如何使用OpenCV/cv2在指定坐标检测恒星轮廓并绘制对应大小白点到新图

Name;RA(deg);Dec(deg);PixelX;PixelY;RPmag;Gmag;BPmag;Parallax;Flags
;313.311502;44.387289;2516;1458;4.850;4.728;4.681;5.8088;000800f0
;313.327406;45.181692;236;2256;4.517;5.190;5.715;3.6700;000800f0
;314.580976;44.472083;3131;4138;4.660;5.304;5.779;14.4950;00800f00
;314.471585;44.788107;2143;4206;4.358;5.673;7.615;1.9323;000800f0
...

前置依赖安装 #

pip install opencv-python numpy pandas

核心实现代码 #

import cv2
import numpy as np
import pandas as pd

# 1. 加载星表数据，分号分隔
star_df = pd.read_csv("your_star_table.csv", sep=";")
# 过滤坐标为空的无效行
star_df = star_df.dropna(subset=["PixelX", "PixelY"])

# 2. 加载观测图像
img = cv2.imread("kaEC6.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
h, w = gray.shape[:2]

# 3. 新建黑色画布用于绘制匹配尺寸的白点
output = np.zeros_like(img)

# 4. 逐个处理恒星坐标
roi_extend = 50  # 中心周围扩展的像素范围，根据恒星最大尺寸调整
threshold_offset = 20  # 二值化阈值偏移，亮于背景阈值offset的区域判定为恒星

for idx, row in star_df.iterrows():
    cx = int(row["PixelX"])
    cy = int(row["PixelY"])
    # 过滤超出图像边界的无效坐标
    if cx < 0 or cx >= w or cy <0 or cy >=h:
        continue
    
    # 截取中心周围的局部ROI，避免全图计算干扰和性能浪费
    x1 = max(0, cx - roi_extend)
    y1 = max(0, cy - roi_extend)
    x2 = min(w, cx + roi_extend)
    y2 = min(h, cy + roi_extend)
    roi = gray[y1:y2, x1:x2]
    # 计算中心在ROI内的相对坐标
    rel_cx = cx - x1
    rel_cy = cy - y1

    # 高斯模糊降噪后二值化，提取亮于背景的恒星区域
    blk = cv2.GaussianBlur(roi, (5,5), 0)
    bg_mean = np.mean(blk)
    _, binary = cv2.threshold(blk, bg_mean + threshold_offset, 255, cv2.THRESH_BINARY)

    # 检测ROI内所有轮廓
    contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 筛选包含当前恒星中心的目标轮廓
    target_contour = None
    for cnt in contours:
        if cv2.pointPolygonTest(cnt, (rel_cx, rel_cy), False) >= 0:
            target_contour = cnt
            break
    
    if target_contour is None:
        # 未检测到轮廓时用默认最小半径，可按需调整
        radius = 2
    else:
        # 计算轮廓最小外接圆半径作为恒星尺寸
        (_, _), radius = cv2.minEnclosingCircle(target_contour)
        radius = int(max(radius, 1))
    
    # 在输出画布绘制对应尺寸的白色实心圆
    cv2.circle(output, (cx, cy), radius, (255,255,255), -1)

# 保存最终结果
cv2.imwrite("star_marked.jpg", output)

参数调整说明 #

• roi_extend：如果图像中恒星尺寸较大，可增大该值保证截取到完整的恒星亮区
• threshold_offset：如果背景噪点被误识别为恒星，可增大该值提高阈值；如果暗星被漏识别，可减小该值
• 若需要更精准的轮廓提取，可将普通阈值替换为cv2.adaptiveThreshold做局部自适应阈值分割

内容的提问来源于stack exchange，提问作者kevin