yolov8

安装环境

装pytorch

conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge

安装用到的包
requirements.txt

ultralytics
tqdm
scikit-learn
pyaml
shapely
pyclipper
scikit-image
imgaug
lmdb
tqdm
rapidfuzz
cython
Pillow
pyyaml
requests

pip install -r requirements.txt

数据标注

标注网站

得到via_region_data.csv，格式

{"42.243.238.32-202406181512034.jpg418013": {"fileref": "","size": 418013,"filename": "42.243.238.32-202406181512034.jpg","base64_img_data": "","file_attributes": {},"regions": [{"shape_attributes": {"name": "rect","x": 324,"y": 179,"width": 279,"height": 108},"region_attributes": {}},{"shape_attributes": {"name": "rect","x": 766,"y": 248,"width": 227,"height": 154},"region_attributes": {}}]},
}

其中(x,y)是左上角坐标， (x + width, y + height)是右下角坐标

经过统计：
平均：12142158
最小：7681360
最大：1440*2560

总 890, 408 background, 482 front
train 712, 325 background
val 89, 39 background
test 89, 41+？ background

转换格式

转成yolov8的格式, 格式为

label_id x y w h

其中

$$x,y,w,h\in \left[0,1\right]$$
转换

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import json
import osimport cv2
from tqdm import tqdmimg_dir = r"E:\ccc\origin_images"
json_path = r"E:\ccc\via_region_data.json"
label_dir = r"E:\ccc\origin_labels"if __name__ == "__main__":with open(json_path, "r") as f:label_data: dict = json.load(f)os.makedirs(label_dir, exist_ok=True)cnt = 0tot = 0for label in tqdm(label_data.values()):filename = label["filename"]img = cv2.imread(os.path.join(img_dir, filename))h, w, _ = img.shapecategory = 0empty_region = Truewith open(os.path.join(label_dir, os.path.splitext(filename)[0] + ".txt"), "w") as f:for region in label["regions"]:empty_region = Falsex = region["shape_attributes"]["x"]y = region["shape_attributes"]["y"]width = region["shape_attributes"]["width"]height = region["shape_attributes"]["height"]x_center = (x + (x + width)) / 2.0y_center = (y + (y + height)) / 2.0x_center_norm = x_center / wy_center_norm = y_center / hwidth_norm = width / wheight_norm = height / hf.write("{} {:.6f} {:.6f} {:.6f} {:.6f}\n".format(category, x_center_norm, y_center_norm, width_norm, height_norm))if empty_region:cnt += 1tot += 1print("non_empty: {}; empty: {}; total: {}".format(tot - cnt, cnt, tot))

划分数据集

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import os
import shutil
from glob import globimport yaml
from sklearn.model_selection import train_test_split
from tqdm import tqdmimg_dir = r"E:\ccc\origin_images"
label_dir = r"E:\ccc\origin_labels"
train_ratio = 0.8
val_ratio = 0.1
test_ratio = 0.1
root_path = r"E:\ccc"
img_train_path = r"images\train"
img_val_path = r"images\val"
img_test_path = r"images\test"
label_train_path = r"labels\train"
label_val_path = r"labels\val"
label_test_path = r"labels\test"
yaml_path = "config.yaml"def copy_files(label_paths, img_dir, target_label_dir, origin_img_dir):os.makedirs(img_dir, exist_ok=True)os.makedirs(target_label_dir, exist_ok=True)for label in tqdm(label_paths):img = os.path.join(origin_img_dir, os.path.splitext(os.path.basename(label))[0] + ".jpg")shutil.copy(img, img_dir)shutil.copy(label, target_label_dir)if __name__ == "__main__":# img_paths = list(glob(os.path.join(img_dir, '*')))label_paths = list(glob(os.path.join(label_dir, "*")))# img_paths.sort()label_paths.sort()is_empty = []for path in label_paths:with open(path, "r") as f:data = f.read().strip()if not data:is_empty.append(True)else:is_empty.append(False)train_val_label, test_label, train_val_is_empty, test_is_empty = train_test_split(label_paths, is_empty, test_size=test_ratio, shuffle=True)train_label, val_label, train_is_empty, val_is_empty = train_test_split(train_val_label,train_val_is_empty,test_size=val_ratio / (train_ratio + val_ratio),shuffle=True,)train_img = [os.path.splitext(path)[0] + ".jpg" for path in train_label]val_img = [os.path.splitext(path)[0] + ".jpg" for path in val_label]test_img = [os.path.splitext(path)[0] + ".jpg" for path in test_label]copy_files(train_label,os.path.join(root_path, img_train_path),os.path.join(root_path, label_train_path),img_dir,)copy_files(val_label,os.path.join(root_path, img_val_path),os.path.join(root_path, label_val_path),img_dir,)copy_files(test_label,os.path.join(root_path, img_test_path),os.path.join(root_path, label_test_path),img_dir,)config = {"path": root_path,"train": img_train_path,"val": img_val_path,"test": img_test_path,"names": {0: "box"},}yaml_dir = os.path.dirname(yaml_path)if yaml_dir:os.makedirs(yaml_dir, exist_ok=True)with open(yaml_path, "w", encoding="utf-8") as f:yaml.dump(config, f)

会自动划分数据集和生成需要的yaml
参考：coco128.yaml

训练yolo

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from ultralytics import YOLOif __name__ == '__main__':# Load a model# model = YOLO("yolov8n.yaml")  # build a new model from YAMLmodel = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)# model = YOLO("yolov8n.yaml").load("yolov8n.pt")  # build from YAML and transfer weights# Train the model# results = model.train(data="config.yaml", epochs=100, imgsz=640, device="cpu", single_cls=True, save=True, workers=0)results = model.train(data="config.yaml", epochs=100, imgsz=640, device=[0], single_cls=True, save=True, workers=8)

测试yolo

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from ultralytics import YOLOif __name__ == "__main__":model = YOLO("runs/detect/train/weights/best.pt")  # load a custom model# Validate the modelmetrics = model.val(data="config.yaml", split="val", plots=True)  # no arguments needed, dataset and settings remembered# metrics = model.val(#     data="config.yaml", split="test", plots=True# )  # no arguments needed, dataset and settings remembered# print(metrics)print("map50-95: ", metrics.box.map)  # map50-95print("map50: ", metrics.box.map50)  # map50print("map75: ", metrics.box.map75)  # map75# print('map50-95 of each category: ',metrics.box.maps)  # a list contains map50-95 of each category

预测yolo

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import os
from glob import globimport cv2
from ultralytics import YOLOmodel_path = "runs/detect/train/weights/best.pt"
imgs_dir = "/mnt/data/ccc/Eudemons_final"
gt_dir = "/mnt/data/ccc/Eudemons_v3/labels/test"
crop_dir = "/mnt/data/ccc/crop"
save_dir = "/mnt/data/ccc/overlay"
draw_predict = True
# draw_gt = True
draw_gt = False
pad = 0.035def get_name_v1(filename, dir_name, idx):return os.path.join(dir_name, "{}{}.jpg".format(filename, f"_{idx}" if idx > 0 else ""))def get_name_v2(path, dir_name, idx):filename, ext = os.path.splitext(os.path.basename(path))mac = os.path.basename(os.path.dirname(path))return os.path.join(dir_name, "{}_{}{}{}".format(mac, filename, f"_{idx}" if idx > 0 else "", ext))if __name__ == "__main__":model = YOLO(model_path)  # load a custom modelpaths = list(glob(os.path.join(imgs_dir, "*", "*.jpg")))draw_predict = draw_predict and save_dirdraw_gt = draw_gt and gt_dir and os.path.exists(gt_dir) and save_dirif draw_predict:os.makedirs(save_dir, exist_ok=True)os.makedirs(crop_dir, exist_ok=True)# results = model(os.path.join(imgs_dir, "*.jpg"), stream=True)results = model(os.path.join(imgs_dir, "*", "*.jpg"), stream=True)for result in results:# result.save(os.path.basename(result.path))path = result.pathfilename = os.path.splitext(os.path.basename(path))[0]h, w = result.orig_shapeorig_img = result.orig_imgboxes = result.boxesboxes_num = boxes.shape[0]confidence = boxes.conf.tolist()xyxy = boxes.xyxy.tolist()for idx, (x1, y1, x2, y2) in enumerate(xyxy):x_pad = (x2 - x1) * pady_pad = (y2 - y1) * padx1 -= x_pady1 -= y_padx2 += x_pady2 += y_padx1 = max(0, int(x1))y1 = max(0, int(y1))x2 = min(w, int(x2))y2 = min(h, int(y2))cv2.imwrite(# get_name_v1(filename, crop_dir, idx),get_name_v2(path, crop_dir, idx),orig_img[y1:y2, x1:x2],)if draw_predict:for x1, y1, x2, y2 in xyxy:x1 = max(0, int(x1))y1 = max(0, int(y1))x2 = min(w - 1, int(x2))y2 = min(h - 1, int(y2))orig_img = cv2.rectangle(orig_img,(x1, y1),(x2, y2),color=(0, 0, 255),lineType=cv2.LINE_AA,)if draw_gt:with open(os.path.join(gt_dir, filename + ".txt"), "r") as f:for line in f:line = line.strip()if not line:continue_, x, y, width, height = line.split(" ")x = float(x)y = float(y)width = float(width)height = float(height)orig_img = cv2.rectangle(orig_img,(int((x - width / 2) * w), int((y - height / 2) * h)),(int((x + width / 2) * w), int((y + height / 2) * h)),color=(0, 255, 0),lineType=cv2.LINE_AA,)cv2.imwrite(# os.path.join(save_dir, filename + ".jpg"),get_name_v2(path, save_dir, 0),orig_img,)