关于YOLOV1
本文主要偏向代码实战，详细的算法原理着墨不多
主要创新：
实现了实时的目标检测
附上YOLOV1论文地址：
https://arxiv.org/abs/1506.02640
关于数据
首先要了解该网络架构的输入和输出，以及网络是如何进行训练的。这里主要讲一下数据集的格式以及使用labelme制作数据集（当然还有其他更好的工具，这里只做参考）
如果已经安装了python，使用以下命令直接安装labelme工具，没安python的先安python
pip install labelme
之后在cmd中输入labelme，然后回车就能看到labelme的前端UI界面了
（ps：在启动labelme时也可以指定很多参数，比如 --flags可以启动时指定有哪些类别，具体不在赘述，感兴趣的读者自行搜索）
labelme --flags F:\python\yolo系列\yolov1\flags.txt
之后OpenDir打开图片所在的文件夹路径，然后鼠标右键Create Rectangle夸夸框就完事了，之后会save为json格式的文件。（把自动保存打开的话会保存到图片的路径下如图：随便标了十五张）
如何你有仔细看过YOLOV1的论文会发现json根本不是我们训练所需要的格式，json多了很多无用的信息，甚至Rectangle也只标记了左上和右下两个点。别慌，我们用脚本转化一下：
import json
import osdef convert(img_size, box):x1 = box[0]y1 = box[1]x2 = box[2]y2 = box[3]center_x = (x1 + x2) * 0.5 / img_size[0]center_y = (y1 + y2) * 0.5 / img_size[1]w = abs((x2 - x1)) * 1.0 / img_size[0]h = abs((y2 - y1)) * 1.0 / img_size[1]return (center_x, center_y, w, h)def decode_json(jsonfloder_path, json_name):txt_name = jsonfloder_path + json_name[0:-5] + '.txt'txt_file = open(txt_name, 'w') # te filesjson_path = os.path.join(json_folder_path, json_name)data = json.load(open(json_path, 'r'))img_w = data['imageWidth'] # json是一个字典的形式img_h = data['imageHeight']for i in data['shapes']:if (i['shape_type'] == 'rectangle'):x1 = int(i['points'][0][0])y1 = int(i['points'][0][1])x2 = int(i['points'][1][0])y2 = int(i['points'][1][1])if x1 < 0 or x2 < 0 or y1 < 0 or y2 < 0:continueelse:bb = (x1, y1, x2, y2)bbox = convert((img_w, img_h), bb)## 这里标签要对其if i['label'] == "person":txt_file.write("0 " + " ".join([str(a) for a in bbox]) + '\n')elif i['label'] == "plane":txt_file.write("1 " + " ".join([str(a) for a in bbox]) + '\n')elif i['label'] == "dog":txt_file.write("2 " + " ".join([str(a) for a in bbox]) + '\n')elif i['label'] == "cat":txt_file.write("3 " + " ".join([str(a) for a in bbox]) + '\n')elif i['label'] == "car":txt_file.write("4 " + " ".join([str(a) for a in bbox]) + '\n')elif i['label'] == "horse":txt_file.write("5 " + " ".join([str(a) for a in bbox]) + '\n')elif i['label'] == "can":txt_file.write("6 " + " ".join([str(a) for a in bbox]) + '\n')elif i['label'] == "train":txt_file.write("7 " + " ".join([str(a) for a in bbox]) + '\n')if __name__ == "__main__":json_folder_path = r'F:\python\yolo系列\yolov1\images' # json的pathjson_names = os.listdir(json_folder_path) # file namefor json_name in json_names: # output all filesprint(json_name)if json_name[-5:] == '.json': # just work for json filesdecode_json(json_folder_path, json_name)不出意外的话会得到如下图所示的txt标签，这才是训练模型所需要的格式
