From d4151c2dabcba70751add97275d4730e091ad6ad Mon Sep 17 00:00:00 2001 From: liyubo Date: Thu, 5 Mar 2026 09:21:07 +0800 Subject: [PATCH] =?UTF-8?q?=E6=A8=A1=E5=9E=8B=E6=96=87=E4=BB=B6=E8=B7=AF?= =?UTF-8?q?=E5=BE=84=E6=94=AF=E6=8C=81=E9=85=8D=E7=BD=AE,=E5=9B=BE?= =?UTF-8?q?=E7=89=87=E4=B8=A4=E8=BE=B9=E6=A0=BC=E5=AD=90=E5=88=87=E6=8E=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- predict/predict_yolo11seg.py | 15 +++++++++++---- util/smb_tool.py | 4 ++-- util/yolo2pix_new.py | 35 +++++++++++++++++++++-------------- 3 files changed, 34 insertions(+), 20 deletions(-) diff --git a/predict/predict_yolo11seg.py b/predict/predict_yolo11seg.py index ad90d72..71685b3 100644 --- a/predict/predict_yolo11seg.py +++ b/predict/predict_yolo11seg.py @@ -22,6 +22,7 @@ from middleware.minio_util import upload_file, downFile, check_zip_size, upload_ from util.yolo2pix_new import * from util.smb_tool import * import threading +import json # 定义红白蓝颜色 (BGR格式) RED = (0, 0, 255) @@ -653,9 +654,15 @@ class YOLOSegmentationInference: # 获取当前脚本文件所在的目录 script_dir = os.path.dirname(__file__) - # 构建模型文件的绝对路径 - asphalt_model_path = os.path.abspath(os.path.join(script_dir, '..', 'pt_save', 'asphalt20260212_0.391.pt')) - cream_model_path = os.path.abspath(os.path.join(script_dir, '..', 'pt_save', 'asphalt20260212_0.391.pt')) + # 获取模型json配置文件 + with open(os.path.join(script_dir, 'model.json'), 'r') as f: + config = json.load(f) + # 构建模型文件的绝对路径 + asphalt_model_path = os.path.abspath(os.path.join(script_dir, '..', 'pt_save', config['asphalt'])) + cream_model_path = os.path.abspath(os.path.join(script_dir, '..', 'pt_save', config['cream'])) + + print(f"asphalt_model_path={asphalt_model_path}") + print(f"cream_model_path={cream_model_path}") if road_data : road_type_cn = road_data['路面类型（沥青/水泥/砂石）'] @@ -1020,4 +1027,4 @@ def predict_images_share_dir(task_id, pt_name, zip_url, user_name, pwd, output_d else : print(f"错误: 输入 {zip_url} 不是有效的文件或目录") - return standardized_path(f"{target_path}_识别/{task_id}/{current_time}/{task_id}.zip"), "success" \ No newline at end of file + return standardized_path(f"{target_path}_识别/{task_id}/{current_time}/{task_id}.zip"), "success" \ No newline at end of file diff --git a/util/smb_tool.py b/util/smb_tool.py index db38107..7579b8f 100644 --- a/util/smb_tool.py +++ b/util/smb_tool.py @@ -809,11 +809,11 @@ def get_road_dict(dir,user_name,pwd) : found_paths = scanner.find_files_by_name( share_path=config['share'], - file_name='每公里指标明细表*.xls', + file_name='每公里指标明细表*.xls*', start_dir=config['dir'], max_depth=4 ) - print(f"\n找到 {len(found_paths)} 个 '每公里指标明细表*.xls' 文件:") + print(f"\n找到 {len(found_paths)} 个 '每公里指标明细表*.xls*' 文件:") for i, path in enumerate(found_paths, 1): print(f"{i}. {path}") diff --git a/util/yolo2pix_new.py b/util/yolo2pix_new.py index 89afc8c..d179349 100644 --- a/util/yolo2pix_new.py +++ b/util/yolo2pix_new.py @@ -22,6 +22,7 @@ CELL_AREA = 0.01 # 每格面积 (平方米) GRID_WIDTH = 108 # 网格像素宽 GRID_HEIGHT = 102 # 网格像素高 COVER_RATIO = 0.01 # mask 覆盖比例阈值 +RESERVED_CELL_NUM = 20 # 道路灾害格子过滤掉两侧数据，只保留每行中间20个格子 # ---------------- 路面类别映射 ---------------- CLASS_MAP_ASPHALT = { @@ -160,7 +161,8 @@ def detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name): road_type = "gravel" return road_code, pile_no, road_type -def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT,cover_ratio=COVER_RATIO): +def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,cell_index_start,cell_index_end, + grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT,cover_ratio=COVER_RATIO): """将YOLO-Seg标签转换成格子编号和类别""" img_file_name = os.path.basename(image_path) road_code, pile_no, road_type = detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name) @@ -172,8 +174,8 @@ def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,grid_wid img = cv2.imread(image_path) if img is None: return "", {} h, w = img.shape[:2] - cols = max(1, w//GRID_WIDTH) - rows = max(1, h//GRID_HEIGHT) + cols = max(1, w//grid_width) + rows = max(1, h//grid_height) result_lines = [] all_class_cells = {} @@ -198,10 +200,10 @@ def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,grid_wid max_y = 0 for r in range(rows): for c in range(cols): - x1,y1 = c*GRID_WIDTH, r*GRID_HEIGHT - x2,y2 = min(w,x1+GRID_WIDTH), min(h,y1+GRID_HEIGHT) + x1,y1 = c*grid_width, r*grid_height + x2,y2 = min(w,x1+grid_width), min(h,y1+grid_height) region = mask[y1:y2, x1:x2] - if np.count_nonzero(region)/region.size>cover_ratio: + if c >= cell_index_start and c < cell_index_end and np.count_nonzero(region)/region.size>cover_ratio: covered_cells.append(r*cols+c+1) # 最小x坐标，y坐标 if min_x > c : @@ -236,7 +238,7 @@ def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,grid_wid }) all_class_cells[cname] = cell_info - return '\n'.join(result_lines), all_class_cells, road_type, rows * cols + return '\n'.join(result_lines), all_class_cells, road_type, rows * RESERVED_CELL_NUM def yoloseg_to_grid(image_path,label_file,cover_ratio=COVER_RATIO): """将YOLO-Seg标签转换成格子编号和类别""" @@ -556,6 +558,12 @@ def format_number_to_k_code(number): # ---------------- 主函数-共享目录 ---------------- def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,cell_width=CELL_WIDTH,cell_height=CELL_HEIGHT,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT): os.makedirs(dir,exist_ok=True) + # 网格像素宽 + road_recognize_width = road_dict.get('识别宽度（米）', 3.6) + grid_width = round(4096 * CELL_WIDTH / road_recognize_width) + # 道路灾害格子过滤掉两侧数据，只保留每行中间20个格子 + cell_index_start = round((road_recognize_width / CELL_WIDTH - RESERVED_CELL_NUM) / 2) + cell_index_end = cell_index_start + RESERVED_CELL_NUM # 解压 # 读取桩号映射 # 遍历图片 @@ -569,13 +577,13 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,cell_width= if not os.path.exists(label_file): print(f"⚠️ 找不到标签: {label_file}") continue - out_txt, class_cells, road_type, all_cell_num = yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file) + out_txt, class_cells, road_type, all_cell_num = yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,cell_index_start,cell_index_end,grid_width=grid_width) # 写每张图独立 _grid.txt grid_txt_path = os.path.splitext(image_path)[0]+"_grid.txt" with open(grid_txt_path,'w',encoding='utf-8') as f: f.write(out_txt) # 生成网格可视化 - draw_grid_on_image(image_path,class_cells,cell_size=(GRID_WIDTH, GRID_HEIGHT),save_path=os.path.splitext(image_path)[0]+"_grid.jpg") + draw_grid_on_image(image_path,class_cells,cell_size=(grid_width, grid_height),save_path=os.path.splitext(image_path)[0]+"_grid.jpg") # 统计各类面积 # counts = {k:[len(v[0])*cell_area, v[1][0], v[1][1]] for k,v in class_cells.items()} counts = {k:[[len(v_child.get("covered_cells"))*cell_area, v_child.get("width"), v_child.get("height")] for v_child in v] for k,v in class_cells.items()} @@ -906,11 +914,10 @@ def get_road_dict(local_dir): dict: 路线编码到路线信息的映射字典 """ # 查找匹配的Excel文件 - pattern_xls = os.path.join(local_dir, '每公里指标明细表*.xls') - pattern_xlsx = os.path.join(local_dir, '每公里指标明细表*.xlsx') - found_paths = glob.glob(pattern_xls) + glob.glob(pattern_xlsx) + pattern = os.path.join(local_dir, '每公里指标明细表*.xls*') + found_paths = glob.glob(pattern) - print(f"\n找到 {len(found_paths)} 个 '每公里指标明细表*.xls/xlsx' 文件:") + print(f"\n找到 {len(found_paths)} 个 '每公里指标明细表*.xls*' 文件:") for i, path in enumerate(found_paths, 1): print(f"{i}. {path}") @@ -989,7 +996,7 @@ if __name__=="__main__": # calc_cell_area, calc_grid_width, calc_grid_height = calc_grid_param(2048, 4096, 3.6, 2) # print(f"calc_cell_area={calc_cell_area}, calc_grid_width={calc_grid_width}, calc_grid_height={calc_grid_height}") - output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/jlp/20260227090742" + output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/jlp/C006500107A" pile_dict = get_pile_dict(output_dir) road_dict = get_road_dict(output_dir) process_dir(road_dict, pile_dict, output_dir)