借用Ultralytics Yolo快速训练一个物体检测器
借用Ultralytics Yolo快速训练一个物体检测器
https://github.com/ultralytics/ultralytics
Step-1 准备数据集
你需要一些待检测物体比如安全帽, 把它从各个角度拍摄一下. 再找一些不相关的背景图片. 然后把安全帽给放大缩小旋转等等贴到背景图片上去, 生成一堆训练数据.
配置文件:
extract_cfg: output_dir: '/datasets/images' fps: 0.25 screen_images_path: '/datasets/待检测图片' max_scale: 1.0 min_scale: 0.1 manual_scale: [ {name: 'logo', min_scale: 0.05, max_scale: 0.3}, {name: 'logo', min_scale: 0.1, max_scale: 0.5}, {name: '箭头', min_scale: 0.1, max_scale: 0.5} ] data_cfgs: [ {id: 0, name: 'logo', min_scale: 0.05, max_scale: 0.3, gen_num: 2}, {id: 1, name: '截屏', min_scale: 0.1, max_scale: 1.0, gen_num: 3, need_full_screen: true}, {id: 2, name: '红包', min_scale: 0.1, max_scale: 0.5, gen_num: 2}, {id: 3, name: '箭头', min_scale: 0.1, max_scale: 0.5, gen_num: 2, rotate_aug: true}, ] save_oss_dir: /datasets/gen_datasets/ gen_num_per_image: 2 max_bg_img_sample: 1
数据集生成:
from pathlib import Path
import io
import random
import cv2
import numpy as np
from PIL import Image
import hydra
from omegaconf import DictConfig
import json
from tqdm import tqdm
# 加载图片
def load_images(background_path, overlay_path):
background = cv2.imread(background_path)
overlay = cv2.imread(overlay_path, cv2.IMREAD_UNCHANGED)
return background, overlay
# 随机缩放和位置
def random_scale_and_position(bg_shape, overlay_shape, max_scale=1.0, min_scale=0.1):
max_height, max_width = bg_shape[:2]
overlay_height, overlay_width = overlay_shape[:2]
base_scale = min(max_height / overlay_height, max_width / overlay_width)
# 随机缩放
scale_factor = random.uniform(
min_scale * base_scale, max_scale * base_scale)
new_height, new_width = int(
overlay_height * scale_factor), int(overlay_width * scale_factor)
# 随机位置
max_x = max_width - new_width - 1
max_y = max_height - new_height - 1
position_x = random.randint(0, max_x)
position_y = random.randint(0, max_y)
return scale_factor, (position_x, position_y)
def get_resized_overlay(overlay, scale):
overlay_resized = cv2.resize(overlay, (0, 0), fx=scale, fy=scale)
return overlay_resized
def rotate_image(img, angle):
if isinstance(img, np.ndarray):
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGRA2RGBA))
# 确保图像具有alpha通道（透明度）
img = img.convert("RGBA")
# 旋转原始图像并粘贴到新的透明图像框架中
rotated_img = img.rotate(angle, resample=Image.BICUBIC, expand=True)
rotated_img = np.asarray(rotated_img)
return cv2.cvtColor(rotated_img, cv2.COLOR_RGBA2BGRA)
# 合成图片
def overlay_image(background, overlay_resized, position, scale):
h, w = overlay_resized.shape[:2]
x, y = position
# 透明度处理
alpha_s = overlay_resized[:, :, 3] / 255.0
alpha_l = 1.0 - alpha_s
for c in range(0, 3):
background[y:y + h, x:x + w, c] = (alpha_s * overlay_resized[:, :, c] +
alpha_l * background[y:y + h, x:x + w, c])
# 画出位置，调试使用
# print("position", x, y, w, h)
# cv2.rectangle(background, (x, y), (x + w, y + h), (0, 255, 0), 2)
background = cv2.cvtColor(background, cv2.COLOR_BGR2RGB)
return Image.fromarray(background)
class Box:
def __init__(self, x, y, width, height, category_id, image_width, image_height):
self.x = x
self.y = y
self.width = width
self.height = height
self.image_width = image_width
self.image_height = image_height
self.category_id = category_id
def to_yolo_format(self):
x_center = (self.x + self.width / 2) / self.image_width
y_center = (self.y + self.height / 2) / self.image_height
width = self.width / self.image_width
height = self.height / self.image_height
box_line = f"{self.category_id} {x_center} {y_center} {width} {height}"
return box_line
class SingleCategoryGen:
def __init__(self, cfg, data_cfg, output_dir):
self.output_dir = output_dir
self.screen_png_images = []
self.coco_images = []
self.coco_annotations = []
screen_images_path = Path(
cfg.screen_images_path.format(user_root=user_root))
self.manual_scale = {}
self.data_cfg = data_cfg
self.category_id = data_cfg.id
self.category_name = self.data_cfg.name
self.max_scale = self.data_cfg.max_scale
self.min_scale = self.data_cfg.min_scale
self.gen_num = self.data_cfg.gen_num
self.rotate_aug = self.data_cfg.get("rotate_aug", False)
self.need_full_screen = self.data_cfg.get("need_full_screen", False)
self.category_num = 0
self.category_names = {}
self.butcket = get_oss_bucket(cfg.bucket_name)
output_dir = Path(output_dir)
save_oss_dir = f"{cfg.save_oss_dir}/{output_dir.parent.name}/{output_dir.name}"
self.save_oss_dir = save_oss_dir
self.images_save_oss_dir = f"{save_oss_dir}/images"
self.label_save_oss_dir = f"{save_oss_dir}/labels"
self.annotations_save_oss_path = f"{save_oss_dir}/annotations.json"
self.load_screen_png_images_and_category(screen_images_path)
def load_screen_png_images_and_category(self, screen_images_dir):
screen_images_dir = Path(screen_images_dir)
category_id = self.category_id
screen_images_path = screen_images_dir / self.category_name
img_files = [p for p in screen_images_path.iterdir() if p.suffix in [
".png", ".jpg"]]
img_files.sort(key=lambda x: x.stem)
for i, img_file in enumerate(img_files):
self.screen_png_images.append(
dict(id=i, name=img_file.stem, supercategory=None, path=str(img_file)))
def add_new_images(self, bg_img_path: Path, gen_image_num=None, subset="train"):
gen_image_num = gen_image_num or self.gen_num
background_origin = cv2.imread(str(bg_img_path))
if background_origin is None:
print(f"open image {bg_img_path} failed")
return
max_box_num = 1
for gen_id in range(gen_image_num):
background = background_origin.copy()
category_id = self.category_id
overlay_img_path = self.sample_category_data()
overlay = cv2.imread(overlay_img_path, cv2.IMREAD_UNCHANGED)
if overlay.shape[2] == 3:
overlay = cv2.cvtColor(overlay, cv2.COLOR_BGR2BGRA)
if self.rotate_aug:
overlay = rotate_image(overlay, random.uniform(-180, 180))
# # 随机裁剪图片
# if random.random() < 0.5:
# origin_height = overlay.shape[0]
# min_height = origin_height // 4
# new_height = random.randint(min_height, origin_height)
# new_top = random.randint(0, origin_height - new_height)
# overlay = overlay[new_top:new_top+new_height, :, :]
box_num = random.randint(1, max_box_num)
# 获取随机缩放和位置
max_scale = self.max_scale
min_scale = self.min_scale
scale, position = random_scale_and_position(
background.shape, overlay.shape, max_scale, min_scale)
# 缩放overlay图片
overlay_resized = get_resized_overlay(overlay, scale)
# 合成后的图片
merged_img = overlay_image(background, overlay_resized, position, scale)
# 保存合成后的图片
filename = f"{bg_img_path.stem}_{category_id}_{gen_id:02d}.png"
merged_img.save(f'{output_dir}/{filename}')
# 生成COCO格式的标注数据
box = Box(*position, overlay_resized.shape[1], overlay_resized.shape[0], category_id, background.shape[1],
background.shape[0])
self.upload_image_to_oss(merged_img, filename, subset, [box])
def sample_category_data(self):
return random.choice(self.screen_png_images)["path"]
image_id = self.gen_image_id()
image_json = {
"id": image_id,
"width": image.width,
"height": image.height,
"file_name": image_name,
}
self.coco_images.append(image_json)
annotation_json = {
"id": image_id,
"image_id": image_id,
"category_id": 0,
"segmentation": None,
"area": bbox[2] * bbox[3],
"bbox": bbox,
"iscrowd": 0
}
self.coco_annotations.append(annotation_json)
def upload_image_to_oss(self, image, image_name, subset, box_list=None):
image_bytesio = io.BytesIO()
image.save(image_bytesio, format="PNG")
self.butcket.put_object(
f"{self.images_save_oss_dir}/{subset}/{image_name}", image_bytesio.getvalue())
if box_list:
label_str = "\n".join([box.to_yolo_format() for box in box_list])
label_name = image_name.split(".")[0] + ".txt"
self.butcket.put_object(
f"{self.label_save_oss_dir}/{subset}/{label_name}", label_str)
def upload_full_screen_image(self):
if not self.need_full_screen:
return
name = self.category_name
category_id = self.category_id
image_list = self.screen_png_images
subset_list = ["train" if i % 10 <= 7 else "val" if i %
10 <= 8 else "test" for i in range(len(image_list))]
for i in range(len(image_list)):
image_data = image_list[i]
subset = subset_list[i]
overlay_img_path = image_data["path"]
image = Image.open(overlay_img_path)
if random.random() < 0.5:
origin_height = image.height
min_height = origin_height // 4
new_height = random.randint(min_height, origin_height)
new_top = random.randint(0, origin_height - new_height)
image = image.crop(
(0, new_top, image.width, new_top + new_height))
filename = f"{name}_{category_id}_{i:05}.png"
box = Box(0, 0, image.width, image.height,
category_id, image.width, image.height)
self.upload_image_to_oss(image, filename, subset, [box])
class ScreenDatasetGen:
def __init__(self, cfg, output_dir):
self.output_dir = output_dir
self.screen_png_images = {}
self.coco_images = []
self.coco_annotations = []
screen_images_path = Path(
cfg.screen_images_path.format(user_root=user_root))
self.max_scale = cfg.max_scale
self.min_scale = cfg.min_scale
self.manual_scale = {}
for info in cfg.manual_scale:
self.manual_scale[info.name] = dict(
max_scale=info.max_scale, min_scale=info.min_scale)
self.category_num = 0
self.category_names = {}
self.category_id_loop = -1
self.butcket = get_oss_bucket(cfg.bucket_name)
output_dir = Path(output_dir)
save_oss_dir = f"{cfg.save_oss_dir}/{output_dir.parent.name}/{output_dir.name}"
self.save_oss_dir = save_oss_dir
self.images_save_oss_dir = f"{save_oss_dir}/images"
self.label_save_oss_dir = f"{save_oss_dir}/labels"
self.annotations_save_oss_path = f"{save_oss_dir}/annotations.json"
self.load_screen_png_images_and_category(screen_images_path)
def add_new_images(self, bg_img_path: Path, gen_image_num=1, subset="train"):
background_origin = cv2.imread(str(bg_img_path))
if background_origin is None:
print(f"open image {bg_img_path} failed")
return
max_box_num = 1
for gen_id in range(gen_image_num):
background = background_origin.copy()
category_id = self.get_category_id_loop()
overlay_img_path = self.sample_category_data(
category_id, subset=subset)
overlay = cv2.imread(overlay_img_path, cv2.IMREAD_UNCHANGED)
if overlay.shape[2] == 3:
overlay = cv2.cvtColor(overlay, cv2.COLOR_BGR2BGRA)
# # 随机裁剪图片
# if random.random() < 0.5:
# origin_height = overlay.shape[0]
# min_height = origin_height // 4
# new_height = random.randint(min_height, origin_height)
# new_top = random.randint(0, origin_height - new_height)
# overlay = overlay[new_top:new_top+new_height, :, :]
box_num = random.randint(1, max_box_num)
# 获取随机缩放和位置
category_name = self.category_names[category_id]
if category_name in self.manual_scale:
max_scale = self.manual_scale[category_name]["max_scale"]
min_scale = self.manual_scale[category_name]["min_scale"]
else:
max_scale = self.max_scale
min_scale = self.min_scale
scale, position = random_scale_and_position(
background.shape, overlay.shape, max_scale, min_scale)
# 缩放overlay图片
overlay_resized = get_resized_overlay(overlay, scale)
# 合成后的图片
merged_img = overlay_image(
background, overlay_resized, position, scale)
# 保存合成后的图片
filename = f"{bg_img_path.stem}_{category_id}_{gen_id:02d}.png"
# merged_img.save(f'{output_dir}/{filename}')
# 生成COCO格式的标注数据
box = Box(*position, overlay_resized.shape[1], overlay_resized.shape[0], category_id, background.shape[1],
background.shape[0])
self.upload_image_to_oss(merged_img, filename, subset, [box])
# self.add_image_annotion_to_coco(box, merged_img, filename)
def upload_full_screen_image(self, category_name=None):
if category_name is None:
return
if not isinstance(category_name, list):
category_name = [category_name]
for category_id in range(self.category_num):
name = self.category_names[category_id]
if name not in category_name:
continue
image_list = self.screen_png_images[category_id]
subset_list = ["train" if i % 10 <= 7 else "val" if i %
10 <= 8 else "test" for i in range(len(image_list))]
for i in range(len(image_list)):
image_data = image_list[i]
subset = subset_list[i]
overlay_img_path = image_data["path"]
image = Image.open(overlay_img_path)
if random.random() < 0.5:
origin_height = image.height
min_height = origin_height // 4
new_height = random.randint(min_height, origin_height)
new_top = random.randint(0, origin_height - new_height)
image = image.crop(
(0, new_top, image.width, new_top + new_height))
filename = f"{name}_{category_id}_{i:05}.png"
box = Box(0, 0, image.width, image.height,
category_id, image.width, image.height)
self.upload_image_to_oss(image, filename, subset, [box])
def load_screen_png_images_and_category(self, screen_images_dir):
screen_images_dir = Path(screen_images_dir)
screen_images_paths = [
f for f in screen_images_dir.iterdir() if f.is_dir()]
screen_images_paths.sort(key=lambda x: x.stem)
for category_id, screen_images_path in enumerate(screen_images_paths):
img_files = [p for p in screen_images_path.iterdir() if p.suffix in [
".png", ".jpg"]]
img_files.sort(key=lambda x: x.stem)
self.screen_png_images[category_id] = []
self.category_names[category_id] = screen_images_path.stem
print(f"{category_id}: {self.category_names[category_id]}")
for i, img_file in enumerate(img_files):
self.screen_png_images[category_id].append(
dict(id=i, name=img_file.stem, supercategory=None, path=str(img_file)))
self.category_num = len(screen_images_paths)
print(f"category_num: {self.category_num}")
def get_category_id_loop(self):
# self.category_id_loop = (self.category_id_loop + 1) % self.category_num
self.category_id_loop = random.randint(0, self.category_num - 1)
return self.category_id_loop
def sample_category_data(self, category_id, subset):
image_data = self.screen_png_images[category_id]
# valid_id = []
# if subset == "train":
# valid_id = [i for i in range(len(image_data)) if i % 10 <= 7]
# elif subset == "val":
# valid_id = [i for i in range(len(image_data)) if i % 10 == 8]
# elif subset == "test":
# valid_id = [i for i in range(len(image_data)) if i % 10 == 9]
# image_data = [image_data[i] for i in valid_id]
return random.choice(image_data)["path"]
def gen_image_id(self):
return len(self.coco_images) + 1
def add_image_annotion_to_coco(self, bbox, image: Image.Image, image_name):
image_id = self.gen_image_id()
image_json = {
"id": image_id,
"width": image.width,
"height": image.height,
"file_name": image_name,
}
self.coco_images.append(image_json)
annotation_json = {
"id": image_id,
"image_id": image_id,
"category_id": 0,
"segmentation": None,
"area": bbox[2] * bbox[3],
"bbox": bbox,
"iscrowd": 0
}
self.coco_annotations.append(annotation_json)
def upload_image_to_oss(self, image, image_name, subset, box_list=None):
image_bytesio = io.BytesIO()
image.save(image_bytesio, format="PNG")
self.butcket.put_object(
f"{self.images_save_oss_dir}/{subset}/{image_name}", image_bytesio.getvalue())
if box_list:
label_str = "\n".join([box.to_yolo_format() for box in box_list])
label_name = image_name.split(".")[0] + ".txt"
self.butcket.put_object(
f"{self.label_save_oss_dir}/{subset}/{label_name}", label_str)
def dump_coco_json(self):
categories = [{key: item[key] for key in ("id", "name", "supercategory")} for item in
self.screen_png_images.values()]
coco_json = {
"images": self.coco_images,
"annotations": self.coco_annotations,
"categories": categories
}
self.butcket.put_object(
self.annotations_save_oss_path, json.dumps(coco_json, indent=2))
# with open(f"{self.output_dir}/coco.json", "w") as fp:
# json.dump(coco_json, fp, indent=2)
@hydra.main(version_base=None, config_path=".", config_name="conf")
def main(cfg: DictConfig):
output_dir = hydra.core.hydra_config.HydraConfig.get().runtime.output_dir
# get_image_and_annotation(output_dir)
# screen_dataset_gen = ScreenDatasetGen(cfg, output_dir)
category_generators = []
for data_cfg in cfg.data_cfgs:
category_generators.append(SingleCategoryGen(cfg, data_cfg, output_dir))
bg_img_files = [f for f in Path(cfg.extract_cfg.output_dir.format(user_root=user_root)).iterdir() if
f.suffix in [".png", ".jpg"]]
if cfg.get("max_bg_img_sample"):
bg_img_files = random.sample(bg_img_files, cfg.max_bg_img_sample)
img_index = 0
for bg_img_file in tqdm(bg_img_files):
subset = "train" if img_index % 10 <= 7 else "val" if img_index % 10 == 8 else "test"
img_index += 1
for category_generator in category_generators:
category_generator.add_new_images(bg_img_path=bg_img_file, subset=subset)
for category_generator in category_generators:
category_generator.upload_full_screen_image()
if __name__ == '__main__':
main()
运行后, 可以在outputs文件夹下生成符合要求的训练数据.
image 就是背景+检测物体
labels 中的内容就是这样的文件:
1 0.6701388888888888 0.289453125 0.5736111111111111 0.57421875 # 类型 box
Step-2 训练模型
这个更简单, 在官网下载一个模型权重, 比如yolo8s.pt, 对付安全帽这种东西, 几M大的模型就够了.
训练配置文件:
names: 0: logo 1: 截屏 2: 红包 path: /outputs test: images/test train: images/train val: images/val
训练代码:
没错就这么一点
from ultralytics import YOLO
model = YOLO('./yolo8s.pt')
model.train(data='dataset.yaml', epochs=100, imgsz=1280)
然后就可以自动化训练了, 结束后会自动保存模型与评估检测效果.
Step-3 检测
class Special_Obj_Detect(object):
def __init__(self, cfg) -> None:
model_path = cfg.model_path
self.model = YOLO(model_path)
self.model.requires_grad_ = False
self.cls_names = {0: 'logo', 1: '截屏', 2: '红包'}
# 单帧图像检测
def detect_image(self, img_path):
results = self.model(img_path)
objects = []
objects_cnt = dict()
objects_area_pct = dict()
for result in results:
result = result.cpu()
boxes = list(result.boxes)
for box in boxes:
if box.conf < 0.8: continue
boxcls = box.cls[0].item()
objects.append(self.cls_names[boxcls])
objects_cnt[self.cls_names[boxcls]] = objects_cnt.get(self.cls_names[boxcls], 0) + 1
area_p = sum([ (xywh[2]*xywh[3]).item() for xywh in box.xywhn])
area_p = min(1, area_p)
objects_area_pct[self.cls_names[boxcls]] = area_p
objects = list(set(objects))
return objects, objects_cnt, objects_area_pct
收工.
本文作者:JiangOil
本文链接: https://www.codebonobo.tech/post/14
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人工智障与神经病网络
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