import cv2
import numpy as np

# ========================
# 配置
# ========================
IMAGE_PATH = "22.png"

CANNY_LOW = 60
CANNY_HIGH = 150

# 判定“近似水平”的阈值
MAX_SLOPE = 0.2   # |dy/dx| < 0.2 认为是水平

# ========================


def main():
    img = cv2.imread(IMAGE_PATH)
    if img is None:
        raise RuntimeError("图片读取失败")

    h, w = img.shape[:2]

    # 1. 灰度
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # 2. 模糊（非常关键，压制粗糙纹理）
    blur = cv2.GaussianBlur(gray, (7, 7), 0)

    # 3. Canny
    edges = cv2.Canny(blur, CANNY_LOW, CANNY_HIGH)

    # 4. 找轮廓（只用来拿点）
    contours, _ = cv2.findContours(
        edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE
    )

    horizontal_points = []

    # 5. 筛选“近似水平”的边缘点
    for cnt in contours:
        if len(cnt) < 50:
            continue

        pts = cnt.squeeze()
        if pts.ndim != 2:
            continue

        x = pts[:, 0]
        y = pts[:, 1]

        dx = x.max() - x.min()
        dy = y.max() - y.min()

        if dx < 100:        # 太短的不要
            continue

        slope = dy / (dx + 1e-6)

        if slope < MAX_SLOPE:
            horizontal_points.append(pts)

    if not horizontal_points:
        print("❌ 没找到合适的水平边")
        return

    # 6. 合并所有候选点
    all_pts = np.vstack(horizontal_points)

    # 7. 选“最靠上的那一条”
    # 方法：按 y 排序，取 y 最小的一部分
    all_pts = all_pts[all_pts[:, 1].argsort()]

    # 取前 20% 作为“上沿候选”
    top_n = int(len(all_pts) * 0.2)
    top_edge_pts = all_pts[:top_n]

    # 8. 用最小二乘拟合直线 y = ax + b
    xs = top_edge_pts[:, 0]
    ys = top_edge_pts[:, 1]

    a, b = np.polyfit(xs, ys, 1)

    # 9. 画线
    x0, x1 = 0, w - 1
    y0 = int(a * x0 + b)
    y1 = int(a * x1 + b)

    vis = img.copy()
    cv2.line(vis, (x0, y0), (x1, y1), (0, 0, 255), 2)

    # 可视化点
    for p in top_edge_pts[::20]:
        cv2.circle(vis, tuple(p), 1, (0, 255, 0), -1)

    cv2.imshow("edges", edges)
    cv2.imshow("top plate edge", vis)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


if __name__ == "__main__":
    main()