# -*- coding: utf-8 -*-
"""
只标注圆心 + 颜色一致性过滤 + 输出过程图
思路：
1) 灰度 + Otsu 阈值；若圆为黑色前景，则反相使圆为白色前景（距离变换以白为前景）
2) 形态学闭运算，修补小孔/断裂
3) 欧氏距离变换（圆心处距离≈半径）
4) 距离模板相关得到峰值响应，阈值化为候选区域
5) 每个候选区域在距离图上取局部极大（圆心），并做"颜色一致性"过滤：
- 圆心像素必须为前景（白）
- 圆心附近小圆盘区域的前景占比 >= min_fg_ratio（默认 0.6）
6) 仅在圆心画十字；保存中间过程图
"""
import os
import cv2
import numpy as np
# ---------------- 配置参数 ----------------
img_path = 'code.jpg'
out_dir = 'out' # 输出过程图目录
invert_binary = True # 若原图是白底黑圆，请保持 True（反相后圆=白色前景）
peak_thresh_ratio = 0.60 # 模板相关响应阈值（相对最大值比例）
min_peak_area = 5 # 峰值连通域最小面积（像素），过滤噪点
borderSize = 75 # 模板/边界扩展参数
gap = 10 # 模板与边缘的空隙
# 颜色一致性检查
disk_fraction = 0.5 # 用于颜色一致性检测的邻域半径 = r * disk_fraction（若 r 小则自适应 >= 4 像素）
min_fg_ratio = 0.60 # 邻域内白色前景比例阈值（0~1），越大越严格
# 标注样式
marker_color = (0, 255, 0)
marker_size = 12
marker_thickness = 1
def ensure_dir(d):
if not os.path.exists(d):
os.makedirs(d)
def to_vis_u8(img_f32):
"""将 float 图像线性归一化到 0~255 的 uint8，便于保存/显示"""
v = cv2.normalize(img_f32, None, 0, 255, cv2.NORM_MINMAX)
return v.astype(np.uint8)
def fg_ratio_in_disk(fg_u8, cx, cy, r_eff):
"""
计算以 (cx, cy) 为圆心、半径 r_eff 的小圆盘内，前景(255)像素比例。
fg_u8: 二值图或形态学结果，前景=255，背景=0
"""
h, w = fg_u8.shape[:2]
if r_eff < 1:
return 0.0
x0, y0 = max(0, cx - r_eff), max(0, cy - r_eff)
x1, y1 = min(w - 1, cx + r_eff), min(h - 1, cy + r_eff)
roi = fg_u8[y0:y1+1, x0:x1+1]
if roi.size == 0:
return 0.0
# 在 ROI 内生成圆形掩膜
mask = np.zeros_like(roi, dtype=np.uint8)
cv2.circle(mask, (cx - x0, cy - y0), r_eff, 255, thickness=-1)
area = int(np.count_nonzero(mask))
if area == 0:
return 0.0
# 前景像素计数（同时满足掩膜与前景）
fg_count = int(np.count_nonzero(cv2.bitwise_and(roi, mask)))
# 因为前景为 255，bitwise_and 后非零即计数；比例按像素个数计算
return fg_count / area
def main():
ensure_dir(out_dir)
# 1) 读图与灰度
im = cv2.imread(img_path, cv2.IMREAD_COLOR)
if im is None:
raise FileNotFoundError(f'无法读取图像：{img_path}')
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
# 2) 二值化（Otsu）
_, bw = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# 反相：使圆为白色前景
if invert_binary:
bw = cv2.bitwise_not(bw)
cv2.imwrite(os.path.join(out_dir, '01_bw.png'), bw)
# 3) 形态学闭运算（修补小孔/细小断裂）
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
morph = cv2.morphologyEx(bw, cv2.MORPH_CLOSE, kernel)
cv2.imwrite(os.path.join(out_dir, '02_morph.png'), morph)
# 4) 距离变换
dist = cv2.distanceTransform(morph, cv2.DIST_L2, 5)
cv2.imwrite(os.path.join(out_dir, '03_dist.png'), to_vis_u8(dist))
# 5) 构造模板并做归一化相关
distborder = cv2.copyMakeBorder(
dist, borderSize, borderSize, borderSize, borderSize,
cv2.BORDER_CONSTANT | cv2.BORDER_ISOLATED, 0
)
kernel2 = cv2.getStructuringElement(
cv2.MORPH_ELLIPSE, (2 * (borderSize - gap) + 1, 2 * (borderSize - gap) + 1)
)
kernel2 = cv2.copyMakeBorder(
kernel2, gap, gap, gap, gap,
cv2.BORDER_CONSTANT | cv2.BORDER_ISOLATED, 0
)
distTempl = cv2.distanceTransform(kernel2, cv2.DIST_L2, 5)
nxcor = cv2.matchTemplate(distborder, distTempl, cv2.TM_CCOEFF_NORMED)
cv2.imwrite(os.path.join(out_dir, '04_nxcor.png'), to_vis_u8(nxcor))
# 6) 阈值化得到峰值区域，并做最小面积过滤
_, mx, _, _ = cv2.minMaxLoc(nxcor)
_, peaks = cv2.threshold(nxcor, mx * peak_thresh_ratio, 255, cv2.THRESH_BINARY)
peaks8u = cv2.convertScaleAbs(peaks)
# 可选：删除太小的连通域（腐蚀/开运算或按轮廓面积过滤）
cnt_res = cv2.findContours(peaks8u, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
contours = cnt_res[0] if len(cnt_res) == 2 else cnt_res[1]
peaks_clean = np.zeros_like(peaks8u)
kept_bboxes = []
for cnt in contours:
area = cv2.contourArea(cnt)
if area >= min_peak_area:
cv2.drawContours(peaks_clean, [cnt], -1, 255, thickness=-1)
kept_bboxes.append(cv2.boundingRect(cnt))
cv2.imwrite(os.path.join(out_dir, '05_peaks_mask.png'), peaks_clean)
# 7) 在每个候选区域中找局部极大，并做颜色一致性过滤
annotated = im.copy()
centers = []
for index, (x, y, w_box, h_box) in enumerate(kept_bboxes):
# 用峰值掩膜作为 mask，限定在候选区域内找最大值（圆心）
mask_roi = peaks_clean[y:y+h_box, x:x+w_box]
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(
dist[y:y+h_box, x:x+w_box], mask=mask_roi
)
cx, cy = int(max_loc[0] + x), int(max_loc[1] + y)
r = int(round(max_val)) # 估计半径
# --- 颜色一致性过滤 ---
# 1) 圆心像素必须为前景（白）
if morph[cy, cx] == 0:
continue
# 2) 圆心邻域的前景占比必须足够高
r_eff = max(4, int(r * disk_fraction))
fg_ratio = fg_ratio_in_disk(morph, cx, cy, r_eff)
if fg_ratio < min_fg_ratio:
continue
centers.append((cx, cy))
# 只画圆心（绿色十字）
cv2.drawMarker(
annotated, (cx, cy),
color=marker_color,
markerType=cv2.MARKER_CROSS,
markerSize=marker_size,
thickness=marker_thickness,
line_type=cv2.LINE_AA
)
cv2.putText(
annotated, f'#{index+1} ({cx}, {cy})', (cx + 5, cy - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, marker_color, 1, cv2.LINE_AA
)
# 8) 输出
print(f'最终圆心数：{len(centers)}')
for i, (cx, cy) in enumerate(centers, start=1):
print(f'#{i}: x={cx}, y={cy}')
cv2.imwrite(os.path.join(out_dir, '06_centers.png'), annotated)
cv2.imshow('centers', annotated)
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
