Hey there, let's dive into some practical, actionable solutions for your ellipsoid detection problem—since you're targeting tableware like bowls and cups, these approaches should address the pain points you've hit with your current methods.

The biggest oversight in your current code is not using OpenCV's built-in cv2.fitEllipse() function, which is designed exactly for detecting elliptical shapes from contour data. Unlike polygon approximation (which relies on corner counts) or Hough circles (which only detect perfect circles), this function fits an ellipse to any closed contour with enough points—perfect for bowls and cups that appear as ellipses in camera frames.

Here's how to modify your getContours() function to use this:

def getContours(img, imgContour):
    contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # Use CHAIN_APPROX_SIMPLE to reduce point count
    for cnt in contours:
        area = cv2.contourArea(cnt)
        # Adjust this threshold based on your camera's field of view and object size
        if area > 1000:
            # Skip contours that are too small or don't have enough points for ellipse fitting
            if len(cnt) < 5:
                continue
            try:
                # Fit an ellipse to the contour
                ellipse = cv2.fitEllipse(cnt)
                # Draw the detected ellipse on your output frame
                cv2.ellipse(imgContour, ellipse, (0, 255, 0), 5)
                
                # Extract ellipse parameters to filter tableware: (center (x,y), axes (major, minor), rotation angle)
                center, axes, angle = ellipse
                major_axis_len = max(axes)
                minor_axis_len = min(axes)
                
                # Filter by aspect ratio to distinguish bowls/cups from other elliptical objects
                # Adjust these values based on your specific tableware (bowls will be closer to 1, cups might vary)
                aspect_ratio = minor_axis_len / major_axis_len
                if 0.6 < aspect_ratio < 1.1:
                    cv2.putText(imgContour, "Tableware", (int(center[0]-60), int(center[1])), 
                                cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 2)
            except:
                # Skip contours that can't be fit to an ellipse (e.g., straight lines, fragmented noise)
                continue

Key notes here:

• Use cv2.CHAIN_APPROX_SIMPLE to reduce the number of contour points (speeds up processing without losing shape info)
• The try-except block handles cases where a contour can't be fit to an ellipse (common with noisy or incomplete edges)
• Aspect ratio filtering helps eliminate non-target ellipses (like round plates or random background shapes)

Your current edge detection (Canny/Sobel) is likely generating noisy or fragmented contours, which throws off shape detection. Try these tweaks:

• Adaptive Thresholding: Instead of fixed Canny thresholds, use adaptive thresholding to handle varying lighting conditions better:

  # Replace your Canny step with this (after Gaussian blur and grayscale conversion)
  img_thresh = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
  # Then apply dilation to connect small gaps in edges
  kernal = np.ones((3, 3))
  imgDil = cv2.dilate(img_thresh, kernal, iterations=1)
  

• Color Segmentation: If your tableware has a distinct color (e.g., white bowls, blue cups), use HSV color space to isolate them first—this eliminates most background noise:

  # Add this right after flipping the frame
  hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
  # Define HSV ranges for your target color (adjust these values with a trackbar for precision)
  lower_color = np.array([0, 0, 200])  # Example for white
  upper_color = np.array([180, 25, 255])
  mask = cv2.inRange(hsv, lower_color, upper_color)
  # Isolate the tableware from the background
  img_isolated = cv2.bitwise_and(frame, frame, mask=mask)
  # Convert to grayscale for edge detection
  img = cv2.cvtColor(img_isolated, cv2.COLOR_BGR2GRAY)
  

If your edges are broken (common with reflective or partially occluded tableware), you can validate potential edge segments by analyzing their curvature:

• For each contour segment, calculate the curvature at key points using adjacent contour points (compute the angle between vectors formed by consecutive points)
• Filter segments with curvature that matches an ellipse's smooth curve (avoid sharp corners or straight lines)
• Group these valid segments and use RANSAC-based ellipse fitting (more robust to outliers) to reconstruct the full ellipse

While implementing custom curvature analysis takes more work, OpenCV's fitEllipse() already has some robustness to fragmented contours if you preprocess well.

• FAST Corner Detection: It's designed for sharp corners, which don't exist on elliptical tableware—this method was never a good fit, so you can drop it entirely.
• Hough Circles: Only detects perfect circles, so it will overfit to elliptical shapes (as you saw with repeated markings). Stick to ellipse fitting instead.
• Polygon Approximation: As you noticed, it relies on corner counts, which fails for curved shapes. Replace this with ellipse fitting.

内容的提问来源于stack exchange，提问作者user14911051