AndroidRemoteController/python/scripts/test_match_pic0_screenshot.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
诊断 pic0 误匹配：找出为何 pic0.png 会匹配到 草稿箱 区域而非正确缩略图。
用法: 在项目根目录执行
  python python/scripts/test_match_pic0_screenshot.py
输出: 不同 scale_min/threshold 下的匹配位置与相关系数，并生成标注图。
"""
from __future__ import print_function
import os
import sys
import json

SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
PROJECT_ROOT = os.path.normpath(os.path.join(SCRIPT_DIR, '..', '..'))
if PROJECT_ROOT not in sys.path:
    sys.path.insert(0, PROJECT_ROOT)

PROCESS_DIR = os.path.join(PROJECT_ROOT, 'static', 'process', 'GenerateNote')
TMP_DIR = os.path.join(PROCESS_DIR, 'tmp')
SCREENSHOT_PATH = os.path.join(TMP_DIR, 'Screenshot-pic0.png')
TEMPLATE_PATH = os.path.join(TMP_DIR, 'pic0.png')
MARKED_WRONG = os.path.join(TMP_DIR, 'Screenshot-pic0-marked-wrong.png')   # 当前错误匹配
MARKED_BEST = os.path.join(TMP_DIR, 'Screenshot-pic0-marked-best.png')    # 推荐参数匹配

try:
    import cv2
    import numpy as np
except ImportError:
    print('请安装: pip install opencv-python numpy')
    sys.exit(1)

import importlib.util
_image_match_path = os.path.join(SCRIPT_DIR, 'image-match.py')
_spec = importlib.util.spec_from_file_location('image_match', _image_match_path)
_image_match = importlib.util.module_from_spec(_spec)
_spec.loader.exec_module(_image_match)
load_image = _image_match.load_image
multi_scale_template_match = _image_match.multi_scale_template_match


def multi_scale_with_correlation(screenshot, template, threshold=0.50, scale_min=0.05, scale_max=2.0, steps=60):
    """多尺度模板匹配，返回 (x, y, w, h, center_x, center_y, best_correlation) 或 None。"""
    gray_screen = cv2.cvtColor(screenshot, cv2.COLOR_BGR2GRAY)
    gray_tpl = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
    sh, sw = screenshot.shape[:2]
    t_h, t_w = template.shape[:2]
    best = None
    best_val = threshold
    step = max(0.02, (scale_max - scale_min) / float(steps))
    for scale in np.arange(scale_min, scale_max + step * 0.5, step):
        w = max(8, int(round(t_w * scale)))
        h = max(8, int(round(t_h * scale)))
        if h > sh or w > sw:
            continue
        resized = cv2.resize(gray_tpl, (w, h), interpolation=cv2.INTER_AREA)
        result = cv2.matchTemplate(gray_screen, resized, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
        if max_val > best_val:
            best_val = max_val
            x, y = int(max_loc[0]), int(max_loc[1])
            center_x = x + w // 2
            center_y = y + h // 2
            best = (x, y, w, h, center_x, center_y, best_val)
    return best


def crop_template_square(template, percent=0.8, base='w'):
    """与 image-match.py 一致的方形中心裁剪。"""
    t_h, t_w = template.shape[:2]
    side_raw = (t_w if base == 'w' else t_h) * percent
    side = min(max(1, int(round(side_raw))), t_w, t_h)
    x0 = (t_w - side) // 2
    y0 = (t_h - side) // 2
    return template[y0:y0 + side, x0:x0 + side].copy()


def draw_match(screenshot, x, y, w, h, center_x, center_y, out_path, label='match'):
    img = screenshot.copy()
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
    cv2.circle(img, (center_x, center_y), 8, (0, 0, 255), 2)
    cv2.putText(img, label, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 1)
    out_dir = os.path.dirname(out_path)
    if out_dir:
        os.makedirs(out_dir, exist_ok=True)
    cv2.imwrite(out_path, img)
    print('已保存:', out_path)


def main():
    screenshot_path = SCREENSHOT_PATH
    template_path = TEMPLATE_PATH
    if len(sys.argv) >= 3:
        screenshot_path = sys.argv[1]
        template_path = sys.argv[2]
    if not os.path.isfile(screenshot_path):
        print('截图不存在:', screenshot_path)
        sys.exit(1)
    if not os.path.isfile(template_path):
        print('模板不存在:', template_path)
        sys.exit(1)

    screenshot = load_image(screenshot_path)
    template = load_image(template_path)
    if screenshot is None or template is None:
        print('无法加载图片')
        sys.exit(1)

    # 与 process 一致：使用 [0.8, "w"] 裁剪模板
    template = crop_template_square(template, percent=0.8, base='w')

    sh, sw = screenshot.shape[:2]
    t_h, t_w = template.shape[:2]
    print('截图尺寸: {}x{}, 裁剪后模板: {}x{}'.format(sw, sh, t_w, t_h))
    print('')

    # 草稿箱通常在右上角；正确缩略图在内容区（偏左/中，偏上）
    def region_label(cx, cy):
        if cx >= sw * 0.65 and cy <= sh * 0.25:
            return '疑似草稿箱'
        return '内容区'

    scale_min_list = [0.05, 0.10, 0.15, 0.20, 0.25]
    threshold_list = [0.40, 0.44, 0.48, 0.52]
    results = []

    for scale_min in scale_min_list:
        for th in threshold_list:
            r = multi_scale_with_correlation(
                screenshot, template,
                threshold=th, scale_min=scale_min, scale_max=2.0, steps=60
            )
            if r is not None:
                x, y, w, h, cx, cy, corr = r
                label = region_label(cx, cy)
                results.append({
                    'scale_min': scale_min,
                    'threshold': th,
                    'x': x, 'y': y, 'w': w, 'h': h,
                    'center_x': cx, 'center_y': cy,
                    'corr': corr,
                    'label': label
                })

    if not results:
        print('未找到任何匹配，请降低阈值或扩大 scale 范围。')
        sys.exit(1)

    # 按相关系数从高到低排序
    results.sort(key=lambda r: -r['corr'])

    print('===== 所有匹配（按相关系数降序）=====')
    print('scale_min  threshold   center_x  center_y  corr    区域')
    print('-' * 60)
    for r in results[:20]:
        print('  {:.2f}      {:.2f}      {:4d}     {:4d}    {:.3f}   {}'.format(
            r['scale_min'], r['threshold'], r['center_x'], r['center_y'], r['corr'], r['label']))

    # 当前逻辑：scale_min=0.05，阈值 0.40～0.52 中第一个命中的
    current = None
    for th in (0.52, 0.48, 0.44, 0.40):
        r = multi_scale_with_correlation(screenshot, template, threshold=th, scale_min=0.05, scale_max=2.0, steps=60)
        if r is not None:
            x, y, w, h, cx, cy, corr = r
            current = (x, y, w, h, cx, cy, corr)
            break
    if current:
        x, y, w, h, cx, cy, corr = current
        draw_match(screenshot, x, y, w, h, cx, cy, MARKED_WRONG, 'current(scale_min=0.05) corr=%.3f' % corr)
        print('')
        print('【当前逻辑】scale_min=0.05 时匹配到: center=({}, {}), corr={:.3f}, 区域={}'.format(
            cx, cy, corr, region_label(cx, cy)))

    # 推荐：取「内容区」且相关系数最高的匹配
    best_content = None
    for r in results:
        if r['label'] == '内容区':
            best_content = r
            break
    if best_content:
        r = best_content
        draw_match(screenshot, r['x'], r['y'], r['w'], r['h'], r['center_x'], r['center_y'],
                   MARKED_BEST, 'best(scale_min=%.2f) corr=%.3f' % (r['scale_min'], r['corr']))
        print('')
        print('【推荐】内容区最佳: scale_min={:.2f}, threshold={:.2f}, center=({}, {}), corr={:.3f}'.format(
            r['scale_min'], r['threshold'], r['center_x'], r['center_y'], r['corr']))
        print('')
        print('建议: 在 image-match.py 中，对相册缩略图(pic) 将 scale_min_use 改为 min(scale_min, 0.15) 或 min(scale_min, 0.20)，')
        print('      避免 scale_min=0.05 时小尺度模板误匹配到右上角 草稿箱 区域。')
    else:
        print('')
        print('未找到内容区匹配，请检查截图与模板是否一致。')

    # 诊断结论
    print('')
    print('===== 诊断结论 =====')
    if current and region_label(current[4], current[5]) == '疑似草稿箱':
        print('原因: scale_min=0.05 时，模板被缩得很小，与右上角 草稿箱 区域产生较高相关系数，被选为最佳匹配。')
        print('解决: 提高相册缩略图的最小缩放 scale_min（如 0.15 或 0.20），只在该尺度以上搜索，避免误匹配。')
    else:
        print('当前参数下匹配位置正常，若仍出现误匹配可适当提高 threshold 或 scale_min。')


if __name__ == '__main__':
    main()