【细胞识别】YOLOv8实现CBC细胞检测

yolo train model=yolov8n.pt data=cbc_dataset.yaml epochs=25 imgsz=640 batch=1

yolo export model=cbc _best.pt format=onnx
yolo predict model=cbc_best.pt source=D:\cbc_analysis\data\image_001.jpg

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()