canshu

README.md

@@ -186,15 +186,19 @@ smart-cam的HTTP接口读取PTZ并记录，等价于：
 2. 沿pan方向尝试若干候选角度，每次抓图后回到起点；
 3. 用ORB特征、RANSAC和单轴画面位移求水平视场；
 4. 沿tilt方向重复测量；
-5. 每个方向会跑完预设的全部候选step，再统一筛选相互一致的视场样本；不因前几个样本
-   离散度低而提前停止；
+5. 每个方向统一筛选相互一致的视场样本；获得4个覆盖充分、离散度不超过8%的
+   可靠样本后提前停止，不再测试更大且通常没有公共画面的step；
 6. 两组结果只有在特征质量高且视场差不超过8%时才接受，否则要求至少三组一致结果；
-7. 水平方向按4%、垂直方向按2%目标重叠计算step；这是根据完整扫图接缝分别设置的轴向补偿；
+7. 水平方向按4.5%、垂直方向按2%目标重叠计算step；这是根据完整扫图接缝分别设置的轴向补偿；
 8. 默认使用ORB特征和RANSAC几何变换；当局部图案重复导致几何内点不足时，自动使用整幅图
    相位相关复核纯单轴位移，相位响应不得低于0.20；
-8. 要求至少50个几何内点且内点率至少45%，否则拒绝结果；
-9. 根据多个水平样本的带符号位移，自动识别该型号摄像头从画面左到右时pan是递增还是递减；
-10. 删除本次自动标定产生的临时截图。
+9. 要求至少50个几何内点且内点率至少45%，否则拒绝结果；
+10. 根据配置中的图像宽高比，用可靠的tilt视场按普通透视公式反算pan视场；若与pan实测相差
+    不超过15%，取“直接pan结果”和“tilt推导结果”的几何平均值，避免任一模型单独产生偏差；
+11. 若pan实测与tilt透视预测相差超过15%，不强制套用比例，并明确提示镜头畸变或不同tilt行
+    水平覆盖不一致；这种摄像头使用单一pan_step无法保证所有行同时零重叠且无缺口；
+12. 根据多个水平样本的带符号位移，自动识别该型号摄像头从画面左到右时pan是递增还是递减；
+13. 删除本次自动标定产生的临时截图。
 
 这一步不能省略。两个角点只能确定扫描范围，无法确定当前摄像头型号和zoom下的一幅画面
 覆盖多少角度。
@@ -299,6 +303,6 @@ docker exec smart-cam curl -fsS -X POST \
 - 不同型号的海康球机可能有相反的pan数值方向；向导会实拍识别，不能用desktop/hanging
   或“默认走短弧”代替；
 - 标定期间不要同时从Web界面控制PTZ；
-- 向导按轴设置目标重叠：水平4%、垂直2%；最终以完整试扫的接缝结果为准；
+- 向导按轴设置目标重叠：水平4.5%、垂直2%；最终以完整试扫的接缝结果为准；
 - 自动标定会完整测试全部候选step，再从所有可靠结果中筛选一致样本；
 - 机械回差、镜头畸变和近景视差可能导致不同位置略有差异，最终必须完整试扫并拼图复核；

interactive_calibrator.py

@@ -8,6 +8,7 @@ from contextlib import redirect_stdout
 from datetime import datetime
 import io
 import json
+import math
 from pathlib import Path
 import re
 import select
@@ -25,7 +26,7 @@ CONFIG_PATH = "/smart-cam/config/config.json"
 # the tested PTZ camera retain more real-world overlap than the center-shift
 # model predicts, while vertical seams need a small safety margin. Keep these
 # axis-specific targets within the generator's accepted 0%..5% range.
-TARGET_OVERLAPS = {"pan": 0.04, "tilt": 0.02}
+TARGET_OVERLAPS = {"pan": 0.045, "tilt": 0.02}
 RED = "\033[31m"
 GREEN = "\033[32m"
 YELLOW = "\033[33m"
@@ -110,6 +111,95 @@ def fov_statistics(samples: list[tuple[int, float, float, dict]]) -> tuple[float
     return estimated_fov, (max(fovs) - min(fovs)) / estimated_fov
 
 
+def horizontal_fov_from_vertical(vertical_fov: float, aspect_ratio: float) -> float:
+    """Convert a rectilinear vertical FOV to horizontal FOV in PTZ units."""
+    vertical_radians = math.radians(vertical_fov / 100.0)
+    horizontal_radians = 2.0 * math.atan(
+        aspect_ratio * math.tan(vertical_radians / 2.0)
+    )
+    return math.degrees(horizontal_radians) * 100.0
+
+
+def constrain_pan_with_tilt(pan_info: dict, tilt_info: dict,
+                            aspect_ratio: float) -> tuple[dict, str]:
+    measured_pan_fov = float(pan_info["derived_from"]["estimated_fov"])
+    measured_tilt_fov = float(tilt_info["derived_from"]["estimated_fov"])
+    optical_pan_fov = horizontal_fov_from_vertical(
+        measured_tilt_fov, aspect_ratio
+    )
+    agreement = measured_pan_fov / optical_pan_fov
+    diagnostic = {
+        "image_aspect_ratio": round(aspect_ratio, 6),
+        "measured_pan_fov": round(measured_pan_fov, 4),
+        "measured_tilt_fov": round(measured_tilt_fov, 4),
+        "tilt_predicted_pan_fov": round(optical_pan_fov, 4),
+        "agreement_ratio": round(agreement, 6),
+    }
+    if 0.85 <= agreement <= 1.15:
+        tilt_target_overlap = TARGET_OVERLAPS["tilt"]
+        tilt_predicted_step = max(
+            1, round(optical_pan_fov * (1.0 - tilt_target_overlap))
+        )
+        direct_pan_step = int(pan_info["value"])
+        # Neither estimate is authoritative on a PTZ dome: direct horizontal
+        # matching is affected by distortion, while the aspect-ratio prediction
+        # assumes an ideal rectilinear lens. Fuse them instead of replacing one
+        # with the other. The geometric mean stays between both measurements.
+        constrained_step = max(
+            1, round(math.sqrt(direct_pan_step * tilt_predicted_step))
+        )
+        effective_overlap = max(
+            0.0, min(core.MAX_GENERATED_OVERLAP,
+                     1.0 - constrained_step / measured_pan_fov)
+        )
+        return {
+            **pan_info,
+            "value": constrained_step,
+            "overlap": effective_overlap,
+            "target_overlap": effective_overlap,
+            "overlap_method": "axis_shift_v1_pan_tilt_fused",
+            "cross_axis_check": {
+                **diagnostic,
+                "applied": True,
+                "direct_pan_step": direct_pan_step,
+                "tilt_predicted_pan_step": tilt_predicted_step,
+                "fused_pan_step": constrained_step,
+            },
+        }, (
+            f"pan/tilt视场比例符合普通透视模型（一致率{agreement*100:.1f}%）；"
+            f"融合pan实测={direct_pan_step}与tilt推导={tilt_predicted_step}"
+            f"后pan_step={constrained_step}"
+        )
+    return {
+        **pan_info,
+        "cross_axis_check": {
+            **diagnostic,
+            "applied": False,
+            "reason": "non_rectilinear_or_axis_scale_mismatch",
+        },
+    }, (
+        f"pan/tilt视场比例仅为普通透视预测的{agreement*100:.1f}%，"
+        "疑似镜头畸变或不同tilt行的水平覆盖不一致；"
+        "不能用tilt比例替代pan实测，固定pan_step应以不缺图为优先"
+    )
+
+
+def configured_image_aspect_ratio() -> float:
+    try:
+        raw = subprocess.run(
+            ["docker", "exec", "smart-cam", "cat", CONFIG_PATH],
+            text=True, capture_output=True, check=True,
+        ).stdout
+        image = json.loads(raw).get("scan", {}).get("image", {})
+        width, height = int(image.get("width", 1280)), int(image.get("height", 720))
+        if width > 0 and height > 0:
+            return width / height
+    except (OSError, ValueError, KeyError, json.JSONDecodeError,
+            subprocess.SubprocessError):
+        pass
+    return 16.0 / 9.0
+
+
 def failure_detail(result: dict) -> str:
     reason = str(result.get("reason") or "unknown")
     labels = {
@@ -360,8 +450,16 @@ def calculate_steps(points: dict, snapshots: set[str], mount: str) -> None:
             color=YELLOW,
         )
     state = core.load_state()
-    state["steps"]["pan"] = estimate_axis("pan", origin, points, snapshots)
-    state["steps"]["tilt"] = estimate_axis("tilt", origin, points, snapshots)
+    pan_info = estimate_axis("pan", origin, points, snapshots)
+    tilt_info = estimate_axis("tilt", origin, points, snapshots)
+    pan_info, cross_axis_message = constrain_pan_with_tilt(
+        pan_info, tilt_info, configured_image_aspect_ratio()
+    )
+    say(f"  {cross_axis_message}", color=(
+        GREEN if pan_info["cross_axis_check"]["applied"] else YELLOW
+    ))
+    state["steps"]["pan"] = pan_info
+    state["steps"]["tilt"] = tilt_info
     core.save_state(state)