OpenCV中物体颜色/透明性导致边界检测及Feret测量异常求助

我正在开展图像物体检测与测量项目，单张图像仅包含一个物体。当前采用OpenCV的背景减法移除背景，再通过feret库计算最大、最小Feret直径。但因物体颜色或透明性问题，背景减法无法精准检测物体边界，进而导致Feret直径测量结果不准确。

运行代码（Jupyter Notebook） #

import cv2 as cv
import os
import numpy as np
import matplotlib.pyplot as plt
# Clean up
plt.close('all')
import os
import numpy as np
from PIL import Image
from tqdm import tqdm
import cv2  # Import OpenCV for image preprocessing
import feret  # Import the feret module

# Function to display images in Jupyter
def display_image(image, title='Image'):
    plt.figure(figsize=(10, 10))
    plt.imshow(image, cmap='gray')
    plt.title(title)
    plt.axis('off')
    plt.show()

# Path to the folder containing images and the background image
image_folder = '.'
background_image_path = 'Main.jpeg'

# Load the background image
background_image = cv.imread(background_image_path, cv.IMREAD_COLOR)
if background_image is None:
    print(f"Unable to load background image: {background_image_path}")
    exit(1)

# Convert background image to grayscale
background_gray = cv.cvtColor(background_image, cv.COLOR_BGR2GRAY)

# Iterate through each file in the folder
for filename in os.listdir(image_folder):
    if filename.endswith('.jpeg') and filename != 'Main.jpeg':
        image_path = os.path.join(image_folder, filename)
        
        # Load the current image
        image = cv.imread(image_path, cv.IMREAD_COLOR)
        if image is None:
            print(f"Unable to load image: {image_path}")
            continue

        # Convert current image to grayscale
        image_gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
        
        # Perform background subtraction
        fgMask = cv.absdiff(background_gray, image_gray)
        
        # Apply a threshold to get the binary image
        _, fgMask = cv.threshold(fgMask, 50, 255, cv.THRESH_BINARY)
    
        # Display the original image and the foreground mask
        display_image(cv.cvtColor(image, cv.COLOR_BGR2RGB), title='Original Image')
        display_image(fgMask, title='Foreground Mask')

        # Plot Feret diameters on the preprocessed image
        feret.plot(fgMask)        
        # Calculate Feret diameters and angles
        maxf_length, minf_length, minf_angle, maxf_angle = feret.all(fgMask)
        
        # Print the results for the current image
        print(f"Filename: {filename}, Max Feret Length: {maxf_length}, Min Feret Length: {minf_length}")
        
    
        # result_path = os.path.join('path_to_save_results', f'fgMask_{filename}')
        # cv.imwrite(result_path, fgMask)

        # Pause to control the display in the notebook
        # This can be adjusted or removed as needed
        input("Press Enter to continue...")

# Clean up
plt.close('all')

# Optionally, print all results at the end
# for res in results:
#     print(f"Filename: {res['filename']}, Max Feret Length: {res['maxf_length']}, Min Feret Length: {res['minf_length']}")

相关素材说明 #

• 背景图：白色底带蓝色网格的标准背景
• 样本图：多张包含透明/浅色不规则物体的图像
• 当前输出：背景分割后的掩码存在边界缺失、物体轮廓不完整的问题

提问 #

请问OpenCV中是否有替代方法或预处理步骤能更好地分离物体与背景？恳请相关建议与指导。

内容的提问来源于stack exchange，提问作者Agura