Yolov8模型推理部署

• OPenVINO2022.1OPenVINO2022.1环境搭建-优快云博客
• TensorRT8.4 TensorRT环境搭建-优快云博客
• ONNXRUNTIME-GPU1.7~1.12Visual stdio2022 opencv cude pytroch与yolov8/可视化工具的环境搭建，不搞VIP，我也要当雷锋-优快云博客
• OPenCV DNN4.6以上

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在ptcharm部署 openvino来实现对象检测

001

output_video

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源码

import cv2 as cv
import time
import numpy as np
import openvino as ov

def load_classes():
    try:
        # 请根据实际情况修改为 classes.txt 的绝对路径
        file_path = "C:/python/yolov8/classes.txt"
        with open(file_path, "r") as f:
            class_list = [cname.strip() for cname in f.readlines()]
        return class_list
    except FileNotFoundError:
        print(f"未找到 {file_path} 文件，请检查文件路径是否正确。")
        return []

def format_yolov8(frame):
    row, col, _ = frame.shape
    _max = max(col, row)
    result = np.zeros((_max, _max, 3), np.uint8)
    result[0:row, 0:col] = frame
    return result

class_list = load_classes()
if not class_list:
    print("由于未找到类别文件，程序无法正常运行。")
    exit(1)

colors = [(255, 255, 0), (0, 255, 0), (0, 255, 255), (255, 0, 0)]

# 初始化 OpenVINO 核心
core = ov.Core()
available_devices = core.available_devices
print("可用设备:", available_devices)

try:
    # 读取模型
    model = core.read_model(model="C:/python/yolov8/yolov8n.onnx")  # 请根据实际情况修改为模型的绝对路径
    # 编译模型
    compiled_model = core.compile_model(model=model, device_name="CPU")
    # 获取输出层
    output_layer = compiled_model.output(0)
except Exception as e:
    print(f"模型加载或编译失败: {e}")
    exit(1)

# 打开视频文件
capture = cv.VideoCapture("C:/python/yolov8/001.mp4")
if not capture.isOpened():
    print("无法打开视频文件，请检查文件路径是否正确。")
    exit(1)

# 获取视频的帧率、宽度和高度
fps = capture.get(cv.CAP_PROP_FPS)
width = int(capture.get(cv.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv.CAP_PROP_FRAME_HEIGHT))

# 指定保存视频的文件夹和文件名
output_folder = "C:/python/yolov8/2/"  # 请根据实际情况修改为你要保存的文件夹路径
output_filename = output_folder + "output_video.mp4"

# 创建 VideoWriter 对象
fourcc = cv.VideoWriter_fourcc(*'mp4v')
out = cv.VideoWriter(output_filename, fourcc, fps, (width, height))

while True:
    ret, frame = capture.read()
    if not ret or frame is None:
        print("视频流结束或读取帧失败。")
        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)

    try:
        res = compiled_model([image])[output_layer]  # 1x84x8400
    except Exception as e:
        print(f"推理过程中出现错误: {e}")
        break

    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] > 0.25:
            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, tuple(box[:2]), (box[0] + box[2], box[1] + box[3]), 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, 0.5, (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+OpenVINO2022.x Object Detection", frame)

    # 将处理后的帧写入输出视频文件
    out.write(frame)

    cc = cv.waitKey(1)
    if cc == 27:
        break

# 释放资源
cv.destroyAllWindows()
capture.release()
out.release()