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import sys
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import os
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# 获取当前文件所在目录，然后添加yolov5路径（yolov5在ArithControl目录内）
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current_dir = os.path.dirname(os.path.abspath(__file__))
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yolov5_path = os.path.join(current_dir, 'yolov5')
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if yolov5_path not in sys.path:
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sys.path.insert(0, yolov5_path)
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import numpy as np
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import torch
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# 直接导入具体模块
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from models.common import DetectMultiBackend
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from utils.torch_utils import select_device, time_sync
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from utils.general import (LOGGER, check_img_size, non_max_suppression, scale_boxes)
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from utils.augmentations import letterbox
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import cv2
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import pathlib
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# 强制替换 PosixPath 为 WindowsPath 或 str
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pathlib.PosixPath = pathlib.WindowsPath
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class ObjectDetector:
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def __init__(self, weights='yolov5s.pt', data='data/coco.yaml',
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imgsz=(640, 640), conf_thres=0.25, iou_thres=0.45,
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max_det=1000, device='0', half=False, dnn=False):
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"""
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初始化物体检测器。
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:param weights: 模型权重文件路径
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:param data: 数据配置文件路径
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:param imgsz: 图像输入尺寸 (height, width)
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:param conf_thres: 置信度阈值
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:param iou_thres: IOU 阈值（用于 NMS）
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:param max_det: 每张图像最大检测目标数
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:param device: 设备 ('cpu' 或 '0' 表示 GPU 0)
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:param half: 是否使用半精度（FP16）
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:param dnn: 是否使用 OpenCV DNN 后端
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"""
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self.weights = weights
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self.data = data
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self.imgsz = imgsz
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self.conf_thres = conf_thres
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self.iou_thres = iou_thres
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self.max_det = max_det
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self.device = select_device(device) # 自动选择设备
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self.half = half
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self.dnn = dnn
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self.classes = None
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self.agnostic_nms = False
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self.augment = False
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self.visualize = False
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# 加载模型
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self.model = DetectMultiBackend(self.weights, device=self.device, dnn=self.dnn,
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data=self.data, fp16=self.half)
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self.stride, self.names, self.pt = self.model.stride, self.model.names, self.model.pt
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self.imgsz = check_img_size(imgsz, s=self.stride)
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# 模型预热
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self.model.warmup(imgsz=(1, 3, *self.imgsz))
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def detect(self, img):
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"""
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对输入图像进行物体检测。
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:param img: 输入图像 (numpy array)
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:return: 检测结果列表，每个元素为 [x1, y1, x2, y2, conf]
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"""
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# 图像预处理
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im0 = img # 原图
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# Resize 图像到网络输入尺寸
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# im = cv2.resize(im0, (self.imgsz[1], self.imgsz[0]), interpolation=cv2.INTER_LINEAR)
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im = letterbox(im0, self.imgsz)[0]
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# 转换格式：HWC to CHW, BGR to RGB
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im = im.transpose((2, 0, 1))[::-1]
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im = np.ascontiguousarray(im)
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t1 = time_sync()
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im = torch.from_numpy(im).to(self.device)
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im = im.half() if self.half else im.float()
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im /= 255 # 归一化到 0.0 - 1.0
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if len(im.shape) == 3:
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im = im[None] # 扩展 batch 维度
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# 模型推理
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pred = self.model(im, augment=self.augment, visualize=self.visualize)
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# 非极大值抑制 (NMS)
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pred = non_max_suppression(pred, self.conf_thres, self.iou_thres,
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self.classes, self.agnostic_nms, max_det=self.max_det)
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detections = []
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for i, det in enumerate(pred):
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if len(det):
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det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
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for *xyxy, conf, cls in reversed(det):
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xyxy = [round(x.item()) for x in xyxy] # 将坐标四舍五入为整数
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detections.append([int(cls)] + xyxy + [float(conf)])
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return detections
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if __name__ == '__main__':
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detector = ObjectDetector(weights='./best_3class.pt', imgsz=(1024, 1280))
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img = cv2.imread('./20250703_bar_017.png')
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if img is None:
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print("Error: 无法加载图像")
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else:
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detections = detector.detect(img)
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for det in detections:
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x1, y1, x2, y2, conf = det # 每个 det 是 [x1, y1, x2, y2, conf]
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# 画框（BGR颜色，粗细2）
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cv2.rectangle(img, (x1, y1), (x2, y2), color=(0, 255, 0), thickness=2)
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# 添加置信度文本
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label = f"{conf:.2f}"
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cv2.putText(img, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX,
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fontScale=0.5, color=(0, 255, 0), thickness=1)
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print(f"检测结果: {det}")
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# 保存图像
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output_path = './output.jpg'
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cv2.imwrite(output_path, img)
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print(f"检测结果图像已保存为: {output_path}")
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