169 lines
5.5 KiB
Python
169 lines
5.5 KiB
Python
import os
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from pathlib import Path
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import cv2
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import numpy as np
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from ultralytics import YOLO
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# ---------------------------
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# 类别映射
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# ---------------------------
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CLASS_NAMES = {
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0: "未堆料",
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1: "小堆料",
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2: "大堆料",
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3: "未浇筑满",
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4: "浇筑满"
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}
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# ---------------------------
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# 加载 ROI 列表
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# ---------------------------
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def load_global_rois(txt_path):
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rois = []
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if not os.path.exists(txt_path):
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print(f"❌ ROI 文件不存在: {txt_path}")
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return rois
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with open(txt_path, 'r') as f:
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for line in f:
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s = line.strip()
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if s:
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try:
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x, y, w, h = map(int, s.split(','))
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rois.append((x, y, w, h))
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except Exception as e:
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print(f"无法解析 ROI 行 '{s}': {e}")
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return rois
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# ---------------------------
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# 裁剪并 resize ROI
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# ---------------------------
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def crop_and_resize(img, rois, target_size=640):
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crops = []
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h_img, w_img = img.shape[:2]
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for i, (x, y, w, h) in enumerate(rois):
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if x < 0 or y < 0 or x + w > w_img or y + h > h_img:
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continue
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roi = img[y:y+h, x:x+w]
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roi_resized = cv2.resize(roi, (target_size, target_size), interpolation=cv2.INTER_AREA)
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crops.append((roi_resized, i))
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return crops
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# ---------------------------
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# class1/class2 加权判断
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# ---------------------------
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def weighted_small_large(pred_probs, threshold=0.4, w1=0.3, w2=0.7):
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p1 = float(pred_probs[1])
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p2 = float(pred_probs[2])
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total = p1 + p2
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if total > 0:
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score = (w1 * p1 + w2 * p2) / total
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else:
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score = 0.0
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final_class = "大堆料" if score >= threshold else "小堆料"
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return final_class, score, p1, p2
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# ---------------------------
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# 单张图片推理函数
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# ---------------------------
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def classify_image_weighted(image, model, threshold=0.4):
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results = model(image)
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pred_probs = results[0].probs.data.cpu().numpy().flatten()
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class_id = int(pred_probs.argmax())
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confidence = float(pred_probs[class_id])
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class_name = CLASS_NAMES.get(class_id, f"未知类别({class_id})")
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# class1/class2 使用加权得分
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if class_id in [1, 2]:
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final_class, score, p1, p2 = weighted_small_large(pred_probs, threshold=threshold)
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else:
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final_class = class_name
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score = confidence
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p1 = float(pred_probs[1])
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p2 = float(pred_probs[2])
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return final_class, score, p1, p2
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# ---------------------------
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# 批量推理主函数
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# ---------------------------
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def batch_classify_images(model_path, input_folder, output_root, roi_file, target_size=640, threshold=0.5):
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# 加载模型
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model = YOLO(model_path)
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# 确保输出根目录存在
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output_root = Path(output_root)
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output_root.mkdir(parents=True, exist_ok=True)
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# 为所有类别创建目录
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class_dirs = {}
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for name in CLASS_NAMES.values():
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d = output_root / name
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d.mkdir(exist_ok=True)
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class_dirs[name] = d
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rois = load_global_rois(roi_file)
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if not rois:
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print("❌ 没有有效 ROI，退出")
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return
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# 遍历图片
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for img_path in Path(input_folder).glob("*.*"):
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if img_path.suffix.lower() not in ['.jpg', '.jpeg', '.png', '.bmp', '.tif']:
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continue
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try:
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img = cv2.imread(str(img_path))
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if img is None:
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continue
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crops = crop_and_resize(img, rois, target_size)
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for roi_resized, roi_idx in crops:
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final_class, score, p1, p2 = classify_image_weighted(roi_resized, model, threshold=threshold)
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# 文件名中保存 ROI、类别、加权分数、class1/class2 置信度
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suffix = f"_roi{roi_idx}_{final_class}_score{score:.2f}_p1{p1:.2f}_p2{p2:.2f}"
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dst_path = class_dirs[final_class] / f"{img_path.stem}{suffix}{img_path.suffix}"
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cv2.imwrite(dst_path, roi_resized)
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print(f"{img_path.name}{suffix} -> {final_class} (score={score:.2f}, p1={p1:.2f}, p2={p2:.2f})")
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except Exception as e:
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print(f"处理失败 {img_path.name}: {e}")
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# ---------------------------
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# 单张图片使用示例（保留 ROI，不保存文件）
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# ---------------------------
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if __name__ == "__main__":
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model_path = r"best.pt"
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image_path = r"./test_image/2.jpg" # 单张图片路径
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roi_file = r"./roi_coordinates/1_rois.txt"
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target_size = 640
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threshold = 0.4 #加权得分阈值可以根据大小堆料分类结果进行调整
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# 加载模型
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model = YOLO(model_path)
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# 读取 ROI
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rois = load_global_rois(roi_file)
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if not rois:
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print("❌ 没有有效 ROI，退出")
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exit(1)
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# 读取图片
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img = cv2.imread(image_path)
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if img is None:
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print(f"❌ 无法读取图片: {image_path}")
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exit(1)
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# 注意：必须裁剪 ROI 并推理，因为训练的时候输入的图像是经过resize的
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crops = crop_and_resize(img, rois, target_size)
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for roi_resized, roi_idx in crops:
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#final_class, score, p1, p2 = classify_image_weighted(roi_resized, model, threshold=threshold)
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final_class,_,_,_ = classify_image_weighted(roi_resized, model, threshold=threshold)
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# 只输出信息，不保存文件
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#print(f"ROI {roi_idx} -> 类别: {final_class}, 加权分数: {score:.2f}, "
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#f"class1 置信度: {p1:.2f}, class2 置信度: {p2:.2f}")
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print(f"类别: {final_class}")
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