126 lines
4.7 KiB
Python
126 lines
4.7 KiB
Python
import cv2
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import numpy as np
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import os
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from ultralytics import YOLO
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# ====================== 用户配置 ======================
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#MODEL_PATH = '/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_ailai2/weights/best.pt'
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MODEL_PATH = 'pointn.pt'
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IMAGE_SOURCE_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/train' # 验证集图片目录
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LABEL_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/train' # 标签目录（与图片同名 .txt）
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OUTPUT_DIR = './output_images'
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IMG_EXTENSIONS = {'.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.tif', '.webp'}
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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# ====================== 可视化函数 ======================
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def draw_keypoints_on_image(image, kpts_xy, colors, label_prefix=''):
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for j, (x, y) in enumerate(kpts_xy):
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x, y = int(x), int(y)
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cv2.circle(image, (x, y), 8, colors[j % len(colors)], -1)
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cv2.putText(image, f'{label_prefix}{j+1}', (x + 10, y - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 1, colors[j % len(colors)], 2)
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return image
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# ====================== 标签读取函数 ======================
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def load_keypoints_from_label(label_path, img_shape):
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"""
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标签格式:
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<class> xc yc w h x1 y1 v1 x2 y2 v2 x3 y3 v3 x4 y4 v4
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共 17 项：1 + 4 + 12
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"""
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if not os.path.exists(label_path):
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print(f"⚠️ 找不到标签文件: {label_path}")
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return None
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H, W = img_shape[:2]
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with open(label_path, 'r') as f:
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line = f.readline().strip().split()
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if len(line) < 17:
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print(f"⚠️ 标签长度不足: {label_path} ({len(line)}项)")
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return None
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floats = [float(x) for x in line[5:]] # 跳过前5个（class + bbox）
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coords = np.array(floats).reshape(-1, 3)[:, :2] # (4,2)
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coords[:, 0] *= W
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coords[:, 1] *= H
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return coords
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# ====================== 主程序 ======================
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if __name__ == "__main__":
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print("🚀 开始验证集关键点误差计算")
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model = YOLO(MODEL_PATH)
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print(f"✅ 模型加载完成: {MODEL_PATH}")
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image_files = [
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f for f in os.listdir(IMAGE_SOURCE_DIR)
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if os.path.splitext(f.lower())[1] in IMG_EXTENSIONS
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]
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if not image_files:
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print("❌ 未找到图像文件")
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exit(1)
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total_errors = []
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skipped = 0
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colors_gt = [(0, 255, 0), (0, 200, 0), (0, 150, 0), (0, 100, 0)]
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colors_pred = [(0, 0, 255)] * 4
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for img_filename in image_files:
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img_path = os.path.join(IMAGE_SOURCE_DIR, img_filename)
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label_path = os.path.join(LABEL_DIR, os.path.splitext(img_filename)[0] + '.txt')
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img = cv2.imread(img_path)
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if img is None:
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print(f"❌ 无法读取图像: {img_path}")
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skipped += 1
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continue
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gt_kpts = load_keypoints_from_label(label_path, img.shape)
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if gt_kpts is None or len(gt_kpts) < 4:
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print(f"⚠️ 跳过 {img_filename}：标签点不足")
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skipped += 1
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continue
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results = model(img, verbose=False)
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if not results or results[0].keypoints is None or len(results[0].keypoints) == 0:
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print(f"⚠️ {img_filename}: 无检测结果，跳过")
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skipped += 1
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continue
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pred_kpts = results[0].keypoints.xy[0].cpu().numpy()
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if pred_kpts.shape[0] != gt_kpts.shape[0]:
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print(f"⚠️ {img_filename}: 点数不匹配 GT={len(gt_kpts)}, Pred={len(pred_kpts)}，跳过")
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skipped += 1
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continue
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# ====================== 只计算关键点 0 和 1 ======================
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selected_idx = [0, 1] # 只算前两个点
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errors = np.linalg.norm(pred_kpts[selected_idx] - gt_kpts[selected_idx], axis=1)
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mean_error = np.mean(errors)
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total_errors.append(mean_error)
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print(f"📸 {img_filename}: 点0&1误差={np.round(errors, 2)} 像素, 平均误差={mean_error:.2f}px")
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# 可视化
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vis_img = img.copy()
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vis_img = draw_keypoints_on_image(vis_img, gt_kpts, colors_gt, label_prefix='GT')
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vis_img = draw_keypoints_on_image(vis_img, pred_kpts, colors_pred, label_prefix='P')
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save_path = os.path.join(OUTPUT_DIR, f"compare_{img_filename}")
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cv2.imwrite(save_path, vis_img)
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# ====================== 结果统计 ======================
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print("\n======================")
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if total_errors:
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print(f"🎯 有效样本数: {len(total_errors)} 张")
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print(f"🚫 跳过样本数: {skipped} 张")
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print(f"📈 平均关键点误差: {np.mean(total_errors):.2f} 像素")
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else:
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print(f"⚠️ 所有样本均被跳过（跳过 {skipped} 张）")
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print("======================")
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