一、数据标注

1. 使用labelme软件标注每个细胞的边界信息，标注结果为JSON格式

2. JSON格式转yolo支持的txt格式

import json
import os
import glob
import os.path as osp'''此函数用来将labelme软件标注好的数据集转换为yolov5_7.0sege中使用的数据集:param jsonfilePath: labelme标注好的*.json文件所在文件夹:param resultDirPath: 转换好后的*.txt保存文件夹:param classList: 数据集中的类别标签
'''  
def labelme2yolov2Seg(jsonfilePath, resultDirPath, classList):# 0.创建保存转换结果的文件夹if (not os.path.exists(resultDirPath)):os.mkdir(resultDirPath)# 1.获取目录下所有的labelme标注好的Json文件，存入列表中jsonfileList = glob.glob(osp.join(jsonfilePath, "*.json") )print(jsonfileList)  # 打印文件夹下的文件名称# 2.遍历json文件，进行转换for jsonfile in jsonfileList:# 3. 打开json文件with open(jsonfile, "r",encoding='UTF-8') as f:file_in = json.load(f)# 4. 读取文件中记录的所有标注目标shapes = file_in["shapes"]# 5. 使用图像名称创建一个txt文件，用来保存数据with open(resultDirPath + "\\" + jsonfile.split("\\")[-1].replace(".json", ".txt"), "w") as file_handle:# 6. 遍历shapes中的每个目标的轮廓for shape in shapes:# 7.根据json中目标的类别标签，从classList中寻找类别的ID，然后写入txt文件中file_handle.writelines(str(classList.index(shape["label"])) + " ")# 8. 遍历shape轮廓中的每个点，每个点要进行图像尺寸的缩放，即x/width, y/heightfor point in shape["points"]:x = point[0] / file_in["imageWidth"]  # mask轮廓中一点的X坐标y = point[1] / file_in["imageHeight"]  # mask轮廓中一点的Y坐标file_handle.writelines(str(x) + " " + str(y) + " ")  # 写入mask轮廓点# 9.每个物体一行数据，一个物体遍历完成后需要换行file_handle.writelines("\n")# 10.所有物体都遍历完，需要关闭文件file_handle.close()# 10.所有物体都遍历完，需要关闭文件f.close()if __name__ == "__main__":jsonfilePath = r"D:/workspace/yolov8/datasets/json"  # 要转换的json文件所在目录resultDirPath = r"D:/workspace/yolov8/datasets/txt"  # 要生成的txt文件夹labelme2yolov2Seg(jsonfilePath=jsonfilePath, resultDirPath=resultDirPath, classList=['danhe','linba','yilin'])  # 更改为自己的类别名

转换结果：

二、训练数据

1. 配置文件

路径：ultralytics\cfg\datasets\cell_seg.yaml

path: ../datasets/cell_seg # dataset root dir
train: images/train # train images (relative to 'path')
val: images/val # val images (relative to 'path')
test: test/images # test images (optional)nc: 2# Classes
names:0: danhe1: linba

2. 训练脚本

# 细胞实例分割训练
from  ultralytics import YOLOmodel = YOLO('yolov8n-seg.pt')
# 训练自己的数据集，  文件路径： ultralytics/cfg/datasets/cell_seg.yaml
model.train(data='cell_seg.yaml', epochs=100, imgsz=320)
# 使用验证集 验证效果
model.val()

3. 训练结果

三、使用上述训练的模型，批量检测细胞、计算细胞面积、灰度值计算

1. 预测细胞类别及轮廓

# 细胞实例分割训练
from  ultralytics import YOLO
# best_seg.pt 训练结果中weight/best.pt
model = YOLO('best_seg.pt')
# 测试数据 批量检测
predict_results = model.predict(source="../datasets/cell/test/images", save=True)

2. 识别轮廓、计算面积

from ultralytics import YOLO
import cv2
import os
import numpy as np# Load a pretrained YOLOv8n model
model =  YOLO('best_seg.pt')# Define path to the directory containing image files
source = '../datasets/cell/test/images'# 指定输出路径
output_dir = './output_images'
if not os.path.exists(output_dir):os.makedirs(output_dir)# 获取待预测文件名称，用于保存同名文件
def get_last_part_of_string(path):return os.path.basename(path)#  hex to BGR
def hex_to_bgr(hex):return tuple(int(hex[i:i+2], 16) for i in (4, 2, 0))# 颜色，同plotting.py的设置
hexs = ("FF3838", "FF9D97", "FF701F", "FFB21D", "CFD231","48F90A", "92CC17", "3DDB86", "1A9334", "00D4BB","2C99A8", "00C2FF", "344593", "6473FF", "0018EC","8438FF", "520085", "CB38FF", "FF95C8", "FF37C7"
)
colors = [hex_to_bgr(h) for h in hexs]# 开始预测
results = model(source=source, save=False)  # list of Results objectsfor result in results:image_path = result.pathimage = cv2.imread(image_path)boxes = result.boxes  # Boxes 对象，用于边界框输出masks = result.masks  # Masks 对象，用于分割掩码输出names = result.names  # 获取类别名称字典for box, mask in zip(boxes, masks):for cls, contour in zip(box.cls, mask.xy):class_id = int(cls.item())  # 获取张量的值并转换为整数color = colors[class_id % len(colors)]  # 获取颜色contour = np.array(contour, dtype=np.int32)  # 确保轮廓是整数类型area = cv2.contourArea(contour)  # 计算轮廓面积class_name = names[class_id]  # 获取类别名称# 计算轮廓的中心M = cv2.moments(contour)if M['m00'] != 0:cx = int(M['m10'] / M['m00'])cy = int(M['m01'] / M['m00'])else:cx, cy = 0, 0'''cv2.drawContours 参数：image: 输入图像，一般是二值图像或彩色图像；contours: 要绘制的轮廓，是一个 Python 列表，每个列表元素都是一个 Numpy 数组，代表一个轮廓；contourIdx: 要绘制的轮廓的索引，默认为 -1, 代表绘制所有轮廓；color: 轮廓的颜色，是一个三元组，分别表示 RGB 颜色；thickness: 线条的粗细，默认为 1;lineType: 线条类型，默认为 cv2.LINE_8;hierarchy: 轮廓的层次关系，是一个 Numpy 数组；maxLevel: 最多绘制的轮廓层'''# 绘制掩码轮廓cv2.drawContours(image, [contour], -1, color, 2)# 在图像上绘制面积和类名text = f'{class_name} {area:.2f}''''cv2.putText 参数：img: 需要绘制文本的图像。‌text: 要绘制的文本内容。‌org: 文本框的左下角坐标，‌即起始点(x,y)。‌fontFace: 字体类型, 如cv2.FONT_HERSHEY_SIMPLEX、cv2.FONT_HERSHEY_PLAIN等。fontScale: 字体大小, float类型, 一般设置为1.0。‌color: 文本颜色, BGR格式。thickness: 文本粗细, 如果设置为None, ‌则表示绘制实心文字。‌lineType: 线型, 可选类型包括cv2.LINE_4、cv2.LINE_8、cv2.LINE_AA等。bottomLeftOrigin: 如果为True, ‌则图像数据原点在左下角（‌默认情况下在左上角）‌。‌'''cv2.putText(image, text, (cx, cy), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)# 保存图像output_path = os.path.join(output_dir, get_last_part_of_string(image_path))'''cv2.imwrite() 参数：filename: 要保存的文件路径和名称，包括路径（‌如果文件不在当前工作目录下）‌和文件扩展名（‌如 .jpg, .png 等）‌。img: 要保存的图像数据，通常是通过 OpenCV 读取或处理得到的。'''cv2.imwrite(output_path, image)print(f'Saved: {output_path}')

3. 灰度值提取

from ultralytics import YOLO
import cv2
import os
import numpy as np# Load a pretrained YOLOv8n model
model =  YOLO('best_seg.pt')# Define path to the directory containing image files
source = '../datasets/cell/test/images'# 指定输出路径
output_dir = './output_images'
if not os.path.exists(output_dir):os.makedirs(output_dir)# 获取待预测文件名称，用于保存同名文件
def get_last_part_of_string(path):return os.path.basename(path)#  hex to BGR
def hex_to_bgr(hex):return tuple(int(hex[i:i+2], 16) for i in (4, 2, 0))# 颜色，同plotting.py的设置
hexs = ("FF3838", "FF9D97", "FF701F", "FFB21D", "CFD231","48F90A", "92CC17", "3DDB86", "1A9334", "00D4BB","2C99A8", "00C2FF", "344593", "6473FF", "0018EC","8438FF", "520085", "CB38FF", "FF95C8", "FF37C7"
)
colors = [hex_to_bgr(h) for h in hexs]# 开始预测
results = model(source=source, save=False)  # list of Results objectsn_points = 5
spacing = 30for result in results:image_path = result.pathimage = cv2.imread(image_path)boxes = result.boxes  # Boxes 对象，用于边界框输出masks = result.masks  # Masks 对象，用于分割掩码输出names = result.names  # 获取类别名称字典# 将图片转换成灰度图gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)for box, mask in zip(boxes, masks):for cls, contour in zip(box.cls, mask.xy):class_id = int(cls.item())  # 获取张量的值并转换为整数color = colors[class_id % len(colors)]  # 获取颜色contour = np.array(contour, dtype=np.int32)  # 确保轮廓是整数类型area = cv2.contourArea(contour)  # 计算轮廓面积class_name = names[class_id]  # 获取类别名称# 计算轮廓的中心M = cv2.moments(contour)if M['m00'] != 0:cx = int(M['m10'] / M['m00'])cy = int(M['m01'] / M['m00'])else:cx, cy = 0, 0'''cv2.drawContours 参数：image: 输入图像，一般是二值图像或彩色图像；contours: 要绘制的轮廓，是一个 Python 列表，每个列表元素都是一个 Numpy 数组，代表一个轮廓；contourIdx: 要绘制的轮廓的索引，默认为 -1, 代表绘制所有轮廓；color: 轮廓的颜色，是一个三元组，分别表示 RGB 颜色；thickness: 线条的粗细，默认为 1;lineType: 线条类型，默认为 cv2.LINE_8;hierarchy: 轮廓的层次关系，是一个 Numpy 数组；maxLevel: 最多绘制的轮廓层'''# 绘制掩码轮廓cv2.drawContours(gray_image, [contour], -1, color, 2)# 在图像上绘制面积和类名text = f'{class_name} {area:.2f}'cv2.putText(gray_image, text, (cx, cy-20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)# 以轮廓为中心，在图像上绘制点for i in range(n_points):# 从中心店左移2个点的距离x = cx + i * spacing - spacing *2y = cy gray_value = gray_image[y, x]# 画点cv2.circle(gray_image, (x, y), 2, (255, 0, 0), 1)# 点描述cv2.putText(gray_image, str(i+1), (x-5, y-5), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 0, 0), 1)# cv2.circle(gray_image, (x-(i * spacing), y), 2, (0, 255, 0), 1) gray_text = f"Point {i+1}({x},{y}): gray value = {gray_value} "print(gray_text)# 将点的灰度值绘制在图片的左上角cv2.putText(gray_image, gray_text, (10, 15+i*15), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 0), 1)# 保存图像output_path = os.path.join(output_dir, get_last_part_of_string(image_path))'''cv2.imwrite() 参数：filename: 要保存的文件路径和名称，包括路径（‌如果文件不在当前工作目录下）‌和文件扩展名（‌如 .jpg, .png 等）‌。img: 要保存的图像数据，通常是通过 OpenCV 读取或处理得到的。'''cv2.imwrite(output_path, gray_image)print(f'Saved: {output_path}')