fix:
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@@ -2092,6 +2092,7 @@ class NetworkManager:
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battery_data = {
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"battery": battery_percent,
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"voltage": round(float(voltage), 3),
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"netType": self.network_type,
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}
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self.safe_enqueue(battery_data, 2)
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self.logger.info(f"电量上报: {battery_percent}%")
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@@ -94,29 +94,33 @@ def detect_circle_v3(frame, laser_point=None):
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# 如果检测到黄色圆圈，再检测红色圆圈进行验证
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if yellow_center and yellow_radius:
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# HSV 红色掩码检测（红色在HSV中跨越0度，需要两个范围）
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# 红色范围1: 0-10度（接近0度的红色）
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lower_red1 = np.array([0, 50, 40])
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upper_red1 = np.array([10, 255, 255])
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# 红色范围1: 0-12度（接近0度的红色）
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# 放宽S/V阈值：S>=30, V>=20 以捕获淡红/暗红
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lower_red1 = np.array([0, 30, 20])
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upper_red1 = np.array([12, 255, 255])
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mask_red1 = cv2.inRange(hsv, lower_red1, upper_red1)
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# 红色范围2: 170-180度（接近180度的红色）
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lower_red2 = np.array([170, 50, 40])
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# 红色范围2: 168-180度（接近180度的红色）
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lower_red2 = np.array([168, 30, 20])
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upper_red2 = np.array([180, 255, 255])
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mask_red2 = cv2.inRange(hsv, lower_red2, upper_red2)
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# 合并两个红色掩码
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mask_red = cv2.bitwise_or(mask_red1, mask_red2)
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# 形态学操作
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# 形态学操作：先CLOSE填充空洞，再DILATE加厚环状区域
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kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
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mask_red = cv2.dilate(mask_red, kernel_red, iterations=1)
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contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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red_pixel_count = np.sum(mask_red > 0)
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print(f"Debug -> 红色掩码: {red_pixel_count} 像素, {len(contours_red)} 个轮廓")
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found_valid_red = False
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if contours_red:
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# 找到所有符合条件的红色圆圈
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for cnt_red in contours_red:
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area_red = cv2.contourArea(cnt_red)
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perimeter_red = cv2.arcLength(cnt_red, True)
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@@ -126,9 +130,10 @@ def detect_circle_v3(frame, laser_point=None):
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else:
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circularity_red = 0
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# 红色圆圈也应该有一定的圆度
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if area_red > 30 and circularity_red > 0.4:
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# 计算红色圆圈的中心和半径
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# 环状轮廓圆度可能偏低，放宽到0.2
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print(f"Debug -> 红轮廓: 面积={area_red:.1f}, 圆度={circularity_red:.2f}" +
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f" (面积>15={area_red > 15}, 圆度>0.2={circularity_red > 0.2})")
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if area_red > 15 and circularity_red > 0.2:
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if len(cnt_red) >= 5:
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(x_red, y_red), (w_red, h_red), angle_red = cv2.fitEllipse(cnt_red)
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radius_red = min(w_red, h_red) / 2
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@@ -139,22 +144,25 @@ def detect_circle_v3(frame, laser_point=None):
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red_center = (int(x_red), int(y_red))
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red_radius = int(radius_red)
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# 计算黄色和红色圆心的距离
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if red_center:
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dx = yellow_center[0] - red_center[0]
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dy = yellow_center[1] - red_center[1]
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distance = np.sqrt(dx * dx + dy * dy)
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# 圆心距离阈值：应该小于黄色半径的某个倍数（比如1.5倍）
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max_distance = yellow_radius * 1.5
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max_distance = yellow_radius * 2.0
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min_r = min(red_radius, yellow_radius)
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max_r = max(red_radius, yellow_radius)
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size_ratio = min_r / max_r if max_r > 0 else 0
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print(f"Debug -> 圆心距={distance:.1f}(阈值={max_distance:.1f}), "
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f"大小比={size_ratio:.2f}(阈值=0.5), "
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f"距离OK={distance < max_distance}, 大小OK={size_ratio > 0.5}")
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# 红色圆圈应该比黄色圆圈大（外圈）
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if distance < max_distance and red_radius > yellow_radius * 0.7:
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# 允许红圈在黄圈外侧或内侧，只要大小相近（较小/较大 >= 0.5）
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if distance < max_distance and size_ratio > 0.5:
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found_valid_red = True
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print(
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f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), 红心({red_center}), 距离:{distance:.1f}, 黄半径:{yellow_radius}, 红半径:{red_radius}")
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# 记录这个有效目标
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valid_targets.append({
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'center': yellow_center,
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'radius': yellow_radius,
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@@ -164,6 +172,16 @@ def detect_circle_v3(frame, laser_point=None):
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break
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if not found_valid_red:
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# 如果黄圈非常可靠（大且圆），在没有红圈验证时仍接受
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if area > 30 and circularity > 0.85:
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print(f"[target] -> 黄圈高置信度(面积:{area:.0f}, 圆度:{circularity:.2f})，跳过红圈验证直接接受")
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valid_targets.append({
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'center': yellow_center,
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'radius': yellow_radius,
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'ellipse': yellow_ellipse,
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'area': area
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})
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else:
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print("Debug -> 未找到匹配的红色圆圈，可能是误识别")
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# 从所有有效目标中选择最佳目标
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@@ -596,7 +614,7 @@ if __name__ == "__main__":
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# 检查文件扩展名
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if any(filename.lower().endswith(ext) for ext in IMAGE_EXTENSIONS):
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# 过滤掉 _result.jpg 后缀的文件
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if not filename.endswith('_result.jpg'):
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if filename.endswith('no_target.jpg'):
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filepath = os.path.join(TARGET_DIR, filename)
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if os.path.isfile(filepath):
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image_files.append(filepath)
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