code

[zhongyihebao]

import cv2
import numpy as np
import matplotlib.pyplot as plt

def detect_and_mark_rectangles(image):
# 降噪
denoised_image = cv2.GaussianBlur(image, (5, 5), -10)

# 将图像转换为灰度
gray_image = cv2.cvtColor(denoised_image, cv2.COLOR_BGR2GRAY)

# 进行二值化处理
_, binary_image = cv2.threshold(gray_image, 122, 255, cv2.THRESH_BINARY)

# 执行 Canny 边缘检测
edges = cv2.Canny(binary_image, 10, 255)

# 找到洞的轮廓
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 找到最大的绿色边界
max_area = 0
max_contour = None
for contour in contours:
    # 计算轮廓的近似多边形
    epsilon = 0.02 * cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, epsilon, True)

    # 如果近似多边形是矩形，则进行标注
    if len(approx) == 4:
        area = cv2.contourArea(contour)
        if area > max_area:
            max_area = area
            max_contour = approx

# 绘制最大的绿色内边界
if max_contour is not None:
    cv2.drawContours(image, [max_contour], 0, (0, 255, 0), 2)

    # 寻找外边界
    outer_contours, _ = cv2.findContours(binary_image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    outer_contours = [cnt for cnt in outer_contours if cv2.contourArea(cnt) > max_area]

    # 绘制红色外边界
    cv2.drawContours(image, outer_contours, -1, (0, 0, 255), 2)

return image

读取和处理多张图片

image_paths = ['test_image{}.jpg'.format(i) for i in range(1, 10)]

for path in image_paths:
# 读取图像
image = cv2.imread(path)

# 进行矩形检测和标注
result = detect_and_mark_rectangles(image)

# 使用Matplotlib显示结果图像
plt.imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.title('Result')
plt.show()

我所期望的到的效果图是这样的