【守护工地安全】YOLOv8实现安全帽检测

学习《OpenCV应用开发：入门、进阶与工程化实践》一书

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数据集

该图像数据集包含8000张图像，两个类别分别是安全帽与人、以其中200多张图像为验证集，其余为训练集。
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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=yolov8s.pt data=hat_dataset.yaml epochs=50 imgsz=640 batch=4

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导出与测试

下面的命令行，导出模型为ONNX格式，同时预测模型的实际推理能力

yolo export model=hat_best.pt format=onnx
yolo predict model=hat_best.pt source=./hats

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###　部署推理
转成ONNX格式文件以后，基于OpenVINO-Python部署推理，相关代码如下

# Read IR
model = ie.read_model(model="hat_best.onnx")
compiled_model = ie.compile_model(model=model, device_name="CPU")
output_layer = compiled_model.output(0)

capture = cv.VideoCapture("D:/images/video/hat_test.mp4")
while True:
    _, frame = capture.read()
    if frame is None:
        print("End of stream")
        break
    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)]
        cv.rectangle(frame, box, color, 2)
        cv.rectangle(frame, (box[0], box[1] - 20), (box[0] + box[2], box[1]), color, -1)
        cv.putText(frame, class_list[class_ids[index]], (box[0], box[1] - 10), cv.FONT_HERSHEY_SIMPLEX, .5, (0, 0, 0))
    end = time.time()
    inf_end = end - start
    fps = 1 / inf_end
    fps_label = "FPS: %.2f" % fps
    cv.putText(frame, fps_label, (20, 45), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

    cv.imshow("YOLOv8 hat Detection", frame)
    cc = cv.waitKey(1)
    if cc == 27:
        break
cv.destroyAllWindows()