diff --git a/.gitignore b/.gitignore
index aba02a1..6aaf2ad 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
 .idea/
+/test.py
diff --git a/bim_im.png b/bim_im.png
index c85ae8b..d6fc3f2 100644
Binary files a/bim_im.png and b/bim_im.png differ
diff --git a/data_sub/test_1/data_sub_full.json b/data_sub/test_1/data_sub_full.json
deleted file mode 100644
index 847e9ff..0000000
--- a/data_sub/test_1/data_sub_full.json
+++ /dev/null
@@ -1,202 +0,0 @@
-[
-    {
-        "label": 0,
-        "prob": 0.9336103200912476,
-        "x": 325,
-        "y": 217,
-        "width": 68,
-        "height": 71
-    },
-    {
-        "label": 0,
-        "prob": 0.9302101731300354,
-        "x": 561,
-        "y": 222,
-        "width": 56,
-        "height": 66
-    },
-    {
-        "label": 0,
-        "prob": 0.9269363284111023,
-        "x": 440,
-        "y": 115,
-        "width": 54,
-        "height": 55
-    },
-    {
-        "label": 0,
-        "prob": 0.9259827136993408,
-        "x": 495,
-        "y": 269,
-        "width": 41,
-        "height": 42
-    },
-    {
-        "label": 0,
-        "prob": 0.915646493434906,
-        "x": 430,
-        "y": 321,
-        "width": 166,
-        "height": 38
-    },
-    {
-        "label": 0,
-        "prob": 0.9102611541748047,
-        "x": 276,
-        "y": 94,
-        "width": 43,
-        "height": 41
-    },
-    {
-        "label": 0,
-        "prob": 0.9072883129119873,
-        "x": 451,
-        "y": 220,
-        "width": 80,
-        "height": 38
-    },
-    {
-        "label": 0,
-        "prob": 0.9049834609031677,
-        "x": 179,
-        "y": 323,
-        "width": 183,
-        "height": 38
-    },
-    {
-        "label": 0,
-        "prob": 0.8983769416809082,
-        "x": 512,
-        "y": 102,
-        "width": 40,
-        "height": 41
-    },
-    {
-        "label": 0,
-        "prob": 0.8979458212852478,
-        "x": 71,
-        "y": 216,
-        "width": 67,
-        "height": 66
-    },
-    {
-        "label": 0,
-        "prob": 0.8557734489440918,
-        "x": 579,
-        "y": 157,
-        "width": 31,
-        "height": 34
-    },
-    {
-        "label": 0,
-        "prob": 0.8004537224769592,
-        "x": 201,
-        "y": 218,
-        "width": 87,
-        "height": 39
-    },
-    {
-        "label": 0,
-        "prob": 0.7972114682197571,
-        "x": 195,
-        "y": 110,
-        "width": 52,
-        "height": 55
-    },
-    {
-        "label": 0,
-        "prob": 0.7899783849716187,
-        "x": 350,
-        "y": 97,
-        "width": 43,
-        "height": 41
-    },
-    {
-        "label": 0,
-        "prob": 0.789060115814209,
-        "x": 34,
-        "y": 93,
-        "width": 42,
-        "height": 39
-    },
-    {
-        "label": 0,
-        "prob": 0.7720834612846375,
-        "x": 573,
-        "y": 107,
-        "width": 37,
-        "height": 40
-    },
-    {
-        "label": 0,
-        "prob": 0.753940761089325,
-        "x": 247,
-        "y": 267,
-        "width": 46,
-        "height": 45
-    },
-    {
-        "label": 0,
-        "prob": 0.7526902556419373,
-        "x": 304,
-        "y": 170,
-        "width": 36,
-        "height": 34
-    },
-    {
-        "label": 0,
-        "prob": 0.7418428063392639,
-        "x": 103,
-        "y": 142,
-        "width": 37,
-        "height": 35
-    },
-    {
-        "label": 0,
-        "prob": 0.7005344033241272,
-        "x": 53,
-        "y": 167,
-        "width": 36,
-        "height": 36
-    },
-    {
-        "label": 0,
-        "prob": 0.6595599055290222,
-        "x": 353,
-        "y": 146,
-        "width": 38,
-        "height": 37
-    },
-    {
-        "label": 0,
-        "prob": 0.6562734842300415,
-        "x": 103,
-        "y": 91,
-        "width": 45,
-        "height": 41
-    },
-    {
-        "label": 0,
-        "prob": 0.6161856651306152,
-        "x": 540,
-        "y": 175,
-        "width": 33,
-        "height": 36
-    },
-    {
-        "label": 0,
-        "prob": 0.07473669946193695,
-        "x": 122,
-        "y": 33,
-        "width": 314,
-        "height": 358
-    },
-    {
-        "label": 0,
-        "prob": 0.056293122470378876,
-        "x": 0,
-        "y": 216,
-        "width": 43,
-        "height": 37
-    }
-]
\ No newline at end of file
diff --git a/data_sub/test_2/data_sub.json b/data_sub/test_2/data_sub.json
new file mode 100644
index 0000000..a3270a0
--- /dev/null
+++ b/data_sub/test_2/data_sub.json
@@ -0,0 +1,130 @@
+[
+    {
+        "label": 0,
+        "prob": 0.9288522601127625,
+        "x": 212,
+        "y": 112,
+        "width": 54,
+        "height": 52
+    },
+    {
+        "label": 0,
+        "prob": 0.9224857687950134,
+        "x": 337,
+        "y": 215,
+        "width": 64,
+        "height": 65
+    },
+    {
+        "label": 0,
+        "prob": 0.9219771027565002,
+        "x": 289,
+        "y": 96,
+        "width": 40,
+        "height": 40
+    },
+    {
+        "label": 0,
+        "prob": 0.92093825340271,
+        "x": 94,
+        "y": 214,
+        "width": 68,
+        "height": 67
+    },
+    {
+        "label": 0,
+        "prob": 0.8973387479782104,
+        "x": 358,
+        "y": 96,
+        "width": 41,
+        "height": 40
+    },
+    {
+        "label": 0,
+        "prob": 0.8777830600738525,
+        "x": 217,
+        "y": 213,
+        "width": 88,
+        "height": 39
+    },
+    {
+        "label": 0,
+        "prob": 0.8754808902740479,
+        "x": 196,
+        "y": 314,
+        "width": 180,
+        "height": 39
+    },
+    {
+        "label": 0,
+        "prob": 0.8678174614906311,
+        "x": 262,
+        "y": 262,
+        "width": 45,
+        "height": 42
+    },
+    {
+        "label": 0,
+        "prob": 0.7945911288261414,
+        "x": 362,
+        "y": 147,
+        "width": 36,
+        "height": 35
+    },
+    {
+        "label": 0,
+        "prob": 0.7796808481216431,
+        "x": 0,
+        "y": 116,
+        "width": 38,
+        "height": 55
+    },
+    {
+        "label": 0,
+        "prob": 0.7364567518234253,
+        "x": 0,
+        "y": 216,
+        "width": 65,
+        "height": 36
+    },
+    {
+        "label": 0,
+        "prob": 0.5554796457290649,
+        "x": 48,
+        "y": 311,
+        "width": 87,
+        "height": 42
+    },
+    {
+        "label": 0,
+        "prob": 0.5389373898506165,
+        "x": 315,
+        "y": 168,
+        "width": 37,
+        "height": 36
+    },
+    {
+        "label": 0,
+        "prob": 0.38828131556510925,
+        "x": 55,
+        "y": 98,
+        "width": 43,
+        "height": 41
+    },
+    {
+        "label": 0,
+        "prob": 0.3368460536003113,
+        "x": 123,
+        "y": 98,
+        "width": 44,
+        "height": 39
+    },
+    {
+        "label": 0,
+        "prob": 0.07753150165081024,
+        "x": 74,
+        "y": 168,
+        "width": 40,
+        "height": 37
+    }
+]
\ No newline at end of file
diff --git a/data_sub/test_2/wide_image.png b/data_sub/test_2/wide_image.png
new file mode 100644
index 0000000..342a553
Binary files /dev/null and b/data_sub/test_2/wide_image.png differ
diff --git a/data_sub/test_3/return_objs_data.json b/data_sub/test_3/return_objs_data.json
new file mode 100644
index 0000000..8b57942
--- /dev/null
+++ b/data_sub/test_3/return_objs_data.json
@@ -0,0 +1,90 @@
+[
+    {
+        "label": 0,
+        "prob": 0.917050302028656,
+        "x": 189,
+        "y": 415,
+        "width": 196,
+        "height": 40
+    },
+    {
+        "label": 0,
+        "prob": 0.9155016541481018,
+        "x": 214,
+        "y": 212,
+        "width": 53,
+        "height": 48
+    },
+    {
+        "label": 0,
+        "prob": 0.9061460494995117,
+        "x": 338,
+        "y": 309,
+        "width": 68,
+        "height": 71
+    },
+    {
+        "label": 0,
+        "prob": 0.9045385122299194,
+        "x": 92,
+        "y": 309,
+        "width": 70,
+        "height": 69
+    },
+    {
+        "label": 0,
+        "prob": 0.8958920240402222,
+        "x": 262,
+        "y": 358,
+        "width": 46,
+        "height": 45
+    },
+    {
+        "label": 0,
+        "prob": 0.8823522329330444,
+        "x": 219,
+        "y": 311,
+        "width": 85,
+        "height": 37
+    },
+    {
+        "label": 0,
+        "prob": 0.8750882744789124,
+        "x": 289,
+        "y": 197,
+        "width": 39,
+        "height": 36
+    },
+    {
+        "label": 0,
+        "prob": 0.7151791453361511,
+        "x": 0,
+        "y": 212,
+        "width": 48,
+        "height": 48
+    },
+    {
+        "label": 0,
+        "prob": 0.569022536277771,
+        "x": 354,
+        "y": 198,
+        "width": 42,
+        "height": 36
+    },
+    {
+        "label": 0,
+        "prob": 0.4601861834526062,
+        "x": 314,
+        "y": 265,
+        "width": 36,
+        "height": 36
+    },
+    {
+        "label": 0,
+        "prob": 0.3012613356113434,
+        "x": 360,
+        "y": 244,
+        "width": 35,
+        "height": 33
+    }
+]
\ No newline at end of file
diff --git a/data_sub/test_3/wide_image.png b/data_sub/test_3/wide_image.png
new file mode 100644
index 0000000..e1a54fb
Binary files /dev/null and b/data_sub/test_3/wide_image.png differ
diff --git a/search.py b/search.py
index 5bb0e29..11d1a82 100644
--- a/search.py
+++ b/search.py
@@ -1,6 +1,6 @@
 import cv2
 
-from utils import filter_points
+from utils import filter_rectangle
 
 print(cv2.__version__) # 4.9.0
 import json
@@ -116,26 +116,30 @@ def cal_c1c2c3c4(param, heigt):
 
 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
+    # type: # line points
+    ## 确定整个bim图的长度和宽度边界
+    max_y = -999999 # y坐标最大值
+    max_y_idx = -1 # y坐标最大值的索引
+    max_x = -999999 # x坐标最大值
+    max_x_idx = -1 # x坐标最大值的索引
+
+    # 遍历,找到矩形x坐标最大值和矩形y坐标最大值
     for i, param in enumerate(params):
+        # x中心点坐标加上宽度的一半 = 当前矩形的x最大坐标
         if param["x"] + param["w"] / 2 > max_x:
+            # 如果是最大的x坐标就更新
             max_x = param["x"] + param["w"] / 2
             max_x_idx = i
+        # y中心点坐标加上高度的一半 = 当前矩形的y最大坐标
         if param["center"] + param["h"] / 2 > max_y:
+            # 如果是最大的y坐标就更新
             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
+
+    padding_value = 0 # 内边距,避免整个bim图片贴着边缘展示
+    bim_width = int(max_x) + padding_value
+    bim_height = int(max_y) + padding_value
+    bim_channels = 3
     im = np.zeros((bim_height, bim_width, bim_channels), dtype=np.uint8)
 
     for i, param in enumerate(params):
@@ -145,7 +149,7 @@ def gen_im_from_params(params, type="lines"):
                 [param['x2'], param['y2']],
                 [param['x3'], param['y3']],
                 [param['x4'], param['y4']]])
-            cv2.polylines(im, [pts], True, (255,0,0), 8) 
+            cv2.polylines(im, [pts], True, (255,255,0), 8)
         elif type == "points":
             cv2.circle(im, (param['x1'], param['y1']), 1, 255, 20)
             cv2.circle(im, (param['x2'], param['y2']), 1, 255, 20)
@@ -155,9 +159,31 @@ def gen_im_from_params(params, type="lines"):
 
     return im
 
-def gen_points_from_params(params):
+
+"""
+判断点是否在区域内部
+    True 在
+    False 不在
+如果没有提供区域,不做判断,返回True
+"""
+def is_inside_roi(point, roi_w, roi_h):
+    if (roi_w != None and roi_h != None):
+        x = point[0]
+        y = point[1]
+        if (x <= 0 or y <= 0 or y >= roi_h or x >= roi_w ):
+            # 不在区域内部
+            return False
+        else:
+            # 在区域内部
+            return True
+    else:
+        # 没有提供区域,不做判断,默认返回True
+        return True
+
+
+def gen_points_from_params(params,roi_w=None,roi_h=None):
     # 依据bim数据生成bim图
-    # type: line points
+    # type line points
     ## 计算bim图长和宽
     max_y = -999999
     max_y_idx = -1
@@ -170,19 +196,26 @@ def gen_points_from_params(params):
         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])
-        # 过滤框框之外的点
+        # 排序点 按照上左、上右、下右、下左的顺时针顺序
+        if (roi_w == None and roi_h == None):
+            cal_c1c2c3c4(param, bim_height)
+        # 过滤点,把在roi区域之外的点全部过滤掉.
+        if(is_inside_roi([param['x1'], param['y1']], roi_w, roi_h)):
+            points.append([param['x1'], param['y1'], i])
+        if(is_inside_roi([param['x2'], param['y2']], roi_w, roi_h)):
+            points.append([param['x2'], param['y2'], i])
+        if(is_inside_roi([param['x3'], param['y3']], roi_w, roi_h)):
+            points.append([param['x3'], param['y3'], i])
+        if(is_inside_roi([param['x4'], param['y4']], roi_w, roi_h)):
+            points.append([param['x4'], param['y4'], i])
+        if(is_inside_roi([param['x_center'], param['y_center']], roi_w, roi_h)):
+            points.append([param['x_center'], param['y_center'], i])
+    if (roi_w != None and roi_h != None):
+        print(f"[经区域过滤之后的点数一共为] ====== [{len(points)}]")
     return points
 
 def topological_similarity(adj1, adj2):
@@ -302,19 +335,12 @@ if __name__ == "__main__":
     # _im_edge_sobel = _sobel(sub_zero)
     # _, _im_thresh = cv2.threshold(_im_edge_sobel, 5, 255, cv2.THRESH_BINARY)
     # cnts = _findContours(_im_thresh)
-    sub_im = cv2.imread("data_sub/test_1/wide_image.png")
-    sub_im2 = cv2.imread("data_sub/test_1/wide_image.png")
+    original_rectangle = read_from_json("data_sub/test_2/data_sub.json")
+    # 过滤矩形
+    # cnts 过滤之后的矩形
+    # sub_im 裁剪之后的图像
+    cnts,sub_im = filter_rectangle("data_sub/test_2/wide_image.png", original_rectangle)
     sub_zero = np.zeros_like(sub_im)
-    cnts = read_from_json("data_sub/test_1/data_sub.json")
-    # cnts的矩形画在sub_im 上
-    for i in range(len(cnts)):
-        p = cnts[i]
-        cv2.rectangle(sub_im2, (p['x'], p['y']), (p['x'] + p['width'], p['y'] + p['height']), (0, 0, 255), 2)
-        # 写编号
-        cv2.putText(sub_im2, str(i), (p['x'], p['y']), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
-    cv2.imshow("sub_im_before_filter", sub_im2)
-    # 过滤点
-    cnts = filter_points("data_sub/test_1/wide_image.png",cnts)
     for contour in cnts:
         # x, y, w, h = cv2.boundingRect(contour)
         x = contour["x"]
@@ -323,12 +349,12 @@ if __name__ == "__main__":
         h = contour["height"]
 
         # 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*0.01) # roi2
-        k = int(h*0.01) # roi1
-        x += k
-        y += k
-        w -= int(k) #
-        h -= int(k) # 2k
+        kh = int(h * 0.01) # roi1
+        kw = int(w * 0.01)  # roi1
+        x += int(kw)
+        y += int(kh)
+        w -= int(kw)
+        h -= int(kh)
         param = {}
         param["x1"] = x
         param["y1"] = y
@@ -403,7 +429,21 @@ if __name__ == "__main__":
     ## 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)
+    # 测试,画出所有的pts
+    # for i, p in enumerate(_sub_sort_params):
+    #     # 画点
+    #     cv2.circle(sub_im, (p["x_center"], p["y_center"]), 2, (0, 255, 255), -1)
+    # cv2.imshow("_sub_sort_params", sub_im)
+    # cv2.waitKey(0)
+    pts = gen_points_from_params(_sub_sort_params,sub_roi_width,sub_roi_height)
+    # # 测试,画出所有的pts
+    for i, p in enumerate(pts):
+        # 画点
+        cv2.circle(sub_im, (p[0], p[1]), 2, (0, 255, 255), -1)
+        # 写编号
+        cv2.putText(sub_im, str(i), (p[0], p[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
+    cv2.imshow("sub_im_points", sub_im)
+    # cv2.waitKey(0)
     polar_list = []
     for i, pt in enumerate(pts):
         r, theta = cartesian_to_polar(polar_origin_x, polar_origin_y, pt[0], pt[1])
@@ -414,6 +454,9 @@ if __name__ == "__main__":
     sum_r /= count * sub_roi_w_base_len
     sum_theta /= count
     print(count, sum_r, sum_theta)
+    print(f"[所有点到该预埋件左上点的个数] ====== [{count}]")
+    print(f"[所有点到该预埋件左上点的平均极半径] ====== [{sum_r}]")
+    print(f"[所有点到该预埋件左上点的平均极角] ====== [{sum_theta}]")
 
     # 初始化候选预埋件
     candi_params = []
@@ -428,7 +471,6 @@ if __name__ == "__main__":
     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
-    times = 0
     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
@@ -509,7 +551,7 @@ if __name__ == "__main__":
             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)
+                                        param["end_point"], 100 * (i+1), 6)
     
     elapsed_time = time.time() - start_time
     print(f"Execution time: {elapsed_time:.4f} seconds")
diff --git a/utils.py b/utils.py
index a833601..9b82d3a 100644
--- a/utils.py
+++ b/utils.py
@@ -4,58 +4,92 @@ import logging
 import cv2
 
 
+"""
+根据裁剪之后的图片,每个点的坐标需要重新计算,以新的图片的宽高作为坐标系
+"""
+def re_cal_point(point,offset):
+    # 相当于所有的x坐标向左平移了offset个距离
+    point['x'] = point['x'] - offset
 
-def filter_points(image_path,points):
-    # 高度过大点过滤参数
+
+
+"""
+过滤矩形
+    1 高度过大的不要
+    2 整个矩形全部身体都在裁剪区域之外的不要
+    
+    返回值:
+        1 过滤之后的矩形
+        2 裁剪之后的图片
+"""
+def filter_rectangle(image_path, points):
+    # 高度过大矩形过滤参数
     max_height_rate = 0.5 # 矩形高度占整个画面高度的最大比例，如果超过该比例，则认为是无效矩形
 
-    # x范围过滤，只保留中间部分的点，只保留矩形完整出现在在画面中间的那部分矩形。
-    left_x_cut_rate=0.2 # 左边界的裁剪比例，从左边开始裁剪百分之多少
-    right_x_cut_rate=0.2 # 右边界的裁剪比例，从右边开始裁剪百分之多少
+    # 裁剪参数
+    left_x_cut_rate=0.15 # 左边界的裁剪比例，从左边开始裁剪百分之多少
+    right_x_cut_rate=0.15 # 右边界的裁剪比例，从右边开始裁剪百分之多少
     image = cv2.imread(image_path)
-    image_height = image.shape[0]
-    image_width = image.shape[1]
-    image_x_min = image_width * left_x_cut_rate
-    image_x_max = image_width * (1 - right_x_cut_rate)
+    image_height = image.shape[0] # 获取图片高度
+    image_width = image.shape[1] # 获取图片宽度
+    image_x_min = int(image_width * left_x_cut_rate) # 左边界的裁剪点
+    image_x_max = int(image_width * (1 - right_x_cut_rate)) # 右边界的裁剪点
 
-    #开始过滤点
+    #开始过滤矩形
     bad_point_index = []
-    print(f'开始过滤点,原有点数为{len(points)}')
+    print(f'开始过滤矩形,原有矩形数为{len(points)}')
     for index in range(len(points)):
         point = points[index]
+
         # 高度过大过滤
         if point['height'] > image_height * max_height_rate:
             bad_point_index.append(index)
             continue
 
-
-        # x坐标范围过滤
-        # 原point中的x和y,是矩形左上角的坐标
-        x_min = point['x'] # 矩形四个点坐标中x的最小值
-        x_max = point['x'] + point['width'] # 矩形四个点坐标中x的最大值
-
-        # 如果矩形x的最小值小于左边界，去除这个矩形
-        if x_min < image_x_min:
+        # x坐标范围过滤,整个矩形全部身体都在裁剪区域之外的不要
+        x_min = point['x'] # 矩形四个矩形坐标中x的最小值
+        x_max = point['x'] + point['width'] # 矩形四个矩形坐标中x的最大值
+        # 如果矩形x的 最大值 小于 左边界，去除这个矩形
+        if x_max < image_x_min:
             bad_point_index.append(index)
             continue
-        # 如果矩形x的最大值大于右边界，去除这个矩形
-        if x_max > image_x_max:
+        # 如果矩形x的 最小值 大于 右边界，去除这个矩形
+        if x_min > image_x_max:
             bad_point_index.append(index)
             continue
 
-    # 删除bad_point_index
+    # 过滤,只保留有效矩形
     filtered_points = []
     for i, point in enumerate(points):
+        # 如果当前矩形的索引在bad_point_index中，则去除这个矩形
         if i not in bad_point_index:
+            # 重新计算点的坐标
+            re_cal_point(point,image_x_min)
+            # 塞入结果
             filtered_points.append(point)
-    print(f'过滤点结束,过滤之后的点数为{len(filtered_points)}')
-    return filtered_points
+    print(f'过滤矩形结束,过滤之后的矩形数为{len(filtered_points)}')
+
+    # 图片裁剪
+    # 裁剪图片 (height方向不变，宽度方向裁剪)
+    cropped_image = image[:, image_x_min:image_x_max]
+    # 展示
+    # cv2.imshow("cropped_image", cropped_image)
+    # cv2.imshow("image", image)
+    # for i in range(len(filtered_points)):
+    #     p = filtered_points[i]
+    #     cv2.rectangle(cropped_image, (p['x'], p['y']), (p['x'] + p['width'], p['y'] + p['height']), (0, 0, 255), 2)
+    #     # 写编号
+    #     cv2.putText(cropped_image, str(i), (p['x'], p['y']), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+    # cv2.imshow("cropped_image_draw", cropped_image)
+    # cv2.waitKey(0)
+    return filtered_points, cropped_image
 
 
 
+# 测试代码
 # def read_from_json(file_path):
 #     with open(file_path, 'r') as f:
 #         loaded_array = json.load(f)
 #     return loaded_array
 # cnts = read_from_json("data_sub/test_1/data_sub.json")
-# filter_points("data_sub/test_1/wide_image.png",cnts)
\ No newline at end of file
+# filter_rectangle("data_sub/test_1/wide_image.png",cnts)
\ No newline at end of file