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new file mode 100644
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diff --git a/data_sub/test_1/data_sub.json b/data_sub/test_1/data_sub.json
new file mode 100644
index 0000000..847e9ff
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diff --git a/data_sub/test_1/data_sub_full.json b/data_sub/test_1/data_sub_full.json
new file mode 100644
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diff --git a/data_sub/test_1/wide_image.png b/data_sub/test_1/wide_image.png
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diff --git a/params.json b/params.json
deleted file mode 100644
index ea7614a..0000000
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-    "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
index 5453e89..da484dd 100644
--- a/search.py
+++ b/search.py
@@ -1,4 +1,7 @@
 import cv2
+
+from utils import filter_points
+
 print(cv2.__version__) # 4.9.0
 import json
 import math
@@ -13,8 +16,9 @@ def get_params(num_param, start, end):
 
 def parmas_to_num(text_param):
     for item in text_param:
-        item['center'] = int(float(item['center']) * 1000)
-        item['x'] = int(item['x'])
+        # item['center'] = int(float(item['center']) * 1000)
+        item['center'] = int(float(item['center']))
+        item['x'] = int(float(item['x']))
         item['w'] = int(item['w'])
         item['h'] = int(item['h'])
     return text_param
@@ -275,7 +279,7 @@ 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["params"] = read_from_json("data_bim.json")
     data_bim["point"] = []
     data_bim["params"] = parmas_to_num(data_bim["params"])
 
@@ -284,21 +288,42 @@ if __name__ == "__main__":
     data_sub["params"] = []
 
     # 创建测试子集
-    sub_im = cv2.imread("wide_image.png")
+    # 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)
+    sub_im = cv2.imread("data_sub/test_1/wide_image.png")
+    sub_im2 = cv2.imread("data_sub/test_1/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)
+    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)
+        if (i == 15):
+            print("=========15=======")
+            print(p)
+    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)
-        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)
+        # x, y, w, h = cv2.boundingRect(contour)
+        x = contour["x"]
+        y = contour["y"]
+        w = contour["width"]
+        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*0.2) # roi2
         k = int(h*0.01) # roi1
@@ -323,9 +348,13 @@ if __name__ == "__main__":
         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)
+    bim_im = gen_im_from_params(data_bim["params"])
+    # cv2.namedWindow("bim", cv2.WINDOW_NORMAL)
+    # cv2.imshow("bim", bim_im)
+    cv2.imwrite("bim_im.png", bim_im)
     # cv2.waitKey(0)
+    cv2.imshow("sub_zero", sub_zero)
+    cv2.waitKey(0)
 
     # data_sub = {}
     # data_sub["type"] = 0
@@ -484,6 +513,15 @@ if __name__ == "__main__":
     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.imshow("2", img_matches)
+    # cnts的矩形画在sub_im 上
+    for i in range(len(cnts)):
+        p = cnts[i]
+        cv2.rectangle(sub_im, (p['x'], p['y']), (p['x']+p['width'],p['y']+p['height']), (0, 0, 255), 2)
+        # 写编号
+        cv2.putText(sub_im, str(i), (p['x'], p['y']), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+        if(i == 15):
+            print("=========15=======")
+            print(p)
+    cv2.imshow("sub_im_after_filter", sub_im)
     cv2.waitKey(0)
\ No newline at end of file
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..002b928
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,63 @@
+import json
+import logging
+
+import cv2
+
+
+
+def filter_points(image_path,points):
+    # 高度过大点过滤参数
+    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
+    image = cv2.imread(image_path)
+    image_height = image.shape[0]
+    image_width = image.shape[1]
+    image_x_min = image_width * left_x_cut_rate
+    print(f"image_x_min====={image_x_min}")
+    image_x_max = image_width * (1 - right_x_cut_rate)
+    print(f"image_x_max====={image_x_max}")
+
+    #开始过滤点
+    bad_point_index = []
+    print(f'开始过滤点,原有点数为{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:
+            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:
+            bad_point_index.append(index)
+            continue
+    print(f'过滤点结束,过滤之后的点数为{len(points)}')
+
+    # 删除bad_point_index
+    filtered_points = []
+    for i, point in enumerate(points):
+        if i not in bad_point_index:
+            filtered_points.append(point)
+    return filtered_points
+
+
+
+# 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