数据集地址
数据集包含 360 张红血细胞图像及其注释文件，分为训练集与验证集。训练文件夹包含 300 张带有注释的图像。测试和验证文件夹都包含 60 张带有注释的图像。我们对原始数据集进行了一些修改以准备此 CBC 数据集，并将数据集分成三部分。在360张涂片图像中，首先使用300张带注释的血细胞图像作为训练集，然后将其余60张带有注释的图像用作测试集。CBC数据集地址如下：
https://github.com/MahmudulAlam/Complete-Blood-Cell-Count-Dataset
模型训练
准备好数据集以后，直接按下面的命令行运行即可：
yolo train model=yolov8n.pt data=cbc_dataset.yaml epochs=25 imgsz=640 batch=1
导出与测试
模型导出与测试
yolo export model=cbc _best.pt format=onnx yolo predict model=cbc_best.pt source=D:\cbc_analysis\data\image_001.jpg
部署推理
转成ONNX格式文件以后，基于OpenVINO-Python部署推理，模型结构如下：
模型支持识别两类细胞分别是：
红细胞 - RBC
白细胞 - WBC
模型推理的代码如下：
ie = Core() for device in ie.available_devices: print(device) # Read IR model = ie.read_model(model="cbc_best.onnx") compiled_model = ie.compile_model(model=model, device_name="CPU") output_layer = compiled_model.output(0) frame = cv.imread("D:/cbc_analysis/data/image_002.jpg") bgr = format_yolov8(frame) img_h, img_w, img_c = bgr.shape start = time.time() image = cv.dnn.blobFromImage(bgr, 1 / 255.0, (640, 640), swapRB=True, crop=False) res = compiled_model([image])[output_layer] # 1x84x8400 rows = np.squeeze(res, 0).T class_ids = [] confidences = [] boxes = [] x_factor = img_w / 640 y_factor = img_h / 640 for r in range(rows.shape[0]): row = rows[r] classes_scores = row[4:] _, _, _, max_indx = cv.minMaxLoc(classes_scores) class_id = max_indx[1] if (classes_scores[class_id] > .25): confidences.append(classes_scores[class_id]) class_ids.append(class_id) x, y, w, h = row[0].item(), row[1].item(), row[2].item(), row[3].item() left = int((x - 0.5 * w) * x_factor) top = int((y - 0.5 * h) * y_factor) width = int(w * x_factor) height = int(h * y_factor) box = np.array([left, top, width, height]) boxes.append(box) indexes = cv.dnn.NMSBoxes(boxes, confidences, 0.25, 0.45) for index in indexes: box = boxes[index] color = colors[int(class_ids[index]) % len(colors)] rr = int((box[2] + box[3])/4) cv.circle(frame, (box[0]+int(box[2]/2), box[1]+int(box[3]/2)), rr-4, color, 2) cv.putText(frame, class_list[class_ids[index]], (box[0] + int(box[2] / 2), box[1] + int(box[3] / 2)), cv.FONT_HERSHEY_SIMPLEX, .5, (0, 0, 0)) cv.putText(frame, "gloomyfish@2024", (20, 45), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) cv.imshow("YOLOv8+OpenVINO2023 RBC(Red Blood Cell) Count", frame) cv.waitKey(0) cv.destroyAllWindows()
