wild_test

interactive_calibrator.py

@@ -72,9 +72,32 @@ def remove_session_snapshots(paths: set[str]) -> None:
 
 
 def candidate_deltas(axis: str) -> list[int]:
+    """Coarse probes from narrow to wide FOV.
+
+    Once any probe provides a usable FOV estimate, estimate_axis replaces the
+    wide tail with scene/camera-specific deltas.  Dense small values are
+    important at high optical zoom, where a jump from 150 to 250 PTZ units can
+    go directly from useful overlap to no common image.
+    """
     if axis == "pan":
-        return [150, 250, 350, 450, 600, 800, 1100, 1500, 2100]
-    return [80, 120, 170, 220, 280, 350, 450, 600, 850, 1150]
+        return [50, 75, 110, 150, 220, 320, 470, 700, 1050, 1500, 2100]
+    return [25, 40, 60, 90, 130, 190, 280, 420, 620, 850, 1150]
+
+
+def fov_guided_deltas(delta: int, overlap: float, axis: str) -> list[int]:
+    """Choose probes that retain enough shared image for robust matching."""
+    shift = 1.0 - overlap
+    if not 0.04 <= shift <= 0.92:
+        return []
+    estimated_fov = delta / shift
+    minimum = 20 if axis == "pan" else 12
+    # Fractions are image displacement/FOV, hence target overlaps are roughly
+    # 82%, 72%, 60%, 48% and 35%.  The middle probes provide the strongest
+    # cross-check, while the last one confirms the wider displacement model.
+    return sorted({
+        max(minimum, round(estimated_fov * fraction))
+        for fraction in (0.18, 0.28, 0.40, 0.52, 0.65)
+    })
 
 
 def choose_direction(axis: str, origin: dict[str, int], points: dict) -> int:
@@ -239,7 +262,14 @@ def estimate_axis(axis: str, origin: dict[str, int], points: dict,
     failure_reasons: dict[str, int] = {}
     say(f"\n正在标定 {axis}_step，摄像头会自动移动并回到起点……")
 
-    for delta in candidate_deltas(axis):
+    pending = candidate_deltas(axis)
+    tested: set[int] = set()
+    adaptive_seeded = False
+    while pending:
+        delta = pending.pop(0)
+        if delta in tested:
+            continue
+        tested.add(delta)
         result = core.measure_delta(
             axis, delta, direction, 1.0, baseline=baseline, origin=origin
         )
@@ -270,6 +300,22 @@ def estimate_axis(axis: str, origin: dict[str, int], points: dict,
         else:
             quality = f"有效特征={inliers}，内点率={ratio*100:.1f}%"
         say(f"  step={delta}: 重叠约 {overlap*100:.1f}%，{quality}")
+        if not adaptive_seeded:
+            guided = fov_guided_deltas(delta, overlap, axis)
+            if guided:
+                # Keep only a small safety tail beyond the guided range. Very
+                # large fixed probes cannot match once the current optical FOV
+                # has been measured and only waste time.
+                cap = round(max(guided) * 1.15)
+                pending = sorted({
+                    value for value in guided + pending
+                    if value not in tested and value <= cap
+                })
+                adaptive_seeded = True
+                say(
+                    f"  已根据首个有效样本自适应生成后续step："
+                    f"{', '.join(str(value) for value in pending)}"
+                )
         feature_reliable = inliers >= 50 and ratio >= 0.45
         phase_reliable = (
             result.get("match_method") == "phase_correlation"
@@ -423,8 +469,19 @@ def estimate_axis(axis: str, origin: dict[str, int], points: dict,
 def calculate_steps(points: dict, snapshots: set[str], mount: str) -> None:
     origin = camera_ptz()
     zooms = [int(points[name]["zoom"]) for name in ("top-left", "bottom-right")]
-    if max(zooms + [origin["zoom"]]) - min(zooms + [origin["zoom"]]) > 10:
-        core.fail("三个位置的zoom不一致；请固定焦段后重新开始")
+    target_zoom = round(sum(zooms) / len(zooms))
+    if origin["zoom"] != target_zoom:
+        say(
+            f"两个角点zoom的平均值为{target_zoom}（{target_zoom/100:.2f}×）；"
+            "正在自动切换到该zoom后再测量视场……"
+        )
+        origin = core.move({**origin, "zoom": target_zoom})
+        time.sleep(1.0)
+        if abs(int(origin["zoom"]) - target_zoom) > 10:
+            core.fail(
+                f"摄像头未能到达平均zoom：目标={target_zoom}，实际={origin['zoom']}"
+            )
+        core.print_ptz(origin, "平均zoom到位")
     top_left = points["top-left"]
     bottom_right = points["bottom-right"]
     # This check is only advisory and runs before signed pan direction has
@@ -624,7 +681,7 @@ def main() -> None:
         mount = "hanging" if stage == "1" else "desktop"
 
         confirm_position(
-            "② 请在Web界面中固定本次扫描使用的zoom，"
+            "② 请在Web界面中设置左上角需要的zoom，"
             "再手动把摄像头移到扫描区域的【左上角】。"
         )
         top_left = camera_ptz()
@@ -633,11 +690,16 @@ def main() -> None:
         core.save_state(state)
 
         confirm_position(
-            "③ 请保持zoom不变，手动把摄像头移到扫描区域的【右下角】。"
+            "③ 请手动把摄像头移到扫描区域的【右下角】。\n"
+            "右下角可以使用不同zoom，程序会取两个角点zoom的平均值作为最终扫描zoom。"
         )
         bottom_right = camera_ptz()
-        if abs(top_left["zoom"] - bottom_right["zoom"]) > 10:
-            core.fail("左右角的zoom不一致，请固定焦段后重新运行")
+        average_zoom = round((top_left["zoom"] + bottom_right["zoom"]) / 2)
+        say(
+            f"两个角点zoom：左上={top_left['zoom']/100:.2f}×，"
+            f"右下={bottom_right['zoom']/100:.2f}×；"
+            f"最终取平均={average_zoom/100:.2f}×。"
+        )
         state = core.load_state()
         state["points"]["bottom-right"] = bottom_right
         core.save_state(state)
@@ -646,10 +708,12 @@ def main() -> None:
         while True:
             confirm_position(
                 "④ 这一步用于测量当前zoom下的水平/垂直视场。\n"
-                "请保持zoom不变，优先把摄像头移到扫描区域内部、"
+                "请优先把摄像头移到扫描区域内部、"
                 "距离较远且纹理分布在整幅画面的位置。\n"
-                "请避开纯天空、纯墙面、水面、玻璃、近距离物体和重复图案；"
-                "回复1后不要再操作Web云台，程序会自动移动、拍照并返回起点。"
+                "请避开纯天空、纯墙面、纯水面、玻璃、近距离物体和重复图案；"
+                "水面场景需要让岸线、浮标或其他固定物体横向分布在画面中；"
+                "回复1后不要再操作Web云台，程序会先切换到两个角点的平均zoom，"
+                "再自动移动、拍照并返回起点。"
             )
             say("\n正在计算，请不要操作摄像头或Web控制页面……")
             try: