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leon 2025-02-28 19:19:41 +08:00
parent 520cb9ed15
commit d471a04c4c
4 changed files with 212 additions and 30 deletions
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162
data_sub/test_4/data_sub.json Normal file
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@ -0,0 +1,162 @@
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data_sub/test_4/wide_image.png Normal file
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76
search.py
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@ -96,7 +96,8 @@ def calculate_second_point(x1, y1, r, theta_radians):
def cal_c1c2c3c4(param, heigt):
'''
按照上左、上右、下右、下左的顺时针顺序
按照上左、上右、下右、下左的顺时针顺序.
返回的数据是转换成了以左上角为原点的坐标系下的坐标点。
'''
param['x1'] = int(param['x'] - param['w'] / 2)
param['y1'] = heigt - int(param['center'] + param['h'] / 2)
@ -138,7 +139,9 @@ def gen_im_from_params(params, type="lines"):
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
print(f"[bim_width] ====== [{bim_width}]")
bim_height = int(max_y) + padding_value
print(f"[bim_height] ====== [{bim_height}]")
bim_channels = 3
im = np.zeros((bim_height, bim_width, bim_channels), dtype=np.uint8)
@ -335,11 +338,11 @@ 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)
original_rectangle = read_from_json("data_sub/test_3/data_sub.json")
original_rectangle = read_from_json("data_sub/test_1/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_3/wide_image.png", original_rectangle)
cnts,sub_im = filter_rectangle("data_sub/test_1/wide_image.png", original_rectangle)
sub_zero = np.zeros_like(sub_im)
for contour in cnts:
# x, y, w, h = cv2.boundingRect(contour)
@ -359,7 +362,7 @@ if __name__ == "__main__":
param["x1"] = x
param["y1"] = y
param["x2"] = x + w
param["y2"] = y
param["y2"] = y
param["x3"] = x + w
param["y3"] = y + h
param["x4"] = x
@ -367,7 +370,7 @@ if __name__ == "__main__":
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["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)
@ -478,8 +481,10 @@ if __name__ == "__main__":
# 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],
@ -505,19 +510,19 @@ if __name__ == "__main__":
# 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
elif abs(tmp_count - count) <= 10: score = 0.4
elif abs(tmp_count - count) <= 20: score = 0.3
elif abs(tmp_count - count) <= 30: score = 0.2
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
score += (1 - abs(tmp_sum_theta - sum_theta) / 3.14) * 0.35
print("score=======", str(score))
if score > 0.6: #????
cartesian_points1 = polar_to_cartesian(np.asarray(tmp_polar_list))
cartesian_points2 = polar_to_cartesian(np.asarray(polar_list))
cartesian_points1 = polar_to_cartesian(np.asarray(tmp_polar_list)) # bim上的坐标
cartesian_points2 = polar_to_cartesian(np.asarray(polar_list)) # sub上的坐标
sc1 = compute_shape_context(cartesian_points1)
sc2 = compute_shape_context(cartesian_points2)
match_score = match_shapes(sc1, sc2)
@ -534,28 +539,43 @@ if __name__ == "__main__":
param['match_score'] = match_score
if min_match_score > match_score:
min_match_score = match_score
print(f"[start_point] ====== [{param["start_point"]}]")
print(f"[end_point] ====== [{param["end_point"]}]")
rst_params.append(param)
# 预埋件匹配
max_score = -99999
max_index = -1
for i, param in enumerate(rst_params):
score = param["score"]
match_score = param["match_score"]
print(f"[match_score] ====== [{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"], 100 * (i+1), 50)
if abs(score[0] / match_score) > max_score:
max_score = abs(score[0] / match_score)
max_index = i
bim_im = cv2.rectangle(bim_im,rst_params[max_index]["start_point"], rst_params[max_index]["end_point"], 100 * (i + 1), 50)
# # 预埋件匹配
# for i, param in enumerate(rst_params):
# score = param["score"]
# match_score = param["match_score"]
# print(f"[match_score] ====== [{match_score}]")
#
# id = param["code"]
#
# if min_match_score == match_score or match_score < 5.0: #????
# # 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}")
# print(f"[start_point] ====== [{param["start_point"]}]")
# print(f"[end_point] ====== [{param["end_point"]}]")
#
# bim_im = cv2.rectangle(bim_im, param["start_point"], param["end_point"], 100 * (i + 1), 50)
elapsed_time = time.time() - start_time
print(f"Execution time: {elapsed_time:.4f} seconds")

4
utils.py
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@ -27,8 +27,8 @@ def filter_rectangle(image_path, points):
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
# 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
left_x_cut_rate=0.1 # 左边界的裁剪比例，从左边开始裁剪百分之多少
right_x_cut_rate=0.1# 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] # 获取图片高度
image_width = image.shape[1] # 获取图片宽度