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新分支 加入了标靶判断

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This commit is contained in:
yrx
2026-05-22 09:45:49 +08:00
parent c754dff4ad
commit 46508e4b31
17 changed files with 356 additions and 34 deletions
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130
shoot_manager.py
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@@ -58,6 +58,7 @@ def analyze_shot(frame, laser_point=None):
# ── Step 1: 确定激光点 ────────────────────────────────────────────────────
laser_point_method = None
distance_m_first = None
best_radius1_temp = None
if config.HARDCODE_LASER_POINT:
laser_point = laser_manager.laser_point
@@ -102,9 +103,22 @@ def analyze_shot(frame, laser_point=None):
r_img, center, radius, method, best_radius1, ellipse_params = cdata
dx, dy = None, None
d_m = distance_m_first
tri_h = None
if center and radius:
dx, dy = laser_manager.compute_laser_position(center, (x, y), radius, method)
d_m = estimate_distance(best_radius1) if best_radius1 else distance_m_first
try:
import numpy as _np
px_per_cm = float(radius) / 10.0
if px_per_cm > 1e-6:
cxp, cyp = float(center[0]), float(center[1])
tri_h = _np.array([
[1.0 / px_per_cm, 0.0, -cxp / px_per_cm],
[0.0, 1.0 / px_per_cm, -cyp / px_per_cm],
[0.0, 0.0, 1.0],
], dtype=float)
except Exception:
tri_h = None
out = {
"success": True,
"result_img": r_img,
@@ -114,6 +128,7 @@ def analyze_shot(frame, laser_point=None):
"laser_point": laser_point, "laser_point_method": laser_point_method,
"offset_method": "yellow_ellipse" if ellipse_params else "yellow_circle",
"distance_method": "yellow_radius",
"tri_homography": tri_h,
}
if yolo_roi_xyxy is not None:
out["yolo_roi_xyxy"] = yolo_roi_xyxy
@@ -129,8 +144,10 @@ def analyze_shot(frame, laser_point=None):
roi_xyxy = None
yolo_ring_ms = 0.0
yolo_black_ms = 0.0
_timing_on = bool(getattr(config, "ARCHERY_TIMING_ENABLE", True))
_sample_on = bool(getattr(config, "TRIANGLE_SAMPLE_ENABLE", False))
if getattr(config, "TRIANGLE_YOLO_ROI_ENABLE", False):
_t_yolo_ring = time_std.perf_counter()
_t_yolo_ring = time_std.perf_counter() if _timing_on else None
try:
from target_roi_yolo import try_get_triangle_roi_from_yolo
roi_xyxy = try_get_triangle_roi_from_yolo(
@@ -140,7 +157,8 @@ def analyze_shot(frame, laser_point=None):
if logger:
logger.warning(f"[YOLO-ROI] {e}")
finally:
yolo_ring_ms = (time_std.perf_counter() - _t_yolo_ring) * 1000.0
if _timing_on and _t_yolo_ring is not None:
yolo_ring_ms = (time_std.perf_counter() - _t_yolo_ring) * 1000.0
_loc_mode = str(
getattr(config, "TRIANGLE_BLACK_TRIANGLE_LOCATE_MODE", "yolo")
@@ -155,7 +173,7 @@ def analyze_shot(frame, laser_point=None):
and roi_xyxy is not None
)
if _run_stage2_black_yolo:
_t_yolo_black = time_std.perf_counter()
_t_yolo_black = time_std.perf_counter() if _timing_on else None
try:
from target_roi_yolo import try_black_triangle_boxes_work
@@ -166,7 +184,8 @@ def analyze_shot(frame, laser_point=None):
if logger:
logger.warning(f"[YOLO-BLACK] {e}")
finally:
yolo_black_ms = (time_std.perf_counter() - _t_yolo_black) * 1000.0
if _timing_on and _t_yolo_black is not None:
yolo_black_ms = (time_std.perf_counter() - _t_yolo_black) * 1000.0
elif (
logger
and _loc_mode == "traditional"
@@ -184,7 +203,7 @@ def analyze_shot(frame, laser_point=None):
try:
logger.info(f"[TRI] begin {datetime.now()}")
logger.info(f"[TRI] K: {K}, dist: {dist_coef}, pos: {pos}, {datetime.now()}")
_t_wall_try = time_std.perf_counter()
_t_wall_try = time_std.perf_counter() if _timing_on else None
tri = try_triangle_scoring(
img_cv, (x, y), pos, K, dist_coef,
size_range=getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500)),
@@ -193,8 +212,8 @@ def analyze_shot(frame, laser_point=None):
yolo_ring_ms=yolo_ring_ms,
yolo_black_ms=yolo_black_ms,
)
_wall_try_ms = (time_std.perf_counter() - _t_wall_try) * 1000.0
if logger and bool(getattr(config, "TRIANGLE_LOG_E2E_TIMING", True)):
_wall_try_ms = (time_std.perf_counter() - _t_wall_try) * 1000.0 if _timing_on else 0.0
if logger and bool(getattr(config, "TRIANGLE_LOG_E2E_TIMING", True)) and _timing_on:
_e2e = float(yolo_ring_ms) + float(yolo_black_ms) + float(_wall_try_ms)
logger.info(
f"[TRI] timing_e2e_triangle_ms={_e2e:.1f} "
@@ -280,6 +299,16 @@ def analyze_shot(frame, laser_point=None):
"tri_markers_completed": tri.get("markers_completed", []),
"tri_homography": tri.get("homography"),
}
try:
import numpy as _np
_H = tri.get("homography")
if _H is not None and _np.all(_np.isfinite(_H)):
_H_inv = _np.linalg.inv(_H)
_pt = _np.array([[[0.0, 0.0]]], dtype=_np.float32)
_center_pt = cv2.perspectiveTransform(_pt, _H_inv)[0][0]
out["tri_center_px"] = [float(_center_pt[0]), float(_center_pt[1])]
except Exception:
pass
if yolo_roi_xyxy is not None:
out["yolo_roi_xyxy"] = yolo_roi_xyxy
return out
@@ -318,6 +347,7 @@ def process_shot(adc_val):
:return: None
"""
logger = logger_manager.logger
_timing_on = bool(getattr(config, "ARCHERY_TIMING_ENABLE", True))
try:
network_manager.safe_enqueue({"shoot_event": "start"}, msg_type=2, high=True)
@@ -356,6 +386,86 @@ def process_shot(adc_val):
)
x, y = laser_point
# 物方采样调试（config.TRIANGLE_SAMPLE_ENABLE）：靶心为原点，取两个对称点判断黑白来区分 40/20 标靶
# 逻辑：若两个采样点 RGB 均 < 阈值 → 全黑 → 40cm 标靶；否则 → 20cm 标靶
sample_target_type = None
_t_sample = time_std.perf_counter() if _timing_on else None
_t_sample_ms = 0.0
sample_points = []
sample_patch_half = 2
if bool(getattr(config, "TRIANGLE_SAMPLE_ENABLE", False)):
sample_obj_radius_cm = float(getattr(config, "TRIANGLE_SAMPLE_RADIUS_CM", 15.0))
sample_obj_angles_deg = (0, 180) # 只取两个对称点：+X 和 -X
sample_patch_half = int(getattr(config, "TRIANGLE_SAMPLE_PATCH_HALF_PX", 2))
sample_black_thresh = float(getattr(config, "TRIANGLE_SAMPLE_BLACK_THRESH", 30.0))
try:
import math as _math
import numpy as _np
import cv2 as _cv2
if tri_homography is not None:
_H_inv = _np.linalg.inv(tri_homography)
for _ang in sample_obj_angles_deg:
_rad = _math.radians(float(_ang))
_pt_obj = _np.array([
[[sample_obj_radius_cm * _math.cos(_rad), sample_obj_radius_cm * _math.sin(_rad)]]
], dtype=_np.float32)
_pt_img = _cv2.perspectiveTransform(_pt_obj, _H_inv)[0][0]
_px, _py = float(_pt_img[0]), float(_pt_img[1])
sample_points.append({
"angle_deg": float(_ang),
"obj_cm": (float(sample_obj_radius_cm * _math.cos(_rad)), float(sample_obj_radius_cm * _math.sin(_rad))),
"img_px": (int(round(_px)), int(round(_py))),
})
elif center and radius:
_px_per_cm = float(radius) / 10.0
for _ang in sample_obj_angles_deg:
_rad = _math.radians(float(_ang))
_px = float(center[0]) + sample_obj_radius_cm * _math.cos(_rad) * _px_per_cm
_py = float(center[1]) + sample_obj_radius_cm * _math.sin(_rad) * _px_per_cm
sample_points.append({
"angle_deg": float(_ang),
"obj_cm": (float(sample_obj_radius_cm * _math.cos(_rad)), float(sample_obj_radius_cm * _math.sin(_rad))),
"img_px": (int(round(_px)), int(round(_py))),
})
# 取样后立即读像素并判断黑白：三角成功用 H_inv；三角失败但圆心成功用 center/radius 近似物方半径
_all_black = False
_sample_infos = []
if sample_points:
_img_cv_for_sample = image.image2cv(result_img, False, False)
_all_black = True
for _sp in sample_points:
_sx, _sy = _sp["img_px"]
_hh = max(1, sample_patch_half)
_patch = []
for _yy in range(_sy - _hh, _sy + _hh + 1):
if _yy < 0 or _yy >= _img_cv_for_sample.shape[0]:
continue
for _xx in range(_sx - _hh, _sx + _hh + 1):
if _xx < 0 or _xx >= _img_cv_for_sample.shape[1]:
continue
_patch.append(_img_cv_for_sample[_yy, _xx].astype(float))
if _patch:
_mean_rgb = _np.mean(_patch, axis=0)
_is_black = bool(_mean_rgb[0] < sample_black_thresh
and _mean_rgb[1] < sample_black_thresh
and _mean_rgb[2] < sample_black_thresh)
if not _is_black:
_all_black = False
_sample_infos.append(
f"{int(_sp['angle_deg'])}°@{_sx},{_sy} rgb=({int(_mean_rgb[0])},{int(_mean_rgb[1])},{int(_mean_rgb[2])})"
)
sample_target_type = "40cm_black" if _all_black else "20cm"
if _sample_infos:
logger.info("[采样] " + " | ".join(_sample_infos) + f" → {sample_target_type}")
except Exception as _e_sample:
sample_points = []
if logger:
logger.warning(f"[采样] 标靶类型判断失败: {_e_sample}")
if _timing_on and _t_sample is not None:
_t_sample_ms = (time_std.perf_counter() - _t_sample) * 1000.0
# 三角形路径成功时 center/radius 为空是正常的；此时用 triangle 方法名用于保存文件名与上报字段 m
if (not method) and tri_markers:
method = "triangle_homography"
@@ -397,6 +507,7 @@ def process_shot(adc_val):
"target_y": float(y),
"offset_method": offset_method,
"distance_method": distance_method,
"target_type": 40 if sample_target_type == "40cm_black" else (20 if sample_target_type == "20cm" else None),
}
if ellipse_params:
@@ -471,6 +582,11 @@ def process_shot(adc_val):
except Exception:
pass
# 物方采样标靶类型判断耗时（合并在上面采样块内，单独统计）
if _timing_on and bool(getattr(config, "TRIANGLE_SAMPLE_ENABLE", False)) and sample_target_type is not None:
logger.info(f"[采样] 标靶类型: {sample_target_type} 耗时: {_t_sample_ms:.2f}ms")
# 叠加信息：落点-圆心距离 / 相机-靶距离等
try:
import math as _math