From 56e8907f28a588e8c90948ec0f977a9ea53ee789 Mon Sep 17 00:00:00 2001 From: ila Date: Wed, 24 Jun 2026 09:42:05 +0800 Subject: [PATCH] feat: implement stamp content matching --- match_stamp.py | 334 ++++++++++++++++++++++++++++++++----------------- tasks.md | 22 ++++ 2 files changed, 244 insertions(+), 112 deletions(-) diff --git a/match_stamp.py b/match_stamp.py index 51d8c5e..c8d3d12 100644 --- a/match_stamp.py +++ b/match_stamp.py @@ -1,50 +1,52 @@ -"""Generate a CSV report matching merged images to source stamp images. +"""Find the source stamp image that best matches a merged clothing image. This script is intentionally standalone and does not import cmbot project code. -Edit the three paths below, then run: +Edit the paths below, or pass them on the command line: - python match_stamp.py + python match_stamp.py --image output\20260623_094529\TY037\1_TY037.png --stamp-dir D:\stamps -It does not copy, move, rename, or delete any image files. +It does not copy, move, rename, delete, or write image files. Results are +printed directly. """ from __future__ import print_function -import csv -from datetime import datetime +import argparse +import sys +import time from pathlib import Path +try: + import cv2 + import numpy as np + from PIL import Image +except ImportError as exc: + print("Missing dependency: {}".format(exc)) + print("Install required packages: pip install opencv-python numpy Pillow") + raise -OUTPUT_DIR = r"D:\chengma\cmbot\output\20260623_094529" + +MERGED_IMAGE = r"D:\chengma\cmbot\output\20260623_094529\TY037\1_TY037.png" STAMP_DIR = r"D:\chengma\印花和底图\已处理印花\卡通71（66大码200斤 KEKE已上）\横1" -REPORT_PATH = r"D:\chengma\cmbot\match_stamp_report.csv" IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp"} -FIELDNAMES = [ - "status", - "code", - "merged_image", - "stamp_image", - "merged_folder", - "message", -] +TOP_N = 5 + +SCALE_MIN = 0.10 +SCALE_MAX = 2.00 +SCALE_STEP = 0.05 + +ALPHA_THRESHOLD = 20 +LOW_SCORE_WARNING_THRESHOLD = 0.55 +TEMPLATE_SCORE_WEIGHT = 0.75 +EDGE_SCORE_WEIGHT = 0.25 +MIN_MATCH_WIDTH = 24 +MIN_MATCH_HEIGHT = 24 def is_image(path): return path.is_file() and path.suffix.lower() in IMAGE_EXTENSIONS -def extract_code_from_merged_name(path): - """Extract stamp code from the final underscore suffix. - - Example: 1_TY037.png -> TY037. - """ - stem = path.stem - if "_" not in stem: - return "" - code = stem.rsplit("_", 1)[1].strip() - return code - - def iter_images(root): root = Path(root) if not root.exists(): @@ -54,103 +56,211 @@ def iter_images(root): yield path -def build_stamp_index(stamp_dir): - index = {} - for path in iter_images(stamp_dir): - code = path.stem.strip() - if not code: +def load_rgba(path): + """Load with Pillow so Windows Chinese paths are handled reliably.""" + with Image.open(str(path)) as image: + return np.array(image.convert("RGBA")) + + +def rgba_to_gray(rgba): + rgb = rgba[:, :, :3] + return cv2.cvtColor(rgb, cv2.COLOR_RGB2GRAY) + + +def rgba_to_rgb(rgba): + return rgba[:, :, :3] + + +def crop_transparent_border(rgba): + alpha = rgba[:, :, 3] + ys, xs = np.where(alpha > ALPHA_THRESHOLD) + if len(xs) == 0 or len(ys) == 0: + mask = np.ones(alpha.shape, dtype=np.uint8) * 255 + return rgba[:, :, :3], mask + + left = int(xs.min()) + right = int(xs.max()) + 1 + top = int(ys.min()) + bottom = int(ys.max()) + 1 + cropped = rgba[top:bottom, left:right] + mask = (cropped[:, :, 3] > ALPHA_THRESHOLD).astype(np.uint8) * 255 + return cropped[:, :, :3], mask + + +def build_scales(): + scales = [] + value = SCALE_MIN + while value <= SCALE_MAX + 0.0001: + scales.append(round(value, 4)) + value += SCALE_STEP + return scales + + +def safe_match_template(source, template, mask=None): + if template.shape[0] > source.shape[0] or template.shape[1] > source.shape[1]: + return None + if template.shape[0] < MIN_MATCH_HEIGHT or template.shape[1] < MIN_MATCH_WIDTH: + return None + if mask is not None and int(np.count_nonzero(mask)) < 9: + return None + + match_mask = mask + if match_mask is not None and template.ndim == 3 and match_mask.ndim == 2: + channels = template.shape[2] + match_mask = cv2.merge([match_mask] * channels) + + try: + result = cv2.matchTemplate(source, template, cv2.TM_CCORR_NORMED, mask=match_mask) + except cv2.error: + return None + + result = np.asarray(result, dtype=np.float32) + result[~np.isfinite(result)] = -1.0 + return np.clip(result, 0.0, 1.0) + + +def resize_for_scale(image, scale, interpolation): + height, width = image.shape[:2] + new_width = max(1, int(round(width * scale))) + new_height = max(1, int(round(height * scale))) + return cv2.resize(image, (new_width, new_height), interpolation=interpolation) + + +def score_stamp(merged_rgb, merged_edges, stamp_path, scales): + stamp_rgba = load_rgba(stamp_path) + stamp_rgb, stamp_mask = crop_transparent_border(stamp_rgba) + stamp_gray = cv2.cvtColor(stamp_rgb, cv2.COLOR_RGB2GRAY) + stamp_edges = cv2.Canny(stamp_gray, 80, 160) + + best = None + for scale in scales: + scaled_rgb = resize_for_scale(stamp_rgb, scale, cv2.INTER_AREA) + scaled_gray = resize_for_scale(stamp_gray, scale, cv2.INTER_AREA) + scaled_edges = resize_for_scale(stamp_edges, scale, cv2.INTER_NEAREST) + scaled_mask = resize_for_scale(stamp_mask, scale, cv2.INTER_NEAREST) + + template_result = safe_match_template(merged_rgb, scaled_rgb, scaled_mask) + if template_result is None: continue - index.setdefault(code, []).append(path.resolve()) - return index + + edge_result = safe_match_template(merged_edges, scaled_edges, scaled_mask) + if edge_result is None: + edge_result = np.zeros(template_result.shape, dtype=np.float32) + + combined = ( + TEMPLATE_SCORE_WEIGHT * template_result + + EDGE_SCORE_WEIGHT * edge_result + ) + _min_val, max_val, _min_loc, max_loc = cv2.minMaxLoc(combined) + x, y = max_loc + template_score = float(template_result[y, x]) + edge_score = float(edge_result[y, x]) + score = float(max_val) + + if best is None or score > best["score"]: + best = { + "score": score, + "template_score": template_score, + "edge_score": edge_score, + "stamp_path": stamp_path, + "x": int(x), + "y": int(y), + "scale": float(scale), + "width": int(scaled_gray.shape[1]), + "height": int(scaled_gray.shape[0]), + } + + return best -def match_merged_image(path, stamp_index): - code = extract_code_from_merged_name(path) - if not code: - return { - "status": "bad_name", - "code": "", - "merged_image": str(path.resolve()), - "stamp_image": "", - "merged_folder": str(path.parent.resolve()), - "message": "File name has no underscore suffix code", - } +def find_best_matches(merged_image, stamp_dir, top_n): + merged_rgba = load_rgba(merged_image) + merged_rgb = rgba_to_rgb(merged_rgba) + merged_gray = rgba_to_gray(merged_rgba) + merged_edges = cv2.Canny(merged_gray, 80, 160) + scales = build_scales() - candidates = stamp_index.get(code, []) - if not candidates: - return { - "status": "missing", - "code": code, - "merged_image": str(path.resolve()), - "stamp_image": "", - "merged_folder": str(path.parent.resolve()), - "message": "No stamp image found for code", - } + results = [] + for stamp_path in iter_images(stamp_dir): + try: + result = score_stamp(merged_rgb, merged_edges, stamp_path, scales) + except Exception as exc: + print("Skipped: {} ({})".format(stamp_path, exc)) + continue + if result is not None: + results.append(result) - stamp_paths = " | ".join(str(p) for p in candidates) - if len(candidates) > 1: - return { - "status": "duplicate", - "code": code, - "merged_image": str(path.resolve()), - "stamp_image": stamp_paths, - "merged_folder": str(path.parent.resolve()), - "message": "Multiple stamp images found for code", - } - - return { - "status": "matched", - "code": code, - "merged_image": str(path.resolve()), - "stamp_image": stamp_paths, - "merged_folder": str(path.parent.resolve()), - "message": "", - } + results.sort(key=lambda item: item["score"], reverse=True) + return results[:top_n], len(results) -def write_csv(rows, report_path): - report_path = Path(report_path) - report_path.parent.mkdir(parents=True, exist_ok=True) - with report_path.open("w", encoding="utf-8-sig", newline="") as f: - writer = csv.DictWriter(f, fieldnames=FIELDNAMES) - writer.writeheader() - for row in rows: - writer.writerow(row) +def print_result(results, matched_count, merged_image, stamp_dir): + print("Merged image: {}".format(Path(merged_image).resolve())) + print("Stamp dir: {}".format(Path(stamp_dir).resolve())) + print("Matched stamp candidates: {}".format(matched_count)) + print("") + + if not results: + print("No valid image match result.") + return + + best = results[0] + print("Best match:") + print("score: {:.4f}".format(best["score"])) + print("template_score: {:.4f}".format(best["template_score"])) + print("edge_score: {:.4f}".format(best["edge_score"])) + print("stamp: {}".format(Path(best["stamp_path"]).resolve())) + print("location: x={}, y={}".format(best["x"], best["y"])) + print("scale: {:.4f}".format(best["scale"])) + print("size: {}x{}".format(best["width"], best["height"])) + if best["score"] < LOW_SCORE_WARNING_THRESHOLD: + print("warning: best score is low; please review manually.") + + print("") + print("Top {}:".format(len(results))) + for index, item in enumerate(results, 1): + print( + "{}. {:.4f} template={:.4f} edge={:.4f} scale={:.4f} {}" + .format( + index, + item["score"], + item["template_score"], + item["edge_score"], + item["scale"], + Path(item["stamp_path"]).name, + ) + ) -def generate_report(output_dir, stamp_dir, report_path): - output_dir = Path(output_dir) - stamp_dir = Path(stamp_dir) - - stamp_index = build_stamp_index(stamp_dir) - rows = [] - for path in iter_images(output_dir): - rows.append(match_merged_image(path, stamp_index)) - - write_csv(rows, report_path) - return rows +def parse_args(argv): + parser = argparse.ArgumentParser( + description="Match one merged clothing image against a stamp directory." + ) + parser.add_argument("--image", default=MERGED_IMAGE, help="Merged clothing image path") + parser.add_argument("--stamp-dir", default=STAMP_DIR, help="Stamp image directory") + parser.add_argument("--top", default=TOP_N, type=int, help="Number of results to print") + return parser.parse_args(argv) -def summarize(rows): - counts = {} - for row in rows: - status = row.get("status", "") - counts[status] = counts.get(status, 0) + 1 - return counts +def main(argv=None): + args = parse_args(argv or sys.argv[1:]) + merged_image = Path(args.image) + stamp_dir = Path(args.stamp_dir) + if not merged_image.is_file(): + print("Merged image not found: {}".format(merged_image)) + return 1 + if not stamp_dir.is_dir(): + print("Stamp directory not found: {}".format(stamp_dir)) + return 1 -def main(): - started = datetime.now() - rows = generate_report(OUTPUT_DIR, STAMP_DIR, REPORT_PATH) - counts = summarize(rows) - print("Generated report: {}".format(Path(REPORT_PATH).resolve())) - print("Merged images: {}".format(len(rows))) - print("Matched: {}".format(counts.get("matched", 0))) - print("Missing: {}".format(counts.get("missing", 0))) - print("Duplicate: {}".format(counts.get("duplicate", 0))) - print("Bad name: {}".format(counts.get("bad_name", 0))) - print("Elapsed: {}".format(datetime.now() - started)) + started = time.time() + results, matched_count = find_best_matches(merged_image, stamp_dir, max(1, args.top)) + print_result(results, matched_count, merged_image, stamp_dir) + print("") + print("Elapsed: {:.2f}s".format(time.time() - started)) + return 0 if __name__ == "__main__": - main() + raise SystemExit(main()) diff --git a/tasks.md b/tasks.md index 1b90a6b..7ea6029 100644 --- a/tasks.md +++ b/tasks.md @@ -1569,3 +1569,25 @@ - [x] 明确第三方库：最小 `opencv-python` + `numpy`，建议用 `Pillow` 处理 Windows 中文路径 - [x] 明确约束：独立脚本、不引用项目代码、不修改图片、第一版不写 CSV/Excel、不接入 GUI - [x] 验证：文档格式检查通过 + +### 19.32 独立脚本：按图片内容匹配合并图印花 — `match_stamp.py` + +前置阅读： + +- `docs/12-stamp-template-matching.md` +- `match_stamp.py` + +背景： + +§19.31 已明确真实需求不是按 `1_TY037.png -> TY037.png` 的文件名规则匹配，而是给定一张衣服 / 合并图和一个印花目录，通过图像内容匹配找出最相似的印花。第一版继续保持为独立脚本，不接入 GUI，不写 CSV，直接打印结果。 + +任务： + +- [x] 将 `match_stamp.py` 从“递归扫描 output 并按文件名写 CSV”改为“单张合并图 + 印花目录内容匹配” +- [x] 使用 `Pillow` 读取图片以兼容 Windows 中文路径，再转为 `numpy` / OpenCV 数组 +- [x] 遍历印花目录内 PNG/JPG/JPEG/WEBP 图片，对透明 PNG 使用 alpha mask 并裁剪透明边界 +- [x] 使用多尺度模板匹配，主分数使用 RGB 彩色 `matchTemplate`，边缘图分数作为辅助 +- [x] 输出最佳匹配和 Top N，包含综合分、模板分、边缘分、坐标、缩放比例和匹配尺寸 +- [x] 找不到高可信结果时打印低分提示；不保存 CSV，不修改任何图片 +- [x] 支持命令行覆盖 `--image`、`--stamp-dir`、`--top` +- [x] 验证：`python -m py_compile match_stamp.py` 通过；临时构造一张合并图和两个候选印花，Top 1 命中正确印花