752 lines
32 KiB
Python
752 lines
32 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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视觉检测模块
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提供靶心检测、距离估算、图像保存等功能
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"""
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import cv2
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import numpy as np
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import os
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import math
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import threading
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import queue
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from maix import image
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import config
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from logger_manager import logger_manager
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# 存图队列 + worker
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_save_queue = queue.Queue(maxsize=16)
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_save_worker_started = False
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_save_worker_lock = threading.Lock()
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def check_laser_point_sharpness(frame, laser_point=None, roi_size=30, threshold=100.0, ellipse_params=None):
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"""
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检测激光点本身的清晰度（不是整个靶子）
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Args:
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frame: 图像帧对象
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laser_point: 激光点坐标 (x, y)，如果为None则自动查找
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roi_size: ROI区域大小（像素），默认30x30
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threshold: 清晰度阈值
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ellipse_params: 椭圆参数 ((center_x, center_y), (width, height), angle)，用于限制激光点必须在椭圆内
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Returns:
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(is_sharp, sharpness_score, laser_pos): (是否清晰, 清晰度分数, 激光点坐标)
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"""
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try:
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# 1. 如果没有提供激光点，先查找
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if laser_point is None:
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from laser_manager import laser_manager
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laser_point = laser_manager.find_red_laser(frame, ellipse_params=ellipse_params)
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if laser_point is None:
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logger_manager.logger.debug(f"未找到激光点")
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return False, 0.0, None
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x, y = laser_point
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# 2. 转换为 OpenCV 格式
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img_cv = image.image2cv(frame, False, False)
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h, w = img_cv.shape[:2]
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# 3. 提取 ROI 区域（激光点周围）
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roi_half = roi_size // 2
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x_min = max(0, int(x) - roi_half)
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x_max = min(w, int(x) + roi_half)
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y_min = max(0, int(y) - roi_half)
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y_max = min(h, int(y) + roi_half)
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roi = img_cv[y_min:y_max, x_min:x_max]
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if roi.size == 0:
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return False, 0.0, laser_point
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# 4. 转换为灰度图（用于清晰度检测）
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gray_roi = cv2.cvtColor(roi, cv2.COLOR_RGB2GRAY)
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# 5. 方法1：检测点的扩散程度（能量集中度）
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# 计算中心区域的能量集中度
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center_x, center_y = roi.shape[1] // 2, roi.shape[0] // 2
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center_radius = min(5, roi.shape[0] // 4) # 中心区域半径
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# 创建中心区域的掩码
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y_coords, x_coords = np.ogrid[:roi.shape[0], :roi.shape[1]]
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center_mask = (x_coords - center_x)**2 + (y_coords - center_y)**2 <= center_radius**2
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# 计算中心区域和周围区域的亮度
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center_brightness = gray_roi[center_mask].mean()
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outer_mask = ~center_mask
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outer_brightness = gray_roi[outer_mask].mean() if np.any(outer_mask) else 0
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# 对比度（清晰的点对比度高）
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contrast = abs(center_brightness - outer_brightness)
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# 6. 方法2：检测点的边缘锐度（使用拉普拉斯）
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laplacian = cv2.Laplacian(gray_roi, cv2.CV_64F)
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edge_sharpness = abs(laplacian).var()
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# 7. 方法3：检测点的能量集中度（方差）
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# 清晰的点：能量集中在中心，方差小
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# 模糊的点：能量分散，方差大
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# 但我们需要的是：清晰的点中心亮度高，周围低，所以梯度大
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sobel_x = cv2.Sobel(gray_roi, cv2.CV_64F, 1, 0, ksize=3)
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sobel_y = cv2.Sobel(gray_roi, cv2.CV_64F, 0, 1, ksize=3)
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gradient = np.sqrt(sobel_x**2 + sobel_y**2)
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gradient_sharpness = gradient.var()
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# 8. 组合多个指标
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# 对比度权重0.3，边缘锐度权重0.4，梯度权重0.3
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sharpness_score = (contrast * 0.3 + edge_sharpness * 0.4 + gradient_sharpness * 0.3)
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is_sharp = sharpness_score >= threshold
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logger = logger_manager.logger
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if logger:
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logger.debug(f"[VISION] 激光点清晰度: 位置=({x}, {y}), 对比度={contrast:.2f}, 边缘={edge_sharpness:.2f}, 梯度={gradient_sharpness:.2f}, 综合={sharpness_score:.2f}, 是否清晰={is_sharp}")
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return is_sharp, sharpness_score, laser_point
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except Exception as e:
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logger = logger_manager.logger
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if logger:
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logger.error(f"[VISION] 激光点清晰度检测失败: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return False, 0.0, laser_point
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def check_image_sharpness(frame, threshold=100.0, save_debug_images=False):
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"""
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检查图像清晰度（针对圆形靶子优化，基于圆形边缘检测）
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检测靶心的圆形边缘，计算边缘区域的梯度清晰度
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Args:
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frame: 图像帧对象
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threshold: 清晰度阈值，低于此值认为图像模糊（默认100.0）
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可以根据实际情况调整：
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- 清晰图像通常 > 200
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- 模糊图像通常 < 100
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- 中等清晰度 100-200
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save_debug_images: 是否保存调试图像（原始图和边缘图），默认False
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Returns:
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(is_sharp, sharpness_score): (是否清晰, 清晰度分数)
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"""
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try:
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logger_manager.logger.debug(f"begin")
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# 转换为 OpenCV 格式
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img_cv = image.image2cv(frame, False, False)
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logger_manager.logger.debug(f"after image2cv")
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# 转换为 HSV 颜色空间
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hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
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h, s, v = cv2.split(hsv)
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logger_manager.logger.debug(f"after HSV conversion")
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# 检测黄色区域（靶心）
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# 调整饱和度策略：稍微增强，不要过度
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s_enhanced = np.clip(s * 1.1, 0, 255).astype(np.uint8)
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hsv_enhanced = cv2.merge((h, s_enhanced, v))
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# HSV 阈值范围（与 detect_circle_v3 保持一致）
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lower_yellow = np.array([7, 80, 0])
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upper_yellow = np.array([32, 255, 255])
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mask_yellow = cv2.inRange(hsv_enhanced, lower_yellow, upper_yellow)
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# 形态学操作，填充小孔洞
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel)
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logger_manager.logger.debug(f"after yellow mask detection")
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# 计算边缘区域：扩展黄色区域，然后减去原始区域，得到边缘区域
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mask_dilated = cv2.dilate(mask_yellow, kernel, iterations=2)
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mask_edge = cv2.subtract(mask_dilated, mask_yellow) # 边缘区域
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# 计算边缘区域的像素数量
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edge_pixel_count = np.sum(mask_edge > 0)
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logger_manager.logger.debug(f"edge pixel count: {edge_pixel_count}")
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# 如果检测不到边缘区域，使用全局梯度作为后备方案
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if edge_pixel_count < 100:
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logger_manager.logger.debug(f"edge region too small, using global gradient")
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# 使用 V 通道计算全局梯度
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sobel_v_x = cv2.Sobel(v, cv2.CV_64F, 1, 0, ksize=3)
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sobel_v_y = cv2.Sobel(v, cv2.CV_64F, 0, 1, ksize=3)
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gradient = np.sqrt(sobel_v_x**2 + sobel_v_y**2)
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sharpness_score = gradient.var()
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logger_manager.logger.debug(f"global gradient variance: {sharpness_score:.2f}")
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else:
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# 在边缘区域计算梯度清晰度
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# 使用 V（亮度）通道计算梯度，因为边缘在亮度上通常很明显
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sobel_v_x = cv2.Sobel(v, cv2.CV_64F, 1, 0, ksize=3)
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sobel_v_y = cv2.Sobel(v, cv2.CV_64F, 0, 1, ksize=3)
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gradient = np.sqrt(sobel_v_x**2 + sobel_v_y**2)
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# 只在边缘区域计算清晰度
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edge_gradient = gradient[mask_edge > 0]
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if len(edge_gradient) > 0:
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# 计算边缘梯度的方差（清晰图像的边缘梯度变化大）
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sharpness_score = edge_gradient.var()
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# 也可以使用均值作为补充指标（清晰图像的边缘梯度均值也较大）
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gradient_mean = edge_gradient.mean()
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logger_manager.logger.debug(f"edge gradient: mean={gradient_mean:.2f}, var={sharpness_score:.2f}, pixels={len(edge_gradient)}")
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else:
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# 如果边缘区域没有有效梯度，使用全局梯度
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sharpness_score = gradient.var()
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logger_manager.logger.debug(f"no edge gradient, using global: {sharpness_score:.2f}")
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# 保存调试图像（如果启用）
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if save_debug_images:
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try:
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debug_dir = config.PHOTO_DIR
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if debug_dir not in os.listdir("/root"):
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try:
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os.mkdir(debug_dir)
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except:
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pass
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# 生成文件名
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try:
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all_images = [f for f in os.listdir(debug_dir) if f.endswith(('.bmp', '.jpg', '.jpeg'))]
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img_count = len(all_images)
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except:
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img_count = 0
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# 保存原始图像
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img_orig = image.cv2image(img_cv, False, False)
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orig_filename = f"{debug_dir}/sharpness_debug_orig_{img_count:04d}.bmp"
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img_orig.save(orig_filename)
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# # 保存边缘检测结果（可视化）
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# # 创建可视化图像：原始图像 + 黄色区域 + 边缘区域
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# debug_img = img_cv.copy()
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# # 在黄色区域绘制绿色
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# debug_img[mask_yellow > 0] = [0, 255, 0] # RGB格式，绿色
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# # 在边缘区域绘制红色
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# debug_img[mask_edge > 0] = [255, 0, 0] # RGB格式，红色
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# debug_img_maix = image.cv2image(debug_img, False, False)
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# debug_filename = f"{debug_dir}/sharpness_debug_edge_{img_count:04d}.bmp"
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# debug_img_maix.save(debug_filename)
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# logger = logger_manager.logger
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# if logger:
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# logger.info(f"[VISION] 保存调试图像: {orig_filename}, {debug_filename}")
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except Exception as e:
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logger = logger_manager.logger
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if logger:
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logger.warning(f"[VISION] 保存调试图像失败: {e}")
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import traceback
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logger.error(traceback.format_exc())
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is_sharp = sharpness_score >= threshold
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logger = logger_manager.logger
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if logger:
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logger.debug(f"[VISION] 清晰度检测: 分数={sharpness_score:.2f}, 边缘像素数={edge_pixel_count}, 是否清晰={is_sharp}, 阈值={threshold}")
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return is_sharp, sharpness_score
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except Exception as e:
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logger = logger_manager.logger
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if logger:
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logger.error(f"[VISION] 清晰度检测失败: {e}")
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import traceback
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logger.error(traceback.format_exc())
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# 出错时返回 False，避免使用模糊图像
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return False, 0.0
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def save_calibration_image(frame, laser_pos, photo_dir=None):
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"""
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保存激光校准图像（带标注）
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在找到的激光点位置绘制圆圈，便于检查算法是否正确
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Args:
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frame: 原始图像帧
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laser_pos: 找到的激光点坐标 (x, y)
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photo_dir: 照片存储目录，如果为None则使用 config.PHOTO_DIR
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Returns:
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str: 保存的文件路径，如果保存失败则返回 None
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"""
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# 检查是否启用图像保存
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if not config.SAVE_IMAGE_ENABLED:
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return None
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if photo_dir is None:
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photo_dir = config.PHOTO_DIR
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try:
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# 确保照片目录存在
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try:
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if photo_dir not in os.listdir("/root"):
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os.mkdir(photo_dir)
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except:
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pass
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# 生成文件名
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try:
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all_images = [f for f in os.listdir(photo_dir) if f.endswith(('.bmp', '.jpg', '.jpeg'))]
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img_count = len(all_images)
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except:
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img_count = 0
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x, y = laser_pos
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filename = f"{photo_dir}/calibration_{int(x)}_{int(y)}_{img_count:04d}.bmp"
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logger = logger_manager.logger
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if logger:
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logger.info(f"保存校准图像: {filename}, 激光点: ({x}, {y})")
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# 转换图像为 OpenCV 格式以便绘制
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img_cv = image.image2cv(frame, False, False)
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# 绘制激光点圆圈（用绿色圆圈标出找到的激光点）
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cv2.circle(img_cv, (int(x), int(y)), 10, (0, 255, 0), 2) # 外圈：绿色，半径10
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cv2.circle(img_cv, (int(x), int(y)), 5, (0, 255, 0), 2) # 中圈：绿色，半径5
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cv2.circle(img_cv, (int(x), int(y)), 2, (0, 255, 0), -1) # 中心点：绿色实心
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# 可选：绘制十字线帮助定位
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cv2.line(img_cv,
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(int(x - 20), int(y)),
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(int(x + 20), int(y)),
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(0, 255, 0), 1) # 水平线
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cv2.line(img_cv,
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(int(x), int(y - 20)),
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(int(x), int(y + 20)),
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(0, 255, 0), 1) # 垂直线
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# 转换回 MaixPy 图像格式并保存
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result_img = image.cv2image(img_cv, False, False)
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result_img.save(filename)
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if logger:
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logger.debug(f"校准图像已保存: {filename}")
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return filename
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except Exception as e:
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logger = logger_manager.logger
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if logger:
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logger.error(f"保存校准图像失败: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return None
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def detect_circle_v3(frame, laser_point=None):
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"""检测图像中的靶心（优先清晰轮廓，其次黄色区域）- 返回椭圆参数版本
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增加红色圆圈检测，验证黄色圆圈是否为真正的靶心
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如果提供 laser_point，会选择最接近激光点的目标
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Args:
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frame: 图像帧
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laser_point: 激光点坐标 (x, y)，用于多目标场景下的目标选择
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Returns:
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(result_img, best_center, best_radius, method, best_radius1, ellipse_params)
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"""
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img_cv = image.image2cv(frame, False, False)
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best_center = best_radius = best_radius1 = method = None
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ellipse_params = None
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# HSV 黄色掩码检测（模糊靶心）
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hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
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h, s, v = cv2.split(hsv)
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# 调整饱和度策略：稍微增强，不要过度
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s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
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hsv = cv2.merge((h, s, v))
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# 放宽 HSV 阈值范围（针对模糊图像的关键调整）
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lower_yellow = np.array([7, 80, 0]) # 饱和度下限降低，捕捉淡黄色
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upper_yellow = np.array([32, 255, 255]) # 亮度上限拉满
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mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# 调整形态学操作
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel)
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contours_yellow, _ = cv2.findContours(mask_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 存储所有有效的黄色-红色组合
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valid_targets = []
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if contours_yellow:
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for cnt_yellow in contours_yellow:
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area = cv2.contourArea(cnt_yellow)
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perimeter = cv2.arcLength(cnt_yellow, True)
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# 计算圆度
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if perimeter > 0:
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circularity = (4 * np.pi * area) / (perimeter * perimeter)
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else:
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circularity = 0
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logger = logger_manager.logger
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if area > 50 and circularity > 0.7:
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if logger:
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logger.info(f"[target] -> 面积:{area}, 圆度:{circularity:.2f}")
|
||
# 尝试拟合椭圆
|
||
yellow_center = None
|
||
yellow_radius = None
|
||
yellow_ellipse = None
|
||
|
||
if len(cnt_yellow) >= 5:
|
||
(x, y), (width, height), angle = cv2.fitEllipse(cnt_yellow)
|
||
yellow_ellipse = ((x, y), (width, height), angle)
|
||
axes_minor = min(width, height)
|
||
radius = axes_minor / 2
|
||
yellow_center = (int(x), int(y))
|
||
yellow_radius = int(radius)
|
||
else:
|
||
(x, y), radius = cv2.minEnclosingCircle(cnt_yellow)
|
||
yellow_center = (int(x), int(y))
|
||
yellow_radius = int(radius)
|
||
yellow_ellipse = 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, 80, 0])
|
||
upper_red1 = np.array([10, 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, 80, 0])
|
||
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
|
||
kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
|
||
mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
|
||
|
||
contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||
|
||
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)
|
||
|
||
if perimeter_red > 0:
|
||
circularity_red = (4 * np.pi * area_red) / (perimeter_red * perimeter_red)
|
||
else:
|
||
circularity_red = 0
|
||
|
||
# 红色圆圈也应该有一定的圆度
|
||
if area_red > 50 and circularity_red > 0.6:
|
||
# 计算红色圆圈的中心和半径
|
||
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
|
||
red_center = (int(x_red), int(y_red))
|
||
red_radius = int(radius_red)
|
||
else:
|
||
(x_red, y_red), radius_red = cv2.minEnclosingCircle(cnt_red)
|
||
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
|
||
|
||
# 红色圆圈应该比黄色圆圈大（外圈）
|
||
if distance < max_distance and red_radius > yellow_radius * 0.8:
|
||
found_valid_red = True
|
||
logger = logger_manager.logger
|
||
if logger:
|
||
logger.info(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,
|
||
'ellipse': yellow_ellipse,
|
||
'area': area
|
||
})
|
||
break
|
||
|
||
if not found_valid_red:
|
||
logger = logger_manager.logger
|
||
if logger:
|
||
logger.debug("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:
|
||
if laser_point:
|
||
# 如果有激光点，选择最接近激光点的目标
|
||
best_target = None
|
||
min_distance = float('inf')
|
||
for target in valid_targets:
|
||
dx = target['center'][0] - laser_point[0]
|
||
dy = target['center'][1] - laser_point[1]
|
||
distance = np.sqrt(dx*dx + dy*dy)
|
||
if distance < min_distance:
|
||
min_distance = distance
|
||
best_target = target
|
||
if best_target:
|
||
best_center = best_target['center']
|
||
best_radius = best_target['radius']
|
||
ellipse_params = best_target['ellipse']
|
||
method = "v3_ellipse_red_validated_laser_selected"
|
||
best_radius1 = best_radius * 5
|
||
else:
|
||
# 如果没有激光点，选择面积最大的目标
|
||
best_target = max(valid_targets, key=lambda t: t['area'])
|
||
best_center = best_target['center']
|
||
best_radius = best_target['radius']
|
||
ellipse_params = best_target['ellipse']
|
||
method = "v3_ellipse_red_validated"
|
||
best_radius1 = best_radius * 5
|
||
|
||
result_img = image.cv2image(img_cv, False, False)
|
||
return result_img, best_center, best_radius, method, best_radius1, ellipse_params
|
||
|
||
|
||
def estimate_distance(pixel_radius):
|
||
"""根据像素半径估算实际距离（单位：米）"""
|
||
if not pixel_radius:
|
||
return 0.0
|
||
return (config.REAL_RADIUS_CM * config.FOCAL_LENGTH_PIX) / pixel_radius / 100.0
|
||
|
||
def estimate_pixel(physical_distance_cm, target_distance_m):
|
||
"""
|
||
根据物理距离和目标距离计算对应的像素偏移
|
||
|
||
Args:
|
||
physical_distance_cm: 物理世界中的距离（厘米），例如激光与摄像头的距离
|
||
target_distance_m: 目标距离（米），例如到靶心的距离
|
||
|
||
Returns:
|
||
float: 对应的像素偏移
|
||
"""
|
||
if not target_distance_m or target_distance_m <= 0:
|
||
return 0.0
|
||
# 公式：像素偏移 = (物理距离_米) * 焦距_像素 / 目标距离_米
|
||
return (physical_distance_cm / 100.0) * config.FOCAL_LENGTH_PIX / target_distance_m
|
||
|
||
|
||
def _save_shot_image_impl(img_cv, center, radius, method, ellipse_params,
|
||
laser_point, distance_m, shot_id=None, photo_dir=None):
|
||
"""
|
||
内部实现：在 img_cv (numpy HWC RGB) 上绘制标注并保存。
|
||
由 save_shot_image（同步）和存图 worker（异步）调用。
|
||
"""
|
||
if not config.SAVE_IMAGE_ENABLED:
|
||
return None
|
||
if photo_dir is None:
|
||
photo_dir = config.PHOTO_DIR
|
||
try:
|
||
try:
|
||
if photo_dir not in os.listdir("/root"):
|
||
os.mkdir(photo_dir)
|
||
except Exception:
|
||
pass
|
||
|
||
x, y = laser_point
|
||
if shot_id:
|
||
if center is None or radius is None:
|
||
filename = f"{photo_dir}/shot_{shot_id}_no_target.bmp"
|
||
else:
|
||
method_str = method or "unknown"
|
||
filename = f"{photo_dir}/shot_{shot_id}_{method_str}.bmp"
|
||
else:
|
||
try:
|
||
all_images = [f for f in os.listdir(photo_dir) if f.endswith(('.bmp', '.jpg', '.jpeg'))]
|
||
img_count = len(all_images)
|
||
except Exception:
|
||
img_count = 0
|
||
if center is None or radius is None:
|
||
method_str = "no_target"
|
||
distance_str = "000"
|
||
else:
|
||
method_str = method or "unknown"
|
||
distance_str = str(round((distance_m or 0.0) * 100))
|
||
filename = f"{photo_dir}/{method_str}_{int(x)}_{int(y)}_{distance_str}_{img_count:04d}.bmp"
|
||
|
||
logger = logger_manager.logger
|
||
if logger:
|
||
if shot_id:
|
||
logger.info(f"[VISION] 保存射箭图像，ID: {shot_id}, 文件名: {filename}")
|
||
if center and radius:
|
||
logger.info(f"结果 -> 圆心: {center}, 半径: {radius}, 方法: {method}")
|
||
if ellipse_params:
|
||
(ec, (ew, eh), ea) = ellipse_params
|
||
logger.info(f"椭圆 -> 中心: ({ec[0]:.1f}, {ec[1]:.1f}), 长轴: {max(ew, eh):.1f}, 短轴: {min(ew, eh):.1f}, 角度: {ea:.1f}°")
|
||
else:
|
||
logger.info(f"结果 -> 未检测到靶心，保存原始图像（激光点: ({x}, {y})）")
|
||
|
||
laser_color = (config.LASER_COLOR[0], config.LASER_COLOR[1], config.LASER_COLOR[2])
|
||
cross_thickness = int(max(getattr(config, "LASER_THICKNESS", 1), 1))
|
||
cross_length = int(max(getattr(config, "LASER_LENGTH", 10), 10))
|
||
cv2.line(img_cv, (int(x - cross_length), int(y)), (int(x + cross_length), int(y)), laser_color, cross_thickness)
|
||
cv2.line(img_cv, (int(x), int(y - cross_length)), (int(x), int(y + cross_length)), laser_color, cross_thickness)
|
||
cv2.circle(img_cv, (int(x), int(y)), 1, laser_color, cross_thickness)
|
||
ring_thickness = 1
|
||
cv2.circle(img_cv, (int(x), int(y)), 10, laser_color, ring_thickness)
|
||
cv2.circle(img_cv, (int(x), int(y)), 5, laser_color, ring_thickness)
|
||
cv2.circle(img_cv, (int(x), int(y)), 2, laser_color, -1)
|
||
|
||
if center and radius:
|
||
cx, cy = center
|
||
if ellipse_params:
|
||
(ell_center, (width, height), angle) = ellipse_params
|
||
cx_ell, cy_ell = int(ell_center[0]), int(ell_center[1])
|
||
cv2.ellipse(img_cv, (cx_ell, cy_ell), (int(width / 2), int(height / 2)), angle, 0, 360, (0, 255, 0), 2)
|
||
cv2.circle(img_cv, (cx_ell, cy_ell), 3, (255, 0, 0), -1)
|
||
minor_length = min(width, height) / 2
|
||
minor_angle = angle + 90 if width >= height else angle
|
||
minor_angle_rad = math.radians(minor_angle)
|
||
dx_minor = minor_length * math.cos(minor_angle_rad)
|
||
dy_minor = minor_length * math.sin(minor_angle_rad)
|
||
pt1 = (int(cx_ell - dx_minor), int(cy_ell - dy_minor))
|
||
pt2 = (int(cx_ell + dx_minor), int(cy_ell + dy_minor))
|
||
cv2.line(img_cv, pt1, pt2, (0, 0, 255), 2)
|
||
else:
|
||
cv2.circle(img_cv, (cx, cy), radius, (0, 0, 255), 2)
|
||
cv2.circle(img_cv, (cx, cy), 2, (0, 0, 255), -1)
|
||
cv2.line(img_cv, (int(x), int(y)), (cx, cy), (255, 255, 0), 1)
|
||
|
||
out = image.cv2image(img_cv, False, False)
|
||
out.save(filename)
|
||
if logger:
|
||
if center and radius:
|
||
logger.debug(f"图像已保存（含靶心标注）: {filename}")
|
||
else:
|
||
logger.debug(f"图像已保存（无靶心，含激光十字线）: {filename}")
|
||
|
||
# 清理旧图片：如果目录下图片超过100张，删除最老的
|
||
try:
|
||
image_files = []
|
||
for f in os.listdir(photo_dir):
|
||
if f.endswith(('.bmp', '.jpg', '.jpeg')):
|
||
filepath = os.path.join(photo_dir, f)
|
||
try:
|
||
mtime = os.path.getmtime(filepath)
|
||
image_files.append((mtime, filepath, f))
|
||
except Exception:
|
||
pass
|
||
|
||
from config import MAX_IMAGES
|
||
if len(image_files) > MAX_IMAGES:
|
||
image_files.sort(key=lambda x: x[0])
|
||
to_delete = len(image_files) - MAX_IMAGES
|
||
deleted_count = 0
|
||
for _, filepath, fname in image_files[:to_delete]:
|
||
try:
|
||
os.remove(filepath)
|
||
deleted_count += 1
|
||
if logger:
|
||
logger.debug(f"[VISION] 删除旧图片: {fname}")
|
||
except Exception as e:
|
||
if logger:
|
||
logger.warning(f"[VISION] 删除旧图片失败 {fname}: {e}")
|
||
if logger and deleted_count > 0:
|
||
logger.info(f"[VISION] 已清理 {deleted_count} 张旧图片，当前剩余 {MAX_IMAGES} 张")
|
||
except Exception as e:
|
||
if logger:
|
||
logger.warning(f"[VISION] 清理旧图片时出错（可忽略）: {e}")
|
||
|
||
return filename
|
||
except Exception as e:
|
||
logger = logger_manager.logger
|
||
if logger:
|
||
logger.error(f"保存图像失败: {e}")
|
||
import traceback
|
||
logger.error(traceback.format_exc())
|
||
return None
|
||
|
||
|
||
def _save_worker_loop():
|
||
"""存图 worker：从队列取任务并调用 _save_shot_image_impl。"""
|
||
while True:
|
||
try:
|
||
item = _save_queue.get()
|
||
if item is None:
|
||
break
|
||
_save_shot_image_impl(*item)
|
||
except Exception as e:
|
||
logger = logger_manager.logger
|
||
if logger:
|
||
logger.error(f"[VISION] 存图 worker 异常: {e}")
|
||
import traceback
|
||
logger.error(traceback.format_exc())
|
||
finally:
|
||
try:
|
||
_save_queue.task_done()
|
||
except Exception:
|
||
pass
|
||
|
||
|
||
def start_save_shot_worker():
|
||
"""启动存图 worker 线程（应在程序初始化时调用一次）。"""
|
||
global _save_worker_started
|
||
with _save_worker_lock:
|
||
if _save_worker_started:
|
||
return
|
||
_save_worker_started = True
|
||
t = threading.Thread(target=_save_worker_loop, daemon=True)
|
||
t.start()
|
||
logger = logger_manager.logger
|
||
if logger:
|
||
logger.info("[VISION] 存图 worker 线程已启动")
|
||
|
||
|
||
def enqueue_save_shot(result_img, center, radius, method, ellipse_params,
|
||
laser_point, distance_m, shot_id=None, photo_dir=None):
|
||
"""
|
||
将存图任务放入队列，由 worker 异步保存。主线程传入 result_img 的复制，不阻塞。
|
||
"""
|
||
if not config.SAVE_IMAGE_ENABLED:
|
||
return
|
||
if photo_dir is None:
|
||
photo_dir = config.PHOTO_DIR
|
||
try:
|
||
img_cv = image.image2cv(result_img, False, False)
|
||
img_copy = np.copy(img_cv)
|
||
except Exception as e:
|
||
logger = logger_manager.logger
|
||
if logger:
|
||
logger.error(f"[VISION] enqueue_save_shot 复制图像失败: {e}")
|
||
return
|
||
task = (img_copy, center, radius, method, ellipse_params, laser_point, distance_m, shot_id, photo_dir)
|
||
try:
|
||
_save_queue.put_nowait(task)
|
||
except queue.Full:
|
||
logger = logger_manager.logger
|
||
if logger:
|
||
logger.warning("[VISION] 存图队列已满，跳过本次保存")
|
||
|
||
|
||
def save_shot_image(result_img, center, radius, method, ellipse_params,
|
||
laser_point, distance_m, shot_id=None, photo_dir=None):
|
||
"""
|
||
保存射击图像（带标注）。同步调用，会阻塞。
|
||
主流程建议使用 enqueue_save_shot；此处保留供校准、测试等场景使用。
|
||
"""
|
||
if not config.SAVE_IMAGE_ENABLED:
|
||
return None
|
||
if photo_dir is None:
|
||
photo_dir = config.PHOTO_DIR
|
||
try:
|
||
img_cv = image.image2cv(result_img, False, False)
|
||
return _save_shot_image_impl(img_cv, center, radius, method, ellipse_params,
|
||
laser_point, distance_m, shot_id, photo_dir)
|
||
except Exception as e:
|
||
logger = logger_manager.logger
|
||
if logger:
|
||
logger.error(f"[VISION] save_shot_image 转换图像失败: {e}")
|
||
return None
|
||
|