【OpenCV 轮廓检测与轮廓筛选】

import cv2
import numpy as np

# 读取图像
image = cv2.imread('object.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 方法1：阈值处理
_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 方法2：Canny边缘检测
edges = cv2.Canny(gray, 50, 150)

# OpenCV 4.x版本：返回contours, hierarchy
contours, hierarchy = cv2.findContours(
    binary, 
    mode=cv2.RETR_EXTERNAL,  # 检索模式：仅外部轮廓
    method=cv2.CHAIN_APPROX_SIMPLE  # 轮廓近似方法：压缩水平、垂直和对角线段
)

filtered_contours = []
for cnt in contours:
    area = cv2.contourArea(cnt)
    if 1000 < area < 50000:  # 仅保留面积在1000到50000之间的轮廓
        filtered_contours.append(cnt)

for cnt in contours:
    x, y, w, h = cv2.boundingRect(cnt)
    aspect_ratio = w / h  # 长宽比
    if 0.8 < aspect_ratio < 1.2:  # 近似正方形的轮廓
        filtered_contours.append(cnt)

for cnt in contours:
    epsilon = 0.02 * cv2.arcLength(cnt, True)  # 近似精度（周长2%）
    approx = cv2.approxPolyDP(cnt, epsilon, True)
    if len(approx) == 4:  # 四边形（如矩形）
        filtered_contours.append(approx)

for cnt in contours:
    if cv2.isContourConvex(cnt):
        filtered_contours.append(cnt)

# 在原图上绘制轮廓
output = cv2.drawContours(
    image, 
    filtered_contours, 
    contourIdx=-1,  # -1表示绘制所有轮廓
    color=(0, 255, 0),  # 绿色
    thickness=2
)

cv2.imshow('Filtered Contours', output)
cv2.waitKey(0)
cv2.destroyAllWindows()

for i, cnt in enumerate(filtered_contours):
    print(f"Contour {i} coordinates: {cnt.squeeze()}")  # 去除冗余维度

blurred = cv2.GaussianBlur(gray, (5, 5), 0)
kernel = np.ones((3, 3), np.uint8)
cleaned = cv2.morphologyEx(blurred, cv2.MORPH_CLOSE, kernel)

dilated = cv2.dilate(edges, kernel, iterations=2)

contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# hierarchy结构：[Next, Previous, First_Child, Parent]

import cv2
import numpy as np

# 1. 读取图像并预处理
image = cv2.imread('objects.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 2. 检测轮廓
contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 3. 筛选轮廓（面积 + 形状）
filtered = []
for cnt in contours:
    area = cv2.contourArea(cnt)
    if area < 1000:
        continue
    approx = cv2.approxPolyDP(cnt, 0.02*cv2.arcLength(cnt, True), True)
    if len(approx) == 4:  # 筛选四边形
        x, y, w, h = cv2.boundingRect(cnt)
        if 0.8 < w/h < 1.2:  # 长宽比接近1
            filtered.append(cnt)

# 4. 绘制结果
output = cv2.drawContours(image, filtered, -1, (0, 255, 0), 2)
cv2.imshow('Result', output)
cv2.waitKey(0)