Mask R-CNN instance segmentation training code
This commit is contained in:
flk
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"""
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工具函数模块
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包含: LabelMe 标注解析、linestrip 合并为多边形、多边形转 mask、可视化等
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"""
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import json
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import os
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import math
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import random
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from typing import List, Dict, Tuple, Optional
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import numpy as np
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from PIL import Image, ImageDraw
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# ============================================================
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# 1. 标签映射
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# ============================================================
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def get_label_map(dataset_dir: str) -> Dict[str, int]:
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"""
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扫描数据集中所有 LabelMe JSON 文件，自动构建标签名→类别ID 映射。
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类别从 1 开始（0 保留给背景）。
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Args:
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dataset_dir: 数据集目录路径
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Returns:
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标签名 → 类别ID 的字典，如 {"crosswalk": 1}
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"""
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labels = set()
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for fname in sorted(os.listdir(dataset_dir)):
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if not fname.endswith(".json"):
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continue
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with open(os.path.join(dataset_dir, fname), "r", encoding="utf-8") as f:
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data = json.load(f)
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for shape in data.get("shapes", []):
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labels.add(shape["label"])
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# 排序保证可复现
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label_map = {label: idx + 1 for idx, label in enumerate(sorted(labels))}
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return label_map
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# ============================================================
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# 2. Linestrip 合并为多边形
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# ============================================================
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def _point_dist(p1, p2) -> float:
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"""计算两点之间的欧氏距离"""
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return math.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2)
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def merge_linestrips_to_polygon(strips: List[List[List[float]]]) -> List[List[float]]:
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"""
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将多条 linestrip 合并为一个闭合多边形。
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策略：以第一条 strip 为起点，每次找到与当前多边形末端点距离最近的
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下一条 strip 的端点，将该 strip（可能反转）拼接到多边形末尾，
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最后闭合多边形。
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对于单条 linestrip，直接闭合。
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Args:
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strips: 多条 linestrip 的点列表，每条 strip 是 [[x, y], ...]
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Returns:
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闭合多边形的点列表 [[x, y], ..., [x, y]]（首尾相同）
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"""
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if len(strips) == 0:
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return []
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# 深拷贝避免修改原始数据
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strips = [list(s) for s in strips]
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if len(strips) == 1:
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polygon = list(strips[0])
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polygon.append(list(polygon[0])) # 闭合
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return polygon
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# 从第一条 strip 开始
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polygon = list(strips[0])
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remaining = strips[1:]
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while remaining:
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last_point = polygon[-1]
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best_idx = 0
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best_dist = float("inf")
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best_reverse = False
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for i, strip in enumerate(remaining):
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# 尝试正向连接（strip 的起点与 polygon 末端最近）
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d_start = _point_dist(last_point, strip[0])
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# 尝试反向连接（strip 的终点与 polygon 末端最近）
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d_end = _point_dist(last_point, strip[-1])
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if d_start < best_dist:
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best_dist = d_start
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best_idx = i
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best_reverse = False
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if d_end < best_dist:
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best_dist = d_end
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best_idx = i
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best_reverse = True
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strip = remaining.pop(best_idx)
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if best_reverse:
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polygon.extend(list(reversed(strip)))
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else:
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polygon.extend(strip)
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# 闭合多边形
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polygon.append(list(polygon[0]))
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return polygon
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# ============================================================
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# 3. 多边形 / Linestrip → Mask
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# ============================================================
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def shapes_to_masks(
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shapes: List[dict],
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height: int,
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width: int,
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label_map: Dict[str, int],
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""
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将 LabelMe 的 shapes 转换为实例分割所需的 masks、labels、boxes。
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处理逻辑：
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- polygon 类型：直接作为多边形填充
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- linestrip 类型：将相同 label 的 linestrip 合并为一个多边形后填充
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- rectangle / circle 类型：转换为多边形后处理
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Args:
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shapes: LabelMe JSON 中的 shapes 列表
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height: 图像高度
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width: 图像宽度
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label_map: 标签名→类别ID 映射
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Returns:
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masks: [N, H, W] uint8 二值 mask（0 或 1）
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labels: [N] int64 类别ID
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boxes: [N, 4] float32 格式 (x1, y1, x2, y2)
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"""
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# 按 label 分组 linestrip（同 label 的 linestrip 合并为一个实例）
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linestrip_groups: Dict[str, List[List]] = {}
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# 非 linestrip 的 shape 逐个处理
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other_shapes: List[dict] = []
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for shape in shapes:
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shape_type = shape.get("shape_type", "polygon")
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if shape_type == "linestrip":
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label = shape["label"]
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linestrip_groups.setdefault(label, []).append(shape["points"])
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else:
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other_shapes.append(shape)
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all_masks = []
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all_labels = []
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# 处理合并后的 linestrip
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for label, strips in linestrip_groups.items():
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polygon = merge_linestrips_to_polygon(strips)
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if len(polygon) < 3:
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continue
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mask = _polygon_to_mask(polygon, height, width)
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if mask.sum() == 0:
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continue
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all_masks.append(mask)
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all_labels.append(label_map.get(label, 1))
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# 处理其他 shape 类型
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for shape in other_shapes:
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label = shape["label"]
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shape_type = shape.get("shape_type", "polygon")
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points = shape["points"]
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if shape_type == "polygon":
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polygon = points
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elif shape_type == "rectangle":
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# 矩形转多边形
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x1, y1 = points[0]
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x2, y2 = points[1]
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polygon = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
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elif shape_type == "circle":
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# 圆形转多边形（近似）
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cx, cy = points[0]
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rx = points[1][0] - cx
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ry = points[1][1] - cy
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polygon = []
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for angle in range(0, 360, 5):
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rad = math.radians(angle)
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polygon.append([cx + rx * math.cos(rad), cy + ry * math.sin(rad)])
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else:
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continue
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if len(polygon) < 3:
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continue
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mask = _polygon_to_mask(polygon, height, width)
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if mask.sum() == 0:
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continue
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all_masks.append(mask)
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all_labels.append(label_map.get(label, 1))
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if len(all_masks) == 0:
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return (
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np.zeros((0, height, width), dtype=np.uint8),
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np.zeros((0,), dtype=np.int64),
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np.zeros((0, 4), dtype=np.float32),
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)
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masks = np.stack(all_masks, axis=0) # [N, H, W]
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labels = np.array(all_labels, dtype=np.int64)
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# 从 mask 计算包围框
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boxes = masks_to_boxes(masks)
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return masks, labels, boxes
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def _polygon_to_mask(
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polygon: List[List[float]],
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height: int,
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width: int,
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) -> np.ndarray:
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"""
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将多边形转换为二值 mask。
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Args:
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polygon: 多边形点列表 [[x, y], ...]
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height: 图像高度
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width: 图像宽度
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Returns:
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[H, W] uint8 二值 mask（0 或 1）
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"""
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img = Image.new("L", (width, height), 0)
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ImageDraw.Draw(img).polygon(
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[(float(p[0]), float(p[1])) for p in polygon],
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fill=1,
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outline=1,
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)
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return np.array(img, dtype=np.uint8)
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def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
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"""
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从一组二值 mask 计算每个实例的包围框。
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Args:
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masks: [N, H, W] uint8
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Returns:
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[N, 4] float32, 格式 (x1, y1, x2, y2)
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"""
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boxes = np.zeros((masks.shape[0], 4), dtype=np.float32)
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for i, mask in enumerate(masks):
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ys, xs = np.where(mask > 0)
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if len(xs) == 0:
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continue
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boxes[i] = [xs.min(), ys.min(), xs.max(), ys.max()]
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return boxes
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# ============================================================
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# 4. 可视化
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# ============================================================
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def visualize_instances(
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image: np.ndarray,
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boxes: np.ndarray,
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masks: np.ndarray,
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labels: np.ndarray,
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scores: Optional[np.ndarray] = None,
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label_map: Optional[Dict[int, str]] = None,
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score_threshold: float = 0.5,
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) -> np.ndarray:
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"""
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在图像上绘制实例分割结果（包围框 + mask 叠加）。
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Args:
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image: [H, W, 3] uint8 BGR 或 RGB 图像
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boxes: [N, 4] 包围框
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masks: [N, H, W] 二值 mask
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labels: [N] 类别ID
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scores: [N] 置信度（可选）
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label_map: 类别ID→名称映射（可选）
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score_threshold: 置信度阈值
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Returns:
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绘制后的图像 [H, W, 3] uint8
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"""
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try:
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import cv2
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except ImportError:
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# 如果没有 cv2，退化为 PIL 绘制
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return _visualize_instances_pil(
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image, boxes, masks, labels, scores, label_map, score_threshold
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)
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img = image.copy()
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if img.dtype != np.uint8:
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img = (img * 255).astype(np.uint8)
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# 生成固定颜色
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np.random.seed(42)
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num_classes = max(int(labels.max()) + 1, 10) if len(labels) > 0 else 10
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colors = np.random.randint(0, 255, size=(num_classes, 3), dtype=np.uint8)
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for i in range(len(boxes)):
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if scores is not None and scores[i] < score_threshold:
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continue
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color = colors[int(labels[i])].tolist()
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x1, y1, x2, y2 = boxes[i].astype(int)
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# 绘制 mask 半透明叠加
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mask = masks[i]
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colored_mask = np.zeros_like(img)
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for c in range(3):
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colored_mask[:, :, c] = mask * color[c]
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img = cv2.addWeighted(img, 1.0, colored_mask, 0.5, 0)
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# 绘制包围框
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cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
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# 绘制标签
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label_text = label_map.get(int(labels[i]), str(labels[i])) if label_map else str(labels[i])
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if scores is not None:
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label_text = f"{label_text}: {scores[i]:.2f}"
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(tw, th), _ = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1)
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cv2.rectangle(img, (x1, y1 - th - 6), (x1 + tw + 4, y1), color, -1)
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cv2.putText(
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img, label_text, (x1 + 2, y1 - 4),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1, cv2.LINE_AA,
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)
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return img
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def _visualize_instances_pil(
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image: np.ndarray,
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boxes: np.ndarray,
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masks: np.ndarray,
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labels: np.ndarray,
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scores: Optional[np.ndarray],
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label_map: Optional[Dict[int, str]],
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score_threshold: float,
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) -> np.ndarray:
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"""使用 PIL 的可视化后备方案（当 cv2 不可用时）"""
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from PIL import Image as PILImage, ImageDraw as PILDraw, ImageFont
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if image.dtype != np.uint8:
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image = (image * 255).astype(np.uint8)
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# 转 RGB
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if image.shape[2] == 3:
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pil_img = PILImage.fromarray(image[..., ::-1]) if image.dtype == np.uint8 else PILImage.fromarray(image)
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else:
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pil_img = PILImage.fromarray(image)
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np.random.seed(42)
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num_classes = max(int(labels.max()) + 1, 10) if len(labels) > 0 else 10
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colors = np.random.randint(0, 255, size=(num_classes, 3), dtype=np.uint8)
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overlay = pil_img.copy()
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draw_overlay = PILDraw.Draw(overlay)
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draw = PILDraw.Draw(pil_img)
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for i in range(len(boxes)):
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if scores is not None and scores[i] < score_threshold:
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continue
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color = tuple(colors[int(labels[i])].tolist())
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mask = masks[i]
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# 用颜色填充 mask 区域
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mask_img = PILImage.new("RGB", pil_img.size, (0, 0, 0))
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mask_draw = PILDraw.Draw(mask_img)
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mask_pil = PILImage.fromarray((mask * 255).astype(np.uint8))
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# 叠加半透明 mask
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for c in range(3):
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channel = np.array(pil_img)[:, :, c].astype(float)
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channel[mask > 0] = channel[mask > 0] * 0.5 + color[c] * 0.5
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pil_img_array = np.array(pil_img)
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pil_img_array[:, :, c] = channel
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pil_img = PILImage.fromarray(pil_img_array.astype(np.uint8))
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draw = PILDraw.Draw(pil_img)
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for i in range(len(boxes)):
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if scores is not None and scores[i] < score_threshold:
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continue
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color = tuple(colors[int(labels[i])].tolist())
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x1, y1, x2, y2 = boxes[i].astype(int)
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draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
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label_text = label_map.get(int(labels[i]), str(labels[i])) if label_map else str(labels[i])
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if scores is not None:
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label_text = f"{label_text}: {scores[i]:.2f}"
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draw.text((x1 + 2, max(0, y1 - 15)), label_text, fill=color)
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return np.array(pil_img)
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# ============================================================
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# 5. 数据集划分
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# ============================================================
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def get_image_json_pairs(dataset_dir: str) -> List[Tuple[str, str]]:
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"""
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扫描数据集目录，返回 (image_path, json_path) 配对列表。
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Args:
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dataset_dir: 数据集目录
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Returns:
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[(image_path, json_path), ...]
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"""
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pairs = []
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for fname in sorted(os.listdir(dataset_dir)):
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if not fname.endswith(".json"):
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continue
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json_path = os.path.join(dataset_dir, fname)
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with open(json_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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image_name = data.get("imagePath", fname.replace(".json", ".jpg"))
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image_path = os.path.join(dataset_dir, image_name)
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if not os.path.exists(image_path):
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# 尝试其他常见扩展名
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for ext in [".jpg", ".jpeg", ".png", ".bmp"]:
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alt_path = os.path.join(dataset_dir, fname.replace(".json", ext))
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if os.path.exists(alt_path):
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image_path = alt_path
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break
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pairs.append((image_path, json_path))
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return pairs
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def split_dataset(
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pairs: List[Tuple[str, str]],
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val_ratio: float = 0.2,
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seed: int = 42,
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) -> Tuple[List, List]:
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"""
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将数据集划分为训练集和验证集。
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Args:
|
||||
pairs: (image_path, json_path) 配对列表
|
||||
val_ratio: 验证集比例
|
||||
seed: 随机种子
|
||||
|
||||
Returns:
|
||||
train_pairs, val_pairs
|
||||
"""
|
||||
rng = random.Random(seed)
|
||||
indices = list(range(len(pairs)))
|
||||
rng.shuffle(indices)
|
||||
val_size = max(1, int(len(pairs) * val_ratio)) if len(pairs) > 1 else 0
|
||||
val_indices = set(indices[:val_size])
|
||||
train_pairs = [pairs[i] for i in indices if i not in val_indices]
|
||||
val_pairs = [pairs[i] for i in val_indices]
|
||||
return train_pairs, val_pairs
|
||||
Reference in New Issue
Block a user