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fix:

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linyimin
2026-06-16 15:18:38 +08:00
parent 5ab4ef2944
commit b169618b16
2 changed files with 37 additions and 18 deletions
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54
test/test_decect_circle_v4.py
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@@ -94,29 +94,33 @@ def detect_circle_v3(frame, laser_point=None):
# 如果检测到黄色圆圈，再检测红色圆圈进行验证
if yellow_center and yellow_radius:
# HSV 红色掩码检测（红色在HSV中跨越0度，需要两个范围）
# 红色范围1: 0-10度（接近0度的红色）
lower_red1 = np.array([0, 50, 40])
upper_red1 = np.array([10, 255, 255])
# 红色范围1: 0-12度（接近0度的红色）
# 放宽S/V阈值：S>=30, V>=20 以捕获淡红/暗红
lower_red1 = np.array([0, 30, 20])
upper_red1 = np.array([12, 255, 255])
mask_red1 = cv2.inRange(hsv, lower_red1, upper_red1)
# 红色范围2: 170-180度（接近180度的红色）
lower_red2 = np.array([170, 50, 40])
# 红色范围2: 168-180度（接近180度的红色）
lower_red2 = np.array([168, 30, 20])
upper_red2 = np.array([180, 255, 255])
mask_red2 = cv2.inRange(hsv, lower_red2, upper_red2)
# 合并两个红色掩码
mask_red = cv2.bitwise_or(mask_red1, mask_red2)
# 形态学操作
# 形态学操作：先CLOSE填充空洞，再DILATE加厚环状区域
kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
mask_red = cv2.dilate(mask_red, kernel_red, iterations=1)
contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
red_pixel_count = np.sum(mask_red > 0)
print(f"Debug -> 红色掩码: {red_pixel_count} 像素, {len(contours_red)} 个轮廓")
found_valid_red = False
if contours_red:
# 找到所有符合条件的红色圆圈
for cnt_red in contours_red:
area_red = cv2.contourArea(cnt_red)
perimeter_red = cv2.arcLength(cnt_red, True)
@@ -126,9 +130,10 @@ def detect_circle_v3(frame, laser_point=None):
else:
circularity_red = 0
# 红色圆圈也应该有一定的圆度
if area_red > 30 and circularity_red > 0.4:
# 计算红色圆圈的中心和半径
# 环状轮廓圆度可能偏低，放宽到0.2
print(f"Debug -> 红轮廓: 面积={area_red:.1f}, 圆度={circularity_red:.2f}" +
f" (面积>15={area_red > 15}, 圆度>0.2={circularity_red > 0.2})")
if area_red > 15 and circularity_red > 0.2:
if len(cnt_red) >= 5:
(x_red, y_red), (w_red, h_red), angle_red = cv2.fitEllipse(cnt_red)
radius_red = min(w_red, h_red) / 2
@@ -139,22 +144,25 @@ def detect_circle_v3(frame, laser_point=None):
red_center = (int(x_red), int(y_red))
red_radius = int(radius_red)
# 计算黄色和红色圆心的距离
if red_center:
dx = yellow_center[0] - red_center[0]
dy = yellow_center[1] - red_center[1]
distance = np.sqrt(dx * dx + dy * dy)
# 圆心距离阈值：应该小于黄色半径的某个倍数（比如1.5倍）
max_distance = yellow_radius * 1.5
max_distance = yellow_radius * 2.0
min_r = min(red_radius, yellow_radius)
max_r = max(red_radius, yellow_radius)
size_ratio = min_r / max_r if max_r > 0 else 0
print(f"Debug -> 圆心距={distance:.1f}(阈值={max_distance:.1f}), "
f"大小比={size_ratio:.2f}(阈值=0.5), "
f"距离OK={distance < max_distance}, 大小OK={size_ratio > 0.5}")
# 红色圆圈应该比黄色圆圈大（外圈）
if distance < max_distance and red_radius > yellow_radius * 0.7:
# 允许红圈在黄圈外侧或内侧，只要大小相近（较小/较大 >= 0.5）
if distance < max_distance and size_ratio > 0.5:
found_valid_red = True
print(
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}")
# 记录这个有效目标
valid_targets.append({
'center': yellow_center,
'radius': yellow_radius,
@@ -164,7 +172,17 @@ def detect_circle_v3(frame, laser_point=None):
break
if not found_valid_red:
print("Debug -> 未找到匹配的红色圆圈，可能是误识别")
# 如果黄圈非常可靠（大且圆），在没有红圈验证时仍接受
if area > 30 and circularity > 0.85:
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")
valid_targets.append({
'center': yellow_center,
'radius': yellow_radius,
'ellipse': yellow_ellipse,
'area': area
})
else:
print("Debug -> 未找到匹配的红色圆圈，可能是误识别")
# 从所有有效目标中选择最佳目标
if valid_targets:
@@ -596,7 +614,7 @@ if __name__ == "__main__":
# 检查文件扩展名
if any(filename.lower().endswith(ext) for ext in IMAGE_EXTENSIONS):
# 过滤掉 _result.jpg 后缀的文件
if not filename.endswith('_result.jpg'):
if filename.endswith('no_target.jpg'):
filepath = os.path.join(TARGET_DIR, filename)
if os.path.isfile(filepath):
image_files.append(filepath)