and reduces noise.

Key Notes for Success #

• Lighting robustness: If your scene has variable light, add a pre-processing step with cv::CLAHE to enhance contrast before running Sobel edge detection.
• Perspective handling: If the box is viewed at an angle, the quadrilateral will be a perspective-distorted rectangle. You can use cv::getPerspectiveTransform to unwarp it for easier analysis, but ARCore’s 3D size calculation can handle distorted coordinates directly.
• Camera alignment: Make sure the image frame from ARCore (Frame.acquireCameraImage()) matches the size and orientation of the frame you’re processing in OpenCV—mismatched dimensions will break coordinate conversion.

内容的提问来源于stack exchange，提问作者PxOxDxR