hongchen/pano_ori_2pinehole/process_pano.py

import cv2
import numpy as np
import py360convert
import requests
import json
import os
import argparse
from pathlib import Path


def imread_url(url):
    """从 URL 读取图片并转换为 OpenCV 格式"""
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()
        image_array = np.asarray(bytearray(response.content), dtype="uint8")
        img = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
        return img
    except Exception as e:
        print(f"❌ 无法从 URL 下载图片: {url}, 错误: {e}")
        return None


def generate_pinhole_from_array(img, points, output_size=(600, 800)):
    """处理图像数组，根据给定的四个点生成 Pinhole 图像"""
    h, w = img.shape[:2]
    pts = np.array(points, dtype=np.float32)

    # 处理全景图跨界逻辑
    if pts[1][0] < pts[0][0]:
        pts[1][0] += w
    if pts[2][0] < pts[3][0]:
        pts[2][0] += w

    center_x = np.mean(pts[:, 0]) % w
    center_y = np.mean(pts[:, 1])

    u_deg = (center_x / w) * 360 - 180
    v_deg = 90 - (center_y / h) * 180

    span_x = np.max(pts[:, 0]) - np.min(pts[:, 0])
    span_y = np.max(pts[:, 1]) - np.min(pts[:, 1])

    fov_h = (span_x / w) * 360 * 1.2
    fov_v = (span_y / h) * 180 * 1.2

    fov_h = min(fov_h, 110)
    fov_v = min(fov_v, 110)

    pinhole_img = py360convert.e2p(
        img,
        fov_deg=(fov_h, fov_v),
        u_deg=u_deg,
        v_deg=v_deg,
        out_hw=output_size,
    )
    return pinhole_img


def main():
    # 1. 定义参数解析器
    parser = argparse.ArgumentParser(description="全景图区域批量切图工具")
    parser.add_argument("--json", type=str, required=True, help="输入的 JSON 文件路径")
    parser.add_argument("--output", type=str, default="cropped_results", help="输出根目录 (默认: cropped_results)")
    parser.add_argument("--width", type=int, default=5671, help="输出图片的宽度 (默认: 800)")
    parser.add_argument("--height", type=int, default=3186, help="输出图片的高度 (默认: 600)")

    args = parser.parse_args()

    # 2. 读取 JSON
    if not os.path.exists(args.json):
        print(f"❌ 找不到文件: {args.json}")
        return

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

    scene_code = data.get("sceneCode", "unknown_scene")
    image_cache = {}
    output_size = (args.height, args.width)  # 注意 opencv 是 (h, w)

    # 3. 循环处理
    for item in data.get("list", []):
        sid = item.get("tag", {}).get("sid", "unknown_sid")
        snaps = item.get("snap", [])

        for idx, snap in enumerate(snaps):
            img_url = snap.get("imageUrl")
            pano_id = snap.get("panoId", "0")
            corners = snap.get("corners", [])

            if not img_url or len(corners) < 4:
                continue

            if img_url not in image_cache:
                print(f"🌐 正在下载全景图: {img_url}")
                image_cache[img_url] = imread_url(img_url)

            origin_img = image_cache[img_url]
            if origin_img is None: continue

            pts_list = [(c['x'], c['y']) for c in corners]

            try:
                print(f"🎨 处理 [SID: {sid}] [Pano: {pano_id}] - 区域 {idx}")
                result_img = generate_pinhole_from_array(origin_img, pts_list, output_size=output_size)

                # 创建保存目录
                save_dir = Path(args.output) / scene_code / sid
                save_dir.mkdir(parents=True, exist_ok=True)

                save_path = save_dir / f"pano_{pano_id}_snap_{idx}.jpg"
                cv2.imwrite(str(save_path), result_img)
            except Exception as e:
                print(f"⚠️ 处理失败: {e}")

    print("\n✨ 处理完成！")


if __name__ == "__main__":
    main()