diff --git a/005查看yolo格式目标检测框.py b/005查看yolo格式目标检测框.py index 62ca623..e368cf8 100644 --- a/005查看yolo格式目标检测框.py +++ b/005查看yolo格式目标检测框.py @@ -3,9 +3,11 @@ import cv2 import numpy as np # 总的检测根目录 -path_root_labels = 'fire-8/valid/labels' +path_root_labels = '/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集20250428/Safty/train/labels_raw' # 总的检测根目录 -path_root_imgs = 'fire-8/valid/images' +path_root_imgs = '/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集20250428/Safty/train/images' + +path_root_save = '/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集20250428/Safty/train/show' remove_path = 'fire-8/remove/valid.txt' @@ -34,9 +36,9 @@ for ii in os.walk(path_root_imgs): line = f.readline() if line: msg = line.split(" ") - if msg[0] == '1': - with open(remove_path, 'a') as t: - t.write(j+'\n') + # if msg[0] == '1': + # with open(remove_path, 'a') as t: + # t.write(j+'\n') # print(j) # print(x_center,",",y_center,",",width,",",height) x1 = int((float(msg[1]) - float(msg[3]) / 2) * w) # x_center - width/2 @@ -45,9 +47,10 @@ for ii in os.walk(path_root_imgs): y2 = int((float(msg[2]) + float(msg[4]) / 2) * h) # y_center + height/2 # print(x1, ",", y1, ",", x2, ",", y2) cv2.rectangle(img_tmp, (x1, y1), (x2, y2), (0, 0, 255), 5) + cv2.putText(img_tmp, msg[0], (x1-5, y1-5), 2, 2, 0, 2) else: break # cv2.imshow("show", img_tmp) # c = cv2.waitKey(0) - cv2.imwrite(f'fire-8/show/{j}', img_tmp) + cv2.imwrite(f'{path_root_save}/{j}', img_tmp) # print(f'写入文件{j}') diff --git a/006yolov8训练.py b/006yolov8训练.py index f00649e..4663c19 100644 --- a/006yolov8训练.py +++ b/006yolov8训练.py @@ -18,9 +18,9 @@ if __name__ == '__main__': # print(torch.cuda.is_available()) - model = YOLO('yolov8n.yaml') + model = YOLO('./yolov8n.yaml') - results = model.train(data='./dataset_20250304.yaml', epochs=200, batch=8, patience=20, imgsz=640, save=True, - workers=0, device=0, project='HelmetShoeHead', name='train', verbose=True) + results = model.train(data='/home/admin-root/haotian/python哈汽锻8安全帽识别/dataset_20250428_BAC.yaml', epochs=200, batch=32, patience=20, imgsz=640, save=True, + workers=0, device=0, project='HelmetHeadBAC', name='train', verbose=True) # print(model) \ No newline at end of file diff --git a/008yolov8预测.py b/008yolov8预测.py index a429b83..5cdcc26 100644 --- a/008yolov8预测.py +++ b/008yolov8预测.py @@ -7,26 +7,32 @@ from ultralytics import YOLO ''' -model = YOLO('5000_HeadHelmet/train2/weights/best.pt') +model = YOLO('/home/admin-root/haotian/python哈汽锻8安全帽识别/HeadShoes/trainshoeV2/weights/best.pt') -images_path = 'test_images' +images_path = '/home/admin-root/haotian/锻8/tensorrtx/yolov8/images' + +save_path = '/home/admin-root/haotian/python哈汽锻8安全帽识别/output/outputShoeV2' all_images = [os.path.join(images_path, t) for t in os.listdir(images_path)] -# conf 置信度在0.5 以上的框才显示。 -results = model(all_images, - conf = 0.5, device=0) -# Process results list -i = 0 -for result in results: - boxes = result.boxes # Boxes object for bounding box outputs - # masks = result.masks # Masks object for segmentation masks outputs - # keypoints = result.keypoints # Keypoints object for pose outputs - # 属于这个类的置信度 - probs = result.probs # Probs object for classification outputs - # obb = result.obb # Oriented boxes object for OBB outputs - print(boxes,' ',probs) - # result.show() # display to screen - result.save(filename=f"result_{i}.jpg") # save to disk - i += 1 \ No newline at end of file +for i in range(len(all_images)): + + # conf 置信度在. + results = model(all_images[i], + conf = 0.2, device=0) + + # # Process results list + # = 0 + # for result in results: + # boxes = result.boxes # Boxes object for bounding box outputs + # # masks = result.masks # Masks object for segmentation masks outputs + # # keypoints = result.keypoints # Keypoints object for pose outputs + + # # 属于这个类的置信度 + # probs = result.probs # Probs object for classification outputs + # # obb = result.obb # Oriented boxes object for OBB outputs + # print(boxes,' ',probs) + # # result.show() # display to screen + results[0].save(filename=f"{save_path}/result_{i}.jpg") # save to disk + # i += 1 \ No newline at end of file diff --git a/009导出yolov8模型.py b/009导出yolov8模型.py index b806ba1..ea3b752 100644 --- a/009导出yolov8模型.py +++ b/009导出yolov8模型.py @@ -1,7 +1,9 @@ from ultralytics import YOLO - +''' + 导出yolo模型并不能转换成tensorRT模型 +''' model = YOLO('5000_HeadHelmet/train2/weights/best.pt') model.export(format="engine", device=0) \ No newline at end of file diff --git a/025划分数据集.py b/025划分数据集.py index 699f1b6..2b9e656 100644 --- a/025划分数据集.py +++ b/025划分数据集.py @@ -4,10 +4,10 @@ import shutil def split_dataset(images_dir, labels_dir, train_ratio=0.8): # 创建 train 和 val 文件夹 - train_images_dir = os.path.join('/home/admin-root/haotian/python哈汽安全帽识别/安全帽鞋头数据集/train', 'images') - val_images_dir = os.path.join('/home/admin-root/haotian/python哈汽安全帽识别/安全帽鞋头数据集/val', 'images') - train_labels_dir = os.path.join('/home/admin-root/haotian/python哈汽安全帽识别/安全帽鞋头数据集/train', 'labels') - val_labels_dir = os.path.join('/home/admin-root/haotian/python哈汽安全帽识别/安全帽鞋头数据集/val', 'labels') + train_images_dir = os.path.join('/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集20250428/2200/train', 'images') + val_images_dir = os.path.join('/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集20250428/2200/val', 'images') + train_labels_dir = os.path.join('/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集20250428/2200/train', 'labels') + val_labels_dir = os.path.join('/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集20250428/2200/val', 'labels') os.makedirs(train_images_dir, exist_ok=True) os.makedirs(val_images_dir, exist_ok=True) @@ -39,8 +39,8 @@ def split_dataset(images_dir, labels_dir, train_ratio=0.8): print(f"数据集已成功划分为训练集和验证集，训练集占比 {train_ratio * 100}%") # 使用示例 -images_dir = '/home/admin-root/haotian/python哈汽安全帽识别/安全帽鞋头数据集/images' # 替换为你的 images 文件夹路径 -labels_dir = '/home/admin-root/haotian/python哈汽安全帽识别/安全帽鞋头数据集/labels_helmetShoeHead_filter' # 替换为你的 labels 文件夹路径 +images_dir = '/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集20250428/2200/images/images' # 替换为你的 images 文件夹路径 +labels_dir = '/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集20250428/2200/images/labels' # 替换为你的 labels 文件夹路径 train_ratio = 0.8 # 训练集占比，默认为 80% split_dataset(images_dir, labels_dir, train_ratio) \ No newline at end of file diff --git a/026保留指定类别的标签.py b/026保留指定类别的标签.py new file mode 100644 index 0000000..8405d06 --- /dev/null +++ b/026保留指定类别的标签.py @@ -0,0 +1,40 @@ +import os + + +# 类别名称列表 +class_names = ['badaoxie', 'banxie', 'baomihuaxie', 'heiyaoshixie', 'huoxingxie', 'jianzhenxie', 'maozhuaxie', 'mianhuatangxie', 'mofangxie', 'moshutiexie', 'qidianxie', 'ranshaoxie', 'shuinixie', 'wangmianxie', 'xiaobaixie', 'xiaochujuxie', 'xiaomaixie', 'yingtangxie', 'yinsuxie', 'yuanyangxie', 'yucixie'] # 替换为你的类别名称 +target_class = ['badaoxie', 'banxie', 'baomihuaxie', 'heiyaoshixie', 'huoxingxie', 'jianzhenxie', 'maozhuaxie', 'mianhuatangxie', 'mofangxie', 'moshutiexie', 'qidianxie', 'ranshaoxie', 'shuinixie', 'wangmianxie', 'xiaobaixie', 'xiaochujuxie', 'xiaomaixie', 'yingtangxie', 'yinsuxie', 'yuanyangxie', 'yucixie'] # 目标类别 +target_class_id = {'badaoxie': 2, 'banxie':2, 'baomihuaxie':2, 'heiyaoshixie':2, 'huoxingxie':2, + 'jianzhenxie':2, 'maozhuaxie':2, 'mianhuatangxie':2, 'mofangxie':2, 'moshutiexie':2, + 'qidianxie':2, 'ranshaoxie':2, 'shuinixie':2, 'wangmianxie':2, 'xiaobaixie':2, + 'xiaochujuxie':2, 'xiaomaixie':2, 'yingtangxie':2, 'yinsuxie':2, 'yuanyangxie':2, 'yucixie':2} # 目标类别的新 ID + +def filter_and_rename_labels(label_directory, output_label_directory): + if not os.path.exists(output_label_directory): + os.makedirs(output_label_directory) # 创建输出标签目录 + + # 遍历标签目录中的所有文件 + for label_file in os.listdir(label_directory): + if label_file.endswith('.txt'): # 确保是标签文件 + with open(os.path.join(label_directory, label_file), 'r') as file: + lines = file.readlines() + + # 过滤标签，只保留目标类别并修改类别 ID + filtered_lines = [] + for line in lines: + try: + class_id, x_center, y_center, box_width, box_height = map(float, line.split()) + if class_names[int(class_id)] in target_class: + # 修改类别 ID 为 0 + filtered_lines.append(f"{target_class_id[class_names[int(class_id)]]} {x_center} {y_center} {box_width} {box_height}\n") + except: + print("标签文件错误", label_file) + + # 将过滤后的标签写入新的文件 + with open(os.path.join(output_label_directory, label_file), 'w') as file: + file.writelines(filtered_lines) + +# 示例用法 +label_directory = '/home/admin-root/haotian/python哈汽锻8安全帽识别/Shoes/shoe.v2i.yolov8/valid/labels_raw' # 替换为你的标签文件夹路径 +output_label_directory = '/home/admin-root/haotian/python哈汽锻8安全帽识别/Shoes/shoe.v2i.yolov8/valid/labels' # 替换为你希望保存过滤后标签的路径 +filter_and_rename_labels(label_directory, output_label_directory) diff --git a/027保留指定类别标签_1.py b/027保留指定类别标签_1.py new file mode 100644 index 0000000..fec4c1f --- /dev/null +++ b/027保留指定类别标签_1.py @@ -0,0 +1,34 @@ +import os + +# 类别名称列表 +class_names = ['0', 'head', 'helmet', 'object', 'person'] # 替换为你的类别名称 +target_class = ['head', 'helmet'] # 目标类别 + +def filter_labels(label_directory, output_label_directory): + if not os.path.exists(output_label_directory): + os.makedirs(output_label_directory) # 创建输出标签目录 + + # 遍历标签目录中的所有文件 + for label_file in os.listdir(label_directory): + if label_file.endswith('.txt'): # 确保是标签文件 + with open(os.path.join(label_directory, label_file), 'r') as file: + lines = file.readlines() + + # 过滤标签，只保留目标类别 + filtered_lines = [] + for line in lines: + try: + class_id, _, _, _, _ = map(float, line.split()) + if class_names[int(class_id)] in target_class: + filtered_lines.append(line) + except: + print(label_file) + + # 将过滤后的标签写入新的文件 + with open(os.path.join(output_label_directory, label_file), 'w') as file: + file.writelines(filtered_lines) + +# 示例用法 +label_directory = '/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集/train/labels_raw' # 替换为你的标签文件夹路径 +output_label_directory = '/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集/train/labels_filtered' # 替换为你希望保存过滤后标签的路径 +filter_labels(label_directory, output_label_directory) diff --git a/028合并标签文件.py b/028合并标签文件.py new file mode 100644 index 0000000..091ee79 --- /dev/null +++ b/028合并标签文件.py @@ -0,0 +1,49 @@ +import os + + +label_path_1 = '/home/admin-root/haotian/python哈汽锻8安全帽识别/头鞋数据集/head_imgs/labels_head' +label_path_2 = '/home/admin-root/haotian/python哈汽锻8安全帽识别/头鞋数据集/head_imgs/labels_shoe' + +label_output_path = '/home/admin-root/haotian/python哈汽锻8安全帽识别/头鞋数据集/head_imgs/labels' + +label_names = os.listdir(label_path_1) + +for label_name in label_names: + + # 原始标签路径, lp1 head: 1, lp2 shoe:2 + lp1 = os.path.join(label_path_1, label_name) + lp2 = os.path.join(label_path_2, label_name) + + # 新的标签路径 + lop = os.path.join(label_output_path, label_name) + + labels = list() + + with open(lp1, 'r') as f: + lines1 = f.readlines() + + for line in lines1: + try: + class_id, x_center, y_center, box_width, box_height = map(float, line.split()) + labels.append(f"1 {x_center} {y_center} {box_width} {box_height}\n") + except: + + print("lp1 解析标签文件出错") + + with open(lp2, 'r') as f: + lines2 = f.readlines() + + for line in lines2: + try: + class_id, x_center, y_center, box_width, box_height = map(float, line.split()) + labels.append(f"2 {x_center} {y_center} {box_width} {box_height}\n") + except: + + print("lp2 解析标签文件出错") + + with open(lop, 'w') as f: + f.writelines(labels) + + + + diff --git a/029将yolo预测结果写为label_studio_json格式.py b/029将yolo预测结果写为label_studio_json格式.py new file mode 100644 index 0000000..3feb7ef --- /dev/null +++ b/029将yolo预测结果写为label_studio_json格式.py @@ -0,0 +1,125 @@ +import os.path +import string +import json +from ultralytics import YOLO + +''' + 注意修改配置文件中的文件夹路径 + + +''' + +# 使用secrets模块（更安全但更慢） +import secrets + +def secure_generate(num: int): + chars = string.ascii_letters + string.digits + '_' + candidate = ''.join(secrets.choice(chars) for _ in range(num)) + + return candidate + + +model = YOLO('/home/admin-root/haotian/劳保鞋识别/jingzhu_245_1112/tensorrtx-master/yolov8/build_1113/best.pt') + +images_path = '/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集20250428/HardHatsV1/train/images' + +save_path = '/home/admin-root/haotian/python哈汽锻8安全帽识别/output/outputShoeV2' + +image_names = os.listdir(images_path) + +all_images = [os.path.join(images_path, t) for t in image_names] + + +json_list = list() + + +for i in range(len(all_images)): + + # conf 置信度在. + results = model(all_images[i], + conf = 0.2, device=0) + + # # Process results list + # = 0 + # for result in results: + # boxes = result.boxes # Boxes object for bounding box outputs + # # masks = result.masks # Masks object for segmentation masks outputs + # # keypoints = result.keypoints # Keypoints object for pose outputs + + # # 属于这个类的置信度 + # probs = result.probs # Probs object for classification outputs + # # obb = result.obb # Oriented boxes object for OBB outputs + # print(boxes,' ',probs) + # # result.show() # display to screen + # results[0].save(filename=f"{save_path}/result_{i}.jpg") # save to disk + + + + names = results[0].names + + xywhns = results[0].boxes.xywhn + clss = results[0].boxes.cls + + t_d = dict() + t_d["data"] = {"image": f"/data/local-files/?d=HardHatsV1/train/images/{image_names[i]}"} + t_d["predictions"] = list() + + t_d_p = dict() + t_d_p["model_version"] = "version 1" + t_d_p["score"] = 0.8 + t_d_p["result"] = list() + + + + orig_img = results[0].orig_shape + + for idx,boxe in enumerate(xywhns): + + + + t_d_p_b = dict() + t_d_p_b["original_width"] = orig_img[1] + t_d_p_b["original_height"] = orig_img[0] + t_d_p_b["image_rotation"] = 0 + + #随机字符串 + t_d_p_b["id"] = secure_generate(9) + t_d_p_b["from_name"] = "label" + t_d_p_b["to_name"] = "image" + t_d_p_b["type"] = "rectanglelabels" + t_d_p_b["origin"] = "manual" + + + x, y, w, h = boxe + class_name = names[clss[idx].item()] + + t_d_p_b["value"]={ + "x": x.item() * 100, + "y": y.item() * 100, + "width" : w.item() * 100, + "height" : h.item() * 100, + "rotation": 0, + "rectanglelabels": [ class_name] + } + + t_d_p["result"].append(t_d_p_b) + + t_d["predictions"].append(t_d_p) + + json_list.append(t_d) + + + break + +with open("test_json.json", "w", encoding="utf-8") as f: + + json.dump(json_list,f, + indent=4, # 4空格缩进 + sort_keys=True, # 按键名字典序排序 + ensure_ascii=False # 支持非ASCII字符 + ) + + + + + # i += 1 \ No newline at end of file diff --git a/030将yolo预测结果写为label_studio_json格式2.py b/030将yolo预测结果写为label_studio_json格式2.py new file mode 100644 index 0000000..f7561e6 --- /dev/null +++ b/030将yolo预测结果写为label_studio_json格式2.py @@ -0,0 +1,128 @@ +import json +import os +from uuid import uuid4 +from ultralytics import YOLO + +def yolo_to_labelstudio(results, class_names, model_version="yolov8_1.0"): + """将 YOLOv8 检测结果转换为 Label Studio 预测格式 + + Args: + results: YOLOv8 Results 对象 + class_names: 类别名称列表 + model_version: 模型版本标识 + + Returns: + Label Studio 兼容的预测字典 + """ + predictions = [] + + # 获取原始图像尺寸 (height, width) + orig_height = results[0].orig_shape[0] + orig_width = results[0].orig_shape[1] + + for box in results[0].boxes: + # 提取检测框信息 + x_min, y_min, x_max, y_max = box.xyxy[0].tolist() + conf = box.conf.item() + cls_idx = int(box.cls.item()) + + # 计算百分比坐标（保留3位小数） + x_percent = round((x_min / orig_width) * 100, 3) + y_percent = round((y_min / orig_height) * 100, 3) + width_percent = round((x_max - x_min) / orig_width * 100, 3) + height_percent = round((y_max - y_min) / orig_height * 100, 3) + + # 构建预测项 + prediction = { + "id": str(uuid4()), # 生成唯一ID + "type": "rectanglelabels", + "from_name": "label", + "to_name": "image", + "original_width": orig_width, + "original_height": orig_height, + "value": { + "x": x_percent, + "y": y_percent, + "width": width_percent, + "height": height_percent, + "rotation": 0, + "rectanglelabels": [class_names[cls_idx]] + }, + "score": round(conf, 4) # 保留4位小数 + } + predictions.append(prediction) + + return { + "model_version": model_version, + "result": predictions + } + +# 使用示例 +if __name__ == "__main__": + # 初始化模型 + # model_0 = YOLO('/home/admin-root/haotian/python哈汽锻8安全帽识别/HelmetHeadBAC/trainHardHatsV1/weights/best.pt') + + # 检测鞋的模型 + model_1 = YOLO('/home/admin-root/haotian/劳保鞋识别/jingzhu_245_1112/tensorrtx-master/yolov8/build_1113/best.pt') + + # 定义类别名称 (需与训练时一致) + CLASS_NAMES_0 = [ + "helmet", "head", + ] + + CLASS_NAMES_1 = [ + "shoe", + ] + + # 头,安全帽 + model_0 = YOLO('/home/admin-root/haotian/python哈汽锻8安全帽识别/HelmetHeadBAC/trainHardHatsV1/weights/best.pt') + + images_path = '/home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽头数据集20250428/HardHatsV1/train/images' + + save_path = '/home/admin-root/haotian/python哈汽锻8安全帽识别/output/outputShoeV2' + + image_names = os.listdir(images_path) + + all_images = [os.path.join(images_path, t) for t in image_names] + + json_list = list() + + for i in range(len(all_images)): + + # conf 置信度在. + results_0 = model_0(all_images[i], + conf = 0.2, device=0) + + predictions_0 = yolo_to_labelstudio(results_0, CLASS_NAMES_0) + + + results_1 = model_1(all_images[i], + conf=0.2, device=0) + + predictions_1 = yolo_to_labelstudio(results_1, CLASS_NAMES_1) + + predictions_0["result"] += predictions_1["result"] + + + t_d = dict() + t_d["data"] = {"image": f"/data/local-files/?d=HardHatsV1/train/images/{image_names[i]}"} + t_d["predictions"] = list() + + t_d["predictions"].append(predictions_0) + + json_list.append(t_d) + + if i == 10: + break + + +with open("test_json.json", "w", encoding="utf-8") as f: + + json.dump(json_list,f, + indent=4, # 4空格缩进 + sort_keys=True, # 按键名字典序排序 + ensure_ascii=False # 支持非ASCII字符 + ) + + + \ No newline at end of file diff --git a/dataset_20250304.yaml b/dataset_20250304.yaml index 1ac80dc..e7a22ad 100644 --- a/dataset_20250304.yaml +++ b/dataset_20250304.yaml @@ -8,7 +8,7 @@ # └── coco8 ← downloads here (1 MB) # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] -path: /home/admin-root/haotian/python哈汽安全帽识别/安全帽鞋头数据集 # dataset root dir +path: /home/admin-root/haotian/python哈汽锻8安全帽识别/安全帽鞋头数据集 # dataset root dir train: train # train images (relative to 'path') 4 images val: val # val images (relative to 'path') 4 images test: # test images (optional) diff --git a/安全帽鞋头数据集.zip b/安全帽鞋头数据集.zip deleted file mode 100644 index cebfcc0..0000000 Binary files a/安全帽鞋头数据集.zip and /dev/null differ diff --git a/安全帽鞋头数据集/train/labels.cache b/安全帽鞋头数据集/train/labels.cache index fd49e64..61406b9 100644 Binary files a/安全帽鞋头数据集/train/labels.cache and b/安全帽鞋头数据集/train/labels.cache differ diff --git a/安全帽鞋头数据集/val/labels.cache b/安全帽鞋头数据集/val/labels.cache index ea6b6ea..2a0a86a 100644 Binary files a/安全帽鞋头数据集/val/labels.cache and b/安全帽鞋头数据集/val/labels.cache differ