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YOLOv5 PyTorch HUB Inference (DetectionModels only)
torch.hub 目前只支持检测模型的推理
1.视频教程:
B站、网易云课堂、腾讯课堂
2.代码地址:
Gitee
Github
3.存储地址:
Google云
百度云:
提取码:
python classify/train.py --model yolov5s-cls.pt --data E:\major\my_dataset--epochs 500 --img 224 --batch 4
python classify/val.py --weights yolov5m-cls.pt --data E:\major\my_dataset --img 224
python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
path: D:\workplace\yolov5-master-latest\datasets\CatDogDet # dataset root dir
train: images/train # train images (relative to 'path')
val: images/val # val images (relative to 'path')
test: # test images (optional)
# Classes
names:
0: dog
1: cat
# -*- coding: utf-8 -*- import os import numpy as np import json from glob import glob import cv2 import shutil import yaml from sklearn.model_selection import train_test_split from tqdm import tqdm # 获取当前路径 ROOT_DIR = os.getcwd() ''' 统一图像格式 ''' def change_image_format(label_path=ROOT_DIR, suffix='.jpg'): """ 统一当前文件夹下所有图像的格式,如'.jpg' :param suffix: 图像文件后缀 :param label_path:当前文件路径 :return: """ externs = ['png', 'jpg', 'JPEG', 'BMP', 'bmp'] files = list() # 获取尾缀在ecterns中的所有图像 for extern in externs: files.extend(glob(label_path + "\\*." + extern)) # 遍历所有图像,转换图像格式 for file in files: name = ''.join(file.split('.')[:-1]) file_suffix = file.split('.')[-1] if file_suffix != suffix.split('.')[-1]: # 重命名为jpg new_name = name + suffix # 读取图像 image = cv2.imread(file) # 重新存图为jpg格式 cv2.imwrite(new_name, image) # 删除旧图像 os.remove(file) ''' 读取所有json文件,获取所有的类别 ''' def get_all_class(file_list, label_path=ROOT_DIR): """ 从json文件中获取当前数据的所有类别 :param file_list:当前路径下的所有文件名 :param label_path:当前文件路径 :return: """ # 初始化类别列表 classes = list() # 遍历所有json,读取shape中的label值内容,添加到classes for filename in tqdm(file_list): json_path = os.path.join(label_path, filename + '.json') json_file = json.load(open(json_path, "r", encoding="utf-8")) for item in json_file["shapes"]: label_class = item['label'] if label_class not in classes: classes.append(label_class) print('read file done') return classes ''' 划分训练集、验证机、测试集 ''' def split_dataset(label_path, test_size=0.3, isUseTest=False, useNumpyShuffle=False): """ 将文件分为训练集,测试集和验证集 :param useNumpyShuffle: 使用numpy方法分割数据集 :param test_size: 分割测试集或验证集的比例 :param isUseTest: 是否使用测试集,默认为False :param label_path:当前文件路径 :return: """ # 获取所有json files = glob(label_path + "\\*.json") files = [i.replace("\\", "/").split("/")[-1].split(".json")[0] for i in files] if useNumpyShuffle: file_length = len(files) index = np.arange(file_length) np.random.seed(32) np.random.shuffle(index) # 随机划分 test_files = None # 是否有测试集 if isUseTest: trainval_files, test_files = np.array(files)[index[:int(file_length * (1 - test_size))]], np.array(files)[ index[int(file_length * (1 - test_size)):]] else: trainval_files = files # 划分训练集和测试集 train_files, val_files = np.array(trainval_files)[index[:int(len(trainval_files) * (1 - test_size))]], \ np.array(trainval_files)[index[int(len(trainval_files) * (1 - test_size)):]] else: test_files = None if isUseTest: trainval_files, test_files = train_test_split(files, test_size=test_size, random_state=55) else: trainval_files = files train_files, val_files = train_test_split(trainval_files, test_size=test_size, random_state=55) return train_files, val_files, test_files, files ''' 生成yolov5的训练、验证、测试集的文件夹 ''' def create_save_file(label_path=ROOT_DIR): """ 按照训练时的图像和标注路径创建文件夹 :param label_path:当前文件路径 :return: """ # 生成训练集 train_image = os.path.join(label_path, 'train', 'images') if not os.path.exists(train_image): os.makedirs(train_image) train_label = os.path.join(label_path, 'train', 'labels') if not os.path.exists(train_label): os.makedirs(train_label) # 生成验证集 val_image = os.path.join(label_path, 'valid', 'images') if not os.path.exists(val_image): os.makedirs(val_image) val_label = os.path.join(label_path, 'valid', 'labels') if not os.path.exists(val_label): os.makedirs(val_label) # 生成测试集 test_image = os.path.join(label_path, 'test', 'images') if not os.path.exists(test_image): os.makedirs(test_image) test_label = os.path.join(label_path, 'test', 'labels') if not os.path.exists(test_label): os.makedirs(test_label) return train_image, train_label, val_image, val_label, test_image, test_label ''' 转换,根据图像大小,返回box框的中点和高宽信息 ''' def convert(size, box): # 宽 dw = 1. / (size[0]) # 高 dh = 1. / (size[1]) x = (box[0] + box[1]) / 2.0 - 1 y = (box[2] + box[3]) / 2.0 - 1 # 宽 w = box[1] - box[0] # 高 h = box[3] - box[2] x = x * dw w = w * dw y = y * dh h = h * dh return x, y, w, h ''' 移动图像和标注文件到指定的训练集、验证集和测试集中 ''' def push_into_file(file, images, labels, label_path=ROOT_DIR, suffix='.jpg'): """ 最终生成在当前文件夹下的所有文件按image和label分别存在到训练集/验证集/测试集路径的文件夹下 :param file: 文件名列表 :param images: 存放images的路径 :param labels: 存放labels的路径 :param label_path: 当前文件路径 :param suffix: 图像文件后缀 :return: """ # 遍历所有文件 for filename in file: # 图像文件 image_file = os.path.join(label_path, filename + suffix) # 标注文件 label_file = os.path.join(label_path, filename + '.txt') # yolov5存放图像文件夹 if not os.path.exists(os.path.join(images, filename + suffix)): try: shutil.move(image_file, images) except OSError: pass # yolov5存放标注文件夹 if not os.path.exists(os.path.join(labels, filename + suffix)): try: shutil.move(label_file, labels) except OSError: pass ''' ''' def json2txt(classes, txt_Name='allfiles', label_path=ROOT_DIR, suffix='.jpg'): """ 将json文件转化为txt文件,并将json文件存放到指定文件夹 :param classes: 类别名 :param txt_Name:txt文件,用来存放所有文件的路径 :param label_path:当前文件路径 :param suffix:图像文件后缀 :return: """ store_json = os.path.join(label_path, 'json') if not os.path.exists(store_json): os.makedirs(store_json) _, _, _, files = split_dataset(label_path) if not os.path.exists(os.path.join(label_path, 'tmp')): os.makedirs(os.path.join(label_path, 'tmp')) list_file = open('tmp/%s.txt' % txt_Name, 'w') for json_file_ in tqdm(files): json_filename = os.path.join(label_path, json_file_ + ".json") imagePath = os.path.join(label_path, json_file_ + suffix) list_file.write('%s\n' % imagePath) out_file = open('%s/%s.txt' % (label_path, json_file_), 'w') json_file = json.load(open(json_filename, "r", encoding="utf-8")) if os.path.exists(imagePath): height, width, channels = cv2.imread(imagePath).shape for multi in json_file["shapes"]: if len(multi["points"][0]) == 0: out_file.write('') continue points = np.array(multi["points"]) xmin = min(points[:, 0]) if min(points[:, 0]) > 0 else 0 xmax = max(points[:, 0]) if max(points[:, 0]) > 0 else 0 ymin = min(points[:, 1]) if min(points[:, 1]) > 0 else 0 ymax = max(points[:, 1]) if max(points[:, 1]) > 0 else 0 label = multi["label"] if xmax <= xmin: pass elif ymax <= ymin: pass else: cls_id = classes.index(label) b = (float(xmin), float(xmax), float(ymin), float(ymax)) bb = convert((width, height), b) out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n') # print(json_filename, xmin, ymin, xmax, ymax, cls_id) if not os.path.exists(os.path.join(store_json, json_file_ + '.json')): try: shutil.move(json_filename, store_json) except OSError: pass ''' 创建yaml文件 ''' def create_yaml(classes, label_path, isUseTest=False): nc = len(classes) if not isUseTest: desired_caps = { 'path': label_path, 'train': 'train/images', 'val': 'valid/images', 'nc': nc, 'names': classes } else: desired_caps = { 'path': label_path, 'train': 'train/images', 'val': 'valid/images', 'test': 'test/images', 'nc': nc, 'names': classes } yamlpath = os.path.join(label_path, "data" + ".yaml") # 写入到yaml文件 with open(yamlpath, "w+", encoding="utf-8") as f: for key, val in desired_caps.items(): yaml.dump({key: val}, f, default_flow_style=False) # 首先确保当前文件夹下的所有图片统一后缀,如.jpg,如果为其他后缀,将suffix改为对应的后缀,如.png def ChangeToYolo5(label_path=ROOT_DIR, suffix='.jpg', test_size=0.1, isUseTest=False): """ 生成最终标准格式的文件 :param test_size: 分割测试集或验证集的比例 :param label_path:当前文件路径 :param suffix: 文件后缀名 :param isUseTest: 是否使用测试集 :return: """ # step1:统一图像格式 change_image_format(label_path) # step2:根据json文件划分训练集、验证集、测试集 train_files, val_files, test_file, files = split_dataset(label_path, test_size=test_size, isUseTest=isUseTest) # step3:根据json文件,获取所有类别 classes = get_all_class(files) # step4:将json文件转化为txt文件,并将json文件存放到指定文件夹 json2txt(classes) # step5:创建yolov5训练所需的yaml文件 create_yaml(classes, label_path, isUseTest=isUseTest) # step6:生成yolov5的训练、验证、测试集的文件夹 train_image, train_label, val_image, val_label, test_image, test_label = create_save_file(label_path) # step7:将所有图像和标注文件,移动到对应的训练集、验证集、测试集 push_into_file(train_files, train_image, train_label, suffix=suffix) # 将文件移动到训练集文件中 push_into_file(val_files, val_image, val_label, suffix=suffix) # 将文件移动到验证集文件夹中 if test_file is not None: # 如果测试集存在,则将文件移动到测试集文件中 push_into_file(test_file, test_image, test_label, suffix=suffix) print('create dataset done') if __name__ == "__main__": ChangeToYolo5()
python train.py --epochs 1000 --weights yolov5s.pt --batch-size 16 --data .\wheatDetect\wheat.yaml --workers 0 --project AI_Model --name wheat --imgsz 1024 --device 0 --rect --exist-ok
--hyp .\data\hyps\hyp.scratch-high.yaml
python train.py --epochs 1000 --weights .\AI_Model\wheat\weights\best.pt --batch-size 18 --data .\wheatDetect\wheat.yaml --workers 0 --project AI_Model --name wheat2 --imgsz 1024 --device 0 --exist-ok --hyp .\data\hyps\hyp.scratch-high.yaml
--evolve
yolov5项目中,遗传算法使用了在两个地方中,
对anchor进行变异优化;
对超参数进行变异优化(yolov5中包含30个左右的超参数来对训练过程进行设置,如此多的超参数如果使用网格搜索来获得最佳结果是比较困难的,所以使用了遗传算法来求出一个局部最优解——获得较好的超参数结果。)
python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache --evolve
python val.py --weights .\AI_Model\wheat2\weights\best.pt --data .\wheatDetect\wheat.yaml --img 1024 --half --batch 12
python val.py --weights .\AI_Model\wheat2\weights\best.pt --data .\wheatDetect\wheat.yaml --img 1024 --half --batch 12 --augment
python val.py --weights .\weights\best1.pt .\weights\best2.pt --data .\wheatDetect\wheat.yaml --img 1024 --half --batch 12
python detect.py --weights .\AI_Model\wheat\weights\best.pt --source .\WheatDataSet\images\val --img 1024
python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
import torch
# Model
model = torch.hub.load('.', 'custom', path=r'best.pt',source='local')
# Images
img = r'D:\PycharmProjects\wheatDetect\WheatDataSet\images\val\0a4408b37.jpg' # or file, PIL, OpenCV, numpy, multiple
# Inference
results = model(img, augment=False) # <--- TTA inference
# Results
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
results.show()
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.pt') # PyTorch
'yolov5s.torchscript') # TorchScript
'yolov5s.onnx') # ONNX
'yolov5s_openvino_model/') # OpenVINO
'yolov5s.engine') # TensorRT
'yolov5s.mlmodel') # CoreML (macOS-only)
'yolov5s.tflite') # TFLite
'yolov5s_paddle_model/') # PaddlePaddle
""" Run a Flask REST API exposing one or more YOLOv5s models """ import argparse import io import torch from flask import Flask, request from PIL import Image app = Flask(__name__) models = {} DETECTION_URL = '/v1/object-detection/<model>' @app.route(DETECTION_URL, methods=['POST']) def predict(model): if request.method != 'POST': return if request.files.get('image'): # Method 1 # with request.files["image"] as f: # im = Image.open(io.BytesIO(f.read())) # Method 2 im_file = request.files['image'] im_bytes = im_file.read() im = Image.open(io.BytesIO(im_bytes)) if model in models: results = models[model](im, size=640) # reduce size=320 for faster inference return results.pandas().xyxy[0].to_json(orient='records') if __name__ == '__main__': parser = argparse.ArgumentParser(description='Flask API exposing YOLOv5 model') parser.add_argument('--port', default=5000, type=int, help='port number') parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s') opt = parser.parse_args() for m in opt.model: # models[m] = torch.hub.load('ultralytics/yolov5', m, force_reload=True, skip_validation=True) models[m] = torch.hub.load('../..', 'custom', path=r'../../best.pt', source='local') app.run(host='0.0.0.0', port=opt.port) # debug=True causes Restarting with stat
import pprint
import requests
DETECTION_URL = 'http://localhost:5000/v1/object-detection/yolov5s'
IMAGE = r'C:\Users\29939\PycharmProjects\wheatDetect\WheatDataSet\images\val\1a94773a1.jpg'
# Read image
with open(IMAGE, 'rb') as f:
image_data = f.read()
response = requests.post(DETECTION_URL, files={'image': image_data}).json()
pprint.pprint(response)
path: D:\workplace\yolov5-master-latest\datasets\DogCat-seg # dataset root dir
train: images/train # train images (relative to 'path')
val: images/val # val images (relative to 'path')
test: # test images (optional)
# Classes
names:
0: dog
1: cat
# -*- coding: utf-8 -*- import os import numpy as np import json from glob import glob import cv2 import shutil import yaml from sklearn.model_selection import train_test_split from tqdm import tqdm from PIL import Image ''' 辅助函数 ''' ''' 转换,根据图像大小,返回box框的中点和高宽信息 ''' def convert(size, points): # 转换后的返回列表 converted_points_list = [] # 宽 dw = 1. / (size[0]) # 高 dh = 1. / (size[1]) for point in points: x = point[0] * dw converted_points_list.append(x) y = point[1] * dh converted_points_list.append(y) return converted_points_list ''' # step1:统一图像格式 ''' def change_image_format(label_path, suffix='.jpg'): """ 统一当前文件夹下所有图像的格式,如'.jpg' :param suffix: 图像文件后缀 :param label_path:当前文件路径 :return: """ print("step1:统一图像格式") # 修改尾缀列表 externs = ['png', 'jpg', 'JPEG', 'bmp'] files = list() # 获取尾缀在externs中的所有图像 for extern in externs: files.extend(glob(label_path + "\\*." + extern)) # 遍历所有图像,转换图像格式 for index,file in enumerate(tqdm(files)): name = ''.join(file.split('.')[:-1]) file_suffix = file.split('.')[-1] if file_suffix != suffix.split('.')[-1]: # 重命名为jpg new_name = name + suffix # 读取图像 image = Image.open(file) image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR) # 重新存图为jpg格式 cv2.imwrite(new_name, image) # 删除旧图像 os.remove(file) ''' # step2:根据json文件划分训练集、验证集、测试集 ''' def split_dataset(ROOT_DIR, test_size=0.3, isUseTest=False, useNumpyShuffle=False): """ 将文件分为训练集,测试集和验证集 :param useNumpyShuffle: 使用numpy方法分割数据集 :param test_size: 分割测试集或验证集的比例 :param isUseTest: 是否使用测试集,默认为False :param label_path:当前文件路径 :return: """ # 获取所有json print('step2:根据json文件划分训练集、验证集、测试集') # 获取所有json文件路径 files = glob(ROOT_DIR + "\\*.json") # 转换为json名称 files = [i.replace("\\", "/").split("/")[-1].split(".json")[0] for i in files] # 是否打乱 if useNumpyShuffle: file_length = len(files) index = np.arange(file_length) np.random.seed(32) np.random.shuffle(index) # 随机划分 test_files = None # 是否有测试集 if isUseTest: trainval_files, test_files = np.array(files)[index[:int(file_length * (1 - test_size))]], np.array(files)[ index[int(file_length * (1 - test_size)):]] else: trainval_files = files # 划分训练集和测试集 train_files, val_files = np.array(trainval_files)[index[:int(len(trainval_files) * (1 - test_size))]], \ np.array(trainval_files)[index[int(len(trainval_files) * (1 - test_size)):]] else: test_files = None # 判断是否启用测试集 if isUseTest: trainval_files, test_files = train_test_split(files, test_size=test_size, random_state=55) else: trainval_files = files if len(trainval_files) != 0: # 划分训练集和验证集 train_files, val_files = train_test_split(trainval_files, test_size=test_size, random_state=55) else: print("数据文件为空") return train_files, val_files, test_files, files ''' # step3:根据json文件,获取所有类别 ''' def get_all_class(file_list, ROOT_DIR): """ 从json文件中获取当前数据的所有类别 :param file_list:当前路径下的所有文件名 :param label_path:当前文件路径 :return: """ print('step3:根据json文件,获取所有类别') # 初始化类别列表 classes = list() # 遍历所有json,读取shape中的label值内容,添加到classes for filename in tqdm(file_list): json_path = os.path.join(ROOT_DIR, filename + '.json') json_file = json.load(open(json_path, "r", encoding="utf-8")) for item in json_file["shapes"]: label_class = item['label'] # 如果没有添加新的类型,则 if label_class not in classes: classes.append(label_class) print('read file done') return classes ''' # step4:将json文件转化为txt文件,并将json文件存放到指定文件夹 ''' def json2txt(classes, txt_Name='allfiles', ROOT_DIR="", suffix='.jpg'): """ 将json文件转化为txt文件,并将json文件存放到指定文件夹 :param classes: 类别名 :param txt_Name:txt文件,用来存放所有文件的路径 :param label_path:当前文件路径 :param suffix:图像文件后缀 :return: """ print('step4:将json文件转化为txt文件,并将json文件存放到指定文件夹') store_json = os.path.join(ROOT_DIR, 'json') if not os.path.exists(store_json): os.makedirs(store_json) _, _, _, files = split_dataset(ROOT_DIR) if not os.path.exists(os.path.join(ROOT_DIR, 'tmp')): os.makedirs(os.path.join(ROOT_DIR, 'tmp')) list_file = open(os.path.join(ROOT_DIR,'tmp/%s.txt'% txt_Name) , 'w') for json_file_ in tqdm(files): # json路径 json_filename = os.path.join(ROOT_DIR, json_file_ + ".json") # 图像路径 imagePath = os.path.join(ROOT_DIR, json_file_ + suffix) # 写入图像文件夹路径 list_file.write('%s\n' % imagePath) # 转换后txt标签文件夹路径 out_file = open('%s/%s.txt' % (ROOT_DIR, json_file_), 'w') # 加载标签json文件 json_file = json.load(open(json_filename, "r", encoding="utf-8")) ''' 核心:标签转换(json转txt) ''' if os.path.exists(imagePath): # 读取图像 image = Image.open(imagePath) image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR) # 获取图像高、宽、通道 height, width, channels = image.shape # 获取shapes的Value值 for multi in json_file["shapes"]: # 如果点位为空 if len(multi["points"][0]) == 0: out_file.write('') continue # 获取单个缺陷的点位(x,y的list) points = np.array(multi["points"]) # 标签 label = multi["label"] # 类别id cls_id = classes.index(label) # 根据图像大小,返回box框的中点和高宽信息 xy_list = convert((width, height), points) # 写txt标签文件 out_file.write(str(cls_id) + " " + " ".join([str(xy) for xy in xy_list]) + '\n') if not os.path.exists(os.path.join(store_json, json_file_ + '.json')): try: shutil.move(json_filename, store_json) except OSError: pass ''' # step5:创建yolov5训练所需的yaml文件 ''' def create_yaml(classes, ROOT_DIR, isUseTest=False): print('step5:创建yolov5训练所需的yaml文件') classes_dict = {} for index, item in enumerate(classes): classes_dict[index] = item if not isUseTest: desired_caps = { 'path': ROOT_DIR, 'train': 'images/train', 'val': 'images/val', 'names': classes_dict } else: desired_caps = { 'path': ROOT_DIR, 'train': 'images/train', 'val': 'images/val', 'test': 'images/test', 'names': classes_dict } yamlpath = os.path.join(ROOT_DIR, "data" + ".yaml") # 写入到yaml文件 with open(yamlpath, "w+", encoding="utf-8") as f: for key, val in desired_caps.items(): yaml.dump({key: val}, f, default_flow_style=False) ''' # step6:生成yolov5的训练、验证、测试集的文件夹 ''' def create_save_file(ROOT_DIR): """ 按照训练时的图像和标注路径创建文件夹 :param label_path:当前文件路径 :return: """ print('step6:生成yolov5的训练、验证、测试集的文件夹') # 生成训练集 train_image = os.path.join(ROOT_DIR, 'images','train') if not os.path.exists(train_image): os.makedirs(train_image) train_label = os.path.join(ROOT_DIR, 'labels','train') if not os.path.exists(train_label): os.makedirs(train_label) # 生成验证集 val_image = os.path.join(ROOT_DIR, 'images', 'val') if not os.path.exists(val_image): os.makedirs(val_image) val_label = os.path.join(ROOT_DIR, 'labels', 'val') if not os.path.exists(val_label): os.makedirs(val_label) # 生成测试集 test_image = os.path.join(ROOT_DIR, 'images', 'test') if not os.path.exists(test_image): os.makedirs(test_image) test_label = os.path.join(ROOT_DIR, 'labels', 'test') if not os.path.exists(test_label): os.makedirs(test_label) return train_image, train_label, val_image, val_label, test_image, test_label ''' # step7:将所有图像和标注文件,移动到对应的训练集、验证集、测试集 ''' def push_into_file(file, images, labels, ROOT_DIR, suffix='.jpg'): """ 最终生成在当前文件夹下的所有文件按image和label分别存在到训练集/验证集/测试集路径的文件夹下 :param file: 文件名列表 :param images: 存放images的路径 :param labels: 存放labels的路径 :param label_path: 当前文件路径 :param suffix: 图像文件后缀 :return: """ print('step7:将所有图像和标注文件,移动到对应的训练集、验证集、测试集') # 遍历所有文件 for filename in tqdm(file): # 图像文件 image_file = os.path.join(ROOT_DIR, filename + suffix) # 标注文件 label_file = os.path.join(ROOT_DIR, filename + '.txt') # yolov5存放图像文件夹 if not os.path.exists(os.path.join(images, filename + suffix)): try: shutil.move(image_file, images) except OSError: pass # yolov5存放标注文件夹 if not os.path.exists(os.path.join(labels, filename + suffix)): try: shutil.move(label_file, labels) except OSError: pass ''' labelme的json标签转yolo的txt标签 ''' def ChangeToYolo5(ROOT_DIR="", suffix='.bmp', test_size=0.1, isUseTest=False,useNumpyShuffle=False,auto_genClasses = False): """ 生成最终标准格式的文件 :param test_size: 分割测试集或验证集的比例 :param label_path:当前文件路径 :param suffix: 文件后缀名 :param isUseTest: 是否使用测试集 :return: """ # step1:统一图像格式 change_image_format(ROOT_DIR, suffix=suffix) # step2:根据json文件划分训练集、验证集、测试集 train_files, val_files, test_file, files = split_dataset(ROOT_DIR, test_size=test_size, isUseTest=isUseTest, useNumpyShuffle=useNumpyShuffle) # step3:根据json文件,获取所有类别 classes = ['Dent','Scratch'] # 是否自动从数据集中获取类别数 if auto_genClasses: classes = get_all_class(files, ROOT_DIR) ''' step4:(***核心***)将json文件转化为txt文件,并将json文件存放到指定文件夹 ''' json2txt(classes, txt_Name='allfiles', ROOT_DIR=ROOT_DIR, suffix=suffix) # step5:创建yolov5训练所需的yaml文件 create_yaml(classes, ROOT_DIR, isUseTest=isUseTest) # step6:生成yolov5的训练、验证、测试集的文件夹 train_image_dir, train_label_dir, val_image_dir, val_label_dir, test_image_dir, test_label_dir = create_save_file(ROOT_DIR) # step7:将所有图像和标注文件,移动到对应的训练集、验证集、测试集file, images, labels, ROOT_DIR, suffix='.jpg' # 将文件移动到训练集文件中 push_into_file(train_files, train_image_dir, train_label_dir,ROOT_DIR=ROOT_DIR, suffix=suffix) # 将文件移动到验证集文件夹中 push_into_file(val_files, val_image_dir, val_label_dir,ROOT_DIR=ROOT_DIR, suffix=suffix) # 如果测试集存在,则将文件移动到测试集文件中 if test_file is not None: push_into_file(test_file, test_image_dir, test_label_dir, ROOT_DIR=ROOT_DIR, suffix=suffix) print('create dataset done') if __name__ == "__main__": ''' 1.ROOT_DIR:图像和json标签的路径 2.suffix:统一图像尾缀 3.test_size:测试集和验证集所占比例 4.isUseTest:是否启用测试集 5.useNumpyShuffle:是否随机打乱 6.auto_genClasses:是否自动根据json标签生成类别列表 ''' ChangeToYolo5(ROOT_DIR = r'D:\dataset\Side_Line_Chang_DataSet\SegYolo2', suffix='.bmp',test_size=0.1, isUseTest=False,useNumpyShuffle=False,auto_genClasses = False)
import numpy as np import cv2 # 读取标签 label_path = r"D:\dataset\Side_Line_Chang_DataSet\SegYolo2\labels\train\item_00000000.txt" # 读取图像 img = cv2.imread(r'D:\dataset\Side_Line_Chang_DataSet\SegYolo\train\images\item_00000000.jpg') height, width, channels = img.shape with open(label_path,'r') as f: current_line = f.readline() while current_line: temp_list = current_line.split(' ') for index in range(1,len(temp_list),2): num1 = float(temp_list[index]) num2 = float(temp_list[index+1]) # 圆点显示 cv2.circle(img,(int(width * num1), int(height * num2)), 5, (0, 0, 255), -1) current_line = f.readline() winname = 'showImg' cv2.namedWindow(winname) cv2.imshow(winname, img) cv2.waitKey(0) cv2.destroyWindow(winname) # 保存图像 cv2.imwrite('test.jpg',img)
python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate
python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
from train import main,parse_opt from classify.train import main as cls_main,parse_opt as cls_parse_opt from segment.train import main as seg_main,parse_opt as seg_parse_opt # 检测任务 # opt = parse_opt() # kwargs = {"epochs": 1000, # "weights": r"yolov5s.pt", # "batch_size": 4, # "data": "ZH.yaml", # "workers": 0, # "project": "AI_Model", # "name": "ZH", # "imgsz": 450, # "device": 0, # "rect": True, # "exist_ok": True} # # for k, v in kwargs.items(): # setattr(opt, k, v) # # main(opt=opt) # 分类任务 # cls_opt = cls_parse_opt() # kwargs = {"epochs": 1000, # "weights": r"yolov5s-cls.pt", # "batch_size": 64, # "data": r"C:\Users\11716\Desktop\dogAndcat-cls\dogAndcat-cls", # "workers": 0, # "project": "AI_Model", # "name": "cat_dog_cls", # "imgsz": 32, # "device": 0, # "rect": True, # "exist_ok": True} # # for k, v in kwargs.items(): # setattr(cls_opt, k, v) # # cls_main(opt=cls_opt) # 分割任务 seg_opt = seg_parse_opt() kwargs = {"epochs": 1000, "weights": r"yolov5s-seg.pt", "batch_size": 4, "data": r"F:\D\Al_Algo\yolov5-master\data\segTest.yaml", "workers": 0, "project": "AI_Model", "name": "cat_dog_seg", "imgsz": 512, "device": 0, "rect": True, "exist_ok": True} for k, v in kwargs.items(): setattr(seg_opt, k, v) seg_main(opt=seg_opt)
if __name__ == '__main__':
opt = parse_opt()
kwargs = {
"weights": r"F:\yolov5_OpenVinoAndTensorRT\seg\cat_dog_seg\weights\best.pt",
"include": 'engine',
"imgsz": [512, 512],
"include": ["openvino"],
}
for k, v in kwargs.items():
setattr(opt, k, v)
main(opt)
def parse_opt(known=False): parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path') parser.add_argument('--cfg', type=str, default='', help='model.yaml path') parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') parser.add_argument('--epochs', type=int, default=100, help='total training epochs') parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--noval', action='store_true', help='only validate final epoch') parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') parser.add_argument('--noplots', action='store_true', help='save no plot files') parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') parser.add_argument('--name', default='exp', help='save to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--quad', action='store_true', help='quad dataloader') parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') parser.add_argument('--seed', type=int, default=0, help='Global training seed') parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') # Logger arguments parser.add_argument('--entity', default=None, help='Entity') parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='Upload data, "val" option') parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval') parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use') return parser.parse_known_args()[0] if known else parser.parse_args()
def main(opt, callbacks=Callbacks()):
# Checks
if RANK in {-1, 0}:
print_args(vars(opt))
check_git_status()
check_requirements(ROOT / 'requirements.txt')
# Resume (from specified or most recent last.pt)
if opt.resume and not check_comet_resume(opt) and not opt.evolve:
last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml
opt_data = opt.data # original dataset
if opt_yaml.is_file():
with open(opt_yaml, errors='ignore') as f:
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