259 lines
9.6 KiB
Python
259 lines
9.6 KiB
Python
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import os
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import re
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import cv2
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import numpy as np
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import shutil
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from concurrent.futures import ThreadPoolExecutor
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# ------------------ 工具函数 ------------------
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def clean_filename(name):
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name = name.strip()
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name = re.sub(r'[\s\r\n\t]+', '', name)
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return name.lower()
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def num_to_coord(num, cols, cell_width, cell_height, offset=1):
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n = num - 1 + offset
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r = n // cols
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c = n % cols
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x1 = c * cell_width
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y1 = r * cell_height
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x2 = x1 + cell_width
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y2 = y1 + cell_height
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return x1, y1, x2, y2
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def polygon_to_yolo(poly, img_width, img_height):
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flat = [coord for point in poly for coord in point]
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return [flat[i] / (img_width if i % 2 == 0 else img_height) for i in range(len(flat))]
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def convex_hull_poly(points):
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if not points:
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return []
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pts = np.array(points, dtype=np.int32)
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hull = cv2.convexHull(pts)
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return hull.reshape(-1, 2).tolist()
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def expand_polygon(poly, expand_px=3):
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"""多边形顶点膨胀 expand_px 像素"""
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if not poly:
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return []
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pts = np.array(poly, dtype=np.int32)
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cx = np.mean(pts[:, 0])
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cy = np.mean(pts[:, 1])
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vec = pts - np.array([[cx, cy]])
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norm = np.linalg.norm(vec, axis=1, keepdims=True)
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norm[norm == 0] = 1
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vec_unit = vec / norm
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pts_expanded = pts + (vec_unit * expand_px).astype(int)
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return pts_expanded.tolist()
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color_map = {
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0: (0, 255, 255), 1: (255, 0, 255), 2: (0, 255, 0), 3: (255, 0, 0),
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4: (0, 0, 255), 5: (255, 255, 0), 6: (128, 128, 0), 7: (128, 0, 128),
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8: (0, 128, 128), 9: (128, 128, 128), 10: (0, 0, 128), 11: (0, 128, 0)
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}
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# ------------------ 匹配图片 ------------------
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def find_matching_image(txt_path, input_root):
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txt_name = os.path.basename(txt_path).lower()
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base_name = re.sub(r'(_partclass)?\.txt$', '', txt_name)
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base_name = re.sub(r'\.jpg$', '', base_name)
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for root, _, files in os.walk(input_root):
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for f in files:
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if f.lower().endswith((".jpg", ".jpeg", ".png")):
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img_base = os.path.splitext(f)[0].lower()
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if base_name == img_base:
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return os.path.join(root, f)
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return None
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# ------------------ 原有处理函数（带多边形膨胀） ------------------
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def process_pixel_txt(img_path, txt_path, class_map, output_root):
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image = cv2.imread(img_path)
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if image is None:
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return False
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h, w = image.shape[:2]
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vis_img = image.copy()
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yolo_labels = []
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unknown_labels = set()
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with open(txt_path, "r", encoding="utf-8") as f:
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for line in f:
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parts = line.strip().split()
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if len(parts) < 5:
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continue
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try:
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x, y, w_box, h_box = map(int, parts[:4])
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except:
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continue
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label = parts[4]
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cls_id = class_map.get(label, -1)
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if cls_id == -1:
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unknown_labels.add(label)
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continue
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poly = [(x, y), (x + w_box, y), (x + w_box, y + h_box), (x, y + h_box)]
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hull = convex_hull_poly(poly)
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hull = expand_polygon(hull, expand_px=3)
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yolo_labels.append(f"{cls_id} " + " ".join(map(str, polygon_to_yolo(hull, w, h))))
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cv2.polylines(vis_img, [np.array(hull, np.int32)], True,
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color=color_map.get(cls_id, (255, 255, 255)), thickness=2)
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if unknown_labels:
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print(f"⚠️ 未知类别 {unknown_labels} 在文件: {txt_path}")
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if not yolo_labels:
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return False
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base = os.path.splitext(os.path.basename(img_path))[0]
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folder_img = os.path.join(output_root, "images")
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os.makedirs(folder_img, exist_ok=True)
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shutil.copy2(img_path, os.path.join(folder_img, os.path.basename(img_path)))
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folder_labels = os.path.join(output_root, "labels")
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os.makedirs(folder_labels, exist_ok=True)
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with open(os.path.join(folder_labels, base + ".txt"), "w", encoding="utf-8") as f:
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f.write("\n".join(yolo_labels))
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folder_vis = os.path.join(output_root, "visual")
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os.makedirs(folder_vis, exist_ok=True)
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cv2.imwrite(os.path.join(folder_vis, base + "-visual.jpg"), vis_img)
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print(f"✅ 已处理 Pixel TXT: {base}")
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return True
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def process_grid_txt(img_path, txt_path, class_map, output_root):
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image = cv2.imread(img_path)
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if image is None:
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return False
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h, w = image.shape[:2]
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cell_width, cell_height = 108, 102
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cols = max(1, w // cell_width)
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vis_img = image.copy()
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overlay = image.copy()
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alpha = 0.5
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yolo_labels = []
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with open(txt_path, "r", encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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numbers = re.findall(r"(\d+)(?=-|$)", line.split()[-1])
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numbers = [int(n) for n in numbers]
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cname = None
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for key in class_map.keys():
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if line.startswith(key):
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cname = key
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break
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if cname is None or not numbers:
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continue
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for num in numbers:
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x1, y1, x2, y2 = num_to_coord(num, cols, cell_width, cell_height)
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cv2.rectangle(overlay, (x1, y1), (x2, y2), color_map.get(class_map[cname], (128, 128, 128)), -1)
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cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0, image)
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points = []
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for num in numbers:
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x1, y1, x2, y2 = num_to_coord(num, cols, cell_width, cell_height)
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points.extend([(x1, y1), (x2, y1), (x2, y2), (x1, y2)])
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hull = convex_hull_poly(points)
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hull = expand_polygon(hull, expand_px=3)
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cls_id = class_map[cname]
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pts = np.array(hull, np.int32).reshape((-1, 1, 2))
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cv2.polylines(vis_img, [pts], True, color_map.get(cls_id, (128, 128, 128)), 2)
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yolo_labels.append(f"{cls_id} " + " ".join(map(str, polygon_to_yolo(hull, w, h))))
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if not yolo_labels:
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return False
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base = os.path.splitext(os.path.basename(img_path))[0]
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for folder_name in ["images", "labels", "visual", "highlighted"]:
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folder = os.path.join(output_root, folder_name)
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os.makedirs(folder, exist_ok=True)
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shutil.copy2(img_path, os.path.join(output_root, "images", os.path.basename(img_path)))
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with open(os.path.join(output_root, "labels", base + ".txt"), "w", encoding="utf-8") as f:
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f.write("\n".join(yolo_labels))
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cv2.imwrite(os.path.join(output_root, "visual", base + "-visual.jpg"), vis_img)
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cv2.imwrite(os.path.join(output_root, "highlighted", base + "-highlighted.jpg"), image)
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print(f"✅ 已处理 Grid TXT: {base}")
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return True
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# ------------------ 批量处理函数 ------------------
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def batch_process_txt_first(input_root, output_root, mode_type="5211", max_workers=4):
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if mode_type == "5211":
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class_maps = {
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"asphalt": class_map_asphalt,
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"cream": class_map_cream,
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"gravel": class_map_gravel,
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}
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elif mode_type == "5210":
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class_maps = {
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"asphalt": class_map_asphalt_road,
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"cream": class_map_cream_road,
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"gravel": class_map_gravel,
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}
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else:
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raise ValueError("mode_type 必须是 '5210' 或 '5211'")
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txt_files = []
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for root, _, files in os.walk(input_root):
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for file in files:
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if file.lower().endswith(".txt"):
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txt_files.append(os.path.join(root, file))
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def process_single(txt_path):
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img_path = find_matching_image(txt_path, input_root)
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if not img_path:
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print(f"⚠️ 未找到匹配图片: {txt_path}")
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return
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with open(txt_path, "r", encoding="utf-8") as f:
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content = f.read().lower()
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if any(k in content for k in ["水泥", "cement", "cream"]):
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road_type = "cream"
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elif any(k in content for k in ["沥青", "asphalt"]):
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road_type = "asphalt"
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else:
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road_type = "gravel"
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selected_map = class_maps[road_type]
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output_folder = os.path.join(output_root, road_type)
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os.makedirs(output_folder, exist_ok=True)
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if "_partclass" in txt_path.lower():
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process_grid_txt(img_path, txt_path, selected_map, output_folder)
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else:
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process_pixel_txt(img_path, txt_path, selected_map, output_folder)
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with ThreadPoolExecutor(max_workers=max_workers) as executor:
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executor.map(process_single, txt_files)
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# ------------------ 类别映射 ------------------
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class_map_asphalt = {
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# "纵向裂缝": 0, "横向裂缝": 1, "网裂": 2, "坑槽": 3, "松散": 4,
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"龟裂": 0, "块状裂缝": 1, "纵向裂缝": 2, "横向裂缝": 3, "沉陷": 4, "车辙": 5,
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"波浪拥包": 6, "坑槽": 7, "松散": 8, "泛油": 9, "修补": 10,
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}
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class_map_cream = {
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"破碎板": 0, "裂缝": 1, "坑洞": 2, "露骨": 3, "错台": 4, "拱起": 5,
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}
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class_map_gravel = {
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"坑槽": 0, "沉陷": 1, "车辙": 2, "波浪搓板": 3,
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}
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class_map_asphalt_road = {
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"龟裂": 0, "块状裂缝": 1, "纵向裂缝": 2, "横向裂缝": 3, "沉陷": 4, "车辙": 5,
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"波浪拥包": 6, "坑槽": 7, "松散": 8, "泛油": 9, "修补": 10,
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}
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class_map_cream_road = {
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"破碎板": 0, "裂缝": 1, "板角断裂": 2, "错台": 3, "拱起": 4, "边角剥落": 5,
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"接缝料损坏": 6, "坑洞": 7, "唧泥": 8, "露骨": 9, "修补": 10,
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}
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# ------------------ 主程序 ------------------
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if __name__ == "__main__":
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input_root = r"D:\work\develop\LF-where\01"
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output_root = r"D:\work\develop\LF-where\out"
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mode_type = "5211" # 5211 或 5210
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batch_process_txt_first(input_root, output_root, mode_type, max_workers=8)
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