126 lines
3.1 KiB
Python
126 lines
3.1 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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# ---------------- 配置 ----------------
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MODEL_PATH = "/home/hx/yolo/ultralytics_yolo11-main/runs/train/61seg/exp2/weights/best.pt"
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IMAGE_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/cls-61/class4c/12.17-18"
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OUT_DIR = "./outputs" # 只保存 labels
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IMG_SIZE = 640
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CONF_THRES = 0.25
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# 多边形简化比例（点数控制核心参数）
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EPSILON_RATIO = 0.001
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IMG_EXTS = (".jpg", ".jpeg", ".png", ".bmp")
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# -------------------------------------
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def simplify_polygon(poly, epsilon_ratio):
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"""
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使用 approxPolyDP 简化多边形
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"""
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poly = poly.astype(np.int32)
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perimeter = cv2.arcLength(poly, True)
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epsilon = epsilon_ratio * perimeter
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approx = cv2.approxPolyDP(poly, epsilon, True)
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return approx.reshape(-1, 2)
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def extract_simplified_masks(result):
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"""
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提取并简化 YOLO mask
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返回：
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[(cls_id, poly), ...]
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"""
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simplified = []
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if result.masks is None:
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return simplified
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boxes = result.boxes
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for i, poly in enumerate(result.masks.xy):
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cls_id = int(boxes.cls[i])
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conf = float(boxes.conf[i])
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if conf < CONF_THRES:
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continue
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poly = simplify_polygon(poly, EPSILON_RATIO)
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if len(poly) < 3:
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continue
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simplified.append((cls_id, poly))
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return simplified
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def save_yolo_seg_labels(masks, img_shape, save_path):
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"""
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保存 YOLO segmentation 标签
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"""
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h, w = img_shape[:2]
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lines = []
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for cls_id, poly in masks:
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poly_norm = []
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for x, y in poly:
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poly_norm.append(f"{x / w:.6f}")
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poly_norm.append(f"{y / h:.6f}")
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lines.append(str(cls_id) + " " + " ".join(poly_norm))
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# ⚠️ 没有目标也生成空 txt（YOLO 训练需要）
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with open(save_path, "w") as f:
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if lines:
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f.write("\n".join(lines))
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def run_folder_inference():
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out_lbl_dir = os.path.join(OUT_DIR, "labels")
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os.makedirs(out_lbl_dir, exist_ok=True)
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# 模型只加载一次
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model = YOLO(MODEL_PATH)
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img_files = sorted([
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f for f in os.listdir(IMAGE_DIR)
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if f.lower().endswith(IMG_EXTS)
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])
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print(f"📂 共检测 {len(img_files)} 张图片")
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for idx, img_name in enumerate(img_files, 1):
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img_path = os.path.join(IMAGE_DIR, img_name)
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img = cv2.imread(img_path)
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if img is None:
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print(f"⚠️ 跳过无法读取: {img_name}")
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continue
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results = model(
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img,
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imgsz=IMG_SIZE,
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conf=CONF_THRES,
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verbose=False
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)
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result = results[0]
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masks = extract_simplified_masks(result)
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base_name = os.path.splitext(img_name)[0]
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label_path = os.path.join(out_lbl_dir, base_name + ".txt")
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save_yolo_seg_labels(masks, img.shape, label_path)
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print(f"[{idx}/{len(img_files)}] ✅ {img_name}")
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print("🎉 标签生成完成（仅保存 YOLO Seg 标签）")
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if __name__ == "__main__":
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run_folder_inference()
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