dsh/analyze_calibration_z1.py

#!/usr/bin/env python3
"""
本地分析 zoom=1 校准扫描结果，用多尺度模板匹配 + 中心 ROI 重新定位。
"""
import json
from pathlib import Path
import cv2
import numpy as np


def match_center_roi(ptz_img: np.ndarray, panorama_img: np.ndarray,
                     roi_ratio=0.5, scales=(0.10, 0.12, 0.15, 0.18, 0.20, 0.25)):
    """用 PTZ 中心 ROI 在全景图中做模板匹配"""
    ph, pw = ptz_img.shape[:2]
    # 提取中心 ROI
    rw, rh = int(pw * roi_ratio), int(ph * roi_ratio)
    x0, y0 = (pw - rw) // 2, (ph - rh) // 2
    roi = ptz_img[y0:y0+rh, x0:x0+rw]

    roi_gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
    pano_gray = cv2.cvtColor(panorama_img, cv2.COLOR_BGR2GRAY)
    pano_h, pano_w = pano_gray.shape

    best = None
    best_score = -1
    for scale in scales:
        sw, sh = int(rw * scale), int(rh * scale)
        if sw > pano_w or sh > pano_h:
            continue
        resized = cv2.resize(roi_gray, (sw, sh), interpolation=cv2.INTER_AREA)
        result = cv2.matchTemplate(pano_gray, resized, cv2.TM_CCOEFF_NORMED)
        _, max_val, _, max_loc = cv2.minMaxLoc(result)
        if max_val > best_score:
            best_score = max_val
            center_x = max_loc[0] + sw / 2
            center_y = max_loc[1] + sh / 2
            best = (center_x / pano_w, center_y / pano_h, scale, float(max_val))
    return best


def main():
    base = Path('/Users/wenhongquan/Desktop/阿里云同步/项目/dnn/德胜河 AI/dsh/calibration_scan_180_360_z1')
    ptz_dir = base / 'ptz_images'
    pano_path = base / 'panorama.jpg'
    raw_path = base / 'mapping_raw.json'

    panorama = cv2.imread(str(pano_path))
    pano_h, pano_w = panorama.shape[:2]
    print(f'Panorama: {pano_w}x{pano_h}')

    with open(raw_path, 'r', encoding='utf-8') as f:
        raw_data = json.load(f)

    results = []
    for r in raw_data['records']:
        filename = r['filename']
        ptz = cv2.imread(str(ptz_dir / filename))
        if ptz is None:
            continue
        res = match_center_roi(ptz, panorama)
        record = dict(r)
        if res:
            x_ratio, y_ratio, scale, score = res
            record['tm_x_ratio'] = round(x_ratio, 4)
            record['tm_y_ratio'] = round(y_ratio, 4)
            record['tm_panorama_x'] = int(x_ratio * pano_w)
            record['tm_panorama_y'] = int(y_ratio * pano_h)
            record['tm_scale'] = round(scale, 3)
            record['tm_score'] = round(score, 3)
            print(f"{filename}: pan={r['pan']:3d} tilt={r['tilt']:+3d} -> x={x_ratio:.3f} y={y_ratio:.3f} scale={scale:.2f} score={score:.3f}")
        results.append(record)

    # 保存结果
    out_path = base / 'mapping_tm_center.json'
    with open(out_path, 'w', encoding='utf-8') as f:
        json.dump({
            'records': results,
            'panorama_size': {'width': pano_w, 'height': pano_h},
        }, f, indent=2, ensure_ascii=False)
    print(f'\nSaved: {out_path}')

    # 生成 lookup table
    valid = [r for r in results if 'tm_x_ratio' in r and r['tm_score'] > 0.3]
    pan_lookup = sorted([[r['tm_x_ratio'], float(r['pan'])] for r in valid], key=lambda x: x[0])
    tilt_lookup = sorted([[r['tm_y_ratio'], float(r['tilt'])] for r in valid], key=lambda x: x[0])
    lookup = {
        'pan_lookup': pan_lookup,
        'tilt_lookup': tilt_lookup,
        'valid_count': len(valid),
    }
    with open(base / 'lookup_table_tm_center.json', 'w', encoding='utf-8') as f:
        json.dump(lookup, f, indent=2, ensure_ascii=False)
    print(f'Lookup table: {len(valid)} valid records')


if __name__ == '__main__':
    main()