【深度学习目标检测】十四、基于深度学习的血细胞计数系统-含GUI（BCD数据集，yolov8）

本文介绍: 血细胞计数是医学上一种重要的检测手段，用于评估患者的健康状况，诊断疾病，以及监测治疗效果。而目标检测是一种计算机视觉技术，用于在图像中识别和定位特定的目标。在血细胞计数中，目标检测技术可以发挥重要作用。首先，血细胞计数通常需要处理大量的血液样本，手动计数每个细胞既耗时又容易出错。使用目标检测算法，可以自动识别和计数图像中的血细胞，大大提高了计数的准确性和效率。其次，不同的血细胞（如红细胞、白细胞和血小板）具有不同的形态和大小，这使得使用传统的图像处理方法进行区分和计数变得困难。

本文介绍使用yolov8进行血细胞检测的方法，在BCD数据集训练，其效果如下图：

yolov8官方文档：主页 – Ultralytics YOLOv8 文档

安装部分参考：官方安装教程

本文使用的数据集是BCD数据集，该数据集包含3个类别：白细胞（WBC）、红细胞（RBC）和血小板（Platelets）。该数据集共364张图片，其中训练集包含255张图片，验证集包含73张图片，测试集包含36张图片。

# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO 2017 dataset http://cocodataset.org by Microsoft
# Example usage: yolo train data=coco.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── coco  ← downloads here (20.1 GB)


# 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: datasets/medical/BCD_YOLO8  # dataset root dir
train: images/train  # train images (relative to 'path') 118287 images
val: images/val  # val images (relative to 'path') 5000 images
test: images/test  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794

# Classes
names:
  0: Platelets
  1: RBC
  2: WBC

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
 
# Parameters
nc: 3  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs
  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
  x: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
 
# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
  - [-1, 3, C2f, [128, True]]
  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
  - [-1, 6, C2f, [256, True]]
  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
  - [-1, 6, C2f, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]]  # 9
 
# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [512]]  # 12
 
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 4], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [256]]  # 15 (P3/8-small)
 
  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 12], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 18 (P4/16-medium)
 
  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 9], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 21 (P5/32-large)
 
  - [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)

yolo detect train project=medical_output name=bcd_yolo8 exist_ok=True optimizer=auto val=True amp=True epochs=100  imgsz=640 model=ultralytics/cfg/models/medical/yolov8.yaml  data=ultralytics/cfg/datasets/bcd.yaml

yolo detect val model=medical_output/bcd_yolo8/weights/best.pt data=ultralytics/ultralytics/cfg/datasets/bcd.yaml

# Ultralytics YOLOv8.1.0 🚀 Python-3.9.18 torch-2.1.0+cu118 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)
# YOLOv8 summary (fused): 168 layers, 3006233 parameters, 0 gradients, 8.1 GFLOPs
# val: Scanning /home/yq/aitools/datasets/medical/BCD_YOLO8/labels/val.cache... 73 images, 0 backgrounds, 0 corrupt: 100%|██████████| 
#                  Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:05<00:00,  1.02s/
#                    all         73        967      0.844      0.915      0.929      0.644
#              Platelets         73         76      0.817      0.882      0.904      0.502
#                    RBC         73        819      0.764      0.864      0.893      0.635
#                    WBC         73         72      0.951          1      0.989      0.797
# Speed: 2.1ms preprocess, 7.8ms inference, 0.0ms loss, 9.1ms postprocess per image
# Results saved to /home/yq/ultralytics/runs/detect/val15
# 💡 Learn more at https://docs.ultralytics.com/modes/val

from PIL import Image
from ultralytics import YOLO
 
# 加载预训练的YOLOv8n模型
model = YOLO('best.pt')
 
image_path = 'BloodImage_00021_jpg.rf.a5330c5a9f79a73c6e526244171cf77b.jpg'
results = model(image_path)  # 结果列表
 
# 展示结果
for r in results:
    im_array = r.plot()  # 绘制包含预测结果的BGR numpy数组
    im = Image.fromarray(im_array[..., ::-1])  # RGB PIL图像
    im.show()  # 显示图像
    im.save('results.jpg')  # 保存图像