189 lines
5.9 KiB
Python
189 lines
5.9 KiB
Python
import os
|
||
import shutil
|
||
from pathlib import Path
|
||
import cv2
|
||
import numpy as np
|
||
from ultralytics import YOLO
|
||
|
||
# ---------------------------
|
||
# 类别映射
|
||
# ---------------------------
|
||
CLASS_NAMES = {
|
||
0: "未堆料",
|
||
1: "小堆料",
|
||
2: "大堆料",
|
||
3: "未浇筑满",
|
||
4: "浇筑满"
|
||
}
|
||
|
||
# ---------------------------
|
||
# 加载 ROI 列表
|
||
# ---------------------------
|
||
def load_global_rois(txt_path):
|
||
rois = []
|
||
if not os.path.exists(txt_path):
|
||
print(f"❌ ROI 文件不存在: {txt_path}")
|
||
return rois
|
||
with open(txt_path, 'r') as f:
|
||
for line in f:
|
||
s = line.strip()
|
||
if s:
|
||
try:
|
||
x, y, w, h = map(int, s.split(','))
|
||
rois.append((x, y, w, h))
|
||
except Exception as e:
|
||
print(f"⚠️ 无法解析 ROI 行 '{s}': {e}")
|
||
return rois
|
||
|
||
# ---------------------------
|
||
# 裁剪并 resize ROI
|
||
# ---------------------------
|
||
def crop_and_resize(img, rois, target_size=640):
|
||
crops = []
|
||
h_img, w_img = img.shape[:2]
|
||
for i, (x, y, w, h) in enumerate(rois):
|
||
if x < 0 or y < 0 or x + w > w_img or y + h > h_img:
|
||
print(f"⚠️ ROI 超出图像边界，跳过: ({x}, {y}, {w}, {h})")
|
||
continue
|
||
roi = img[y:y+h, x:x+w]
|
||
roi_resized = cv2.resize(roi, (target_size, target_size), interpolation=cv2.INTER_AREA)
|
||
crops.append((roi_resized, i))
|
||
return crops
|
||
|
||
# ---------------------------
|
||
# class1/class2 加权判断
|
||
# ---------------------------
|
||
def weighted_small_large(pred_probs, threshold=0.4, w1=0.3, w2=0.7):
|
||
p1 = float(pred_probs[1])
|
||
p2 = float(pred_probs[2])
|
||
total = p1 + p2
|
||
if total > 0:
|
||
score = (w1 * p1 + w2 * p2) / total
|
||
else:
|
||
score = 0.0
|
||
final_class = "大堆料" if score >= threshold else "小堆料"
|
||
return final_class, score, p1, p2
|
||
|
||
# ---------------------------
|
||
# 单张图片推理函数
|
||
# ---------------------------
|
||
def classify_image_weighted(image, model, threshold=0.5):
|
||
results = model(image)
|
||
pred_probs = results[0].probs.data.cpu().numpy().flatten()
|
||
class_id = int(pred_probs.argmax())
|
||
confidence = float(pred_probs[class_id])
|
||
class_name = CLASS_NAMES.get(class_id, f"未知类别({class_id})")
|
||
|
||
# 对于 小堆料/大堆料 使用加权逻辑
|
||
if class_id in [1, 2]:
|
||
final_class, score, p1, p2 = weighted_small_large(pred_probs, threshold=threshold)
|
||
else:
|
||
final_class = class_name
|
||
score = confidence
|
||
p1 = float(pred_probs[1])
|
||
p2 = float(pred_probs[2])
|
||
|
||
return final_class, score, p1, p2
|
||
|
||
# ---------------------------
|
||
# 批量推理主函数（移动文件）
|
||
# ---------------------------
|
||
def batch_classify_images_move(model_path, input_folder, output_root, roi_file, target_size=640, threshold=0.5):
|
||
# 加载模型
|
||
print("🚀 加载模型...")
|
||
model = YOLO(model_path)
|
||
|
||
# 输出目录
|
||
output_root = Path(output_root)
|
||
output_root.mkdir(parents=True, exist_ok=True)
|
||
print(f"📁 输出目录: {output_root}")
|
||
|
||
# 为所有类别创建子目录
|
||
class_dirs = {}
|
||
for name in CLASS_NAMES.values():
|
||
d = output_root / name
|
||
d.mkdir(exist_ok=True)
|
||
class_dirs[name] = d
|
||
|
||
# 加载 ROI 区域
|
||
rois = load_global_rois(roi_file)
|
||
if not rois:
|
||
print("❌ 没有有效 ROI，退出程序")
|
||
return
|
||
|
||
print(f"🎯 已加载 {len(rois)} 个 ROI 区域")
|
||
|
||
# 定义缺陷严重性等级（数字越小越严重）
|
||
severity_rank = {
|
||
"未堆料": 0,
|
||
"大堆料": 1,
|
||
"小堆料": 2,
|
||
"未浇筑满": 3,
|
||
"浇筑满": 4
|
||
}
|
||
|
||
# 遍历输入文件夹
|
||
input_folder = Path(input_folder)
|
||
image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff'}
|
||
processed_count = 0
|
||
|
||
for img_path in sorted(input_folder.glob("*.*")):
|
||
if img_path.suffix.lower() not in image_extensions:
|
||
continue
|
||
|
||
try:
|
||
print(f"\n📄 处理: {img_path.name}")
|
||
img = cv2.imread(str(img_path))
|
||
if img is None:
|
||
print(f"❌ 无法读取图像: {img_path.name}")
|
||
continue
|
||
|
||
# 裁剪并缩放 ROI
|
||
crops = crop_and_resize(img, rois, target_size)
|
||
if not crops:
|
||
print(f"⚠️ 无有效 ROI 裁剪区域: {img_path.name}")
|
||
continue
|
||
|
||
detected_classes = []
|
||
|
||
# 遍历每个 ROI 分类
|
||
for roi_img, roi_idx in crops:
|
||
final_class, score, p1, p2 = classify_image_weighted(roi_img, model, threshold=threshold)
|
||
detected_classes.append(final_class)
|
||
print(f" 🔍 ROI{roi_idx}: {final_class} (score={score:.2f})")
|
||
|
||
# 选择最严重类别
|
||
most_severe_class = min(detected_classes, key=lambda x: severity_rank.get(x, 99))
|
||
dst_path = class_dirs[most_severe_class] / img_path.name
|
||
|
||
# 移动文件
|
||
shutil.move(str(img_path), str(dst_path))
|
||
print(f"✅ 移动 -> [{most_severe_class}] {dst_path}")
|
||
|
||
processed_count += 1
|
||
|
||
except Exception as e:
|
||
print(f"❌ 处理失败 {img_path.name}: {e}")
|
||
|
||
print(f"\n🎉 批量处理完成！共移动 {processed_count} 张图像。")
|
||
|
||
|
||
# ---------------------------
|
||
# 使用示例
|
||
# ---------------------------
|
||
if __name__ == "__main__":
|
||
model_path = "/home/hx/yolo/ultralytics_yolo11-main/runs/train/cls_resize1/exp_cls2/weights/best.pt"
|
||
input_folder = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/61"
|
||
output_root = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/61"
|
||
roi_file = "./roi_coordinates/2.txt"
|
||
threshold = 0.4
|
||
|
||
batch_classify_images_move(
|
||
model_path=model_path,
|
||
input_folder=input_folder,
|
||
output_root=output_root,
|
||
roi_file=roi_file,
|
||
target_size=640,
|
||
threshold=threshold
|
||
)
|