154 lines
4.8 KiB
Python
154 lines
4.8 KiB
Python
|
import os
|
|||
|
|
import cv2
|
|||
|
|
import numpy as np
|
|||
|
|
from ultralytics import YOLO
|
|||
|
|
|
|||
|
|
# ================== 配置参数 ==================
|
|||
|
|
MODEL_PATH = r"/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_obb4/weights/best.pt"
|
|||
|
|
IMAGE_SOURCE_DIR = r"/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/obb2/test"
|
|||
|
|
LABEL_SOURCE_DIR = IMAGE_SOURCE_DIR # 假设标签和图像在同一目录
|
|||
|
|
OUTPUT_DIR = "./inference_results"
|
|||
|
|
|
|||
|
|
IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff', '.webp'}
|
|||
|
|
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
|||
|
|
|
|||
|
|
# 加载模型
|
|||
|
|
print("🔄 加载 YOLO OBB 模型...")
|
|||
|
|
model = YOLO(MODEL_PATH)
|
|||
|
|
print("✅ 模型加载完成")
|
|||
|
|
|
|||
|
|
# 获取图像列表
|
|||
|
|
image_files = [
|
|||
|
|
f for f in os.listdir(IMAGE_SOURCE_DIR)
|
|||
|
|
if os.path.splitext(f.lower())[1] in IMG_EXTENSIONS
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
if not image_files:
|
|||
|
|
print(f"❌ 错误：未找到图像文件")
|
|||
|
|
exit(1)
|
|||
|
|
print(f"📁 发现 {len(image_files)} 张图像待处理")
|
|||
|
|
all_angle_errors = [] # 存储每张图的夹角误差（度）
|
|||
|
|
|
|||
|
|
# ================== 工具函数 ==================
|
|||
|
|
|
|||
|
|
def parse_obb_label_file(label_path):
|
|||
|
|
"""解析 OBB 标签文件，返回 [{'cls': int, 'points': (4,2)}]"""
|
|||
|
|
boxes = []
|
|||
|
|
if not os.path.exists(label_path):
|
|||
|
|
return boxes
|
|||
|
|
with open(label_path, 'r') as f:
|
|||
|
|
for line in f:
|
|||
|
|
parts = line.strip().split()
|
|||
|
|
if len(parts) != 9:
|
|||
|
|
continue
|
|||
|
|
cls_id = int(parts[0])
|
|||
|
|
coords = list(map(float, parts[1:]))
|
|||
|
|
points = np.array(coords).reshape(4, 2)
|
|||
|
|
boxes.append({'cls': cls_id, 'points': points})
|
|||
|
|
return boxes
|
|||
|
|
|
|||
|
|
|
|||
|
|
def compute_main_direction(points):
|
|||
|
|
"""
|
|||
|
|
根据四个顶点计算旋转框的主方向（长边方向），
|
|||
|
|
返回 [0, π) 范围内的弧度值。
|
|||
|
|
"""
|
|||
|
|
edges = []
|
|||
|
|
for i in range(4):
|
|||
|
|
p1 = points[i]
|
|||
|
|
p2 = points[(i + 1) % 4]
|
|||
|
|
vec = p2 - p1
|
|||
|
|
length = np.linalg.norm(vec)
|
|||
|
|
if length > 1e-6:
|
|||
|
|
edges.append((length, vec))
|
|||
|
|
|
|||
|
|
if not edges:
|
|||
|
|
return 0.0
|
|||
|
|
|
|||
|
|
# 找最长边
|
|||
|
|
longest_edge = max(edges, key=lambda x: x[0])[1]
|
|||
|
|
angle_rad = np.arctan2(longest_edge[1], longest_edge[0])
|
|||
|
|
|
|||
|
|
# 归一化到 [0, π)
|
|||
|
|
angle_rad = angle_rad % np.pi
|
|||
|
|
return angle_rad
|
|||
|
|
|
|||
|
|
|
|||
|
|
def compute_min_angle_between_two_dirs(dir1_rad, dir2_rad):
|
|||
|
|
"""计算两个方向之间的最小夹角（0 ~ 90°），返回角度制"""
|
|||
|
|
diff = abs(dir1_rad - dir2_rad)
|
|||
|
|
min_diff_rad = min(diff, np.pi - diff)
|
|||
|
|
return np.degrees(min_diff_rad) # 返回 0~90°
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ================== 主循环 ==================
|
|||
|
|
for img_filename in image_files:
|
|||
|
|
img_path = os.path.join(IMAGE_SOURCE_DIR, img_filename)
|
|||
|
|
label_path = os.path.join(LABEL_SOURCE_DIR, os.path.splitext(img_filename)[0] + ".txt")
|
|||
|
|
|
|||
|
|
print(f"\n🖼️ 处理: {img_filename}")
|
|||
|
|
|
|||
|
|
# 读图
|
|||
|
|
img = cv2.imread(img_path)
|
|||
|
|
if img is None:
|
|||
|
|
print("❌ 无法读取图像")
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# 推理
|
|||
|
|
results = model(img, imgsz=640, conf=0.15, verbose=False)
|
|||
|
|
result = results[0]
|
|||
|
|
pred_boxes = result.obb
|
|||
|
|
|
|||
|
|
# === 提取预测框主方向 ===
|
|||
|
|
pred_dirs = []
|
|||
|
|
if pred_boxes is not None and len(pred_boxes) >= 2:
|
|||
|
|
for box in pred_boxes[:2]: # 只取前两个
|
|||
|
|
xywhr = box.xywhr.cpu().numpy()[0]
|
|||
|
|
cx, cy, w, h, r_rad = xywhr
|
|||
|
|
main_dir = r_rad if w >= h else r_rad + np.pi / 2
|
|||
|
|
pred_dirs.append(main_dir % np.pi)
|
|||
|
|
pred_angle = compute_min_angle_between_two_dirs(pred_dirs[0], pred_dirs[1])
|
|||
|
|
else:
|
|||
|
|
print("❌ 预测框不足两个")
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# === 提取真实框主方向 ===
|
|||
|
|
true_boxes = parse_obb_label_file(label_path)
|
|||
|
|
if len(true_boxes) < 2:
|
|||
|
|
print("❌ 标签框不足两个")
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
true_dirs = []
|
|||
|
|
for tb in true_boxes[:2]: # 取前两个
|
|||
|
|
d = compute_main_direction(tb['points'])
|
|||
|
|
true_dirs.append(d)
|
|||
|
|
true_angle = compute_min_angle_between_two_dirs(true_dirs[0], true_dirs[1])
|
|||
|
|
|
|||
|
|
# === 计算夹角误差 ===
|
|||
|
|
error_deg = abs(pred_angle - true_angle)
|
|||
|
|
all_angle_errors.append(error_deg)
|
|||
|
|
|
|||
|
|
print(f" 🔹 预测夹角: {pred_angle:.2f}°")
|
|||
|
|
print(f" 🔹 真实夹角: {true_angle:.2f}°")
|
|||
|
|
print(f" 🔺 夹角误差: {error_deg:.2f}°")
|
|||
|
|
|
|||
|
|
# ================== 输出统计 ==================
|
|||
|
|
print("\n" + "=" * 60)
|
|||
|
|
print("📊 夹角误差统计（基于两框间最小夹角）")
|
|||
|
|
print("=" * 60)
|
|||
|
|
if all_angle_errors:
|
|||
|
|
mean_error = np.mean(all_angle_errors)
|
|||
|
|
std_error = np.std(all_angle_errors)
|
|||
|
|
max_error = np.max(all_angle_errors)
|
|||
|
|
min_error = np.min(all_angle_errors)
|
|||
|
|
|
|||
|
|
print(f"有效图像数: {len(all_angle_errors)}")
|
|||
|
|
print(f"平均夹角误差: {mean_error:.2f}°")
|
|||
|
|
print(f"标准差: {std_error:.2f}°")
|
|||
|
|
print(f"最大误差: {max_error:.2f}°")
|
|||
|
|
print(f"最小误差: {min_error:.2f}°")
|
|||
|
|
else:
|
|||
|
|
print("❌ 无有效数据用于统计")
|
|||
|
|
|
|||
|
|
print("=" * 60)
|
|||
|
|
print("🎉 所有图像处理完成！")
|