yolo 获取结果异常脚本：
import os
import shutil
import json
import timeimport torch
import cv2
from PIL import Image
import numpy as npfrom ultralytics import YOLO
from torchvision.ops import batched_nmsclass YOLO_Class():def __init__(self, model_path, device="cuda:0"):self.model = YOLO(model_path, verbose=False).to(device)self.last_log_time = time.time()self.frame_counter = 0self.conf_threshold = 0.5def iou(self, box1, box2):"""计算两个矩形框的 IoU"""x1_min, y1_min, x1_max, y1_max = box1x2_min, y2_min, x2_max, y2_max = box2inter_xmin = max(x1_min, x2_min)inter_ymin = max(y1_min, y2_min)inter_xmax = min(x1_max, x2_max)inter_ymax = min(y1_max, y2_max)inter_w = max(0, inter_xmax - inter_xmin)inter_h = max(0, inter_ymax - inter_ymin)inter_area = inter_w * inter_harea1 = (x1_max - x1_min) * (y1_max - y1_min)area2 = (x2_max - x2_min) * (y2_max - y2_min)union_area = area1 + area2 - inter_areaif union_area == 0:return 0return inter_area / union_areadef get_normalized_box(self, points):"""将LabelMe点转换为 [x_min, y_min, x_max, y_max] 格式"""x_coords = [point[0] for point in points]y_coords = [point[1] for point in points]x_min = min(x_coords)y_min = min(y_coords)x_max = max(x_coords)y_max = max(y_coords)return [x_min, y_min, x_max, y_max]def load_labelme_annotations(self, json_path):"""加载LabelMe标注"""with open(json_path, 'r', encoding='utf-8') as f:data = json.load(f)annotations = []for shape in data.get('shapes', []):box = self.get_normalized_box(shape['points'])annotations.append({'label': shape['label'],'box': box,'points': shape['points']})return annotationsdef detect_img(self, image):"""YOLO推理"""start_time = time.time()with torch.no_grad():results = self.model(image, verbose=False)cls = results[0].boxes.cls.int().cpu()labels = results[0].boxes.clsboxes = results[0].boxes.xyxyscores = results[0].boxes.confindices = torch.where(scores > 0.3)[0]labels = labels[indices]boxes = boxes[indices]scores = scores[indices]if len(boxes) == 0:return []# 使用PyTorch的batched_nmstry:# PyTorch 1.10.0及以上版本keep_indices = batched_nms(boxes, scores, labels, 0.5)except ImportError:# 兼容旧版本keep_indices = []unique_classes = torch.unique(labels)for cls_id in unique_classes:cls_mask = (labels == cls_id)cls_boxes = boxes[cls_mask]cls_scores = scores[cls_mask]if len(cls_boxes) == 0:continuefrom torchvision.ops import nmscls_keep = nms(cls_boxes, cls_scores, 0.2)global_cls_indices = torch.where(cls_mask)[0]keep_indices.extend(global_cls_indices[cls_keep].tolist())keep_indices = torch.tensor(keep_indices, device=boxes.device)# 根据保留的索引筛选结果labels = labels[keep_indices]boxes = boxes[keep_indices]scores = scores[keep_indices]detections = []for i in range(len(boxes)):detections.append({'label': self.model.names[int(labels[i])],'box': boxes[i].cpu().numpy().tolist(),'score': scores[i].cpu().numpy().item()})return detectionsif 0:detections = []for i in range(len(boxes)):detections.append({'label': self.model.names[int(labels[i])],'box': boxes[i].cpu().numpy().tolist(),'score': scores[i].cpu().numpy().item()})return detectionsdef draw_detections_on_image(self, image_path, detections, output_path):"""在图片上绘制YOLO检测结果（绿色框）"""# 读取图片image = cv2.imread(image_path)if image is None:print(f"无法读取图片: {image_path}")return False# 绘制每个检测框for det in detections:box = det['box']label = det['label']score = det['score']# 转换为整数坐标x1, y1, x2, y2 = map(int, box)# 绘制绿色矩形框cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)# 添加标签和置信度label_text = f"{label}: {score:.2f}"label_size = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)[0]# 绘制标签背景cv2.rectangle(image, (x1, y1 - label_size[1] - 10),(x1 + label_size[0], y1), (0, 255, 0), -1)# 绘制标签文字cv2.putText(image, label_text, (x1, y1 - 5),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)# 保存带检测结果的图片cv2.imwrite(output_path, image)return Truedef compare_detections(self, image_path, output_base_dir, iou_threshold=0.5):"""对比YOLO检测和LabelMe标注"""# 获取对应的JSON文件路径base_name = os.path.splitext(image_path)[0]json_path = base_name + '.json'if not os.path.exists(json_path):print(f"未找到标注文件: {json_path}")return# 加载图像和标注image = Image.open(image_path)yolo_detections = self.detect_img(image)labelme_annotations = self.load_labelme_annotations(json_path)# 匹配检测框和标注框matched_pairs = []unmatched_detections = yolo_detections.copy()unmatched_annotations = labelme_annotations.copy()# 匹配IOU大于阈值的框for det in yolo_detections:for ann in labelme_annotations:iou_score = self.iou(det['box'], ann['box'])if iou_score >= iou_threshold and det['label'] == ann['label']:matched_pairs.append((det, ann, iou_score))if det in unmatched_detections:unmatched_detections.remove(det)if ann in unmatched_annotations:unmatched_annotations.remove(ann)break# 分类判断yolo_count = len(yolo_detections)labelme_count = len(labelme_annotations)matched_count = len(matched_pairs)category = ""if yolo_count > labelme_count:category = "more" # 多检elif yolo_count < labelme_count:category = "miss" # 漏检else:category = "good" # 数量相同但IOU低# 如果有IOU小于0.5的匹配对，也归为低IOU类别low_iou_exists = any(iou_score < 0.5 for _, _, iou_score in matched_pairs)if low_iou_exists:category = "low_iou"if category in ['good']:return # 保存到对应目录output_dir = os.path.join(output_base_dir, category)os.makedirs(output_dir, exist_ok=True)image_filename = os.path.basename(image_path)# 复制原图片和标注文件shutil.copy2(image_path, output_dir)shutil.copy2(json_path, output_dir)# 生成带检测结果的图片result_image_path = os.path.join(output_dir, f"{image_filename}")self.draw_detections_on_image(image_path, yolo_detections, result_image_path)# 生成对比信息文件info_file = os.path.join(output_dir, f"{os.path.basename(base_name)}_info.txt")with open(info_file, 'w', encoding='utf-8') as f:f.write(f"图片: {image_path}\n")f.write(f"分类: {category}\n")f.write(f"YOLO检测数: {yolo_count}\n")f.write(f"LabelMe标注数: {labelme_count}\n")f.write(f"匹配数: {matched_count}\n")f.write(f"多检数: {len(unmatched_detections)}\n")f.write(f"漏检数: {len(unmatched_annotations)}\n")f.write("\n匹配详情:\n")for det, ann, iou_score in matched_pairs:f.write(f" {det['label']}: IOU={iou_score:.3f}\n")if unmatched_detections:f.write("\n多检框:\n")for det in unmatched_detections:f.write(f" {det['label']}: score={det['score']:.3f}\n")if unmatched_annotations:f.write("\n漏检框:\n")for ann in unmatched_annotations:f.write(f" {ann['label']}\n")print(f"处理完成: {os.path.basename(image_path)} -> {category}")def evaluate_dataset(self, image_dir, output_base_dir, iou_threshold=0.3):image_extensions = ['.jpg', '.jpeg', '.png', '.bmp']img_files = ['%s/%s' % (i[0].replace("\\", "/"), j) for i in os.walk(image_dir) for j in i[-1] ifj.lower().endswith('.jpg')]for img_path in img_files:self.compare_detections(img_path, output_base_dir, iou_threshold)if __name__ == '__main__':yolo = YOLO_Class(r'D:\project\yolov12-main_new\weights\chan4\weights\best.pt', device="cuda:0")val = [r"D:\data\det_chantu\val_penzi",r"D:\data\det_chantu\val_1032",r"D:\data\det_chantu\val_pen",]for image_dir in val:output_base_dir = r"D:\data\det_chantu\evaluation_results2" # 输出目录yolo.evaluate_dataset(image_dir, output_base_dir, iou_threshold=0.4)
