299 lines
11 KiB
Python
299 lines
11 KiB
Python
import os
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import cv2
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import torch
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import argparse
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import numpy as np
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from ultralytics import YOLO
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from pathlib import Path
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# ====================== 配置参数 ======================3
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MODEL_PATH = "best.pt"
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#SOURCE_IMG_DIR = "/home/hx/yolo/output_masks" # 原始输入图像目录
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SOURCE_IMG_DIR = "/home/hx/yolo/yemian/test_image" # 原始输入图像目录
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OUTPUT_DIR = "/home/hx/yolo/output_masks2" # 推理输出根目录
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ROI_COORDS_FILE = "./roi_coordinates/1_rois2.txt" # 必须与训练时相同
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CONF_THRESHOLD = 0.25
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IOU_THRESHOLD = 0.45
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DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
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SAVE_TXT = True
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SAVE_MASKS = True
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VIEW_IMG = False
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LINE_WIDTH = 2
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TARGET_SIZE = 640 # 必须与训练输入尺寸一致
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def load_roi_coords(txt_path):
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"""
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加载 ROI 坐标 (x, y, w, h)
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支持多个 ROI（例如多视野检测）
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"""
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rois = []
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if not os.path.exists(txt_path):
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raise FileNotFoundError(f"❌ ROI 文件未找到: {txt_path}")
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with open(txt_path, 'r') as f:
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for line in f:
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line = line.strip()
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if line and not line.startswith('#'):
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try:
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x, y, w, h = map(int, line.split(','))
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rois.append((x, y, w, h))
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print(f"📌 加载 ROI: (x={x}, y={y}, w={w}, h={h})")
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except Exception as e:
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print(f"⚠️ 无法解析 ROI 行: '{line}' | 错误: {e}")
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return rois
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def plot_result_with_opacity_and_roi(
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result,
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orig_img_shape,
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roi_box,
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line_width=2,
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mask_opacity=0.5
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):
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"""
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绘制分割结果，支持透明叠加，并适配 ROI → 原图坐标的还原
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:param result: YOLO 推理结果（在 cropped-resized 图像上）
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:param orig_img_shape: 原图 (H, W)
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:param roi_box: (x, y, w, h) 当前 ROI 区域
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:return: 在原图上绘制的结果图像
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"""
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h_orig, w_orig = orig_img_shape
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img = np.zeros((h_orig, w_orig, 3), dtype=np.uint8) # 创建黑底原图大小画布
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x, y, w, h = roi_box
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# 获取模型输出的裁剪+resize后的图像上的结果
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if result.masks is not None:
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masks = result.masks.data.cpu().numpy() # (N, 640, 640)
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# 将 mask resize 回 ROI 尺寸
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resized_masks = []
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for mask in masks:
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m = cv2.resize(mask, (w, h), interpolation=cv2.INTER_NEAREST)
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m = (m > 0.5).astype(np.uint8)
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resized_masks.append(m)
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resized_masks = np.array(resized_masks)
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# 上色叠加
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overlay = img.copy()
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colors = np.random.randint(0, 255, size=(len(resized_masks), 3), dtype=np.uint8)
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for i, mask in enumerate(resized_masks):
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color = colors[i].tolist()
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roi_region = overlay[y:y+h, x:x+w]
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roi_region[mask == 1] = color
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cv2.addWeighted(overlay, mask_opacity, img, 1 - mask_opacity, 0, img)
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# 绘制边界框和标签
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if result.boxes is not None:
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boxes = result.boxes.xyxy.cpu().numpy()
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classes = result.boxes.cls.cpu().numpy().astype(int)
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confidences = result.boxes.conf.cpu().numpy()
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colors = np.random.randint(0, 255, size=(len(classes), 3), dtype=np.uint8)
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font = cv2.FONT_HERSHEY_SIMPLEX
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font_scale = 0.6
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thickness = 1
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for i in range(len(boxes)):
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# 映射回 ROI 像素坐标
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box = boxes[i]
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x1 = int(box[0] * w / TARGET_SIZE) + x
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y1 = int(box[1] * h / TARGET_SIZE) + y
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x2 = int(box[2] * w / TARGET_SIZE) + x
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y2 = int(box[3] * h / TARGET_SIZE) + y
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cls_id = classes[i]
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conf = confidences[i]
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color = colors[i].tolist()
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cv2.rectangle(img, (x1, y1), (x2, y2), color, line_width)
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label = f"{cls_id} {conf:.2f}"
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(text_w, text_h), baseline = cv2.getTextSize(label, font, font_scale, thickness)
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cv2.rectangle(img, (x1, y1 - text_h - 6), (x1 + text_w, y1), color, -1)
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cv2.putText(img, label, (x1, y1 - 4), font, font_scale,
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(255, 255, 255), thickness, cv2.LINE_AA)
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return img
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def run_segmentation_inference_with_roi(
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model_path,
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source,
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output_dir,
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roi_coords_file,
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conf_threshold=0.25,
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iou_threshold=0.45,
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device="cuda:0",
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save_txt=True,
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save_masks=True,
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view_img=False,
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line_width=2,
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):
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print(f"🚀 加载模型: {model_path}")
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print(f"💻 使用设备: {device}")
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# 加载模型
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model = YOLO(model_path)
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# 加载 ROI
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rois = load_roi_coords(roi_coords_file)
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if len(rois) == 0:
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print("❌ 没有加载到任何有效的 ROI，程序退出。")
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return
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# 创建输出目录
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output_dir = Path(output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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txt_dir = output_dir / "labels"
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mask_dir = output_dir / "masks"
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vis_dir = output_dir / "visualize"
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txt_dir.mkdir(parents=True, exist_ok=True)
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mask_dir.mkdir(parents=True, exist_ok=True)
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vis_dir.mkdir(parents=True, exist_ok=True) # ✅ 改这里：无条件创建！
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# 获取图像文件
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source = Path(source)
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if source.is_file():
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img_files = [source]
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else:
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img_files = list(source.glob("*.jpg")) + \
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list(source.glob("*.jpeg")) + \
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list(source.glob("*.png")) + \
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list(source.glob("*.bmp"))
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if not img_files:
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print(f"❌ 在 {source} 中未找到图像文件")
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return
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print(f"🖼️ 共 {len(img_files)} 张图片待推理...")
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# 推理循环
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for img_path in img_files:
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print(f"\n🔍 正在处理: {img_path.name}")
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orig_img = cv2.imread(str(img_path))
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if orig_img is None:
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print(f"❌ 无法读取图像: {img_path}")
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continue
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h_orig, w_orig = orig_img.shape[:2]
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# 初始化全图级结果存储
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full_vis_img = orig_img.copy() # 可视化用
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all_txt_lines = []
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# 对每个 ROI 进行推理
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for idx, (x, y, w, h) in enumerate(rois):
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# 越界检查
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if x < 0 or y < 0 or x + w > w_orig or y + h > h_orig:
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print(f"⚠️ ROI 越界，跳过: ({x},{y},{w},{h})")
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continue
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# 提取 ROI 并 resize 到模型输入尺寸
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roi_img = orig_img[y:y+h, x:x+w]
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if roi_img.size == 0:
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print(f"⚠️ 空 ROI 区域: {idx}")
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continue
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resized_img = cv2.resize(roi_img, (TARGET_SIZE, TARGET_SIZE), interpolation=cv2.INTER_LINEAR)
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# 推理
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results = model(
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source=resized_img,
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conf=conf_threshold,
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iou=iou_threshold,
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imgsz=TARGET_SIZE,
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device=device,
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verbose=False
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)
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result = results[0]
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# 可视化：将结果映射回原图
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vis_part = plot_result_with_opacity_and_roi(
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result=result,
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orig_img_shape=(h_orig, w_orig),
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roi_box=(x, y, w, h),
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line_width=line_width,
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mask_opacity=0.5
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)
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full_vis_img = cv2.addWeighted(full_vis_img, 1.0, vis_part, 0.7, 0)
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# 保存标签（YOLO 格式，归一化到原图）
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if save_txt and result.masks is not None:
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scale_x = w / TARGET_SIZE
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scale_y = h / TARGET_SIZE
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for i in range(len(result.boxes)):
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cls_id = int(result.boxes.cls[i])
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seg = result.masks.xy[i] # (N, 2) in 640 space
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# 映射回 ROI 像素坐标
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seg[:, 0] = seg[:, 0] * scale_x + x
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seg[:, 1] = seg[:, 1] * scale_y + y
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# 归一化到原图
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seg_norm = seg / [w_orig, h_orig]
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seg_flat = seg_norm.flatten()
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line = f"{cls_id} {' '.join(f'{val:.6f}' for val in seg_flat)}"
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all_txt_lines.append(line)
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# 保存单个 ROI 的 mask（可选）
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if save_masks and result.masks is not None:
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mask_data = result.masks.data.cpu().numpy()
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combined_mask = (mask_data.sum(axis=0) > 0).astype(np.uint8) * 255
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# resize 回 ROI 尺寸
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roi_mask = cv2.resize(combined_mask, (w, h), interpolation=cv2.INTER_NEAREST)
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full_mask = np.zeros((h_orig, w_orig), dtype=np.uint8)
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full_mask[y:y+h, x:x+w] = roi_mask
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mask_save_path = mask_dir / f"mask_{img_path.stem}_roi{idx}.png"
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cv2.imwrite(str(mask_save_path), full_mask)
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# 保存最终可视化图像
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vis_save_path = vis_dir / f"vis_{img_path.name}"
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cv2.imwrite(str(vis_save_path), full_vis_img)
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print(f"✅ 保存可视化结果: {vis_save_path}")
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# 保存合并的文本标签
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if save_txt and all_txt_lines:
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txt_save_path = txt_dir / f"{img_path.stem}.txt"
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with open(txt_save_path, 'w') as f:
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f.write("\n".join(all_txt_lines) + "\n")
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print(f"📝 保存标签: {txt_save_path}")
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# 实时显示
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if view_img:
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display_img = cv2.resize(full_vis_img, (960, 540))
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cv2.imshow("Segmentation Result", display_img)
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if cv2.waitKey(0) == 27: # ESC
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cv2.destroyAllWindows()
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break
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if view_img:
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cv2.destroyAllWindows()
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print(f"\n🎉 推理完成！结果保存在: {output_dir}")
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# ====================== 主程序 ======================
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", default=MODEL_PATH, help="模型权重路径")
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parser.add_argument("--source", default=SOURCE_IMG_DIR, help="图片路径或文件夹")
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parser.add_argument("--output", default=OUTPUT_DIR, help="输出目录")
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parser.add_argument("--roi-file", default=ROI_COORDS_FILE, help="ROI 坐标文件路径")
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parser.add_argument("--conf", type=float, default=CONF_THRESHOLD, help="置信度阈值")
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parser.add_argument("--iou", type=float, default=IOU_THRESHOLD, help="IoU 阈值")
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parser.add_argument("--device", default=DEVICE, help="设备: cuda:0, cpu")
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parser.add_argument("--view-img", action="store_true", help="显示图像")
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parser.add_argument("--save-txt", action="store_true", help="保存标签")
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parser.add_argument("--save-masks", action="store_true", help="保存掩码")
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opt = parser.parse_args()
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run_segmentation_inference_with_roi(
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model_path=opt.model,
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source=opt.source,
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output_dir=opt.output,
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roi_coords_file=opt.roi_file,
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conf_threshold=opt.conf,
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iou_threshold=opt.iou,
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device=opt.device,
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save_txt=opt.save_txt or SAVE_TXT,
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save_masks=opt.save_masks or SAVE_MASKS,
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view_img=opt.view_img,
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line_width=LINE_WIDTH,
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) |