commit d6684ac2f27e2b42f6dc84c04ef2a142f09ae043
Author: leon <tleon666@qq.com>
Date:   Thu Feb 27 11:09:58 2025 +0800

    init

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..aba02a1
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.idea/
diff --git a/params.json b/params.json
new file mode 100644
index 0000000..ea7614a
--- /dev/null
+++ b/params.json
@@ -0,0 +1,272 @@
+[
+  {
+    "code": "1",
+    "type": "250x1450",
+    "x": "196.0",
+    "y": 0,
+    "center": "351",
+    "w": "1450",
+    "h": "250",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "2",
+    "type": "300x300",
+    "x": "174.0",
+    "y": 0,
+    "center": "754",
+    "w": "300",
+    "h": "300",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "3",
+    "type": "501x500",
+    "x": "889.5",
+    "y": 0,
+    "center": "1054.5",
+    "w": "500",
+    "h": "501",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "4",
+    "type": "250x648",
+    "x": "-32.0",
+    "y": 0,
+    "center": "1149",
+    "w": "648",
+    "h": "250",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "5",
+    "type": "250x250",
+    "x": "588.0",
+    "y": 0,
+    "center": "1543",
+    "w": "250",
+    "h": "250",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "6",
+    "type": "249x249",
+    "x": "1031.5",
+    "y": 0,
+    "center": "1750.5",
+    "w": "249",
+    "h": "249",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "7",
+    "type": "400x399",
+    "x": "-199.5",
+    "y": 0,
+    "center": "1955",
+    "w": "399",
+    "h": "400",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "8",
+    "type": "300x302",
+    "x": "394.0",
+    "y": 0,
+    "center": "2159",
+    "w": "302",
+    "h": "300",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "9",
+    "type": "300x302",
+    "x": "996.0",
+    "y": 0,
+    "center": "2157",
+    "w": "302",
+    "h": "300",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "10",
+    "type": "248x1447",
+    "x": "2190.0",
+    "y": 0,
+    "center": "345",
+    "w": "1447",
+    "h": "248",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "11",
+    "type": "298x297",
+    "x": "2198.5",
+    "y": 0,
+    "center": "747",
+    "w": "297",
+    "h": "298",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "12",
+    "type": "499x499",
+    "x": "2891.0",
+    "y": 0,
+    "center": "1040.5",
+    "w": "499",
+    "h": "499",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "13",
+    "type": "245x648",
+    "x": "1994.5",
+    "y": 0,
+    "center": "1146.5",
+    "w": "648",
+    "h": "245",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "14",
+    "type": "247x247",
+    "x": "2589.5",
+    "y": 0,
+    "center": "1550.5",
+    "w": "247",
+    "h": "247",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "15",
+    "type": "246x247",
+    "x": "2998.5",
+    "y": 0,
+    "center": "1757",
+    "w": "247",
+    "h": "246",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "16",
+    "type": "398x392",
+    "x": "1799.5",
+    "y": 0,
+    "center": "1955",
+    "w": "392",
+    "h": "398",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "17",
+    "type": "300x293",
+    "x": "2394.5",
+    "y": 0,
+    "center": "2147",
+    "w": "293",
+    "h": "300",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "18",
+    "type": "298x297",
+    "x": "3001.5",
+    "y": 0,
+    "center": "2144",
+    "w": "297",
+    "h": "298",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "19",
+    "type": "245x1447",
+    "x": "4191.5",
+    "y": 0,
+    "center": "344.5",
+    "w": "1447",
+    "h": "245",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "20",
+    "type": "295x300",
+    "x": "4175.0",
+    "y": 0,
+    "center": "743.5",
+    "w": "300",
+    "h": "295",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "21",
+    "type": "498x495",
+    "x": "4897.5",
+    "y": 0,
+    "center": "1030",
+    "w": "495",
+    "h": "498",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "22",
+    "type": "245x648",
+    "x": "3977.0",
+    "y": 0,
+    "center": "1150.5",
+    "w": "648",
+    "h": "245",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "23",
+    "type": "245x246",
+    "x": "4547.5",
+    "y": 0,
+    "center": "1541.5",
+    "w": "246",
+    "h": "245",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "24",
+    "type": "245x245",
+    "x": "4992.5",
+    "y": 0,
+    "center": "1735.5",
+    "w": "245",
+    "h": "245",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "25",
+    "type": "398x392",
+    "x": "3796.5",
+    "y": 0,
+    "center": "1955",
+    "w": "392",
+    "h": "398",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "26",
+    "type": "298x296",
+    "x": "4392.5",
+    "y": 0,
+    "center": "2142",
+    "w": "296",
+    "h": "298",
+    "angle": "0\u00b0"
+  },
+  {
+    "code": "27",
+    "type": "297x295",
+    "x": "4991.5",
+    "y": 0,
+    "center": "2146.5",
+    "w": "295",
+    "h": "297",
+    "angle": "0\u00b0"
+  }
+]
\ No newline at end of file
diff --git a/search.py b/search.py
new file mode 100644
index 0000000..5453e89
--- /dev/null
+++ b/search.py
@@ -0,0 +1,489 @@
+import cv2
+print(cv2.__version__) # 4.9.0
+import json
+import math
+import copy
+import numpy as np
+import time
+from scipy.spatial import distance
+from scipy.optimize import linear_sum_assignment
+
+def get_params(num_param, start, end):
+    return copy.deepcopy(num_param[start:end+1])
+
+def parmas_to_num(text_param):
+    for item in text_param:
+        item['center'] = int(float(item['center']) * 1000)
+        item['x'] = int(item['x'])
+        item['w'] = int(item['w'])
+        item['h'] = int(item['h'])
+    return text_param
+
+def parmas_to_text(num_param):
+    for item in num_param:
+        item['center'] = str(item['center'])
+        item['x'] = str(item['x'])
+        item['w'] = str(item['w'])
+        item['h'] = str(item['h'])
+        item['x1'] = str(item['x1'])
+        item['y1'] = str(item['y1'])
+        item['x2'] = str(item['x2'])
+        item['y2'] = str(item['y2'])
+        item['x3'] = str(item['x3'])
+        item['y3'] = str(item['y3'])
+        item['x4'] = str(item['x4'])
+        item['y4'] = str(item['y4'])
+        item['x_center'] = str(item['x_center'])
+        item['y_center'] = str(item['y_center'])
+    return num_param
+
+def sort_params(params):
+    sorted_params = sorted(params, key=lambda item: (item['center'], item['x']))
+    return sorted_params
+
+def print_params(sort_params):
+    for param in sort_params:
+        print(param["center"], param["x"], param["w"], param["h"])
+
+def print_path(search_path):
+    for path in search_path:
+        print(path[0], path[1])
+
+def search_path(sort_params):
+    searchPath = []
+    for i in range(len(sort_params) - 1):
+        (r, theta) = cartesian_to_polar(sort_params[i]["x"], sort_params[i]["center"], 
+                        sort_params[i + 1]["x"], sort_params[i + 1]["center"])
+        searchPath.append([r, theta])
+    return searchPath
+
+def normalize_params_and_path(sort_params, search_path, index=0):
+    base = sort_params[index]["h"] 
+    for param in sort_params:
+        param['center'] /= base
+        param['x'] /= base
+        param['w'] /= base
+        param['h'] /= base
+        param['w/h'] = param['w'] / param['h']
+    if search_path != None:
+        for path in search_path:
+            path[0] /= base
+            # path[1] /= base
+    return sort_params, search_path
+
+def read_from_json(file_path):
+    with open(file_path, 'r') as f:
+        loaded_array = json.load(f)
+    return loaded_array
+
+def cartesian_to_polar(x1, y1, x2, y2):
+    dx = x2 - x1
+    dy = y2 - y1
+    r = math.sqrt(dx**2 + dy**2)
+    theta = math.atan2(dy, dx)
+    return r, theta
+
+def calculate_second_point(x1, y1, r, theta_radians):
+    # theta_radians = math.radians(theta_degrees)
+    x2 = x1 + r * math.cos(theta_radians)
+    y2 = y1 + r * math.sin(theta_radians)
+    
+    return x2, y2
+
+def cal_c1c2c3c4(param, heigt):
+    '''
+    按照上左、上右、下右、下左的顺时针顺序
+    '''
+    param['x1'] = int(param['x'] - param['w'] / 2)
+    param['y1'] = heigt - int(param['center'] + param['h'] / 2)
+
+    param['x2'] = int(param['x'] + param['w'] / 2)
+    param['y2'] = heigt - int(param['center'] + param['h'] / 2)
+
+    param['x3'] = int(param['x'] + param['w'] / 2)
+    param['y3'] = heigt - int(param['center'] - param['h'] / 2)
+
+    param['x4'] = int(param['x'] - param['w'] / 2)
+    param['y4'] = heigt - int(param['center'] - param['h'] / 2)
+
+    param['x_center'] = int((param['x1'] + param['x2']) / 2)
+    param['y_center'] = int((param['y1'] + param['y3']) / 2)
+    return param
+
+def gen_im_from_params(params, type="lines"):
+    # 依据bim数据生成bim图
+    # type: line points
+    ## 计算bim图长和宽
+    max_y = -999999
+    max_y_idx = -1
+    max_x = -999999
+    max_x_idx = -1
+    for i, param in enumerate(params):
+        if param["x"] + param["w"] / 2 > max_x:
+            max_x = param["x"] + param["w"] / 2
+            max_x_idx = i
+        if param["center"] + param["h"] / 2 > max_y:
+            max_y = param["center"] + param["h"] / 2
+            max_y_idx = i
+    ## 为了不贴边，x，y固定加 500
+    FILL_VALUE = 0
+    bim_width = int(max_x)
+    bim_height = int(max_y)
+    bim_width += FILL_VALUE
+    bim_height += FILL_VALUE
+    bim_channels = 1
+    im = np.zeros((bim_height, bim_width, bim_channels), dtype=np.uint8)
+
+    for i, param in enumerate(params):
+        cal_c1c2c3c4(param, bim_height)
+        if type == "lines":
+            pts = np.asarray([[param['x1'], param['y1']],
+                [param['x2'], param['y2']],
+                [param['x3'], param['y3']],
+                [param['x4'], param['y4']]])
+            cv2.polylines(im, [pts], True, (255,0,0), 8) 
+        elif type == "points":
+            cv2.circle(im, (param['x1'], param['y1']), 1, 255, 20)
+            cv2.circle(im, (param['x2'], param['y2']), 1, 255, 20)
+            cv2.circle(im, (param['x3'], param['y3']), 1, 255, 20)
+            cv2.circle(im, (param['x4'], param['y4']), 1, 255, 20)
+            cv2.circle(im, (param['x'], int((param['y3'] + param['y2']) / 2 )), 1, (255,0,0), 8)
+
+    return im
+
+def gen_points_from_params(params):
+    # 依据bim数据生成bim图
+    # type: line points
+    ## 计算bim图长和宽
+    max_y = -999999
+    max_y_idx = -1
+    max_x = -999999
+    max_x_idx = -1
+    for i, param in enumerate(params):
+        if param["x"] + param["w"] / 2 > max_x:
+            max_x = param["x"] + param["w"] / 2
+            max_x_idx = i
+        if param["center"] + param["h"] / 2 > max_y:
+            max_y = param["center"] + param["h"] / 2
+            max_y_idx = i
+    ## 为了不贴边，x，y固定加 500
+    bim_height = int(max_y)
+    points = []
+    adjacency = []  # 邻接矩阵
+    for i, param in enumerate(params):
+        cal_c1c2c3c4(param, bim_height)
+        points.append([param['x1'], param['y1'], i])
+        points.append([param['x2'], param['y2'], i])
+        points.append([param['x3'], param['y3'], i])
+        points.append([param['x4'], param['y4'], i])
+        # points.append([param['x'], int((param['y3'] + param['y2']) / 2 )])
+        points.append([param['x_center'], param['y_center'], i])
+    return points
+
+def topological_similarity(adj1, adj2):
+    """
+    计算两个拓扑结构的相似度
+    """
+    # 计算邻接矩阵的相似度
+    similarity = np.sum(adj1 == adj2) / (adj1.shape[0] * adj2.shape[1])
+    return similarity
+
+def find_topological_matches(points1, adj1, points2, adj2, threshold=0.8):
+    """
+    基于拓扑结构寻找匹配点集
+    """
+    matches = []
+    for i in range(len(points1)):
+        for j in range(len(points2)):
+            # 计算拓扑相似度
+            sim = topological_similarity(adj1[i], adj2[j])
+            if sim > threshold:
+                matches.append((i, j, sim))
+    # 按相似度排序
+    matches.sort(key=lambda x: x[2], reverse=True)
+    return matches
+
+from sklearn.linear_model import RANSACRegressor
+
+def ransac_shape_matching(points, reference_points):
+    model_ransac = RANSACRegressor(random_state=42)
+    try:
+        model_ransac.fit(reference_points, points[:len(reference_points)])  # 假设点数相同
+    except ValueError as e:
+        print("Error fitting the model:", e)
+        return []
+    
+    inlier_mask = model_ransac.inlier_mask_
+    best_match_subset = points[inlier_mask]
+
+    return best_match_subset
+
+def polar_to_cartesian(polar_points):
+    r = polar_points[:, 0]
+    theta = polar_points[:, 1]
+    x = r * np.cos(theta)
+    y = r * np.sin(theta)
+    return np.vstack((x, y)).T
+
+def compute_shape_context(points, nbins_r=5, nbins_theta=12):
+    n = points.shape[0]
+    shape_contexts = []
+    r_max = np.max(distance.pdist(points))
+    r_edges = np.logspace(-1, np.log10(r_max), nbins_r)
+    theta_edges = np.linspace(-np.pi, np.pi, nbins_theta + 1)
+
+    for i in range(n):
+        current_point = points[i]
+        relative_points = points - current_point
+        rs = np.hypot(relative_points[:, 0], relative_points[:, 1])
+        thetas = np.arctan2(relative_points[:, 1], relative_points[:, 0])
+        H, _, _ = np.histogram2d(thetas, rs, bins=[theta_edges, r_edges])
+        H /= np.sum(H) + 1e-8  # 归一化
+        shape_contexts.append(H.flatten())
+    
+    return np.array(shape_contexts)
+
+def match_shapes(sc1, sc2):
+    cost_matrix = distance.cdist(sc1, sc2, metric='sqeuclidean')
+    row_ind, col_ind = linear_sum_assignment(cost_matrix)
+    return cost_matrix[row_ind, col_ind].sum()
+
+def _sobel(image):
+    '''
+        _sobel
+    '''
+    if image.ndim > 2:
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    # todo 增加几个参数 http://blog.csdn.net/sunny2038/article/details/9170013
+    _sobelx = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=1)
+    _sobely = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=1)
+
+    _sobelx = np.uint8(np.absolute(_sobelx))
+    _sobely = np.uint8(np.absolute(_sobely))
+    _sobelcombine = cv2.bitwise_or(_sobelx,_sobely)
+    return _sobelcombine
+
+def _findContours(image):
+    '''
+    _findContours
+    http://blog.csdn.net/mokeding/article/details/20153325
+    '''
+    contours, _ = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+    return sorted(contours, key=cv2.contourArea, reverse=True)
+
+if __name__ == "__main__":
+    # 读取并处理数据
+    data_bim = {}
+    data_bim["type"] = 0
+    data_bim["params"] = read_from_json("./params.json")
+    data_bim["point"] = []
+    data_bim["params"] = parmas_to_num(data_bim["params"])
+
+    data_sub = {}
+    data_sub["type"] = 0
+    data_sub["params"] = []
+
+    # 创建测试子集
+    sub_im = cv2.imread("wide_image.png")
+    sub_zero = np.zeros_like(sub_im)
+    _im_gray = cv2.cvtColor(sub_im, cv2.COLOR_BGR2GRAY)
+    _im_gray = cv2.GaussianBlur(_im_gray, (5, 5), 0)
+    _im_edge_sobel = _sobel(_im_gray)
+    _, _im_thresh = cv2.threshold(_im_edge_sobel, 5, 255, cv2.THRESH_BINARY)
+    cnts = _findContours(_im_thresh)
+    for contour in cnts:
+        x, y, w, h = cv2.boundingRect(contour)
+        cv2.rectangle(sub_zero, (x, y), (x + w, y + h), (255, 255, 255), -1)
+    _im_edge_sobel = _sobel(sub_zero)
+    _, _im_thresh = cv2.threshold(_im_edge_sobel, 5, 255, cv2.THRESH_BINARY)
+    cnts = _findContours(_im_thresh)
+    for contour in cnts:
+        x, y, w, h = cv2.boundingRect(contour)
+        # 由于定位框大小大于预埋件大小，因此这里需要做缩放处理
+        k = int(h*0.2) # roi2
+        k = int(h*0.01) # roi1
+        x += k
+        y += k
+        w -= int(k) #
+        h -= int(k) # 2k
+        param = {}
+        param["x1"] = x
+        param["y1"] = y
+        param["x2"] = x + w
+        param["y2"] = y 
+        param["x3"] = x + w
+        param["y3"] = y + h
+        param["x4"] = x
+        param["y4"] = y + h
+        param['x_center'] = int((param['x1'] + param['x2']) / 2)
+        param['y_center'] = int((param['y1'] + param['y3']) / 2)
+        param["w"] = param["x2"] - param["x1"]
+        param["h"] = param["y3"] - param["y1"] 
+        param["x"] = int((param["x1"] + param["x2"]) / 2)
+        param['center'] = sub_im.shape[0] - int((param["y1"] + param["y3"]) / 2)
+        data_sub["params"].append(param)
+        cv2.rectangle(sub_zero, (x, y), (x + w, y + h), (0, 255, 0), 1)
+    
+    # cv2.imshow("1", sub_zero)
+    # cv2.waitKey(0)
+
+    # data_sub = {}
+    # data_sub["type"] = 0
+    # data_sub["params"] = get_params(data_bim["params"], 1,8)
+    # data_sub["point"] = []
+    # data_sub["params"][0]["center"] += 20
+    # data_sub["params"][0]["x"] += 13
+    # data_sub["params"][0]["w"] += 40
+
+    ##################### 开始计算 ####################
+    # 1、计算sub的搜索路径
+    ## 1.1 排序 y升序，x升序
+    data_sub["params"] = sort_params(data_sub["params"])
+    _sub_sort_params = data_sub["params"]
+    start_time = time.time()
+
+    # sub_roi_height = int(max_y) #????
+    # sub_roi_width = int(max_x) #????
+    sub_roi_height = sub_im.shape[0]
+    sub_roi_width = sub_im.shape[1]
+    sub_roi_params_select_id = -1
+    sub_roi_w_base_len = 0 # 选中预埋件的宽度作为基础长度
+    sub_roi_divide_w_h = 1
+    polar_origin_x = 0 # 极坐标原点 x
+    polar_origin_y = 0 # 极坐标原点 y
+    start_x = 0
+    start_y = 0
+    ## 1.2 选择一块完整的预埋件
+    for i, param in enumerate(_sub_sort_params):
+        if 0 < param['x1'] and param['x2'] < sub_roi_width \
+            and 0 < param['y1'] and param['y3'] < sub_roi_height:
+            sub_roi_params_select_id = i
+            sub_roi_w_base_len = param['x2'] - param['x1']
+            sub_roi_divide_w_h = param['w'] / param['h']
+            polar_origin_x = int(param['x1']) # 当前选择的预埋件的左上角 x 坐标
+            polar_origin_y = int(param['y1']) # 当前选择的预埋件的左上角 y 坐标
+            break
+    if sub_roi_params_select_id == -1 or sub_roi_w_base_len == 0 :
+        print("[ERROR]\t 拍摄的图像中没有完整的预埋件信息\n")
+        assert(0)
+    ## 1.2.2 将其他预埋件相对于它的极坐标进行填写
+    for i, param in enumerate(_sub_sort_params):
+        if i != sub_roi_params_select_id:
+            param['r'], param['theta'] = cartesian_to_polar(_sub_sort_params[sub_roi_params_select_id]['x_center'],
+                                                            _sub_sort_params[sub_roi_params_select_id]['y_center'],
+                                                            param['x_center'],param['y_center'])
+    
+    ## 1.3计算所有点到该预埋件左上点的，点个数，平均极半径 和 平均极角度
+    sum_r, sum_theta = 0.0,0
+    count = 0
+    pts = gen_points_from_params(_sub_sort_params)
+    polar_list = []
+    for i, pt in enumerate(pts):
+        r, theta = cartesian_to_polar(polar_origin_x, polar_origin_y, pt[0], pt[1])
+        sum_r += r
+        sum_theta += theta
+        count += 1
+        polar_list.append([r, theta])
+    sum_r /= count * sub_roi_w_base_len
+    sum_theta /= count
+    print(count, sum_r, sum_theta)
+
+    # 初始化候选预埋件
+    candi_params = []
+    for i, param in enumerate(data_bim["params"]):
+        temp_div_w_h = param['w'] / param['h']
+        if temp_div_w_h / sub_roi_divide_w_h < 1.5 and temp_div_w_h / sub_roi_divide_w_h > 0.66:
+            candi_params.append(param)
+    print(f"形状筛选后还剩下：{len(candi_params)} 个候选, w / h = {sub_roi_divide_w_h}")
+
+    rst_params = []
+    bim_all_pts = gen_points_from_params(data_bim["params"])
+    bim_im = gen_im_from_params(data_bim["params"])
+    # sub_im = gen_im_from_params(data_sub["params"])# 需要读取
+    min_match_score = 999999
+    for i, param in enumerate(candi_params):
+        tmp_roi_w_base_len = param['x2'] - param['x1']
+        scale = tmp_roi_w_base_len / sub_roi_w_base_len
+        tmp_roi_width = int(scale * sub_roi_width) 
+        tmp_roi_height = int(scale * sub_roi_height)
+        # 相对于bim图的坐标
+        tmp_roi_start_x = int(param['x1'] - scale * polar_origin_x)
+        tmp_roi_end_x = tmp_roi_start_x + tmp_roi_width
+        tmp_roi_start_y = int(param['y1'] - scale * polar_origin_y)
+        tmp_roi_end_y = tmp_roi_start_y + tmp_roi_height
+        tmp_roi_conners = [[tmp_roi_start_x, tmp_roi_start_y],
+                   [tmp_roi_end_x, tmp_roi_start_y],
+                   [tmp_roi_end_x, tmp_roi_end_y],
+                   [tmp_roi_start_x, tmp_roi_end_y]]
+        tmp_sum_r, tmp_sum_theta = 0.0, 0
+        tmp_count = 0
+        tmp_polar_list = []
+        # 这里需要把选中的预埋件也提取出来
+        param['effective_points'] = []
+        for j, pt in enumerate(bim_all_pts):
+            if cv2.pointPolygonTest(np.asarray(tmp_roi_conners), (pt[0],pt[1]), False) > 0:
+                r, theta = cartesian_to_polar(param['x1'], param['y1'], pt[0], pt[1])
+                tmp_sum_r += r
+                tmp_sum_theta += theta
+                tmp_count += 1
+                tmp_polar_list.append([r, theta])
+                # 搜集有效点，以供后续继续处理
+                param['effective_points'].append(pt)
+        tmp_sum_r /= tmp_count * tmp_roi_w_base_len
+        tmp_sum_theta /= tmp_count
+        
+        # 预埋件数量相差 30%，则不进行计算
+        # if tmp_count / count > 1.3 or tmp_count / count < 0.77: continue
+
+        if abs(tmp_count - count) == 0: score = 0.5
+        elif abs(tmp_count - count) <= 10: score = 0.3
+        elif abs(tmp_count - count) <= 20: score = 0.2
+        elif abs(tmp_count - count) <= 30: score = 0.1
+        else: score = 0.0
+
+        # else: score = (1 / abs(tmp_count - count) ) * 0.7
+        score += (1 - abs(tmp_sum_r - sum_r) / sub_roi_width) * 0.25
+        score += (1 - abs(tmp_sum_theta - sum_theta) / 3.14) * 0.25
+
+        if score > 0.6: #????
+            cartesian_points1 = polar_to_cartesian(np.asarray(tmp_polar_list))
+            cartesian_points2 = polar_to_cartesian(np.asarray(polar_list))
+            sc1 = compute_shape_context(cartesian_points1)
+            sc2 = compute_shape_context(cartesian_points2)
+            match_score = match_shapes(sc1, sc2)
+            print(score, match_score, tmp_count, tmp_sum_r, tmp_sum_theta)
+            if match_score < 5.0: #????
+                param["start_point"] = (tmp_roi_start_x, tmp_roi_start_y)
+                param["end_point"] = (tmp_roi_end_x, tmp_roi_end_y)
+                param["score"] = (score, tmp_count, tmp_sum_r*tmp_roi_w_base_len, tmp_sum_theta)
+                param['match_score'] = match_score
+                if min_match_score > match_score:
+                    min_match_score = match_score
+                rst_params.append(param)
+    
+    # 预埋件匹配
+    for i, param in enumerate(rst_params):
+        score = param["score"]
+        match_score = param["match_score"]
+        id = param["code"]
+        if min_match_score == match_score or match_score < 0.5: #????
+        # if True:   
+            index_list = []
+            for j in range(len(param['effective_points'])):
+                pt = param['effective_points'][j]
+                if pt[2] not in index_list:
+                    index_list.append(param['effective_points'][j][2])
+            # for i, param in enumerate(_sub_sort_params):
+            #     param[]
+            print(f"起始预埋件ID：{id}，置信度为：{score[0]}，点数量：{score[1]}，平均长度为：{score[2]}，平均角度为：{score[3]}")
+            print(f"match_sscore为：{match_score}")
+            bim_im = cv2.rectangle(bim_im, param["start_point"], \
+                                        param["end_point"], 255, 6)
+    
+    elapsed_time = time.time() - start_time
+    print(f"Execution time: {elapsed_time:.4f} seconds")
+    img_matches = cv2.resize(bim_im, (int(bim_im.shape[1]/6), int(bim_im.shape[0]/6)))
+    # sub_im = cv2.resize(sub_im, (int(sub_im.shape[1]/10), int(sub_im.shape[0]/10)))
+    cv2.imshow("1", img_matches)
+    cv2.imshow("2", sub_im)
+    cv2.waitKey(0)
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